CN102361353B - Method for aligning section raw data on basis of double time scale delay evaluation - Google Patents
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Abstract
The invention relates to a method for aligning section raw data on the basis of double time scale quantitative evaluation measurement point delay and performing prediction and modification. By the method, multi-source section raw data with different delay can be aligned. According to data delay differences among different regions, different stations and different transmission types, the method comprises the following steps of: quantitatively evaluating delay time of each measurement point by using sampling sequences with two time scales; predicting and modifying the current numerical value through an autoregression-sliding average model; evaluating the precision grade of the numerical value and a delay distribution interval; and providing for a state estimator. By the method, the data delay of different measurement points can be quantified accurately; the current section numerical value of the measurement point can be predicted and modified according to the delay; and precision weightis endowed, so that the aligned multi-source real-time data is integrated into a section closer to the operation condition of the current power grid.
Description
Technical field
The invention belongs to the power system automation technology field, the control centre's multi-source that relates to electric power system is more specifically given birth to the method for online data alignment.
Background technology
Being interconnected at of electrical network realizes wider most optimum distribution of resources, thereby when producing great economic benefit, brings problems also for operation, analysis and the coordination control of electrical network.Because the fault of adjacent electrical network generation can directly have influence on the safety of internal electric network, therefore in electricity net safety stable is analyzed, need model and the real-time profile data of adjacent electrical network.
Section refers to reflect the set that analog quantity such as meritorious, idle, the voltage, electric current of a certain moment power system operation operating mode and switching value data such as switch, disconnecting link state constitute.In control centre's database, at any one time, there is the numerical value in multiple source in each measuring point, comprise RTU measured value, PMU measured value, the state estimation value of discontinuity surface last the time, and power of the assembling unit planned value of providing of generation schedule, the load power predicted value that load prediction provides etc., all there is certain error in the actual value of they and current time, is referred to as section and gives birth to data.
The modern power network control centre mainly obtains the real-time power network running state information by two kinds of metric data acquisition systems.A kind of is that traditional data are collected and supervisory control system (SCADA:Supervisory Control And Data Acquisition), and it obtains analog quantity and the quantity of state of reflection power operating state by the remote-terminal unit (RTU:Remote Terminal Unit) that is installed in transformer station and power plant.Another kind is emerging in recent years wide area measurement system (WAMS:Wide Area Measurement System), target analog quantity when synchronous phasor measurement unit (PMU:PhasorMeasurementUnit) collection in its transformer station by being installed in backbone network and power plant has the GPS high-precise synchronization.Because technology and economic dispatch factor, in quite long period, WAMS can't substitute SCADA, and both will exist simultaneously.
Therefore, in the real-time data base of control centre, the metric data that has two kinds of forms (dimension): the continuous cross-section data of band GPS synchronous time mark (be that each measuring point has been stored the sequence of values in a period of time, rather than a single point) and the single profile data of target (each measuring point has only been stored a point) when not having.The continuous cross-section data are gathered by the PMU in the WAMS system, a plurality of sections (being generally 10 seconds) of storage certain time length, on send frequency generally at 25～100Hz, PMU measures has only a few tens of milliseconds time-delay, owing to have the GPS synchronous time mark, can think that the data of each measuring point were gathered in the identical moment; Single profile data comprises the data of RTU image data and the EMS of other control centres system forwards in the SCADA system, and the data that RTU gathers generally have several seconds time-delay, and the RTU time-delay at different factory station is also had nothing in common with each other; The time-delay of the data of transmitting by the EMS of other control centres is then longer, and two kinds of forwarding forms are generally arranged: a kind of is to transmit remote measurement, remote signalling data in real time, and time-delay in 10～30 seconds is generally arranged; Another kind is to transmit EMS state estimation result, and general time-delay can reach a few minutes.China electrical network carry out state, net, province,, county's graded dispatching, electric network model and the data acquisition of the own administrative area of the EMS of control centre system maintenances at different levels, the model of higher level control centre is generally formed by each subordinate's control centre's model splicing, except straight accent factory station data is direct collection, other data are generally transmitted by the subordinate control centre.Therefore, there is very big-difference in the delay between the data.
All do not have synchronous time mark because SCADA data and EMS transmit data, its time-delay can not directly obtain.Traditional section is given birth to data and is integrated the delay difference of ignoring different measuring points usually, perhaps the error that time-delay is caused is used as the error in measurement processing, perhaps simply think only to postpone one-period, do not have the time of delay of qualitative assessment different measuring points, carry out corresponding data correction then.If the data that these several delay differences are very big are simply sent into state estimator, can restrain even calculate, the profile data that obtains also certainly will depart from the electrical network actual operating mode, particularly works as system loading and changes the tangible period.The application's method can accurately quantize the data time-delay of different measuring points, and revise the numerical value of the current section of measuring point according to time-delay prediction, and estimate its numerical precision grade and time-delay distributed area, a metric data section of pressing close to the current actual operating mode of electrical network more is provided.
Summary of the invention
There is time-delay in various degree at present control centre multi-source real time data but do not carry out quantitative estimation and revise the problem of its delay time error.The present invention proposes a kind ofly based on dual-time yardstick qualitative assessment measuring point time-delay and predict correction, be fit to the profile data alignment schemes of large scale electric network.
The present invention is specifically by the following technical solutions:
A kind of section that time-delay is estimated based on the dual-time yardstick is given birth to the alignment of data method, this method is at the difference of zones of different, different factory station, the time-delay of different transport-type data, the sample sequence of two kinds of time scales of employing is quantitatively estimated the time of delay of each measuring point, revise the currency of each measuring point data by autoregression-moving average model prediction, be integrated into a section of pressing close to current operation of power networks operating mode more thereby the real-time section of multi-source is given birth to data; It is characterized in that: described section is given birth to the alignment of data method and be may further comprise the steps:
(1) to sending the real time data of transmitting with the EMS of other control centres to carry out the sampling of dual-time yardstick in real time through synchronous phasor measurement unit PMU, remote-terminal unit RTU, the time window of sampling and sampling density are according to the time-delay distributed area setting of measuring point statistics separately, the long time scale sample sequence adopts first sampling step length big than second sampling step length, is used for locating postpone roughly interval according to the overall trend of corresponding measuring point real time data time-delay; Short time yardstick sample sequence adopts second sampling step length little than first sampling step length, is used for that real time data is carried out further accurately time-delay and estimates;
(2) when the section integration period arrives, two groups of sample sequences to the dual-time yardstick, be reference with the synchronous phasor measurement unit PMU sampling real time data sequence that has the GPS synchronous time mark, the generalized correlation Y-factor method Y that adopts time delay to estimate is estimated the time of delay of the real time data sequence that remote-terminal unit RTU sampling real time data sequence and the EMS of other control centres transmit, at first locate remote-terminal unit RTU sampling real time data with the long time scale sample sequence respectively according to the overall trend of corresponding measuring point real time data time-delay, the real time data time of delay roughly that the EMS of other control centres transmits, use short time yardstick sequence to remote-terminal unit RTU sampling real time data then, the real time data that the EMS of other control centres transmits is further accurate to be estimated to delay time, and obtains the delay estimated value D of each measuring point;
(3) select step-length based on each measuring point time-delay estimated value D, adopt the currency of each measuring point data of autoregression-moving average (ARMA) model prediction, for the currency of predicting the real time data of each measuring point collection that obtains according to each measuring point time-delay estimated value D, the weight of this currency precision of reflection is set, time of delay, more little weight was more big, vice versa, and the weight value is between (0,1);
(4) store the currency of each measuring point real time data, delay time estimated value D and weight, carry out the interval statistical analysis that distributes of time-delay of a plurality of sections, the alignment profile data of revising and the data assessment report that generates each measuring point obtain delaying time.
The beneficial effect of the inventive method is, employing sequence based on the dual-time yardstick, realize first each measuring point because the quantitative estimation of the time of delay that cause at zones of different, different factory station, different transmission means, and go out each by the continuous sampling sequence prediction and postpone measuring point at the numerical value of current section, the precision difference that weight embodies predicted value is set, and can improve the back at measuring point data and dynamically revise the sampling time window and adopt step-length, be applicable to the section alignment of interconnected network multi-source real time data.
Description of drawings
Fig. 1 is the principle flow chart of the inventive method.
Fig. 2 is that the dual-time yardstick postpones to estimate schematic diagram.
Embodiment
Below in conjunction with Figure of description technical scheme of the present invention is described in further detail.
The present invention proposes the section alignment schemes of quantitatively delaying time and estimating and predict correction based on the real time data of dual-time yardstick, the real-time sequence of the band GPS synchronous time mark that this method can be gathered according to PMU, the time-delay of send data on the different RTU of quantitative estimation, the EMS of other control centres transmitting data, and the error by predicting that the correction time-delay causes, make the transmission data in various sources be integrated into a section of more pressing close to current actual operating mode, reduce not matching of whole network data greatly.Its step following (Fig. 1 is the principle flow chart of the inventive method):
(1) to sending the real time data of transmitting with EMS to carry out the sampling of dual-time yardstick in real time through synchronous phasor measurement unit PMU, remote-terminal unit RTU, the time window of sampling and sampling density arrange according to the time-delay distributed area of measuring point statistics, the sampling step length that the employing of long time scale sample sequence is bigger (5s～60s), be used for locating postpone roughly interval according to overall trend, short time yardstick sample sequence adopts less sampling step length (0.1s～1s) be used for further accurate time-delay to estimate.Net, provincial control centre all set up the WAMS main website at present, receive the PMU high sampling rate data (25Hz that send on each PMU substation, area under one's jurisdiction, 50Hz, 100Hz), a plurality of sections of general storage a period of time (for example 10 seconds), no matter be real remote measurement, the remote signalling that SCADA gathers, or the estimated result data of other EMS system forwards all are the sections of up-to-date warehouse-in, do not have accurate markers in the EMS system.
In step 1, preferably according to the time-delay distributed area of data transmission and statistics, time-delay can be divided into second level, ten seconds levels and minute level, corresponding time windows and the sample rate of adopting, short time window sampling is equivalent to that several step-lengths of long-time window sequence are carried out the part amplifies, and both cooperate to reduce memory data output and amount of calculation; For the measuring point that PMU does not gather, step-length adopts a second level to get final product, and is used for distinguishing data transmission, and for example EMS transmits data needs a few minutes to upgrade once.Preferred first step-length is 5s-60s, and second step-length is 0.1s-1s.
(2) when the section integration period arrives, adopt the generalized correlation Y-factor method Y, by two groups of sample sequences of dual-time yardstick, be reference with the PMU sequence that has the GPS synchronous time mark, estimate that RTU sample sequence and EMS transmit the time of delay of sequence.At first with the long time scale sample sequence according to overall trend location time of delay roughly, estimate time-delay with short time yardstick sequence is further accurate then, thereby quantize D time of delay of measuring point;
In step 2, the WAMS sample sequence x (n) of measuring point and SCADA/EMS sample sequence y (n) are to twice of same measuring point separate sampling, can obtain two time-delay estimated values between sequence by the seasonal effect in time series cross-correlation method, consider the observation noise of WAMS and SCADA, in order to obtain to delay time preferably estimated result, need carry out smoothly can adopting the broad sense cross-correlation method to cross-correlation function:
R
xy(τ)＝F
-1[P
xy(w)W(w)]＝R
xy(τ)*ω(τ)(1)
* represents convolution in the formula, and P
Xy(w)=F[R
Xy(τ)] be cross-correlation function R
XyFourier conversion (τ), the i.e. crosspower spectrum of x (n) and y (n).The smoothing windows function can adopt level and smooth correlating transforms window, maximum likelihood window or Hannan-Thompson window.
1. curve is the acquisition sequence of PMU band GPS synchronous time mark among Fig. 2, and curve 3. gathers for SCADA or EMS transmits sequence, and the time-delay estimation by long time scale is subjected to the restriction of first step-length, only can estimation curve 2. and the time-delay Δ T of curve between 3.
1, it is the integral multiple of first step-length, and less than the time-delay Δ T of first step-length
2, namely curve 2. with curve time-delay 1., it is the integral multiple of second step-length, need further to estimate with short time yardstick sample sequence, thereby obtain curve 1. and the time-delay of curve between 3. be Δ T=Δ T
1+ Δ T
2
(3) based on each measuring point time-delay estimated value D, adopt autoregression-moving average (ARMA) model prediction to postpone the currency of measuring point.For the currency that obtains according to the time-delay prediction, the weight of its precision of reflection is set, it is more big to postpone more little weight, and vice versa, and the weight value can be between (0,1);
In step 3, based on each measuring point time-delay estimated value D, adopt autoregression-moving average (ARMA) model prediction to postpone the currency of measuring point, that is: not only relevant with the value x (k-n) in its preceding N step at k value x (k) constantly, but also it is relevant with the disturbing signal value a (k-m) in preceding M step, can set up ARMA as follows (N, M) model to it so.
(4) storage time-delay estimated result and weight, in order to carry out the interval statistical analysis that distributes of the time-delay of a plurality of sections, and the alignment profile data that the output time-delay is revised further handles for subsequent module.
In step 4, for the currency according to the time-delay prediction, should distinguish its precision, therefore, the weighted value of reflection precision can be set, and it is more big to postpone more little weight, and vice versa, the weight value can be between (0,1), and subsequent module can be further processed according to the precision weight.The interval statistics that distributes of time-delay is used for improving time window and the sampling step length setting of dual-time yardstick sampling.
In order to improve the practicality of method, the present invention is also according to characteristics such as electrical network multi-zone supervision and geographical distributions, can divide into groups to measuring point according to control area, factory station, the measuring point that adopts part PMU the to gather estimation of delaying time, represent to put in order the time-delay of organizing measuring point and predict correction, for unusual big data point time of delay, can use load prediction, unit output planned value etc. to substitute.
Claims (5)
1. the section that time-delay is estimated based on the dual-time yardstick is given birth to the alignment of data method, this method is at the difference of zones of different, different factory station, the time-delay of different transport-type data, the sample sequence of two kinds of time scales of employing is quantitatively estimated the delay time of each measuring point, revise the currency of each measuring point data by autoregression-moving average model prediction, be integrated into a section of pressing close to current operation of power networks operating mode more thereby the real-time section of multi-source is given birth to data; It is characterized in that: described section is given birth to the alignment of data method and be may further comprise the steps:
(1) to sending the real time data of transmitting with the EMS of other control centres to carry out the sampling of dual-time yardstick in real time through synchronous phasor measurement unit PMU, remote-terminal unit RTU, the time window of sampling and sampling density are according to the time-delay distributed area setting of measuring point statistics separately, the long time scale sample sequence adopts first sampling step length big than second sampling step length, is used for locating the roughly interval of time-delay according to the overall trend of corresponding measuring point real time data time-delay; Short time yardstick sample sequence adopts second sampling step length little than first sampling step length, is used for that real time data is carried out further accurately time-delay and estimates;
(2) when the section integration period arrives, two groups of sample sequences to the dual-time yardstick, be reference with the synchronous phasor measurement unit PMU sampling real time data sequence that has the GPS synchronous time mark, the generalized correlation Y-factor method Y that adopts time delay to estimate is estimated the delay time of the real time data sequence that remote-terminal unit RTU sampling real time data sequence and the EMS of other control centres transmit, at first locate remote-terminal unit RTU sampling real time data with the long time scale sample sequence respectively according to the overall trend of corresponding measuring point real time data time-delay, the real time data delay time roughly that the EMS of other control centres transmits, use short time yardstick sample sequence to remote-terminal unit RTU sampling real time data then, the real time data that the EMS of other control centres transmits is further accurate to be estimated to delay time, and obtains the time-delay estimated value D of each measuring point;
(3) select step-length based on each measuring point time-delay estimated value D, adopt the currency of each measuring point data of autoregression-moving average (ARMA) model prediction, for the currency of predicting the real time data of each measuring point collection that obtains according to each measuring point time-delay estimated value D, the weight of this currency precision of reflection is set, the more little weight of delay time is more big, vice versa, and the weight value is between (0,1);
(4) store the currency of each measuring point real time data, delay time estimated value D and weight, carry out the interval statistical analysis that distributes of time-delay of a plurality of sections, the alignment profile data of revising and the data assessment report that generates each measuring point obtain delaying time.
2. section according to claim 1 is given birth to the alignment of data method, it is characterized in that:
The long time scale sampling step length is determined according to the delay time distributed area of past statistics in the step (1), can be divided into a second level, ten seconds levels and minute level; The corresponding adjustment of short time yardstick sampling step length is to guarantee the estimated accuracy of delaying time.
3. section according to claim 2 is given birth to the alignment of data method, it is characterized in that:
First sampling step length that described long time scale sample sequence adopts is 5s-60s, and second sampling step length that described short time yardstick sample sequence adopts is 0.1s-1s.
4. section according to claim 1 is given birth to the alignment of data method, it is characterized in that:
The precision weight of predicted value is determined according to length and the historical evaluated error of delay time in the step (3), sends into state estimator as the reference measurement weight of measuring point.
5. section according to claim 1 is given birth to the alignment of data method, it is characterized in that:
The time-delay estimated value of each measuring point and precision weight can regularly be carried out statistical analysis in the step (4), generate form.
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| CN102902894B (en) * | 2012-10-29 | 2015-04-22 | 东北电网有限公司 | Method for evaluating the data quality and estimating the angle error of PMU (Phasor Measurement Unit) of control center based on difference comparison |
| CN103955623B (en) * | 2014-05-15 | 2017-02-08 | 华北电力大学 | External network measuring and sampling error correction method based on prediction and correction algorithm |
| CN104462813B (en) * | 2014-12-05 | 2017-07-14 | 国家电网公司 | A kind of double yardstick data fusion methods of transformer station's structure changes |
| CN104392069B (en) * | 2014-12-11 | 2017-09-08 | 国家电网公司 | A kind of WAMS delay character modeling method |
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| CN114785441B (en) * | 2022-04-22 | 2024-03-22 | 湖南腾河智慧能源科技有限公司 | Method, system, equipment and storage medium for synchronizing cycle-level power data |
| CN115117887B (en) * | 2022-08-09 | 2023-01-03 | 中国电力科学研究院有限公司 | Power grid regulation and control operation simulation time sequence power flow simulation method, device, equipment and medium |
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| CN101807798B (en) * | 2010-04-20 | 2013-01-16 | 国网电力科学研究院 | Section data integration method for power system safety and stability online analysis |
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