CN105242143B - Bad data modification method is estimated based on multi-period precision measurement unit data mode - Google Patents
Bad data modification method is estimated based on multi-period precision measurement unit data mode Download PDFInfo
- Publication number
- CN105242143B CN105242143B CN201510689210.0A CN201510689210A CN105242143B CN 105242143 B CN105242143 B CN 105242143B CN 201510689210 A CN201510689210 A CN 201510689210A CN 105242143 B CN105242143 B CN 105242143B
- Authority
- CN
- China
- Prior art keywords
- pmu
- data
- state estimation
- measurement
- result
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005259 measurement Methods 0.000 title claims abstract description 55
- 238000002715 modification method Methods 0.000 title abstract 2
- 238000000034 method Methods 0.000 claims abstract description 18
- 238000012797 qualification Methods 0.000 claims abstract description 6
- 238000012937 correction Methods 0.000 claims description 10
- 238000005096 rolling process Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 2
- 230000035772 mutation Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
Landscapes
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention relates to bad data modification method is estimated based on multi-period precision measurement unit data mode, belong to electric power system energy management system technical field, this method includes：State estimation was carried out for the cycle with 5 minutes, value gives a mark PMU data on the basis of the result of state estimation, scoring of the marking result one day comprehensive as the PMU data, chooses higher data of giving a mark and is measured as credible PMU；When state estimation qualification rate is undergone mutation, to SCADA data compared with state estimation result, statistics exceedes the data of some threshold values as bad data；Measured with credible PMU and measured instead of the SCADA of the equipment, state estimation is re-started, using the next state estimated result as final result.It the method increase the average qualification rate of Power Network Status Estimation and the stability of data.
Description
Technical Field
The invention belongs to the technical field of power system energy management systems, and particularly relates to a state estimation bad data correction method based on multi-period precision measurement unit PMU data.
Background
The intelligent power grid dispatching technology supports online operation of high-level applications in the system, and provides higher and higher requirements for the quality of basic parameter data of the power grid. How to further improve the state estimation qualification rate and the stability of the measured data becomes a bottleneck problem which restricts the further improvement of the application level of the power grid dispatching technical support system. According to the traditional power grid state estimation method, a monitoring module (SCADA) is used for measuring and is used as data input, the problems of low measurement precision, unstable equipment operation and the like generally exist, the state estimation qualified rate is low, and meanwhile data are easy to generate sudden change. And novel collection device PMU of measurationing has the characteristics of high acquisition precision, stability is strong. In recent years, with the gradual popularization of PMU devices in the power grid, it has become possible to adopt PMU measurement to participate in state estimation calculation.
Disclosure of Invention
The invention aims to further improve the qualification rate and stability of power grid state estimation and the application level of a power grid dispatching technical support system, and provides a state estimation bad data correction method based on multi-period PMU data.
The invention provides a state estimation bad data correction method based on multi-period PMU data, which comprises the following steps: performing state estimation in a period of 5 minutes, scoring the PMU data by taking a result of the state estimation as a reference value, taking a scoring result of one day as a score of the PMU data, and selecting data with higher score as a credible PMU measurement; when the state estimation qualification rate mutates, comparing the SCADA data with a state estimation result, and counting data exceeding a certain threshold value as bad data; and replacing the SCADA measurement of the equipment by the credible PMU measurement, and performing state estimation again to obtain a final state estimation result.
The state estimation bad data correction method based on multi-period PMU data provided by the invention has the following advantages:
1. in the state estimation bad data correction method based on multi-period PMU data, the data acquired by 2 different acquisition devices are integrated, so that compared with the traditional state estimation based on SCADA measurement, the state estimation method based on SCADA measurement has higher reliability and stronger stability.
2. The method disclosed by the invention is used for scoring based on PMU data in a certain time window, and rolling correction can be continuously carried out on the score value along with the change of time, so that a certain data can be effectively prevented from generating larger random disturbance.
Drawings
FIG. 1 is a block flow diagram of the method of the present invention.
Detailed Description
The invention provides a state estimation bad data correction method based on multi-period PMU data, which is explained in detail by combining the drawings and the embodiment as follows:
the flow of the state estimation bad data correction method based on multi-period PMU data provided by the invention is shown in FIG. 1, and the method comprises the following steps:
1) Obtaining the latest model and real-time data of a power grid in a period of 5 minutes, carrying out state estimation calculation, scoring PMU data by taking the calculation result as a reference value, taking 288 acquisition points in 24 hours as a time window, carrying out weighting processing on the scoring result in the time window to be used as the score of the PMU data, and selecting PMU measurement with higher score as credible PMU measurement;
1-1) defining the deviation between the PMU measurement and the state estimation result at each state estimation as shown in equation (1):
F pmu ＝P pmu -P se (1)
in the formula (1), P pmu A PMU measurement that represents a device; p se A state estimation value representing the measurement point; f pmu Indicating the deviation between the device's PMU measurements and state estimation results.
1-2) base values for scoring calculations are defined as shown in equation (2):
F p-base ＝|P se |+10 (2)
in the formula (2), F p-base Base values calculated for scoring the PMUs;
1-3) scoring PMU measurements after a single state estimation, as shown in (3):
in the formula (3), S i pmu Scoring the PMU measurements after a single state estimation;
1-4) taking 24 hours as a time window, counting the scoring conditions of all PMU measurements in the time window, and calculating the overall scoring result, wherein the total scoring result is shown in (4):
wherein S pmu Scoring the rolling score of the PMU when S pmu &85, the PMU measurement is considered as a credible measurement.
2) When the state estimation is carried out, the state estimation result is used as a reference value, the SCADA data and the state estimation result are compared, the data exceeding a certain threshold value are counted to be used as bad data, the reliable PMU measurement is used for replacing the SCADA measurement of the equipment, the state estimation is carried out again, and the state estimation result is used as a final result;
2-1) defining the deviation Se of the measured value of the measuring point from the state estimated value err As shown in formula (5):
in formula (1), P meas A measurement value representing a measurement point of a certain line; p se A state estimation value representing the measurement point; p base Represents a reference value, P base Related to the voltage class at which the line is located;
2-2) judging whether the measuring point is a large error point according to different thresholds set by the measuring data types:
when the measured data is active measured data, the set threshold value is 3 percent, and when the deviation Se is err &And gt, 3%, the measurement point is bad data;
when the measured data is reactive measured data, the set threshold value is 4%, and when the deviation Se is err &4%, the measurement point is bad data;
and (3) replacing bad data by the credible PMU measurement calculated in the step 1), and re-performing state estimation calculation.
Claims (2)
1. A bad data correction method based on multi-period precision measurement unit data state estimation is characterized by comprising the following steps: performing state estimation in a period of 5 minutes, scoring PMU data by taking a state estimation result as a reference value, taking a one-day scoring result as a score of the PMU data, and selecting data with higher score as a credible PMU measurement; when the state estimation qualification rate mutates, comparing the SCADA data with a state estimation result, and counting data exceeding a certain threshold value as bad data; and replacing SCADA measurement of the equipment by credible PMU measurement, re-estimating the state, and taking the state estimation result as a final result.
2. The method of claim 1, wherein the method comprises the steps of:
1) Obtaining the latest model and real-time data of a power grid in a period of 5 minutes, carrying out state estimation calculation, scoring PMU data by taking the calculation result as a reference value, taking 288 acquisition points in 24 hours as a time window, carrying out weighting processing on the scoring result in the time window to be used as the score of the PMU data, and selecting PMU measurement with higher score as credible PMU measurement;
1-1) defining the deviation between the PMU measurement and the state estimation result at each state estimation as shown in equation (1):
F pmu ＝P pmu -P se (1)
in the formula (1), P pmu A PMU measurement that represents a device; p is se A state estimation value representing the measurement point; f pmu Indicating a deviation between PMU measurements and state estimation results for the device;
1-2) base values for scoring calculations are defined as shown in equation (2):
F p-base ＝|P se |+10 (2)
in the formula (2), F p-base Base values calculated for scoring the PMUs;
1-3) scoring PMU measurements after a single state estimation, as shown in (3):
in the formula (3), S i pmu Scoring the PMU measurements after a single state estimation;
1-4) taking 24 hours as a time window, counting the scoring conditions of all PMU measurements in the time window, and calculating the overall scoring result, wherein the total scoring result is shown in (4):
wherein S pmu Scoring the PMU rolling score as S pmu &85 hours, the PMU measurement is considered as a credible measurement;
2) When the state estimation is carried out, the state estimation result is used as a reference value, the SCADA data and the state estimation result are compared, the data exceeding a certain threshold value are counted to be used as bad data, the reliable PMU measurement is used for replacing the SCADA measurement of the equipment, the state estimation is carried out again, and the state estimation result is used as a final result;
2-1) defining the deviation Se of the measurement value of the measurement point from the state estimation value err As shown in formula (5):
in the formula (5), P meas A measurement value representing a measurement point of a certain line; p is se A state estimation value representing the measurement point; p base Represents a reference value, P base Related to the voltage class at which the line is located;
2-2) judging whether the measuring point is a large error point according to different thresholds set by the measuring data types:
when the measured data is active measured data, the set threshold value is 3 percent, and when the deviation Se is err &And gt, 3%, the measurement point is bad data;
when the measured data is reactive measured data, the set threshold value is 4 percent, and when the deviation Se is err &4%, the measurement point is bad data;
and (3) replacing bad data by the credible PMU measurement calculated in the step 1), and performing state estimation calculation again.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510689210.0A CN105242143B (en) | 2015-10-21 | 2015-10-21 | Bad data modification method is estimated based on multi-period precision measurement unit data mode |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510689210.0A CN105242143B (en) | 2015-10-21 | 2015-10-21 | Bad data modification method is estimated based on multi-period precision measurement unit data mode |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105242143A CN105242143A (en) | 2016-01-13 |
| CN105242143B true CN105242143B (en) | 2018-05-11 |
Family
ID=55039856
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510689210.0A Active CN105242143B (en) | 2015-10-21 | 2015-10-21 | Bad data modification method is estimated based on multi-period precision measurement unit data mode |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105242143B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108667017A (en) * | 2018-06-06 | 2018-10-16 | 国网江西省电力有限公司 | A kind of matching process of SCADA and PMU metric data section times |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101305283A (en) * | 2005-11-08 | 2008-11-12 | Abb技术有限公司 | Method and apparatus for verifying the accuracy of state estimation calculations |
| CN103618385A (en) * | 2013-12-03 | 2014-03-05 | 国家电网公司 | State estimation data correction system and method for improving accuracy |
| CN103745109A (en) * | 2014-01-10 | 2014-04-23 | 国家电网公司 | Bad data detection and identification method based on measurement of PMU (Phasor Measurement Unit) and measurement of SCADA (Supervisory Control and Data Acquisition) |
| CN104090166A (en) * | 2014-07-14 | 2014-10-08 | 国家电网公司 | Power grid line parameter on-line identification method considering state estimation large error points |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9627886B2 (en) * | 2012-03-27 | 2017-04-18 | Mitsubishi Electric Research Laboratoriies, Inc. | State estimation for power system using hybrid measurements |
-
2015
- 2015-10-21 CN CN201510689210.0A patent/CN105242143B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101305283A (en) * | 2005-11-08 | 2008-11-12 | Abb技术有限公司 | Method and apparatus for verifying the accuracy of state estimation calculations |
| CN103618385A (en) * | 2013-12-03 | 2014-03-05 | 国家电网公司 | State estimation data correction system and method for improving accuracy |
| CN103745109A (en) * | 2014-01-10 | 2014-04-23 | 国家电网公司 | Bad data detection and identification method based on measurement of PMU (Phasor Measurement Unit) and measurement of SCADA (Supervisory Control and Data Acquisition) |
| CN104090166A (en) * | 2014-07-14 | 2014-10-08 | 国家电网公司 | Power grid line parameter on-line identification method considering state estimation large error points |
Non-Patent Citations (3)
| Title |
|---|
| 基于PMU/SCADA混合数据的电力系统状态估计的研究;李爽;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20131215;C042-1236 * |
| 基于PMU/SCADA混合量测状态估计及不良数据检测方法;许勇;《四川电力技术》;20150831;第38卷(第4期);第51-55页 * |
| 基于PMU/SCADA混合量测的电力系统状态估计;刘晓义 等;《电测与仪表》;20120731;第49卷(第559期);第11-15页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105242143A (en) | 2016-01-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhao et al. | Data-driven correction approach to refine power curve of wind farm under wind curtailment | |
| CN103927695B (en) | Ultrashort-term wind power prediction method based on self study complex data source | |
| CN110648249B (en) | Annual power balance measuring and calculating method, device and equipment | |
| CN103631681B (en) | A kind of method of online reparation abnormal data of wind power plant | |
| CN103455716B (en) | A kind of power system voltage stabilization margin calculation method based on super short-period wind power prediction | |
| CN110533092B (en) | Wind generating set SCADA data classification method based on operation condition and application | |
| CN108629520B (en) | Method for evaluating running state of high-voltage transmission line in microclimate environment | |
| CN110137947B (en) | Grid voltage sag severity assessment method based on ITIC curve | |
| WO2013174144A1 (en) | Continuous power flow calculation method based on wind power fluctuation rule | |
| CN106570790B (en) | Wind power plant output data restoration method considering wind speed data segmentation characteristics | |
| CN105137177A (en) | Harmonic voltage responsibility calculation alarm method for single-point monitoring of power distribution network | |
| CN103280830B (en) | Overload control method suitable for large-scale wind power centralized access | |
| CN105590027A (en) | Identification method for photovoltaic power abnormal data | |
| CN107654342A (en) | A kind of abnormal detection method of Wind turbines power for considering turbulent flow | |
| CN108287320A (en) | A kind of battery capacity inspection optimization method | |
| CN109921426A (en) | Wind-electricity integration system probability load flow calculation method based on CV-KDE | |
| CN103927597A (en) | Ultra-short-term wind power prediction method based on autoregression moving average model | |
| CN107221933B (en) | Probabilistic load flow calculation method | |
| CN105242143B (en) | Bad data modification method is estimated based on multi-period precision measurement unit data mode | |
| CN111799798B (en) | Method and system for improving accuracy of future state load flow calculation result | |
| CN113533895B (en) | Abnormal electricity utilization judgment method and device based on three-phase three-wire circuit load curve | |
| CN114285091B (en) | Regional power grid data acquisition abnormality detection method comprising multiple photovoltaic power generation | |
| CN105932669B (en) | Wind power swing component decomposer and wind power output wave characteristic appraisal procedure | |
| CN103927594A (en) | Wind power prediction method based on self-learning composite data source autoregression model | |
| CN110808614B (en) | New energy consumption capacity calculation method, system and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |