CN111900731A - PMU-based power system state estimation performance evaluation method - Google Patents

PMU-based power system state estimation performance evaluation method Download PDF

Info

Publication number
CN111900731A
CN111900731A CN202010746249.2A CN202010746249A CN111900731A CN 111900731 A CN111900731 A CN 111900731A CN 202010746249 A CN202010746249 A CN 202010746249A CN 111900731 A CN111900731 A CN 111900731A
Authority
CN
China
Prior art keywords
power system
state estimation
pmu
performance evaluation
value
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.)
Granted
Application number
CN202010746249.2A
Other languages
Chinese (zh)
Other versions
CN111900731B (en
Inventor
谢伟
陆超
宋文超
华斌
方陈
林俊杰
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.)
Tsinghua University
State Grid Shanghai Electric Power Co Ltd
Original Assignee
Tsinghua University
State Grid Shanghai Electric Power 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 Tsinghua University, State Grid Shanghai Electric Power Co Ltd filed Critical Tsinghua University
Priority to CN202010746249.2A priority Critical patent/CN111900731B/en
Publication of CN111900731A publication Critical patent/CN111900731A/en
Priority to AU2020327340A priority patent/AU2020327340B2/en
Priority to PCT/CN2020/134406 priority patent/WO2022021726A1/en
Application granted granted Critical
Publication of CN111900731B publication Critical patent/CN111900731B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • 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
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/22—Flexible AC transmission systems [FACTS] or power factor or reactive power compensating or correcting units

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Evolutionary Computation (AREA)
  • Evolutionary Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Power Engineering (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Educational Administration (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • Development Economics (AREA)
  • Artificial Intelligence (AREA)
  • Tourism & Hospitality (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Primary Health Care (AREA)
  • General Health & Medical Sciences (AREA)
  • Water Supply & Treatment (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Public Health (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to a PMU-based power system state estimation performance evaluation method, which specifically comprises the following steps: actual measurement of measurement value S of observation object by PMU of power systemmObtaining SmState estimation value S ofseWill SmAnd SseInputting a trained classification model to obtain classification accuracy and a marking value and calculating a state estimation performance evaluation index, wherein the training process comprises the following steps: obtaining a measurement error data set X of an observed object through a power system simulation platform, carrying out normalization processing on the X and calculating a probability density function of the X
Figure DDA0002608452500000011
Obtaining S through a power system simulation platformtBy superposition of StAnd
Figure DDA0002608452500000012
to obtain SmObtaining each SmState estimation value S ofse，αmTaking 0 or 1, using St、Sm、SseAnd alphamAnd training a classification model. Compared with the prior art, the method has the advantages of high accuracy, simplicity and convenience in operation, labor saving and the like.

Description

PMU-based power system state estimation performance evaluation method
Technical Field
The invention relates to a technology for evaluating the state estimation performance of a force system, in particular to a method for evaluating the state estimation performance of a power system based on a PMU.
Background
The evaluation method for the state estimation performance of the power system is a key technology for operation and control of the power system, can measure key information such as precision of a state estimation result, and the like, and can ensure correct operation and control of the power system by an accurate and reasonable state estimation result. With the integration of a large amount of renewable energy, the complexity of a power transmission network, the diversification of loads, and the rapid change of the operation mode of a power system. A Phasor Measurement Unit (PMU) based linear state estimation may better reflect the current state of the system. PMU errors, however, are a critical issue affecting the accuracy of the linearity state estimation. In practical studies, PMU errors are generally considered to follow a gaussian distribution. However, there are many factors that affect the accuracy of PMU measurement data, and there are mainly amplitude errors and phase angle errors of the voltage transformer and the current transformer, transmission errors of the cable channel, and synchronous clock errors. Therefore, the PMU error should follow a more complex distribution. Meanwhile, in an actual power system, a real value of a PMU measurement point and a real state of the power system are not available, so that the state estimation performance in the actual power system is difficult to evaluate.
Currently, the main index for evaluating the state estimation performance is the yield η, which is defined as:
Figure BDA0002608452480000011
Figure BDA0002608452480000012
wherein m is a measurement quantity, riThe measurement residual of the measurement point i isiIs a threshold value;
but the yield depends on a threshold for distinguishing between passing and failingi，iThe constant is set according to engineering experience, and no practical theoretical basis exists;
meanwhile, research utilizes the concept of cross entropy in the information theory as a state estimation performance evaluation standard, but the method only reflects the relation between a measured value and an estimated value and does not relate to the real state of the power system.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides the PMU-based power system state estimation performance evaluation method which is high in accuracy, simple and convenient to operate and labor-saving.
The purpose of the invention can be realized by the following technical scheme:
a PMU-based power system state estimation performance evaluation method specifically comprises the following steps:
n through the power system2Actual measurement of n by each PMU2Measured value S of each observed objectmThe observation object comprises one or more of voltage amplitude, voltage phase angle, current amplitude and current phase angle, and each S is obtained through state estimationmState estimation value S ofseN is to be2Group SmAnd SseRespectively correspond to input n2Grouping the trained classification models using n2S given by a classification modelmAnd SseThe criteria for the division are such that,correspond to obtain n2A mark value alphamAnd calculating a state estimation performance evaluation index lambda, wherein the calculation formula is as follows:
Figure BDA0002608452480000021
wherein p ismiAnd alphamiRespectively the ith classification accuracy pmAnd a mark value alphamSaid p ismThe calculation formula of (2) is as follows:
Figure BDA0002608452480000022
wherein n isrAnd nfRespectively carrying out correct quantity and wrong quantity of classification on the classification model after training;
wherein, said n2The training process of the group classification model is as follows:
obtaining a measurement error data set X of an observed object by a PMU of a power system simulation platform, normalizing the X for convenient analysis and comparison, and calculating a probability density function of the X
Figure BDA0002608452480000023
Obtaining n through a power system simulation platform2Group Observation truth StBy superimposing StAnd
Figure BDA0002608452480000024
theoretical solution of n2Group SmObtaining respective S by state estimationmState estimation value S ofseJudging each group St、SmAnd SseWhether the judgment formula is satisfied, if so, the marking value alpha with the corresponding generation value of 1 is generatedmOtherwise, generate α with value 0mThe judgment formula is as follows:
|Sim-Sit|＞|Sise-Sit|
wherein Si ism、SitAnd SiseAre respectively the ith group Sm、StAnd Sse；
N is to be2Group St、Sm、SseAnd alphamAs training data, performing classification model training to correspondingly obtain n2And (4) classifying the models.
Further, the classification model comprises an SVM model, a binary tree model or a neural network model, and the kernel function of the training classification model is a Gaussian kernel function.
Further, the acquisition process of the measurement error data set X is as follows:
measuring observation value S of observation object by PMU on node of power system simulation platformmQuerying S of nodes on the simulation platform of the power system through simulation softwaretBy calculating SmAnd StThe difference value of (A) is used to obtain the measurement error, and X is formed by a plurality of groups of measurement errors.
Further, the formula of the normalization process is as follows:
Figure BDA0002608452480000031
wherein,
Figure BDA0002608452480000032
is the j normalized measurement error, E (X) is the expectation of X, var (X) is the variance of X, XjIs the jth measurement error in X.
Further, the
Figure BDA0002608452480000033
The calculation formula of (a) is as follows:
Figure BDA0002608452480000034
where K is the Gaussian kernel function, h is the kernel density estimation window width, xjFor the jth observation in X, n1Number of samples of X;
the calculation formula of the kernel density estimation window width h is as follows:
Figure BDA0002608452480000035
wherein, sigma is the standard deviation of X, R is the four-quadrant spacing of X, N is the number of observed data in X, if h is too large, or decrease
Figure BDA0002608452480000036
If h is too small, this will result in
Figure BDA0002608452480000037
The fluctuation is large and discontinuous, and the error is large.
Furthermore, the state estimation algorithm can reduce the measurement error and increase the accuracy and the availability of the measured data, and the basic idea is based on a weighted least square method to solve an optimization problem:
Figure BDA0002608452480000038
s.t.Sm＝H(St)+w
wherein H is a measurement equation, and the H establishes SmAnd StW is a measurement error, W is a weight matrix, W is a diagonal sparse matrix, and diagonal elements are inverses of variances of the corresponding measurement errors.
Compared with the prior art, the invention has the following beneficial effects:
(1) according to the method, the error characteristic of PMU measurement data is obtained through a power system simulation platform, the error characteristic is superposed on a true value of an observation object, a measurement value of the observation object is calculated theoretically to form training data of a classification model, finally, an object measurement value and a corresponding state estimation value of each node of a power system are obtained on a new time section, a plurality of groups of trained models are input, and finally, a state estimation performance evaluation index lambda is calculated;
(2) the invention can adopt SVM model, binary tree model or neural network model as classification model, and has wide application range.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
A method for evaluating state estimation performance of a power system based on a PMU, as shown in fig. 1, specifically includes:
n is arranged on the power system2Each monitoring node is provided with a PMU, a measurement error data set X of an observed object is obtained through the PMU of the power system simulation platform, normalization processing is carried out on the data set, and the probability density function of the X is calculated
Figure BDA0002608452480000041
The method comprises the steps of obtaining n through a power system simulation platform by virtue of PMU measurement error distribution characteristics2Group Observation truth StBy superimposing StAnd
Figure BDA0002608452480000042
theoretical solution of n2Group SmObtaining respective S by state estimationmState estimation value S ofseJudging each group St、SmAnd SseWhether the judgment formula is satisfied, if so, the marking value alpha with the corresponding generation value of 1 is generatedmOtherwise, generate α with value 0mThe judgment formula is as follows:
|Sim-Sit|＞|Sise-Sit|
wherein Si ism、SitAnd SiseS of the ith nodem、StAnd Sse；
N is a value obtained by subtracting the true value of the observation target from the true value of the observation target2Group St、Sm、SseAnd alphamPerforming SVM training as training data, wherein the kernel function of the training is a Gaussian kernel function, and correspondingly obtaining n2A trained SVM model;
n through the power system on a time slice to be evaluated2Actual measurement of n by each PMU2Measured value S of each observed objectmThe observed objects are the voltage amplitude and the voltage phase angle, i.e. SmIncluding voltage amplitude measurements and voltage phase angle measurements, each S is obtained by state estimationmState estimation value S ofseN is to be2Group SmAnd SseRespectively correspond to input n2Grouping the trained classification models using n2S given by a classification modelmAnd SseDividing the standard to obtain n2A mark value alphamAnd calculating a state estimation performance evaluation index lambda, wherein the calculation formula is as follows:
Figure BDA0002608452480000051
wherein p ismiAnd alphamiRespectively the ith classification accuracy pmAnd a mark value alpham，pmThe calculation formula of (2) is as follows:
Figure BDA0002608452480000052
wherein n isrAnd nfRespectively carrying out correct quantity and wrong quantity of classification on the classification model after training;
the calculation process of the state estimation is an optimization solving process based on a weighted least square method, and the calculation formula is as follows:
Figure BDA0002608452480000053
s.t.Sm＝H(St)+w
wherein H is a measurement equation, and S is establishedmAnd StW is the measurement error, W is the weight matrix, which is a diagonal sparse matrix, and the diagonal elements are the reciprocal of the variance of the corresponding measurement error.
The acquisition process of the measurement error data set X is:
measuring observation value S of observation object by PMU on node of power system simulation platformmQuerying S of nodes on the simulation platform of the power system through simulation softwaretBy calculating SmAnd StThe difference value of (A) is used to obtain the measurement error, and X is formed by a plurality of groups of measurement errors.
The formula of the normalization process is:
Figure BDA0002608452480000054
wherein,
Figure BDA0002608452480000055
is the j normalized measurement error, E (X) is the expectation of X, var (X) is the variance of X, XjIs the jth measurement error in X.
Figure BDA0002608452480000056
The calculation formula of (a) is as follows:
Figure BDA0002608452480000057
where K is the Gaussian kernel function, h is the kernel density estimation window width, xjFor the jth observation in X, n1Number of samples of X;
the calculation formula of the kernel density estimation window width h is as follows:
Figure BDA0002608452480000058
wherein, sigma is the standard deviation of X, R is the four-quadrant spacing of X, N is the number of observed data in X, if h is too large, or decrease
Figure BDA0002608452480000059
If h is too small, this will result in
Figure BDA00026084524800000510
The fluctuation is large and discontinuous, and the error is large.
The embodiment provides a PMU-based power system state estimation performance evaluation method, which includes the steps of firstly obtaining error characteristics of PMU measurement data through a power system simulation platform, then superposing the error characteristics on an observation object true value, theoretically calculating observation object measurement values to form training data of an SVM model, finally obtaining object measurement values and corresponding state estimation values of all nodes of a power system on a new time section, inputting a plurality of trained SVM models, and finally calculating a state estimation performance evaluation index lambda.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (10)

1. A PMU-based power system state estimation performance evaluation method is characterized by comprising the following steps:
n through the power system2Actual measurement of n by each PMU2Measured value S of each observed objectmObtaining respective S by state estimationmState estimation value S ofseN is to be2Group SmAnd SseAre respectively provided withCorresponding to input n2The trained classification models are combined, and n is obtained correspondingly2A mark value alphamAnd calculating a state estimation performance evaluation index lambda, wherein the calculation formula is as follows:
Figure FDA0002608452470000011
wherein p ismiAnd alphamiRespectively the ith classification accuracy pmAnd a mark value alphamSaid p ismThe calculation formula of (2) is as follows:
Figure FDA0002608452470000012
wherein n isrAnd nfRespectively carrying out correct quantity and wrong quantity of classification on the classification model after training;
wherein, said n2The training process of the group classification model is as follows:
obtaining a measurement error data set X of an observed object through a PMU of a power system simulation platform, carrying out normalization processing on the X, and calculating a probability density function of the X
Figure FDA0002608452470000013
Obtaining n through a power system simulation platform2Group Observation truth StBy superimposing StAnd
Figure FDA0002608452470000014
theoretical solution of n2Group SmObtaining respective S by state estimationmState estimation value S ofseJudging each group St、SmAnd SseWhether the judgment formula is satisfied, if so, the marking value alpha with the corresponding generation value of 1 is generatedmOtherwise, generate α with value 0mThe judgment formula is as follows:
|Sim-Sit|＞|Sise-Sit|
wherein Si ism、SitAnd SiseAre respectively the ith group Sm、StAnd Sse；
Using n2Group St、Sm、SseAnd alphamCarrying out classification model training to correspondingly obtain n2And (4) classifying the models.
2. The PMU-based power system state estimation performance evaluation method according to claim 1, characterized in that the state estimation calculation formula is:
Figure FDA0002608452470000015
s.t.Sm＝H(St)+w
wherein, H is the measurement equation, W is the measurement error, and W is the weight matrix.
3. The PMU-based power system state estimation performance evaluation method according to claim 1, characterized in that the method includes
Figure FDA0002608452470000016
The calculation formula of (a) is as follows:
Figure FDA0002608452470000021
where K is the kernel density function, h is the kernel density estimation window width, xjFor the jth observation in X, n1Number of samples of X.
4. The PMU-based power system state estimation performance evaluation method according to claim 3, characterized in that the calculation formula of the kernel density estimation window width h is:
Figure FDA0002608452470000022
where σ is the standard deviation of X, R is the four-bit distance of X, and N is the number of observed data in X.
5. The PMU-based power system state estimation performance evaluation method of claim 3, characterized in that K is a Gaussian kernel function.
6. The PMU-based power system state estimation performance evaluation method of claim 1, wherein the observed object includes one or more of a voltage magnitude, a voltage phase angle, a current magnitude, and a current phase angle.
7. The PMU-based power system state estimation performance evaluation method of claim 1, characterized in that the kernel function of the training classification model is a Gaussian kernel function.
8. The PMU-based power system state estimation performance evaluation method according to claim 1, wherein the measurement error dataset X is obtained by:
PMU measurement observation value S of observation object through power system simulation platformmInquiring the true value S of the observation object through the power system simulation platformtBy calculating SmAnd StThe difference value of (A) is used to obtain the measurement error, and X is formed by a plurality of groups of measurement errors.
9. The PMU-based power system state estimation performance evaluation method according to claim 1, characterized in that the formula of the normalization process is:
Figure FDA0002608452470000023
wherein,
Figure FDA0002608452470000024
for the j normalized measurement errorE (X) is the expectation of X, var (X) is the variance of X, XjIs the jth measurement error in X.
10. The PMU-based power system state estimation performance evaluation method according to claim 1, characterized in that the classification model includes an SVM model, a binary tree model or a neural network model.
CN202010746249.2A 2020-07-29 2020-07-29 PMU-based power system state estimation performance evaluation method Active CN111900731B (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CN202010746249.2A CN111900731B (en) 2020-07-29 2020-07-29 PMU-based power system state estimation performance evaluation method
AU2020327340A AU2020327340B2 (en) 2020-07-29 2020-12-08 Method for evaluating state estimation performance of power system based on PMU
PCT/CN2020/134406 WO2022021726A1 (en) 2020-07-29 2020-12-08 Pmu-based power system state estimation performance evaluation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010746249.2A CN111900731B (en) 2020-07-29 2020-07-29 PMU-based power system state estimation performance evaluation method

Publications (2)

Publication Number Publication Date
CN111900731A true CN111900731A (en) 2020-11-06
CN111900731B CN111900731B (en) 2021-10-08

Family

ID=73182638

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010746249.2A Active CN111900731B (en) 2020-07-29 2020-07-29 PMU-based power system state estimation performance evaluation method

Country Status (3)

Country Link
CN (1) CN111900731B (en)
AU (1) AU2020327340B2 (en)
WO (1) WO2022021726A1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022021726A1 (en) * 2020-07-29 2022-02-03 国网上海市电力公司 Pmu-based power system state estimation performance evaluation method

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110705887A (en) * 2019-10-10 2020-01-17 国网湖北省电力有限公司计量中心 Low-voltage transformer area operation state comprehensive evaluation method based on neural network model
CN115379551B (en) * 2022-08-19 2024-05-17 合肥联信电源有限公司 Clock calibration method applied to energy storage type emergency power supply
CN115906353B (en) * 2022-11-17 2023-08-08 国网上海市电力公司 Power distribution network PMU (Power management unit) optimal configuration method based on node evaluation
CN115859690B (en) * 2023-02-15 2023-06-06 西安热工研究院有限公司 Multi-level QMU (quality-of-the-tube) evaluation method and system for electromagnetic threat of equipment
CN116840765B (en) * 2023-08-31 2023-11-07 武汉格蓝若智能技术股份有限公司 Voltage transformer error state evaluation method based on multivariate time sequence analysis
CN117039893B (en) * 2023-10-09 2024-01-26 国网天津市电力公司电力科学研究院 Power distribution network state determining method and device and electronic equipment

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103489009A (en) * 2013-09-17 2014-01-01 北方信息控制集团有限公司 Pattern recognition method based on self-adaptation correction neural network
CN104866714A (en) * 2015-05-14 2015-08-26 同济大学 Self-adaptive nuclear density robust state estimation method for power system

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101615213B (en) * 2009-07-21 2011-05-11 清华大学 Evaluation method of power system state estimated result based on expanded uncertainty
US10324132B2 (en) * 2010-06-07 2019-06-18 Abb Inc. Systems and methods for power line event zone identification
GB201314611D0 (en) * 2013-08-15 2013-10-02 Univ The Of Birmingham Power system control
US10635519B1 (en) * 2017-11-30 2020-04-28 Uptake Technologies, Inc. Systems and methods for detecting and remedying software anomalies
CN108182257A (en) * 2017-12-29 2018-06-19 东北电力大学 A kind of GSA bad data detection and identification methods based on the optimization of areal concentration statistical method
CN110490378A (en) * 2019-08-07 2019-11-22 中国南方电网有限责任公司 The calculation method of Power Network Status Estimation precision based on cloud SCADA big data
CN110543720B (en) * 2019-09-03 2021-06-08 北京交通大学 State estimation method based on SDAE-ELM pseudo-measurement model
CN110942109A (en) * 2019-12-17 2020-03-31 浙江大学 PMU false data injection attack prevention method based on machine learning
CN111221811A (en) * 2020-02-15 2020-06-02 光一科技股份有限公司 Low-voltage distribution network line parameter estimation method based on centralized meter reading system
CN111900731B (en) * 2020-07-29 2021-10-08 国网上海市电力公司 PMU-based power system state estimation performance evaluation method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103489009A (en) * 2013-09-17 2014-01-01 北方信息控制集团有限公司 Pattern recognition method based on self-adaptation correction neural network
CN104866714A (en) * 2015-05-14 2015-08-26 同济大学 Self-adaptive nuclear density robust state estimation method for power system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
商凯: "东团堡风电场短期风电功率预测研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022021726A1 (en) * 2020-07-29 2022-02-03 国网上海市电力公司 Pmu-based power system state estimation performance evaluation method

Also Published As

Publication number Publication date
WO2022021726A1 (en) 2022-02-03
AU2020327340B2 (en) 2022-03-24
AU2020327340A1 (en) 2022-02-17
CN111900731B (en) 2021-10-08

Similar Documents

Publication Publication Date Title
CN111900731B (en) PMU-based power system state estimation performance evaluation method
Yuan et al. Short-term wind power prediction based on LSSVM–GSA model
CN111860982A (en) Wind power plant short-term wind power prediction method based on VMD-FCM-GRU
CN110417011B (en) Online dynamic security assessment method based on mutual information and iterative random forest
CN103488869A (en) Wind power generation short-term load forecast method of least squares support vector machine
CN109088407B (en) Power distribution network state estimation method based on deep belief network pseudo-measurement modeling
CN111628494B (en) Low-voltage distribution network topology identification method and system based on logistic regression method
CN113126019B (en) Remote estimation method, system, terminal and storage medium for error of intelligent ammeter
CN108336739A (en) A kind of Probabilistic Load Flow on-line calculation method based on RBF neural
CN103971175B (en) Short-term load prediction method of multistage substations
CN111598165A (en) Density clustering outlier detection method based on extreme learning machine
CN116821832A (en) Abnormal data identification and correction method for high-voltage industrial and commercial user power load
CN109861220B (en) Method for constructing tensor input of deep convolutional neural network for power system analysis
CN117060440A (en) Power system partition frequency stability judging method
CN115510961A (en) Community comprehensive energy system operation safety assessment method based on active learning
Xia et al. A semi-supervised learning method for electricity theft detection based on CT-GAN
CN113537821A (en) Rapid assessment method and system for state of power system
CN106097141A (en) The ternary interval number appraisal procedure of photovoltaic generating system confidence capacity
CN112651183A (en) Reliability evaluation method for quantum distributed countermeasure unified deep hash network
CN105699043A (en) Method for improving measuring stability and precision of wind tunnel sensor
CN113988360B (en) Wind power prediction method and device based on wind speed fluctuation characteristic typing
Zhu et al. Short-term Forecast for Photovoltaic Generation Based on Improved Restricted Boltzmann Machine Algorithm
Miao et al. A Transformer District Line Loss Calculation Method Based on Data Mining and Machine Learning
Zheng et al. Short-Term Load Forecast Based on Feature Reconstruction and Bidirectional LSTM
CN116702629B (en) Power system transient stability evaluation method with migration capability

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
GR01 Patent grant
GR01 Patent grant