KR20150002914A - Data estimation method of power distribution - Google Patents
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Abstract
Description
The present invention relates to a power system data estimation method, and more particularly, to a power system data estimation method capable of ensuring data integrity by determining defective data or deficient data from measured values acquired in a power system and estimating the state will be.
With the development of the industry and the improvement of the standard of living of the people, the demand for electric power worldwide is rapidly increasing year by year. Accordingly, securing the reliability and reliability of the power system has become a very important issue in the system operation. To solve this problem, it is very important to secure a precise system analysis capability.
Precise modeling of the power system must precede accurate system analysis. At present, equipment related to power infrastructure located in power generation and transmission are relatively inaccurate, even though precise models have been developed. Therefore, the accuracy of the load model is the most important factor that determines the accuracy of the system analysis results.
Load modeling is a process of defining the response characteristics of loads in response to voltage fluctuations and is divided into individual load-based load modeling and measurement-based load modeling according to the modeling method. Individual load-based load modeling is not burdensome to install measuring equipment, but is not suitable for reflecting the characteristics of a rapidly varying load, so research is not active at present.
On the other hand, a technique for performing load modeling based on the Kalman filter algorithm is disclosed in Korean Patent Laid-Open Publication No. 2013-0004742 (load modeling method and system).
The load modeling method for designing a model representing a load characteristic of the power system for analyzing a power system and stably operating the power system using the analysis result includes the steps of collecting power data and voltage data ; And estimating a load model parameter by applying a Kalman filter to the power data and the voltage data.
In addition, measurement-based load modeling has the disadvantage of requiring measurement equipment for every bus line to be modeled. However, since the accuracy of the load model can be improved by directly measuring and reflecting the characteristics of the load to be modeled, Based modeling.
On the other hand, measurement-based load modeling is divided into dynamic load modeling and static load modeling. Dynamic load modeling has the advantage of accurately reflecting the transient characteristics of the load, but this requires very frequent data acquisition. Static load modeling, on the other hand, does not reflect the transient nature of the load, but has the advantage of being able to model using data acquired at relatively low frequencies. In other words, it is impossible to construct a dynamic load model when equipment capable of high frequency data measurement such as Digital Fault Recorder (DFR) and Phasor Measurement Unit (PMU) can not be installed on all buses. Characteristics.
Dynamic load models include state variable equation models and induction motor models. The state variable equation model has the advantage of accurately reflecting the characteristics of the load, but it has the disadvantage that it can not represent the physical meaning of the load. On the other hand, the induction motor model can properly reflect the physical meaning of the induction motor load which occupies most of the actual load.
On the other hand, the static load model has the ZIP model and the exponential model, and the ZIP model has the advantage of showing the physical meaning of the load.
In order to operate the power system, various analytical techniques exist, and the data is acquired from each power plant, substation and distribution station and operated on the basis of it. However, the analog data thus acquired contains errors. Therefore, topology analysis and state estimation of the system are performed so that the data acquired from each power plant, substation, and distribution station is processed in real time and used as information for analysis of the power system. Through this process, data having errors and deficient parts are processed, .
Here, although the state estimation technique of the power system has been approached by various methods, it is very rare that the acquired data is processed and analyzed in real time in the operational aspect. The reason for this is that the technology for real-time system analysis and its operation is not completely secured, and even if the analysis algorithm is completed, it is necessary to determine the degree of reliability of acquired data to filter out and correct deficient data and defective data It is difficult.
In addition, in order to secure data integrity in a power system, there has been a problem in that data is manipulated by hand and there are some error and error values in the data, so that it is difficult to apply the systematic information to be interpretable.
The present invention relates to a power system capable of ensuring data integrity by using measurement techniques obtained from a power system including bad data or insufficient data and converting the measurement values into final result values that can be analyzed in a power system using Newton-Raphson method Of the data.
Among the embodiments, a power system data estimating method is a power system data estimating method for real-time processing data acquired from a power system and applying the acquired data to an analysis of a power system to stably operate the power system comprising the steps of: a) obtaining a measured value for state estimation including voltage (V), current (I), active power (P) and reactive power (Q) in said power system; b) calculating an objective function that minimizes an error using the measurement value, the weight, and the error by applying the Weight Least Square (WLS) technique with the current power system as a constraint condition; c) calculating a supply / demand equation by summing up the outflow power of the line on a bus line basis, the expectation value corresponding to the actual measured value acting as a variable, the line outflow power amount being expressed as a function, d) deriving a result value including bad data or insufficient data by applying the Newton-Raphson method using the optimization technique to the supply / demand equation; And e) removing the bad data or insufficient data from the result value from the objective function to which the WLS technique is applied, and performing state estimation to calculate a final result value
In this case, in step b), the weight may be set to '0' when the measured value is bad data or insufficient data.
The step d) comprises the steps of: d-1) expressing the supply and demand equation by a Lagrangian function by applying an optimization problem having an equal constraint condition in which the input power and the bus power of each bus are the same; d-2) deriving a first-order differential vector and a second-order differential matrix by differentiating the Lagrangian function by a first derivative and a second derivative; And d-3) performing a state estimation including an error including the defective data or the deficient data by performing a tidal calculation by inputting a random error to the measured value.
The objective function can be expressed by Equation (1) below, and the supply / demand equation can be expressed by Equation (3) below, and the Lagrangian function can be expressed by Equation (5) below.
The power system data estimation method of the present invention utilizes optimization techniques, including bad data or insufficient data, obtained from a power system, and converts the measurement values into final result values that can be analyzed in the power system using the Newton-Raphson method, The system information can be quickly derived by analyzing the power system using the integrity data, and the system can be usefully used to develop an operation support system through analysis of the power system.
Brief Description of the Drawings Fig. 1 is a diagram for explaining a general nine bus line sample system according to one embodiment of the present invention; Fig.
Figure 2 is a diagram illustrating a typical facility-based 9-bus sample system;
3 is a flowchart for explaining a power system data estimation method according to an embodiment of the present invention;
4 is a view for explaining π equivalent line modeling with reference to a bus line;
5 shows a normal distribution curve (? = 26.1229) based on Table 6;
The description of the present invention is merely an example for structural or functional explanation, and the scope of the present invention should not be construed as being limited by the embodiments described in the text. That is, the embodiments are to be construed as being variously embodied and having various forms, so that the scope of the present invention should be understood to include equivalents capable of realizing technical ideas. Also, the purpose or effect of the present invention should not be construed as limiting the scope of the present invention, since it does not mean that a specific embodiment should include all or only such effect.
Meanwhile, the meaning of the terms described in the present invention should be understood as follows.
The terms "first "," second ", and the like are intended to distinguish one element from another, and the scope of the right should not be limited by these terms. For example, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.
It is to be understood that when an element is referred to as being "connected" to another element, it may be directly connected to the other element, but there may be other elements in between. On the other hand, when an element is referred to as being "directly connected" to another element, it should be understood that there are no other elements in between. On the other hand, other expressions that describe the relationship between components, such as "between" and "between" or "neighboring to" and "directly adjacent to" should be interpreted as well.
It should be understood that the singular " include "or" have "are to be construed as including a stated feature, number, step, operation, component, It is to be understood that the combination is intended to specify that it does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.
In each step, the identification code (e.g., a, b, c, etc.) is used for convenience of explanation, the identification code does not describe the order of each step, Unless otherwise stated, it may occur differently from the stated order. That is, each step may occur in the same order as described, may be performed substantially concurrently, or may be performed in reverse order.
All terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, unless otherwise defined. Commonly used predefined terms should be interpreted to be consistent with the meanings in the context of the related art and can not be interpreted as having ideal or overly formal meaning unless explicitly defined in the present invention.
FIG. 1 is a diagram for explaining a general 9-bus line sample system, and FIG. 2 is a diagram for explaining a general equipment-based 9-bus line sample line. 1 and 2, the numbers are the admittance values in PU units. 3 is a flowchart for explaining a power system data estimation method according to an embodiment of the present invention.
Referring to Fig. 1, the substation has three generation generators and three load generators. However, in reality, the power plant and the load are equivalent, and as a system consisting of three substations and three power stations, .
Referring to Figs. 2 and 3, a power system data estimation method acquires measurement values for state estimation. In this case, in FIG. 2, the facility reference means a data acquisition location, meaning that almost all data are obtained at each of the distribution stations and the substations. Variables V, I, P and Q can be obtained at substations and distribution stations, Are used as measurement values for state estimation. (Refer to step S1)
When obtaining the voltage of each bus, the active power flowing into each line, and the reactive power, the variables can be summarized as shown in Table 1 below. However, the transformer in Table 1 is omitted.
[Table 1]
The measured value obtained from each object can be weighted least square (WLS) technique to minimize error. Here, the error is the difference between the measured value and the expected value of each variable, that is,
If all the measurable variable vectors are X and each weight vector is W, the formula for minimizing the error using the WLS technique can be summarized as shown in Equation 1 below.
[Equation 1]
In Equation (1), the constraint condition h (x) = 0 represents the supply / demand condition of the current power system. Objective function
[Table 2]
As shown in Table 2, the expected value corresponding to the actual measured value can be used as a variable and also as a function. In this case, the error function is represented by the difference between the expected value and the measured value, and the function obtained by adding the squared error to the weighted value is the objective function.
In the WLS technique, the weights determine to what extent the measurement will affect the expected value calculation in the calculation. Therefore, the accuracy of the measured values is a major factor in applying the weights.
[Table 3]
As shown in Table 3, the typical specifications of the instrument show that the voltage is ± 0.2% and the power is ± 0.5%. Therefore, the weight should be a relatively larger value for the voltage value, and a value that is arithmetically 2.5 times larger than the weight of the power measurement value may be used.
The calculated value for this weight is to use a value that is two times larger than the weight of the power measurement value when the error of all the measuring equipments is correct. If the error of the measured value is very large, this weight should be relatively small , The weight should be set to zero if bad data can not be acquired or data can not be acquired due to a system problem.
4 is a diagram for explaining π equivalent line modeling with reference to a bus line.
Referring to FIG. 4, the expected value is a list of variables or functions corresponding to the measured values, and the line outlet power, which acts as a variable but flows into the line, can be expressed as a function. The line outlet power can be expressed as a function (2).
&Quot; (2) "
The equation of supply and demand is expressed by summarizing Equation (2) as a bus-base, and a matrix generated based on line impedance and connection information (fr, to)
&Quot; (3) "
Inlet power of each bus
&Quot; (4) "
Equation (1) is expressed by the following equation (5) if the optimization problem having an equal-degree constraint is expressed by a Lagrangian function.
&Quot; (5) "
Equation (5) is a one-system differentiated optimal condition equation is as shown in Equation (6).
&Quot; (6) "
&Quot; (7) "
If Equation (7) is expressed again for each variable, a first-order differential vector and a second-order differential matrix can be derived as Equations (8) and (9).
&Quot; (8) "
&Quot; (9) "
The initial values of the simulations of the nine buses shown in Fig. 1 are shown in the following Table 3.
[Table 4]
Assuming that the initial value of Table 4 is a measured value, a random error (voltage ± 0.2%, power ± 0.5%) for the measured value is input, and Equation 8 and Equation 9 using the optimization technique are used (See step S4), the following results can be obtained.
[Table 5]
As shown in Table 5, the optimization technique allows the dL value to be less than 10 -5 And the equilibrium of supply expressed in Equation 3 is down to 10 -8 or less.
The results of the state estimation including the error show that the estimation of the voltage level falls below the error range of 0.01%. In the case of the power variable, the error range falls to 0.3% or less due to the characteristics of the voltage variation.
However, in the case of a normal integrity data value, the state estimation approximates the actual value by perfectly matching the supply and demand equation, but it can be estimated by correcting the bad data value when a bad data value is input. For example, assuming that the V5 value is bad data, the following Table 6 can be obtained.
[Table 6]
5 is a diagram showing a normal distribution curve (? = 26.1229) based on Table 6.
Since the value calculated in Table 6 is a measurement value which is not an actual value and the resultant value is estimated based on the measurement result, when the standard deviation is obtained based on the measurement result reference error, the expected value is 0, so σ = 26.1229, Based on this, a normal distribution curve is drawn as shown in FIG.
Based on this, it can be said that a value that is out of 2σ, that is, out of 95.4% is not a normal result (-52.24 <x <+52.24). Therefore, if the error values are V5 and Qd1 and the state is estimated after removing the WLS equation from these variables, an accurate value can be found as shown in Table 7 (refer to step S5).
[Table 7]
As described above, the defective data value includes an error value, and the voltage level with respect to the actual value is estimated within an error range of 0.01% or less, and the power value is estimated within an error range of 0.3% or less. If the calculation error that eliminates the maximum error is not within 1%, it is necessary to check and remove the error value again from the current result value. To increase the accuracy, remove only the confirmed bad data V5, Can be more accurately estimated using the measured values of the values excluded from the calculation including V5.
This state estimation calculation can be performed using MATLAB, and the result can be obtained through three or four iterations. In addition, even if there are bad data, if the error value is found and redefined, it is possible to obtain an almost perfect result (voltage error ± 0.01%, power error ± 0.3%) through about 7 to 8 state estimation calculations . This makes it possible to quickly obtain real interpretable power system information, and it is very useful in developing an operational support system through analysis.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the present invention as defined by the following claims It can be understood that
Claims (6)
comprising the steps of: a) obtaining a measured value for state estimation including voltage (V), current (I), active power (P) and reactive power (Q) in said power system;
b) calculating an objective function that minimizes an error using the measurement value, the weight, and the error by applying the Weight Least Square (WLS) technique with the current power system as a constraint condition;
c) calculating a supply / demand equation by summing up the outflow power of the line on a bus line basis, the expectation value corresponding to the actual measured value acting as a variable, the line outflow power amount being expressed as a function,
d) deriving a result value including bad data or insufficient data by applying the Newton-Raphson method using the optimization technique to the supply / demand equation; And
and e) removing the bad data or the insufficient data from the result value from the objective function using the WLS technique, and performing state estimation to calculate a final result value.
Wherein the step b) sets the weight to '0' when the measured value is bad data or insufficient data.
The step d)
d-1) expressing the supply and demand equation as a Lagrangian function by applying an optimization problem having an equal constraint condition in which the input power and the bus power of each bus are the same;
d-2) deriving a first-order differential vector and a second-order differential matrix by differentiating the Lagrangian function by a first derivative and a second derivative; And
d-3) performing a state estimation including an error including the defective data or the deficient data by performing a tidal calculation by inputting a random error to the measured value, Way.
Wherein the objective function is represented by the following equation: h (X) = 0 in the equation is a supply / demand condition of the current power system, and the variable vector of the measured value is X and each weight vector is W. / RTI >
The equation of supply and demand is based on a matrix generated based on line impedance and connection information (fr, to)
Wherein the Lagrangian function is expressed by the following equation.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
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| KR20160092242A (en) * | 2015-01-27 | 2016-08-04 | 전남대학교산학협력단 | Simplified bus voltage estimation method for changes in sending end voltage at radial network and estimation program |
| KR20160098896A (en) * | 2015-02-11 | 2016-08-19 | 엘에스산전 주식회사 | Method for detecting power value in a hvdc system |
| US10380101B2 (en) | 2015-07-29 | 2019-08-13 | Lsis Co., Ltd. | Apparatus and method for managing of database in energy management system |
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| KR20230069601A (en) * | 2021-11-12 | 2023-05-19 | 한국에너지기술연구원 | Voltage sensitivity measurement method and measurement device using distributed controller measurement value |
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| KR20160092242A (en) * | 2015-01-27 | 2016-08-04 | 전남대학교산학협력단 | Simplified bus voltage estimation method for changes in sending end voltage at radial network and estimation program |
| KR20160098896A (en) * | 2015-02-11 | 2016-08-19 | 엘에스산전 주식회사 | Method for detecting power value in a hvdc system |
| US10380101B2 (en) | 2015-07-29 | 2019-08-13 | Lsis Co., Ltd. | Apparatus and method for managing of database in energy management system |
| CN112660054A (en) * | 2021-01-05 | 2021-04-16 | 北京家人智能科技有限公司 | Method and device for triggering external airbags in grading manner and electronic equipment |
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| KR102596582B1 (en) * | 2022-12-23 | 2023-10-31 | 숭실대학교 산학협력단 | Integrated visualization method for analyzing large-scale time-series data of power system |
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