CN108830456B - Sensitive equipment and power grid voltage sag compatibility analysis method and device - Google Patents

Sensitive equipment and power grid voltage sag compatibility analysis method and device Download PDF

Info

Publication number
CN108830456B
CN108830456B CN201810443088.2A CN201810443088A CN108830456B CN 108830456 B CN108830456 B CN 108830456B CN 201810443088 A CN201810443088 A CN 201810443088A CN 108830456 B CN108830456 B CN 108830456B
Authority
CN
China
Prior art keywords
probability
sensitive equipment
fault
sag
equipment
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810443088.2A
Other languages
Chinese (zh)
Other versions
CN108830456A (en
Inventor
马智远
莫文雄
许中
王勇
周凯
栾乐
王红斌
林金洪
叶志峰
张群峰
钟锦群
邱智民
程振华
梁旭懿
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Original Assignee
Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd filed Critical Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority to CN201810443088.2A priority Critical patent/CN108830456B/en
Publication of CN108830456A publication Critical patent/CN108830456A/en
Application granted granted Critical
Publication of CN108830456B publication Critical patent/CN108830456B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Development Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Educational Administration (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Theoretical Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The application relates to a method and a device for analyzing compatibility of sensitive equipment and power grid voltage sag, wherein the method for analyzing compatibility of the sensitive equipment and the power grid voltage sag comprises the following steps: generating fault sample data of an uncertain region of the sensitive equipment according to a tolerance curve of the sensitive equipment, acquiring sample data from the fault sample data of the uncertain region of the sensitive equipment according to a preset rule, fitting the sample data to obtain a data fitting point, acquiring a comprehensive fault probability function of the sensitive equipment in the uncertain region according to a pre-established equipment side fault probability function and a power grid side probability density function, and acquiring a compatibility probability of the sensitive equipment in the uncertain region according to the data fitting point and the comprehensive fault probability function of the sensitive equipment in the uncertain region. By analyzing the compatibility probability in the uncertain region of the tolerance curve of the equipment, the tolerance characteristic of the sensitive equipment is more accurately described and is more consistent with the actual condition, so that the evaluation precision is greatly improved.

Description

Sensitive equipment and power grid voltage sag compatibility analysis method and device
Technical Field
The invention relates to the field of power grid analysis, in particular to a method and a device for analyzing compatibility of sensitive equipment and power grid voltage sag.
Background
In recent years, with the large-scale application of digital automatic control technology and the rapid development of new energy technology industry, more and more devices sensitive to power supply voltage are continuously connected to a power grid, and the sensitive devices comprise a computer (PC), a variable frequency speed regulator (ASD), a Programmable Logic Controller (PLC), a photovoltaic inverter, an alternating current contactor and the like. The voltage sag can cause the blue screen and the power supply of the computer to trip, thereby losing data; the direct-current voltage of the variable-frequency speed regulator is reduced, low-voltage or overcurrent protection is triggered, and the production process is interrupted; causing instruction disorder and even shutdown of the programmable logic controller; the photovoltaic inverter is disconnected, so that the power grid is unstable in operation; the abnormal tripping or low-voltage protection action of an alternating current contactor and a relay is caused, and the shutdown of an elevator and a motor is caused. Therefore, it is necessary to study the compatibility problem of sensitive devices to the voltage sag of the power grid.
At present, the compatibility analysis method adopted in the conventional technology is the most direct method for calculating the number of times of compatibility (or failure), and is called as a measurement statistical method. The equipment fault region is considered to be arranged at the lower right of the tolerance curve, the normal operation region is considered to be arranged at the upper left of the tolerance curve, therefore, multiple sag events are marked on a voltage amplitude-duration plane (VT plane for short), the tolerance curve of the equipment is drawn at the same time, and the sag times below the equipment tolerance curve are counted, namely the equipment fault times, so that the compatibility parameters of the sensitive equipment to the grid voltage sag are obtained.
However, in the implementation process, the inventor finds that at least the following disadvantages exist in the conventional technology: because the tolerance curve of general equipment has an uncertain region, the error of the measurement statistical method is large, and the actual compatible situation of the equipment on the voltage sag of the power grid cannot be accurately reflected.
Disclosure of Invention
Therefore, it is necessary to provide a method and an apparatus for analyzing compatibility between a sensitive device and a power grid voltage sag, aiming at the problem of large error in the conventional compatibility analysis method.
In one aspect, an embodiment of the present invention provides a method for analyzing compatibility between a sensitive device and a power grid voltage sag, including:
generating fault sample data of an uncertain region of the sensitive equipment according to a tolerance curve of the sensitive equipment;
acquiring sampling sample data from fault sample data in an uncertain area of equipment according to a preset rule, and fitting the sampling sample data to obtain a data fitting point;
and obtaining the compatibility probability of the sensitive equipment in the uncertain region according to the data fitting point, the pre-established equipment side fault probability function and the power grid side probability density function.
In one embodiment, the method for analyzing compatibility between the sensitive device and the grid voltage sag further includes:
generating sag sample data of a normal operation area of the sensitive equipment according to a tolerance curve of the sensitive equipment;
after obtaining the compatibility probability of the sensitive device in the uncertain region, the method also comprises the following steps:
and obtaining the compatible times of the sensitive equipment and the power grid voltage sag according to the compatible probability of the sensitive equipment in the uncertain region, the fault sample data of the uncertain region of the sensitive equipment and the sag sample data of the normal operation region of the sensitive equipment.
In one embodiment, the step of obtaining sample data from fault sample data in an uncertain region of equipment according to a preset rule includes:
in (T)min,Tmax) Within the interval, from TminStarting to generate first fault sample points of equal interval increasing duration from TminStarting to generate first normal sample points at equal intervals of decreasing duration;
in (U)min,Umax) Within the interval, from UmaxStarting to reduce the voltage amplitude at equal intervals to generate a second fault sample point, from UmaxStarting to increase the voltage amplitude at equal intervals to generate a second normal sample point;
taking a set consisting of the first fault sample point and the second fault sample point as a fault sample data space of the uncertain region of the sensitive equipment;
taking a set consisting of the first normal sample point and the second normal sample point as a sag sample data space of a normal operation area of the sensitive equipment;
wherein, Tmax、TminMaximum and minimum values, U, of the critical duration values at the critical points of the normal operating region and the fault operating region, respectivelymax、UminThe maximum value and the minimum value of the sag voltage critical value at the critical part of the normal operation area and the fault operation area are respectively.
In one embodiment, the step of obtaining the compatibility probability of the sensitive device in the uncertain region according to the data fitting point, the pre-established device-side fault probability function and the grid-side probability density function includes:
and obtaining the compatibility probability of the sensitive equipment in the uncertain region under different voltage sag types according to the data fitting point, the voltage sag type, the pre-established equipment side fault probability function and the power grid side probability density function.
In one embodiment, the sensitive device and grid voltage sag compatibility analysis method divides an uncertain region into A, B, C three regions:
in the region A, temporarily reducing the voltage Umin<U<UmaxAnd has a duration Tmin<T<Tmax；
In the B region, temporarily reducing the voltage U<UminAnd has a duration Tmin<T<Tmax；
In the C region, temporarily reducing the voltage Umin<U<UmaxAnd has a duration Tmax<T；
The step of fitting the sampling sample data to obtain the data fitting point comprises the following steps:
according to fault sample data in an uncertain region of the sensitive equipment and the number of preset histograms, respectively counting the distribution probability of the fault sample data in the region B along with the duration and the distribution probability of the fault sample data in the region C along with the temporary decreasing amplitude to obtain a data fitting point.
In one embodiment, the device-side failure probability function comprises a failure probability function f of the sensitive device in the B regiond,x(T) and the probability of failure function f in the C regiond,y(U)；
The grid-side probability density function comprises a probability distribution function f of the duration of the grid sagg,x(T) and the probability distribution function f of the sag amplitudeg,y(U)；
The pre-established establishing steps of the fault probability function of the uncertain region at the equipment side and the probability function of the voltage sag amplitude and the voltage sag duration at the power grid side comprise:
the probability density function expression obtained according to the maximum entropy principle is:
Figure GDA0002568284710000031
where x is any real number, a is a linear transformation factor and
Figure GDA0002568284710000032
λi(i ═ 0, 1.. and n) are lagrangian multipliers, i is the number of the lagrangian multipliers, and n is the number of the lagrangian multipliers. a. Lambda [ alpha ]iIs a parameter to be solved;
let ui(i ═ 1, 2.. times, n) is the ith origin moment of the random variable, which can be calculated from the fault sample data, and then derived from the maximum entropy principle:
Figure GDA0002568284710000041
according to the improved maximum entropy method, the logarithm transformation is carried out on the maximum entropy method, and then the equation is substituted, so that the following formula can be obtained:
Figure GDA0002568284710000042
according to the sag type, carrying out logarithmic transformation on the data fitting points, and solving unknown parameters a and lambda by using a linear least square methodiObtaining the fault probability function f of the sensitive equipment in the B area under different sag typesd,x(T) and the probability of failure function f in the C regiond,y(U) and probability distribution function f of the duration of the grid sagg,x(T) and the probability distribution function f of the sag amplitudeg,x(U)。
In one embodiment, the step of obtaining the compatibility probability of the sensitive device in the uncertain region according to the data fitting point, the pre-established device side fault probability function and the power grid side probability density function comprises:
according to the fault probability function f of the sensitive equipment in the B aread,x(T) and the probability distribution function f of the duration of the grid sagg,x(T) and a data fitting point to obtain the fault probability P of the sensitive equipment in the B area1：
Figure GDA0002568284710000043
Wherein, taumaxMaximum value of the duration of the voltage sag sample;
according to the fault probability P of sensitive equipment1Obtaining sensitive devices in the B-zoneCompatibility probability R1＝1-P1；
According to the fault probability function f of the sensitive equipment in the C aread,y(U), probability distribution function f of voltage sag amplitudeg,y(U) and data fitting point to obtain the fault probability P of the sensitive equipment in the C area2：
Figure GDA0002568284710000044
Wherein u isminThe minimum value of the sag amplitude value in the sag sample of the power grid is obtained;
according to the fault probability P of sensitive equipment2Obtaining the compatibility probability R of the sensitive equipment in the C area2＝1-P2；
Setting the sag amplitude U and the duration T as mutually independent variables to obtain the fault probability P of the sensitive equipment in the area A3＝P1·P2；
According to the fault probability P of sensitive equipment3Obtaining the compatibility probability R of the sensitive equipment in the area A3＝1-P3。
In one embodiment, the step of obtaining the compatible times of the sensitive equipment and the power grid voltage sag according to the compatibility probability of the sensitive equipment in the uncertain region, fault sample data of the uncertain region of the sensitive equipment and sag sample data of the normal operating region of the sensitive equipment comprises the following steps:
obtaining the sag times N of the sensitive device in regions A, B and C respectively2、N’2And N "2And the number N of the temporary drop of the sensitive equipment in the normal operation area1；
According to compatibility probability R of sensitive equipment in A, B and C regions respectively1、R2And R3Obtaining the compatible times N of the nameplate sensing equipment and the voltage sag of the power grid1+N2×R1+N’2×R2+N”2×R3。
In another aspect, an embodiment of the present invention further provides an apparatus for analyzing compatibility between a sensitive device and a power grid voltage sag, including:
the fault sample acquisition unit is used for generating fault sample data of the uncertain region of the sensitive equipment according to the tolerance curve of the sensitive equipment;
the data fitting unit is used for acquiring sampling data from fault sample data in the uncertain region of the equipment according to a preset rule and fitting the sampling data to obtain a data fitting point;
and the compatible probability obtaining unit is used for obtaining the compatible probability of the sensitive equipment in the uncertain region according to the data fitting point, the pre-established equipment side fault probability function and the power grid side probability density function.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above method steps when executing the program.
One or more embodiments provided by the invention have at least the following beneficial effects: according to the method for analyzing compatibility of the sensitive equipment and the power grid voltage sag, fault sample data of an uncertain region of the sensitive equipment are generated according to a tolerance curve of the sensitive equipment, sample data are obtained from the fault sample data of the uncertain region of the equipment according to a preset rule, the sample data are fitted to obtain data fitting points, and the compatibility probability of the sensitive equipment in the uncertain region is obtained according to the data fitting points and a pre-established equipment side fault probability function and a pre-established power grid side probability density function. The method comprises the steps of analyzing a tolerance curve to obtain fault sample data of an uncertain region, performing data fitting on the fault sample data to obtain uniform and effective data fitting points, combining established probability functions of an equipment side and a power grid side in a fitting range formed by the data fitting points to obtain the compatibility probability of the sensitive equipment in the uncertain region, calculating the compatibility probability of the equipment in the uncertain region to obtain comprehensive data of the compatibility of the sensitive equipment to power grid voltage sag, and greatly reducing errors of compatibility analysis of the sensitive equipment and the power grid voltage sag.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for analyzing voltage sag compatibility of a sensing device with a power grid according to an embodiment;
FIG. 2 is a schematic flow chart illustrating a method for analyzing the compatibility of a sensing device with a grid voltage sag in another embodiment;
FIG. 3 is a schematic flow chart illustrating a step of obtaining sample data from failure sample data in an uncertain region of a device according to a preset rule in one embodiment;
FIG. 4 is a diagram illustrating an uncertainty region of a tolerance curve of a sensing device divided into A, B, C three regions according to an embodiment;
FIG. 5(a) is a schematic diagram illustrating an uncertain region of a tolerance curve of a low-voltage sensitive device in a three-phase sag type according to an embodiment;
FIG. 5(b) is a schematic diagram illustrating an uncertain region of a tolerance curve of a low-voltage sensitive device in a two-phase sag type according to an embodiment;
FIG. 5(c) is a schematic diagram illustrating an uncertain region of a tolerance curve of a low voltage sensitive device under the single-phase sag type in one embodiment;
FIG. 6(a) is a fault probability histogram and fitted curve of a sensitive device in a three-phase sag B-zone in an embodiment;
FIG. 6(b) is a failure probability histogram and fitted curve of the sensitive device in the C region under three-phase sag in one embodiment;
FIG. 7 is a histogram and fitted curve of the probability of failure of the uncertain region sensitive device under two-phase sag in one embodiment;
FIG. 8 is a single phase sag uncertainty region sensitive device fault probability histogram and fitting curve according to one embodiment;
FIG. 9 is a three-dimensional graph of magnitude-duration-frequency statistics of total samples of voltage sags in a power grid according to one embodiment;
FIG. 10(a) is a probability histogram of sag durations of three phases of a power grid and a fitted curve according to an embodiment;
FIG. 10(b) is a probability histogram and a fitting curve of three-phase sag amplitudes of the power grid according to an embodiment;
FIG. 11 is a probability histogram and fitted curve of the durations and amplitudes of two-phase sag of a power grid in one embodiment;
FIG. 12 is a probability histogram and fitted curve of the duration and amplitude of a single-phase sag of a power grid in one embodiment;
FIG. 13(a) is a table of fitting parameters for the device-side maximum entropy probability density function in one embodiment;
FIG. 13(b) is a table of fitting parameters for a grid-side maximum entropy probability density function in one embodiment;
FIG. 13(c) is a table of compatible probabilities for uncertain regions for different types of dips in one embodiment;
FIG. 13(d) is a table of total compatibility counts for different sag types in one embodiment;
FIG. 14(a) is a sag amplitude and duration profile for a three-phase sag type in one embodiment;
FIG. 14(b) is a sag amplitude and duration profile for a two-phase type sag type in one embodiment;
FIG. 14(c) is a sag amplitude and duration profile for a single-phase sag type in one embodiment;
FIG. 15 is a block diagram of an exemplary embodiment of a device for analyzing voltage sag compatibility of a sensing device with a power grid;
FIG. 16 is a block diagram that illustrates a computer device, in accordance with an embodiment.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "connected" to another element, it can be directly connected to the other element and be integral therewith, or intervening elements may also be present. The terms "mounted," "one end," "the other end," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
In one aspect, as shown in fig. 1, an embodiment of the present invention provides a method for analyzing compatibility between a sensitive device and a power grid voltage sag, including:
s120: generating fault sample data of an uncertain region of the sensitive equipment according to a tolerance curve of the sensitive equipment;
s140: acquiring sampling sample data from fault sample data in an uncertain area of equipment according to a preset rule, and fitting the sampling sample data to obtain a data fitting point;
s160: and obtaining the compatibility probability of the sensitive equipment in the uncertain region according to the data fitting point, the pre-established equipment side fault probability function and the power grid side probability density function.
Wherein, the tolerance curve can be the performance parameter of the equipment which is carried by the factory or obtained by the test. The preset rule is a selection rule of valid data of a fault sample preset according to a device type or an experiment, for example, some data may be selected at equal intervals as sampling data, or equal amounts of data may be selected as sampling data according to sections, respectively. The sampling sample data refers to data which is selected from all sample data and is needed to be used for next processing. The data fitting points refer to data points on a fitting curve corresponding to the original sampling points on a curve formed by the fitted sampling data. The equipment side fault probability function refers to a fault probability density function in an uncertain region of a tolerance curve of the sensitive equipment, and the power grid side probability density function refers to a probability distribution function of the amplitude and duration of power grid sag.
Specifically, fault sample data of an uncertain region are generated according to an existing tolerance curve of the sensitive equipment, then the fault sample data are sampled to obtain sample data, the sample data are fitted to obtain a fitting curve, and data fitting points on the fitting curve corresponding to sampling points corresponding to the sample data are selected from the fitting curve to serve as data bases of subsequent processing. According to the characteristics of the uncertain region, in a data space range formed by each data fitting point in the data fitting points, the influence factors of the equipment side fault probability function and the power grid side probability density function on the sensitive equipment during voltage sag are solved, the fault probability of the sensitive equipment and the voltage sag is solved, and then the compatibility probability of the sensitive equipment and the power grid voltage sag is obtained through the fault probability solution. Therefore, the compatibility of the sensitive equipment and the power grid voltage sag in an uncertain area is achieved, the compatibility of the sensitive equipment and the power grid voltage sag can be comprehensively evaluated, the reliability and effectiveness of an analysis result are greatly improved, and errors are reduced.
In one embodiment, as shown in fig. 2, the method for analyzing compatibility between a sensitive device and a grid voltage sag further includes:
s180: and generating sag sample data of the normal operation area of the sensitive equipment according to the tolerance curve of the sensitive equipment. After obtaining the compatibility probability of the sensitive device in the uncertain region, the method also comprises the following steps: and obtaining the compatible times of the sensitive equipment and the power grid voltage sag according to the compatible probability of the sensitive equipment in the uncertain region, the fault sample data of the uncertain region of the sensitive equipment and the sag sample data of the normal operation region of the sensitive equipment.
The sag sample data of the normal operation area of the sensitive equipment refers to data corresponding to the sag samples in the normal operation area of the tolerance curve, and represents data which can be used for the sensitive equipment to operate normally when the voltage sag occurs. After the compatibility probability of the sensitive equipment in the uncertain region is obtained, the product of the fault sample data of the sensitive equipment in the uncertain region and the compatibility probability in the uncertain region is the compatible times of the sensitive equipment and the power grid voltage sag in the uncertain region, and the sag times of the sensitive equipment in the normal operation region are added, so that the total compatible times of the sensitive equipment and the power grid voltage sag are formed. By obtaining the compatible times, the voltage sag compatibility of the sensitive equipment can be evaluated more intuitively.
In one embodiment, as shown in fig. 3, the step of obtaining sample data from fault sample data in an uncertain region of a device according to a preset rule includes:
s141: in (T)min,Tmax) Within the interval, from TminStarting to generate first fault sample points of equal interval increasing duration from TminStarting to generate first normal sample points at equal intervals of decreasing duration;
s142: in (U)min,Umax) Within the interval, from UmaxStarting to reduce the voltage amplitude at equal intervals to generate a second fault sample point, from UmaxStarting to increase the voltage amplitude at equal intervals to generate a second normal sample point;
s143: taking a set consisting of the first fault sample point and the second fault sample point as a fault sample data space of the uncertain region of the sensitive equipment;
s144: taking a set consisting of the first normal sample point and the second normal sample point as a sag sample data space of a normal operation area of the sensitive equipment;
wherein, Tmax、TminMaximum and minimum values, U, of the critical duration values at the critical points of the normal operating region and the fault operating region, respectivelymax、UminThe maximum value and the minimum value of the sag voltage critical value at the critical part of the normal operation area and the fault operation area are respectively.
Since the tolerance curve of the sensitive device is generally rectangular, as shown in fig. 4, its uncertainty region can be divided into A, B, C three regions. It is easy to understand that the probability function of failure in zone B is only related to the duration, in the zone B, for each endurance curve, a temporary drop of maximum value of duration greater than the duration threshold at the critical point of the curve will cause failure of the sensitive equipment, and a temporary drop of minimum value of duration less than the duration threshold at the critical point will not cause failure of the sensitive equipment, and therefore, in (T) atmin,Tmax) Within the interval, from TminStarting to generate first fault sample points of equal interval increasing duration from TminBeginning to decrease at equal intervalsThe small duration generates the first normal sample point. The C-zone fault probability function is only related to the sag magnitude and is therefore (U)min,Umax) Within the interval, from UmaxStarting to reduce the voltage amplitude at equal intervals to generate a second fault sample point, from UmaxStarting to increase the voltage amplitude at equal intervals to generate second normal sample points. The failure probability of the area A can be regarded as the joint probability of the area B and the area C, so that the failure probability of the area B and the failure probability of the area C only need to be counted. By adopting the data sampling mode with equal intervals, the sampling uniformity can be ensured, and the final analysis error is not larger due to unbalanced data sampling caused by subjective reasons.
In one embodiment, the step of obtaining the compatibility probability of the sensitive device in the uncertain region according to the data fitting point, the pre-established device side fault probability function and the power grid side probability density function comprises: and obtaining the compatibility probability of the sensitive equipment in the uncertain region under different voltage sag types according to the data fitting point, the voltage sag type, the pre-established equipment side fault probability function and the power grid side probability density function.
Because the three-phase power supply sensitive devices, such as frequency converters, are very sensitive to sag types (mainly classified into three-phase symmetrical sag, two-phase sag and single-phase sag), some sensitive devices are even not in fault when in single-phase sag or two-phase sag, and therefore, the problem of compatibility between the three-phase power supply sensitive devices and the grid voltage sag needs to be analyzed under different sag types.
In one embodiment, as shown in fig. 4, the method for analyzing compatibility of a sensitive device and a power grid voltage sag can divide an uncertain region into A, B, C three regions:
in the region A, temporarily reducing the voltage Umin<U<UmaxAnd has a duration Tmin<T<Tmax；
In the B region, temporarily reducing the voltage U<UminAnd has a duration Tmin<T<Tmax；
In the C region, temporarily reducing the voltage Umin<U<UmaxAnd has a duration Tmax<T；
The step of fitting the sampling sample data to obtain the data fitting point comprises the following steps:
s145: according to fault sample data in an uncertain region of the sensitive equipment and the number of preset histograms, respectively counting the distribution probability of the fault sample data in the region B along with the duration and the distribution probability of the fault sample data in the region C along with the temporary decreasing amplitude to obtain a data fitting point.
The preset number of the histograms can be a reasonable number of the histograms selected according to multiple historical voltage sag events. Specifically, according to fault sample data in an uncertain region, selecting a reasonable number of histograms, and respectively counting the probability of the distribution of the fault sample data in the region B along with the duration and the probability of the distribution of the fault sample data in the region C along with the temporary reduced amplitude to obtain a data fitting point.
And (3) taking the low-voltage frequency converter as a compatibility evaluation object, and generating fault sample data of an uncertain region under different sag types according to the tolerance curve cluster obtained by the test. In order to represent the general characteristics of the low-voltage frequency converter, 7 brands (2 brands in China) including ABB, Siemens, Tdad, Enweiteng, Huichuan, Danfoss and Weiken and 8 frequency converters (18.5kW and 7.5kW) are selected for testing.
FIG. 5(a) shows the uncertainty region of the tolerance curve cluster obtained by the test in the three-phase sag, where the boundary value T ismin＝10ms，Tmax＝66ms，Umin＝64％,Umax76%. In the B region, for each tolerance curve, at (T)min,Tmax) Within a time interval from a time threshold TminStarting to generate first fault sample points of equal interval increasing duration from TminThe first normal sample point is generated starting at an equal interval decreasing duration, here taking the time interval as 1 ms. For region C, in (U)min,Umax) Within interval, from voltage critical value UmaxStarting to reduce the voltage amplitude at equal intervals to generate a second fault sample point, starting from the voltage threshold value UmaxStarting to increase the voltage amplitude at equal intervals to generate second normal sample points, the amplitude interval is 1%.
In the case of two-phase sag, the uncertainty region formed by the tolerance curve cluster obtained by the test is approximately rectangular, and the boundary value U is shown in FIG. 5(b)min＝0，Umax66%. In the case of single-phase sag, the uncertainty region formed by the tolerance curve cluster obtained by the test is approximately rectangular, and the boundary value U thereof is shown in fig. 5(c)min＝0，Umax46%. The uncertain areas of the two-phase sag and the single-phase sag can be treated as a C area, and the distribution of the fault probability is only related to the sag amplitude U. In (U)min,Umax) Within interval, from voltage critical value UmaxStarting to reduce the voltage amplitude at equal intervals to generate a second fault sample point, starting from the voltage threshold value UmaxStarting to increase the voltage amplitude at equal intervals to generate second normal sample points, the amplitude interval is 1%.
And drawing a distribution histogram of the fault probability of the uncertain region according to the fault sample data of the uncertain region obtained in the step (1). For the three-phase sag, the distribution probabilities of the fault sample data points in the B area and the C area are respectively counted, and the distribution probability is divided by the total number of samples to obtain a fault probability distribution histogram, as shown in fig. 6. Wherein, because the precision of the duration is 1ms and the distribution range is 10 ms-66 ms, the maximum number of the bar graphs does not exceed 57, and 25 is taken out; similarly, the accuracy of the sag amplitude value is 1%, the distribution range is 64% -76%, the maximum number of the bar graphs does not exceed 13, and 12 is taken here to obtain a data fitting point. For the two-phase and single-phase sag, the distribution probability of the fault points falling in the uncertain region is counted and divided by the total number of samples to obtain the distribution of the fault probability, and a probability histogram is drawn, as shown in fig. 7 and 8. Wherein, the number of the bar graphs is 14, and a data fitting point is obtained.
In the process of establishing the power grid side probability density function, the data fitting can be realized in the following modes: 355 groups of actual voltage sags captured by a certain 4 provincial and urban electric energy quality monitoring system in 1 month in 2009-2015 7 months serve as historical voltage sag data, wherein the historical voltage sag data comprise a single-phase sag 205 group, a two-phase sag 90 group and a three-phase sag 60 group. According to the statistical table in the national standard GBT 30137, a three-dimensional graph of the amplitude value-duration time-frequency statistics of the total samples is obtained as shown in FIG. 9. It can be seen that the amplitude of most sag events is distributed between 60% and 90%, the duration is distributed between 0.02s and 0.1s, and a small portion of sag events have longer duration or lower amplitude. Selecting reasonable number of bar graphs, and respectively counting probability distribution histograms of three-phase sag amplitudes and durations and two-phase and single-phase sag amplitudes, as shown in fig. 10 to 12, to obtain data fitting points.
In one embodiment, the device-side failure probability function comprises a failure probability function f of the sensitive device in the B regiond,x(T) and the probability of failure function f in the C regiond,y(U)；
The grid-side probability density function comprises a probability distribution function f of the duration of the grid sagg,x(T) and the probability distribution function f of the sag amplitudeg,y(U)；
The pre-established establishing steps of the fault probability function of the uncertain region at the equipment side and the probability function of the voltage sag amplitude and the voltage sag duration at the power grid side comprise:
the probability density function expression obtained according to the maximum entropy principle is:
Figure GDA0002568284710000121
where x is any real number, a is a linear transformation factor and
Figure GDA0002568284710000122
λi(i ═ 0, 1.. and n) are lagrangian multipliers, i is the number of the lagrangian multipliers, and n is the number of the lagrangian multipliers. a. Lambda [ alpha ]iIs a parameter to be solved;
let ui(i ═ 1, 2.. times, n) is the ith origin moment of the random variable, which can be calculated from the fault sample data, and then derived from the maximum entropy principle:
Figure GDA0002568284710000123
according to the improved maximum entropy method, the logarithm transformation is carried out on the maximum entropy method, and then the equation is substituted, so that the following formula can be obtained:
Figure GDA0002568284710000124
according to the sag type, carrying out logarithmic transformation on the data fitting points, and solving unknown parameters a and lambda by using a linear least square methodiObtaining the fault probability function f of the sensitive equipment in the B area under different sag typesd,x(T) and the probability of failure function f in the C regiond,y(U) and probability distribution function f of the duration of the grid sagg,x(T) and the probability distribution function f of the sag amplitudeg,x(U)。
Specifically, under different sag types, the i-order central moment mu is obtained according to a data fitting point of the fault probability of the uncertain region of the tolerance curve of the sensitive equipmenti(i-1, 2.., n), and taking n-6. The probabilities of the sample points are logarithmically transformed, and the unknown parameters a and λ i (i ═ 1, 2.. times.6) are fitted by using the least square method, as shown in fig. 13(a), and are substituted into the probabilities
Figure GDA0002568284710000131
The fault probability density function of the sensitive device B, C area can be obtained, as shown in fig. 6 to 8, it can be seen that the maximum entropy probability density function conforms to the variation trend of data, and the fitting effect is good.
The solving of the probability distribution function with respect to the amplitude and duration of the grid sag may be realized in particular by the following procedure: under different sag types, an i-order central moment mu i (i is 1,2, n) is obtained according to the data fitting points of the amplitude and the duration distribution probability of the sag of the power grid, and n is 6. The probabilities of the sample points are logarithmically transformed, and the unknown parameters a and λ i (i ═ 1, 2.. times.6) are fitted by using the least square method, as shown in fig. 13(b), and are substituted into the probabilities
Figure GDA0002568284710000132
The probability distribution function of the amplitude and the duration of the power grid sag can be obtained, and as shown in fig. 10 to 12, the probability distribution function can be seen mostThe large entropy probability density function accords with the change trend of data, and the fitting effect is good.
It should be noted that the terms such as the pause type and the like are the same as those in the above embodiments, and are not described herein again. The maximum entropy method fitting equipment side fault probability function and the power grid side probability density function based on logarithm transformation improvement provided by the embodiment of the invention convert fitting of exponential functions into fitting of polynomials by taking logarithms, so that the probability value of a fitting point and the change range of the probability value are correspondingly enlarged, and parameter solution of maximum entropy is facilitated.
In one embodiment, the step of obtaining the compatibility probability of the sensitive device in the uncertain region according to the data fitting point, the pre-established device side fault probability function and the power grid side probability density function comprises:
according to the fault probability function f of the sensitive equipment in the B aread,x(T) and the probability distribution function f of the duration of the grid sagg,x(T) and a data fitting point to obtain the fault probability P of the sensitive equipment in the B area1：
Figure GDA0002568284710000133
Wherein, taumaxMaximum value of the duration of the voltage sag sample;
according to the fault probability P of sensitive equipment1Obtaining the compatibility probability R of the sensitive equipment in the B region1＝1-P1；
According to the fault probability function f of the sensitive equipment in the C aread,y(U), probability distribution function f of voltage sag amplitudeg,y(U) and data fitting point to obtain the fault probability P of the sensitive equipment in the C area2：
Figure GDA0002568284710000134
Wherein u isminThe minimum value of the sag amplitude value in the sag sample of the power grid is obtained;
according to the fault probability P of sensitive equipment2Obtaining the compatibility probability R of the sensitive equipment in the C area2＝1-P2；
Setting the sag amplitude U and the duration T as mutually independent variables to obtain the fault probability P of the sensitive equipment in the area A3＝P1·P2；
According to the fault probability P of sensitive equipment3Obtaining the compatibility probability R of the sensitive equipment in the area A3＝1-P3。
As can be seen from FIG. 4, for region B, fd,x(T) dT is the probability that the sensitive equipment is failed by the temporary drop with the duration within (T-1/2dT, T +1/2dT), and the sensitive equipment is failed by the temporary drop with the duration greater than T in the power grid
Figure GDA0002568284710000141
Wherein tau ismaxThe maximum value of the duration of the grid sag sample. The probability of the fault of the sensitive equipment caused by the grid sag in (T-1/2dT, T +1/2dT) is the product of the two probabilities, and T is equal to (T)min,Tmax) Integrating the data to obtain the failure probability P of the sensitive equipment in the B area1：
Figure GDA0002568284710000142
Compatibility probability R of the B region1＝1-P1. The failure probability P of the C area can be obtained in the same way2：
Figure GDA0002568284710000143
In the formula uminAnd the minimum value of the amplitude of the sag sample of the power grid is obtained.
Compatibility probability R of C region2＝1-P2. Assuming that the sag amplitude U and the duration T are independent variables, the failure probability P of the region A is3＝P1·P2Compatible probability R of3＝1-P3. Optionally, the grid sagThe integration of the probability density function of (a) should be performed within the fitting interval because the fitting error within the fitting interval is minimal and the fitting data error outside the interval is large.
In a specific embodiment, the step of obtaining the compatibility probability of the sensitive device in the uncertain region under different voltage sag types according to the data fitting point, the voltage sag type, the pre-established device side fault probability function and the power grid side probability density function includes:
substituting related maximum entropy probability density functions under different sag types into P1And P2Expression (c):
Figure GDA0002568284710000144
Figure GDA0002568284710000145
according to P3＝P1·P2，R1＝1-P1，R2＝1-P2，R3＝1-P3Then calculate its compatibility probability R1、R2、R3. Wherein, because the frequency converter has A, B, C three uncertain regions when three phases temporarily drop, R is needed to be calculated1、R2、R3While the uncertain areas of the two-phase sag and the single-phase sag are equivalent to the C area, only R needs to be calculated2The calculation result is shown in fig. 13 (c). When three phases are temporarily reduced, the compatibility probability of the area A is the highest, the compatibility probability of the area B is the lowest, and the compatibility probability of the area C is between the compatibility probabilities. On the whole, the compatibility probability is highest when the single-phase sag is performed, the compatibility probability is second when the two-phase sag is performed, and the compatibility probability is lowest when the three-phase sag is performed, so that the sensitivity characteristic of the frequency converter to different types of sag is met.
In one embodiment, the step of obtaining the compatible times of the sensitive equipment and the power grid voltage sag according to the compatibility probability of the sensitive equipment in the uncertain region, fault sample data of the uncertain region of the sensitive equipment and sag sample data of the normal operating region of the sensitive equipment comprises the following steps:
obtaining the sag times N of the sensitive device in regions A, B and C respectively2、N’2And N "2And the number N of the temporary drop of the sensitive equipment in the normal operation area1；
According to compatibility probability R of sensitive equipment in A, B and C regions respectively1、R2And R3Obtaining the compatible times N of the nameplate sensing equipment and the voltage sag of the power grid1+N2×R1+N’2×R2+N”2×R3。
For example, according to the fault sample data and the sag sample data, sag scatter diagrams and universal tolerance curves of the frequency converter under three sag types on the sag voltage-duration plane can be obtained, as shown in fig. 14. Counting the sag times N in the normal operation area of the equipment tolerance curve1And the number of dips N located in the uncertainty region2So as to obtain the total compatible times N ═ N1+N2And x R. The calculation result of the total number of compatibilities is shown in fig. 13 (d). The measurement statistical method considers that the lower right side of the tolerance curve is an equipment fault area, and the upper left side of the tolerance curve is a normal operation area, so that the temporary drop times of the sensitive equipment in the normal operation area are directly counted to serve as compatible times, namely temporary drop sample data of the normal operation area. Because of the two tolerance curves for the three-phase dip, there were two results (45 or 39) for the compatibility counts obtained by the measurement statistics. If the compatibility probability (such as a measurement statistical method) of the uncertain region in the tolerance curve is not considered, the converter is considered to be completely or completely immune to the sag in the uncertain region, and compared with the maximum entropy method considering the uncertain region, the influence of the sag on the converter is overestimated or underestimated to a greater extent. In other words, the method provided by the invention can effectively overcome over-evaluation or under-evaluation of a measurement statistical method, and greatly improve the evaluation precision.
It should be noted that, because some frequency converters are immune to the two-phase sag and the single-phase sag, their tolerance curves cannot be drawn on the sag voltage-duration plane, the tolerance curves shown in fig. 5(b) to (c) are only the upper limit of the tolerance curve of the tested frequency converter, and represent the most sensitive tolerance characteristics of the frequency converter, and actually, the tolerance capability of the remaining frequency converters to the single-phase and two-phase sag is far stronger than the tolerance characteristics in fig. 5(b) to (c), and the calculated compatibility times represent the compatibility characteristics of the entire 8 frequency converters, so that it can be seen from fig. 14(b) to (c) that although the sag events below the tolerance curves are more, the two-phase sag and single-phase sag events are basically not failed due to the higher immune capability of the entire frequency converters.
It should be understood that although the various steps in the flow charts of fig. 1-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
According to the method for analyzing the compatibility of the sensitive equipment and the power grid voltage sag, provided by the embodiment of the invention, the tolerance characteristic of the sensitive equipment is more accurately described by analyzing the compatibility probability in the uncertain region of the tolerance curve of the equipment, and the tolerance characteristic is more consistent with the actual condition, so that the evaluation precision is greatly improved; a probability density function model is established by adopting a maximum entropy method based on logarithmic transformation improvement, so that the accuracy of evaluation is further improved; the voltage sag compatibility analysis method provided by the invention is suitable for various voltage sensitive devices and has wide adaptability.
The method for analyzing the voltage sag compatibility of the sensitive equipment and the power grid can provide accurate equipment and power grid sag compatibility evaluation results for power grids, governing manufacturers and sensitive equipment users, provide important reference for analysis of sag compatibility of the sensitive equipment, can effectively overcome over-evaluation or under-evaluation of the conventional evaluation method, and has wide adaptability.
Another aspect of the embodiments of the present invention further provides an apparatus for analyzing compatibility between a sensitive device and a power grid voltage sag, as shown in fig. 15, including:
the fault sample acquisition unit is used for generating fault sample data of the uncertain region of the sensitive equipment according to the tolerance curve of the sensitive equipment;
the data fitting unit is used for acquiring sampling data from fault sample data in the uncertain region of the equipment according to a preset rule and fitting the sampling data to obtain a data fitting point;
and the compatible probability obtaining unit is used for obtaining the compatible probability of the sensitive equipment in the uncertain region according to the data fitting point, a pre-established equipment side fault probability function and a power grid side probability density function.
For specific limitations of the device for analyzing compatibility between the sensitive device and the power grid voltage sag, reference may be made to the above limitations of the method for analyzing compatibility between the sensitive device and the power grid voltage sag, which are not described herein again. All or part of the modules in the device for analyzing the voltage sag compatibility of the sensitive equipment and the power grid can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 16. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for analyzing compatibility of a sensitive device with a grid voltage sag. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 16 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
s120: generating fault sample data of an uncertain region of the sensitive equipment according to a tolerance curve of the sensitive equipment;
s140: acquiring sampling sample data from fault sample data in an uncertain area of equipment according to a preset rule, and fitting the sampling sample data to obtain a data fitting point;
s160: and obtaining the compatibility probability of the sensitive equipment in the uncertain region according to the data fitting point, the pre-established equipment side fault probability function and the power grid side probability density function.
The terms such as tolerance curve are the same as those in the above embodiments, and are not described herein, it is to be understood that a computer device may implement any step in the above method embodiments.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
s120: generating fault sample data of an uncertain region of the sensitive equipment according to a tolerance curve of the sensitive equipment;
s140: acquiring sampling sample data from fault sample data in an uncertain area of equipment according to a preset rule, and fitting the sampling sample data to obtain a data fitting point;
s160: and obtaining the compatibility probability of the sensitive equipment in the uncertain region according to the data fitting point, the pre-established equipment side fault probability function and the power grid side probability density function.
The definitions of the terms such as the tolerance curve are the same as those in the above embodiments, and are not described herein.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (9)

1. A method for analyzing compatibility of sensitive equipment and power grid voltage sag is characterized by comprising the following steps:
generating fault sample data of an uncertain region of the sensitive equipment according to a tolerance curve of the sensitive equipment;
acquiring sampling sample data from fault sample data in the uncertain region of the equipment according to a preset equal-interval sampling rule, and fitting the sampling sample data to obtain a data fitting point;
obtaining the compatibility probability of the sensitive equipment in an uncertain region according to the data fitting point, a pre-established equipment side fault probability function and a power grid side probability density function; the equipment side fault probability function is a fault probability density function of a tolerance curve uncertain region of the sensitive equipment; the power grid side probability density function is a probability distribution function of the sag amplitude and the duration of the power grid;
the equipment side fault probability function comprises a fault probability function fd, x (T) of the sensitive equipment in a B area and a fault probability function fd, y (U) of the sensitive equipment in a C area;
the power grid side probability density function comprises a probability distribution function fg of the power grid sag duration, x (T) and a probability distribution function fg of sag amplitude, y (U);
the pre-established device side uncertain region fault probability function and the pre-established network side voltage sag amplitude and duration probability function establishing step comprises the following steps:
the probability density function expression obtained according to the maximum entropy principle is:
Figure FDA0002770112490000011
where x is any real number, a is a linear transformation factor and
Figure FDA0002770112490000012
λ i (i ═ 0, 1.·, n) is a lagrange multiplier, i is the number of the lagrange multipliers, n is the number of the lagrange multipliers, and a and λ i are parameters to be solved;
let μ i (i ═ 1, 2.. times, n) be the ith order origin moment of a random variable, which can be calculated from the fault sample data, then it can be derived from the maximum entropy principle:
Figure FDA0002770112490000013
according to the improved maximum entropy method, it is logarithmically transformed and then introduced
Figure FDA0002770112490000014
The following can be obtained:
Figure FDA0002770112490000015
according to the sag types, carrying out logarithmic transformation on the data fitting points, and solving unknown parameters a and lambda i by using a linear least square method to obtain fault probability functions fd and x (T) of the sensitive equipment in the B area, fault probability functions fd and y (U) of the sensitive equipment in the C area, probability distribution functions fg and x (T) of the sag duration time of the power grid and probability distribution functions fg and x (U) of sag amplitude values.
2. The method for analyzing compatibility of a sensitive device and a power grid voltage sag according to claim 1, further comprising:
generating sag sample data of a normal operation area of the sensitive equipment according to a tolerance curve of the sensitive equipment;
after the obtaining of the compatibility probability of the sensitive device in the uncertain region, the method further comprises the following steps:
and obtaining the compatible times of the sensitive equipment and the power grid voltage sag according to the compatible probability of the sensitive equipment in the uncertain region, the fault sample data of the uncertain region of the sensitive equipment and the sag sample data of the normal operation region of the sensitive equipment.
3. The method for analyzing compatibility between sensitive equipment and power grid voltage sag according to claim 2, wherein the step of obtaining sample data from the fault sample data of the uncertain region of the equipment according to a preset equal-interval sampling rule comprises the following steps:
generating a first failure sample point at equal intervals of increasing duration from Tmin and a first normal sample point at equal intervals of decreasing duration from Tmin within the (Tmin, Tmax) section;
in the interval (Umin, Umax), reducing the voltage amplitude at equal intervals from Umax to generate a second fault sample point, and increasing the voltage amplitude at equal intervals from Umax to generate a second normal sample point;
taking a set consisting of the first fault sample point and the second fault sample point as a fault sample data space of the uncertain region of the sensitive equipment;
taking a set formed by the first normal sample point and the second normal sample point as a sag sample data space of a normal operation area of the sensitive equipment;
tmax and Tmin are respectively the maximum value and the minimum value of the duration critical value at the critical position of the normal operation region and the fault operation region, and Umax and Umin are respectively the maximum value and the minimum value of the sag voltage critical value at the critical position of the normal operation region and the fault operation region.
4. The method for analyzing compatibility of sensitive equipment and power grid voltage sag according to any one of claims 1 to 3, wherein the step of obtaining the compatibility probability of the sensitive equipment in an uncertain region according to the data fitting point, a pre-established equipment-side fault probability function and a power grid-side probability density function comprises:
and obtaining the compatibility probability of the sensitive equipment in an uncertain region under different voltage sag types according to the data fitting point, the voltage sag type, a pre-established equipment side fault probability function and a power grid side probability density function.
5. The method for analyzing compatibility of sensitive equipment and power grid voltage sag according to claim 4, wherein the uncertain region is divided into A, B, C three regions:
in the region A, the voltage Umin is temporarily reduced to be less than U < Umax, and the duration Tmin is less than T < Tmax;
in the region B, the voltage U is temporarily reduced to be less than Umin and the duration Tmin is less than T and less than Tmax;
in the region C, the voltage Umin is temporarily reduced to be less than U < Umax, and the duration Tmax is less than T;
the step of fitting the sampling sample data to obtain the data fitting point comprises the following steps:
and respectively counting the distribution probability of the fault sample data in the region B along with the duration and the distribution probability of the fault sample data in the region C along with the temporary reduced amplitude according to the fault sample data in the uncertain region of the sensitive equipment and the number of preset histograms to obtain a data fitting point.
6. The method for analyzing compatibility between sensitive equipment and power grid voltage sag according to claim 5, wherein the step of obtaining the compatibility probability of the sensitive equipment in the uncertain region according to the data fitting point, the pre-established equipment-side fault probability function and the power grid-side probability density function comprises:
obtaining the fault probability P1 of the sensitive equipment in the B area according to the fault probability function fd, x (T) of the sensitive equipment in the B area, the probability distribution function fg, x (T) of the duration time of the power grid sag and the data fitting point:
Figure FDA0002770112490000031
wherein, taumaxMaximum value of the duration of the voltage sag sample;
according to the sensitive equipment fault probability P1, obtaining the compatibility probability R1 of the sensitive equipment in the B region as 1-P1;
obtaining the fault probability P2 of the sensitive equipment in the C area according to the fault probability function fd, y (U) of the sensitive equipment in the C area, the probability distribution function fg, y (U) of the voltage sag amplitude and the data fitting point:
Figure FDA0002770112490000032
wherein umin is the minimum value of sag amplitude values in the power grid sag samples;
according to the sensitive equipment fault probability P2, obtaining the compatibility probability R2 of the sensitive equipment in the C region as 1-P2;
setting the temporary drop amplitude U and the duration T as mutually independent variables, and obtaining the fault probability P3 of the sensitive equipment in the area A as P1. P2;
and obtaining the compatibility probability R3 of the sensitive equipment in the A region as 1-P3 according to the sensitive equipment fault probability P3.
7. The method for analyzing compatibility between sensitive equipment and grid voltage sag according to claim 6, wherein the step of obtaining the compatible times of the sensitive equipment and the grid voltage sag according to the compatibility probability of the sensitive equipment in the uncertain region, the fault sample data of the uncertain region of the sensitive equipment and the sag sample data of the normal operation region of the sensitive equipment comprises:
acquiring the number N2, N '2 and N' 2 of sag times of the sensitive equipment in regions A, B and C respectively, and the number N1 of sag times of the sensitive equipment in a normal operation region;
and obtaining the compatible times N of the sensitive equipment with the voltage sag of the power grid according to the compatibility probabilities R1, R2 and R3 of the sensitive equipment in regions A, B and C, namely N1+ N2 × R1+ N '2 × R2+ N' 2 × R3.
8. A sensitive equipment and grid voltage sag compatibility analysis device is characterized by comprising:
the fault sample acquisition unit is used for generating fault sample data of the uncertain region of the sensitive equipment according to the tolerance curve of the sensitive equipment;
the data fitting unit is used for acquiring sampling sample data from the fault sample data of the uncertain region of the equipment according to a preset equal-interval sampling rule and fitting the sampling sample data to obtain a data fitting point;
a compatible probability obtaining unit, configured to obtain a compatible probability of the sensitive device in an uncertain region according to the data fitting point, a pre-established device-side failure probability function and a power grid-side probability density function; the equipment side fault probability function is a fault probability density function of a tolerance curve uncertain region of the sensitive equipment; and the power grid side probability density function is a probability distribution function of the sag amplitude and the duration of the power grid.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the method steps of any of claims 1-7 are implemented when the program is executed by the processor.
CN201810443088.2A 2018-05-10 2018-05-10 Sensitive equipment and power grid voltage sag compatibility analysis method and device Active CN108830456B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810443088.2A CN108830456B (en) 2018-05-10 2018-05-10 Sensitive equipment and power grid voltage sag compatibility analysis method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810443088.2A CN108830456B (en) 2018-05-10 2018-05-10 Sensitive equipment and power grid voltage sag compatibility analysis method and device

Publications (2)

Publication Number Publication Date
CN108830456A CN108830456A (en) 2018-11-16
CN108830456B true CN108830456B (en) 2021-02-02

Family

ID=64147776

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810443088.2A Active CN108830456B (en) 2018-05-10 2018-05-10 Sensitive equipment and power grid voltage sag compatibility analysis method and device

Country Status (1)

Country Link
CN (1) CN108830456B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109283416A (en) * 2018-11-20 2019-01-29 广东电网有限责任公司 A kind of calculation method and device of sensitive equipment failure rate
CN111260188B (en) * 2020-01-08 2022-06-07 国网福建省电力有限公司 Voltage sag treatment device configuration method
CN111308977B (en) * 2020-03-05 2021-03-23 四川大学 Industrial process modeling method for sag and short-time interruption severity assessment and treatment
CN113049928B (en) * 2021-03-10 2023-06-09 海南电网有限责任公司电力科学研究院 Sub-process failure evaluation method in industrial process voltage sag
CN113315133A (en) * 2021-05-31 2021-08-27 广东电网有限责任公司广州供电局 Control method and device of voltage sag treatment device and electronic equipment
CN113675842B (en) * 2021-07-27 2024-04-12 华南理工大学 Voltage sag compatibility evaluation method, system, device and medium

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102901895A (en) * 2012-09-29 2013-01-30 上海市电力公司 Method for evaluating voltage dip sensitivity of sensitive equipment
CN103424654A (en) * 2013-08-02 2013-12-04 华南理工大学 Method for assessing voltage sag sensitivity of sensitive equipment

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102901895A (en) * 2012-09-29 2013-01-30 上海市电力公司 Method for evaluating voltage dip sensitivity of sensitive equipment
CN103424654A (en) * 2013-08-02 2013-12-04 华南理工大学 Method for assessing voltage sag sensitivity of sensitive equipment

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Experimental Study on the Tolerance Ability of Adjustable Speed Drives to Voltage Sags;Chenyi Li等;《The 2nd International Conference on power and renewable energy》;20171231;第320-325页 *
敏感设备电压暂降故障水平的多不确定性评估;肖先勇等;《中国电机工程学报》;20100405;第36-42页 *
电铁牵引负荷扰动电流概率分布模型研究;王亚奇;《中国优秀硕士学位论文全文数据库 工程科技II辑》;20180315;第C042-1498页 *
考虑不确定性的电压暂降评价指标与评估方法研究;艾丽;《中国优秀硕士学位论文全文数据库 工程科技II辑》;20170115;第C042-328页 *

Also Published As

Publication number Publication date
CN108830456A (en) 2018-11-16

Similar Documents

Publication Publication Date Title
CN108830456B (en) Sensitive equipment and power grid voltage sag compatibility analysis method and device
Chen et al. Efficient identification method for power line outages in the smart power grid
Hou et al. Cluster computing-based trajectory sensitivity analysis application to the WECC system
CN110751385B (en) Non-invasive load identification method, terminal device and storage medium
RU2018114569A (en) METHOD FOR IDENTIFYING FAILURE EVENTS IN THE ELECTRIC DISTRIBUTION NETWORK SECTOR
Chusovitin et al. Three-phase state estimation model for distribution grids
CN110275076B (en) Method and device for testing immunity of equipment voltage sag, computer equipment and medium
CN113189500B (en) Battery electric quantity calculation method and device, computer equipment and storage medium
CN108802628B (en) Method, device and equipment for measuring battery electric quantity
CN112659927B (en) Method, apparatus, device and medium for determining performance characteristics of fuel cell stack for vehicle
CN110544031B (en) Method and device for predicting reliability of electric energy meter, computer equipment and storage medium
JP6161783B2 (en) Method for obtaining impedance of power transmission / distribution network by computer support, power generation apparatus and computer program for implementing the method
Bolzoni et al. Real-time auto-regressive modelling of electric power network frequency
Dambrowski Validation of impedance-data and of impedance-based modeling approach of electrochemical cells by means of mathematical system theory
Rizvi et al. Real-time ZIP load parameter tracking using adaptive window and variable elimination with realistic synthetic synchrophasor data
CN112269059B (en) Power grid load test method and device, computer equipment and storage medium
CN110929999B (en) Voltage sag severity calculation method considering tolerance characteristics of different devices
CN110196354B (en) Method and device for detecting switching event of load
CN109193563B (en) Current loss fault monitoring method and device based on three-phase three-wire meter equipment
CN110187167B (en) Method and device for detecting load switch event based on manifold classification
Fu et al. Bayesian estimation based parameter estimation for composite load
Addy et al. Understanding the effect of baseline modeling implementation choices on analysis of demand response performance
Pal Effect of different load models on the three-sample based quadratic prediction algorithm
Angulo-Paniagua et al. Comparing chi-square-based bad data detection algorithms for distribution system state estimation
Huang et al. A recursive Bayesian approach to load phase detection in unbalanced distribution system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right

Effective date of registration: 20200916

Address after: 510620 Tianhe District, Guangzhou, Tianhe South Road, No. two, No. 2, No.

Applicant after: Guangzhou Power Supply Bureau of Guangdong Power Grid Co.,Ltd.

Address before: 510620 Tianhe District, Guangzhou, Tianhe South Road, No. two, No. 2, No.

Applicant before: GUANGZHOU POWER SUPPLY Co.,Ltd.

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant