CN112946422A - Power distribution network line parameter identification method considering PMU measurement outliers - Google Patents
Power distribution network line parameter identification method considering PMU measurement outliers Download PDFInfo
- Publication number
- CN112946422A CN112946422A CN202110147426.XA CN202110147426A CN112946422A CN 112946422 A CN112946422 A CN 112946422A CN 202110147426 A CN202110147426 A CN 202110147426A CN 112946422 A CN112946422 A CN 112946422A
- Authority
- CN
- China
- Prior art keywords
- measurement
- distribution network
- data
- group
- power distribution
- 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
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
-
- 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
Abstract
The invention provides a power distribution network line parameter identification method considering PMU measurement outliers. The method comprises the following steps: arranging PMU devices at two ends of a power distribution network line to be measured, measuring and accumulating multi-time discontinuous surface measurement through the PMU devices, randomly selecting a certain quantity of all measured data as a group, and selecting multiple groups; calculating the line parameter value and the measurement residual error of the power distribution network of each group by using a total least square method; eliminating the measurement with large residual error, and calculating the sum of the line parameter value and the measurement residual error by using the total least square method; and determining the line parameters of the power distribution network corresponding to the group of measurement data with the minimum sum of the measurement residuals as the line parameter values of the power distribution network to be measured. The method realizes the combined use of the TLS method and the LTS method, can resist the measured outlier, the coefficient matrix error and the observed data error, and realizes the effective identification of the line parameters of the power distribution network under the condition of measuring the outlier and the error.
Description
Technical Field
The invention relates to the technical field of power distribution network line parameter identification, in particular to a power distribution network line parameter identification method considering PMU (phasor measurement Unit) measurement outliers.
Background
In a power distribution network, line parameters are basic data of power distribution network state estimation, fault location, reactive power optimization and the like, but aging or environmental factors bring about changes of the line parameters, so that research on line parameter identification based on relevant measurement is very important. However, PMU (phasor measurement unit) measurements may have outliers, i.e. deviate from the data of most measurements. Therefore, PMU measurements with outliers reduce the accuracy of power distribution network line parameter identification.
The prior art parameter identification scheme includes: in the power distribution network, TLS (total least square) method is adopted to realize the identification of the line parameters of the power distribution network by combining PMU measurement and AMI (advanced measurement in front of measurement system) measurement; alternatively, a robust least squares method of Huber estimation and median estimation is employed to counter the effects of metrology outliers on line parameter identification. In the power transmission network, a chi-square detection method is adopted to detect and measure outliers in some schemes, so that the estimation accuracy is improved; some schemes use an LTS (least squares) method to identify the line parameters.
The above-mentioned line parameter identification technology in the prior art has the following disadvantages: in the existing method, the TLS method can consider the line parameter identification under the condition of coefficient matrix error and observation data error, but can not process the measured outlier. The LTS method adopted in the existing method can process the measured outliers, but the coefficient matrix error is not considered.
Disclosure of Invention
The embodiment of the invention provides a power distribution network line parameter identification method considering PMU measurement outliers, so as to effectively identify line parameters of a power distribution network.
In order to achieve the purpose, the invention adopts the following technical scheme.
A power distribution network line parameter identification method considering PMU measurement outliers comprises the following steps:
arranging PMU devices at two ends of a power distribution network line to be measured, measuring and accumulating multi-time section measurement by the PMU devices within a certain time period, randomly selecting measurement data of a plurality of time sections from the measurement data of all time sections as a group of data, and selecting a plurality of groups of data;
calculating the line parameter value and the measurement residual error of the power distribution network corresponding to each group of measurement data by using a total least square method;
under the condition of line parameter values corresponding to each group of data, removing measurement with large residual errors, and recalculating the sum of the line parameter values and the measurement residual errors of the power distribution network corresponding to each group of measurement data by using a total least square method;
and comparing the sum of the measurement residual errors corresponding to each group of measurement data, and determining the line parameter of the power distribution network corresponding to the group of measurement data with the minimum sum of the measurement residual errors as the line parameter value of the power distribution network to be measured.
Preferably, the two ends of the power distribution network line to be measured are provided with PMU devices, the PMU devices measure and accumulate multi-time discontinuous measurement in a certain period of time, measurement data of a plurality of time sections are arbitrarily selected from measurement data of all time sections as a group of data, and a plurality of groups of data are selected, including:
arranging PMU devices at two ends of a power distribution network line to be measured, measuring and accumulating n time section measurements by the PMU devices in a certain time period, measuring node voltage phasor measurement by the PMU devices, and measuring current phasor measurement of a branch connected with the node;
and randomly selecting the measurement data of p time sections from the measurement data of n time sections as a group of measurement data, wherein p is less than n, the group of measurement data comprises the current phasor measurement and the voltage phasor measurement of p time sections, and K groups of measurement data are selected.
Preferably, the calculating the line parameter value and the measurement residual error of the power distribution network corresponding to each set of measurement data by using a total least square method includes:
writing the equation of ohm's law of the power distribution network line into a linear equation:
AX＝Y
in the formula: a represents a current phasor measurement matrix of a plurality of time sections, namely a coefficient matrix, Y represents a voltage drop phasor matrix of a plurality of time sections, namely observation data, and X represents line parameter resistance and reactance;
aiming at a group of measured data, the TLS method is adopted to realize the line parameter identification of the power distribution network through singular value decomposition:
in the formula: sigmaT+1To expand the matrix [ A, Y]Minimum singular value of, XTLSThe resistance R and the reactance X comprise power distribution network line parameters;
each time section corresponds to a measurement residual error, and the measurement residual error of the ith time section
The calculation formula of (2) is as follows:
aifor the current phasor measurement data of the ith time section, yiVoltage drop phasor data for the ith time slice.
Preferably, under the condition of the line parameter value corresponding to each group of data, the measurement with large residual error is removed, and the sum of the line parameter value and the measurement residual error of the power distribution network corresponding to each group of measurement data is recalculated by using a total least square method, further comprising:
under the condition of line parameter values corresponding to a group of measurement data, sorting the measurement residual errors of n time sections from small to large, eliminating n-h measurement data with large residual errors, and processing each group of measurement data by adopting the same method;
and (3) calculating the line parameters corresponding to each group of measured data by adopting the TLS method again according to the formulas 1 and 2, and calculating the sum of the measurement residual errors of h time sections in each group of measured data.
Preferably, the comparing the sum of the measurement residuals corresponding to each measurement data set, and determining the line parameter of the power distribution network corresponding to the measurement data set with the smallest sum of the measurement residuals as the line parameter value of the power distribution network to be measured includes:
and comparing the sum of the measurement residual errors corresponding to each group of measurement data, determining the line parameter corresponding to the group of measurement data with the minimum sum of the measurement residual errors, and determining the line parameter corresponding to the group of measurement data with the minimum sum of the measurement residual errors as the line parameter value of the power distribution network to be measured.
According to the technical scheme provided by the embodiment of the invention, the method provided by the embodiment of the invention realizes the combined use of the TLS method and the LTS method, can resist the measured outliers, the coefficient matrix errors and the observation data errors, and realizes the effective identification of the line parameters of the power distribution network under the condition that the measured outliers and the outliers exist.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a processing flow chart of a power distribution network line parameter identification method considering PMU measurement outliers according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including 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. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The TLS method can realize line parameter identification under the condition of considering coefficient matrix errors and observation data errors, and the LTS method can resist measured outliers in line parameter identification. The method combines the TLS method and the LTS method, retains the advantages of two algorithms, can resist measurement outliers, coefficient matrix errors and observation data errors, and realizes the identification of the power distribution network line parameters.
A processing flow chart of the method for identifying parameters of a power distribution network line considering PMU measurement outliers according to the embodiment of the present invention is shown in fig. 1, and includes the following processing steps:
and S1, arranging PMU devices at two ends of a power distribution network line to be measured, and measuring and accumulating n time section measurements by the PMU devices.
According to ohm's law:
IZ＝U1-U2＝ΔU
wherein I represents the current measurement of the line, Z represents the line parameters resistance and reactance, U1Represents the line first voltage measurement, U2Represents the line end voltage measurement and Δ U represents the voltage drop of the line. And under the condition that PMU devices are respectively installed at two ends of a power distribution network line, measuring the current phasor and measuring the voltage phasor of the accumulated multi-time discontinuous surface.
The PMU device is used for measuring the voltage phasor measurement of a node and the current phasor measurement of a branch connected with the node.
And S2, selecting a certain amount of measurement data.
In the n time section measurement data, the measurement data of p (p < n) time sections are arbitrarily selected as a group of measurement data, the group of measurement data comprises the current phasor measurement and the voltage phasor measurement of the p time sections, and K groups are selected.
And S3, calculating a line parameter value and a measurement residual error corresponding to each group of measurement data by using a total least square method.
And identifying the line parameters of the K groups of measured data by adopting a TLS (transport layer security) method respectively to obtain the line parameter value and the measured residual error corresponding to each group of measured data.
Writing the equation of ohm's law of the power distribution network line into a linear equation:
AX＝Y
in the formula: a represents a current phasor measurement matrix, namely a coefficient matrix, of a plurality of time sections, Y represents a voltage drop phasor matrix, namely observation data, of a plurality of time sections, and X represents line parameter resistance and reactance.
Aiming at a group of measured data, the TLS method is adopted to realize the line parameter identification of the power distribution network through singular value decomposition:
in the formula: sigmaT+1To expand the matrix [ A, Y]Minimum singular value of, XTLSIncluding the resistance R and reactance X of the distribution network line parameters.
Each time section corresponds to a measurement residual error, and the measurement residual error of the ith time section
The calculation formula of (2) is as follows:
aifor the current phasor measurement data of the ith time section, yiVoltage drop phasor data for the ith time slice.
S4, measuring large rejection error
Under the condition of line parameter values corresponding to a group of measurement data, sorting the measurement residual errors of n time sections from small to large, and removing n-h measurement data with large residual errors, for example, sorting as follows:
and eliminating the measurement with large residual error by adopting the same method for the line parameter values corresponding to each group of data.
S5, recalculating line parameters and errors
And (3) calculating the line parameters corresponding to each group of measured data by adopting the TLS method again according to the formulas 1 and 2, and calculating the sum of the measurement residual errors of h time sections in each group of measured data.
S6, obtaining line parameter values
And comparing the sum of the measurement residual errors corresponding to each group of measurement data, determining the line parameter corresponding to the group of measurement data with the minimum sum of the measurement residual errors, and determining the line parameter corresponding to the group of measurement data with the minimum sum of the measurement residual errors as the line parameter value of the power distribution network to be measured.
Therefore, the scheme realizes the identification of the power distribution network line parameters according to the following objective functions:
in the formula: h belongs to [ n/2, n ], and is an integer.
In summary, the method of the embodiment of the present invention realizes the combined use of the TLS method and the LTS method, retains the advantages of the two methods, can combat the measurement outliers, the coefficient matrix errors, and the observation data errors, and can effectively identify the line parameters of the power distribution network in the case of the measurement errors and outliers.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. A power distribution network line parameter identification method considering PMU measurement outliers is characterized by comprising the following steps:
arranging PMU devices at two ends of a power distribution network line to be measured, measuring and accumulating multi-time section measurement by the PMU devices within a certain time period, randomly selecting measurement data of a plurality of time sections from the measurement data of all time sections as a group of data, and selecting a plurality of groups of data;
calculating the line parameter value and the measurement residual error of the power distribution network corresponding to each group of measurement data by using a total least square method;
under the condition of line parameter values corresponding to each group of data, removing measurement with large residual errors, and recalculating the sum of the line parameter values and the measurement residual errors of the power distribution network corresponding to each group of measurement data by using a total least square method;
and comparing the sum of the measurement residual errors corresponding to each group of measurement data, and determining the line parameter of the power distribution network corresponding to the group of measurement data with the minimum sum of the measurement residual errors as the line parameter value of the power distribution network to be measured.
2. The method according to claim 1, wherein the PMU device is provided at two ends of the distribution network to be measured, the PMU device measures and accumulates multiple time section measurements in a certain period of time, the measured data of multiple time sections are arbitrarily selected from the measured data of all time sections as a group of data, and multiple groups of data are selected, and the method comprises the following steps:
arranging PMU devices at two ends of a power distribution network line to be measured, measuring and accumulating n time section measurements by the PMU devices in a certain time period, measuring node voltage phasor measurement by the PMU devices, and measuring current phasor measurement of a branch connected with the node;
and randomly selecting the measurement data of p time sections from the measurement data of n time sections as a group of measurement data, wherein p is less than n, the group of measurement data comprises the current phasor measurement and the voltage phasor measurement of p time sections, and K groups of measurement data are selected.
3. The method of claim 2, wherein the calculating the line parameter values and the measurement residuals of the distribution network corresponding to each set of measurement data using a total least squares method comprises:
writing the equation of ohm's law of the power distribution network line into a linear equation:
AX＝Y
in the formula: a represents a current phasor measurement matrix of a plurality of time sections, namely a coefficient matrix, Y represents a voltage drop phasor measurement matrix of a plurality of time sections, namely observation data, and X represents line parameter resistance and reactance;
aiming at a group of measured data, the TLS method is adopted to realize the line parameter identification of the power distribution network through singular value decomposition:
in the formula: sigmaT+1To expand the matrix [ A, Y]Minimum singular value of, XTLSThe resistance R and the reactance X comprise power distribution network line parameters;
each time section corresponds to a measurement residual error, and the measurement residual error of the ith time section
The calculation formula of (2) is as follows:
aifor the current phasor measurement data of the ith time section, yiVoltage drop phasor data for the ith time slice.
4. The method according to claim 3, wherein under the condition of the line parameter value corresponding to each group of data, the measurement with large residual error is eliminated, and the sum of the line parameter value and the measurement residual error of the power distribution network corresponding to each group of measurement data is recalculated by using a total least square method, further comprising:
under the condition of line parameter values corresponding to a group of measurement data, sorting the measurement residual errors of n time sections from small to large, eliminating n-h measurement data with large residual errors, and processing the line parameter values corresponding to each group of measurement data by adopting the same method;
and (3) calculating the line parameters corresponding to each group of measured data by adopting the TLS method again according to the formulas 1 and 2, and calculating the sum of the measurement residual errors of h time sections in each group of measured data.
5. The method according to claim 4, wherein the comparing the sum of the measurement residuals corresponding to each measurement data set, and determining the line parameter of the distribution network corresponding to the measurement data set with the smallest sum of the measurement residuals as the line parameter value of the distribution network to be measured comprises:
and comparing the sum of the measurement residual errors corresponding to each group of measurement data, determining the line parameter value corresponding to the group of measurement data with the minimum sum of the measurement residual errors, and determining the line parameter corresponding to the group of measurement data with the minimum sum of the measurement residual errors as the line parameter value of the power distribution network to be measured.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110147426.XA CN112946422B (en) | 2021-02-03 | 2021-02-03 | Power distribution network line parameter identification method considering PMU measurement outliers |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110147426.XA CN112946422B (en) | 2021-02-03 | 2021-02-03 | Power distribution network line parameter identification method considering PMU measurement outliers |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112946422A true CN112946422A (en) | 2021-06-11 |
| CN112946422B CN112946422B (en) | 2023-03-21 |
Family
ID=76241995
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110147426.XA Active CN112946422B (en) | 2021-02-03 | 2021-02-03 | Power distribution network line parameter identification method considering PMU measurement outliers |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112946422B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103869184A (en) * | 2014-02-11 | 2014-06-18 | 广东电网公司电力科学研究院 | PMU data-based identification method for single electric transmission line zero-sequence parameter robustness |
| US20190339320A1 (en) * | 2018-05-03 | 2019-11-07 | Siemens Aktiengesellschaft | Determination of power transmission line parameters using asynchronous measurements |
| US20190379209A1 (en) * | 2018-06-08 | 2019-12-12 | The Regents Of The University Of California | Method and system for locating the source of events in power distribution systems using distribution-level pmu data |
| CN110676940A (en) * | 2019-10-21 | 2020-01-10 | 国网上海市电力公司 | Power distribution network PMU configuration method and system for improving parameter identification precision |
| CN110687352A (en) * | 2019-10-21 | 2020-01-14 | 国网上海市电力公司 | PMU-based power distribution network T-type line parameter identification method and system |
| CN110907720A (en) * | 2019-11-06 | 2020-03-24 | 国网天津市电力公司电力科学研究院 | Complete parameter identification method for short-circuit same-tower double-circuit line based on PMU measurement |
| CN110927519A (en) * | 2019-11-20 | 2020-03-27 | 东南大学 | Active power distribution network fault positioning method based on mu PMU measurement value |
| CN112115568A (en) * | 2020-09-01 | 2020-12-22 | 贵州电网有限责任公司 | Improved coupling least square power transmission line parameter identification method based on PMU measurement |
-
2021
- 2021-02-03 CN CN202110147426.XA patent/CN112946422B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103869184A (en) * | 2014-02-11 | 2014-06-18 | 广东电网公司电力科学研究院 | PMU data-based identification method for single electric transmission line zero-sequence parameter robustness |
| US20190339320A1 (en) * | 2018-05-03 | 2019-11-07 | Siemens Aktiengesellschaft | Determination of power transmission line parameters using asynchronous measurements |
| US20190379209A1 (en) * | 2018-06-08 | 2019-12-12 | The Regents Of The University Of California | Method and system for locating the source of events in power distribution systems using distribution-level pmu data |
| CN110676940A (en) * | 2019-10-21 | 2020-01-10 | 国网上海市电力公司 | Power distribution network PMU configuration method and system for improving parameter identification precision |
| CN110687352A (en) * | 2019-10-21 | 2020-01-14 | 国网上海市电力公司 | PMU-based power distribution network T-type line parameter identification method and system |
| CN110907720A (en) * | 2019-11-06 | 2020-03-24 | 国网天津市电力公司电力科学研究院 | Complete parameter identification method for short-circuit same-tower double-circuit line based on PMU measurement |
| CN110927519A (en) * | 2019-11-20 | 2020-03-27 | 东南大学 | Active power distribution network fault positioning method based on mu PMU measurement value |
| CN112115568A (en) * | 2020-09-01 | 2020-12-22 | 贵州电网有限责任公司 | Improved coupling least square power transmission line parameter identification method based on PMU measurement |
Non-Patent Citations (6)
| Title |
|---|
| 原康康等: "基于多新息最小二乘算法的电力线路参数辨识", 《电力工程技术》 * |
| 李鸿奎等: "线路参数辨识方法――基于PMU量测", 《山东工业技术》 * |
| 王玉彬等: "基于改进鲁棒自适应UKF的配电网动态状态估计方法", 《电力系统自动化》 * |
| 薛安成等: "基于自适应抗差最小二乘的线路正序参数在线辨识方法", 《电工技术学报》 * |
| 陆进军等: "基于PMU量测的线路参数辨识算法", 《电网与清洁能源》 * |
| 陈晓刚等: "基于WAMS/SCADA混合量测的电网参数辨识与估计", 《电力系统自动化》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112946422B (en) | 2023-03-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110322371A (en) | The area Gao Suntai multiplexing electric abnormality user based on multiple linear regression analysis detects localization method | |
| Ahmadi et al. | Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy | |
| Magnago et al. | A unified approach to robust meter placement against loss of measurements and branch outages | |
| US8583582B2 (en) | Robust automated hierarchical determination for power monitoring systems | |
| AU2016266732A1 (en) | Automatic network device electrical phase identification | |
| JP2018169161A (en) | Deterioration diagnosis apparatus, deterioration diagnosis method, and deterioration diagnosis system for battery | |
| JP2018179733A (en) | Battery life diagnostic device and battery life diagnostic method | |
| CN113114530B (en) | Network element health state detection method and equipment | |
| CN112684401B (en) | Method and system for processing operation error data of electric energy meter in light-load station area | |
| CN105938161B (en) | A kind of test method and system of the internal resistance of cell | |
| CN111144435A (en) | Electric energy abnormal data monitoring method based on LOF and verification filtering framework | |
| Farajollahi et al. | Tracking state estimation in distribution networks using distribution-level synchrophasor data | |
| CN104539471A (en) | Bandwidth metering method and device and computer equipment | |
| CN112946422B (en) | Power distribution network line parameter identification method considering PMU measurement outliers | |
| CN114460529A (en) | Electric energy meter error online evaluation method, device, equipment and storage medium | |
| CN114290960A (en) | Method and device for acquiring battery health degree of power battery and vehicle | |
| CN105426647B (en) | Cold stand-by systems reliablity estimation method based on the fusion of reliability prior information | |
| CN112345972B (en) | Power distribution network line transformation relation abnormity diagnosis method, device and system based on power failure event | |
| CN116298927B (en) | Battery capacity estimation method and device, computer equipment and storage medium | |
| CN105512803B (en) | Processing method and processing device of power data | |
| CN109193639B (en) | Robust estimation method for power system | |
| CN112886579B (en) | Power distribution network line parameter identification method considering unmatched PMU and SCADA measurement time scales | |
| CN116577552A (en) | Method and device for diagnosing electricity larceny of intelligent measuring switch | |
| CN113484818B (en) | Sliding window-based accurate positioning method for high-frequency acquisition anomaly-resistant electric energy meter | |
| CN113009363A (en) | Method and apparatus for measuring ohmic resistance of battery |
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 |