CN113552528A - Transformer substation bus unbalance rate abnormity analysis method based on improved genetic algorithm - Google Patents

Transformer substation bus unbalance rate abnormity analysis method based on improved genetic algorithm Download PDF

Info

Publication number
CN113552528A
CN113552528A CN202110725432.9A CN202110725432A CN113552528A CN 113552528 A CN113552528 A CN 113552528A CN 202110725432 A CN202110725432 A CN 202110725432A CN 113552528 A CN113552528 A CN 113552528A
Authority
CN
China
Prior art keywords
electric quantity
bus
electric
genetic algorithm
electric energy
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.)
Pending
Application number
CN202110725432.9A
Other languages
Chinese (zh)
Inventor
徐迅
夏澍
韩浩江
沈磊
杨剑
顾劲岳
郑天奇
史媛
张帆
陈佳瑜
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.)
State Grid Shanghai Electric Power Co Ltd
Original Assignee
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 State Grid Shanghai Electric Power Co Ltd filed Critical State Grid Shanghai Electric Power Co Ltd
Priority to CN202110725432.9A priority Critical patent/CN113552528A/en
Publication of CN113552528A publication Critical patent/CN113552528A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
    • G01R35/04—Testing or calibrating of apparatus covered by the other groups of this subclass of instruments for measuring time integral of power or current

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

A transformer substation bus unbalance rate abnormity analysis method based on an improved genetic algorithm belongs to the field of detection. Deducing and calculating the daily electric quantity value of the electric energy meter according to actual operation data provided by the SCADA system; according to the corresponding relation between the electric quantity data provided by the electric acquisition system and the real electric quantity when a fault occurs, the SCADA system calculates to obtain the corresponding relation between the electric quantity value and the real electric quantity, and a daily electric quantity comparison table of different fault types under different data sources is established; establishing a bus unbalance rate abnormity analysis model based on the electric quantity data provided by the bus balance report of the electric power acquisition system; and solving the abnormal analysis model of the unbalance rate of the bus by using an improved genetic algorithm, and judging the fault electric energy meter and the fault type according to the optimization result. The method can quickly and accurately judge the line electric energy meter causing the abnormal imbalance rate of the bus, and has important significance for improving the monitoring rate of the on-site electric energy meter. The method is suitable for the field of transformer substation bus unbalance rate abnormity analysis and electric energy meter secondary circuit fault elimination.

Description

Transformer substation bus unbalance rate abnormity analysis method based on improved genetic algorithm
The technical field is as follows:
the invention relates to the field of detection, in particular to a transformer substation bus unbalance rate abnormity analysis method based on an improved genetic algorithm.
Background art:
the unbalance rate of the substation bus is one of important assessment indexes of power supply enterprises, and the line loss rate index is directly influenced by the quality of the index.
The electric energy meters in the transformer substation are numerous and complex in wiring, and the imbalance rate index of the bus is very easy to be abnormal, so that an effective analysis method is found, the loss reduction and energy saving work is facilitated, the state of the electric energy meter can be timely monitored, the labor time cost is saved, and the working efficiency is improved.
Most of the existing documents analyze the condition that the imbalance rate of the bus is abnormal due to the fault of a single electric energy meter. However, the above documents only study the causes and phenomena of faults, and only aim at a single electric energy meter, and the number of electric energy meters in the substation is large, so that the troubleshooting work of the abnormal unbalance rate of the bus of the substation cannot be effectively guided.
A secondary circuit fault identification method is provided in the literature of ' electric measurement and instrument ', summer 28557, Xuying Cheng, Wangxikiki, and the like, 2017,54(11):99-105 '), and is one of effective methods for analyzing the unbalance rate of the conventional bus, but the method does not consider the abnormal condition of the transformation ratio and cannot effectively identify the conditions of partial voltage loss, partial current loss or faults of a plurality of electric energy meters.
A plurality of system platforms in the power system can provide data related to the unbalance rate of the substation bus. For example, a bus balance report in the electric power generation system displays the transformation ratio and the electric quantity condition of each line electric energy meter in the transformer substation every day; and the SCADA system displays the bus voltage and the phase current of each line. However, the current analysis of the unbalance rate of the bus mainly depends on an electric power acquisition system, and the data of other system platforms are not effectively utilized. How to reasonably integrate data from different sources and mine the internal association and rules of the data needs further research.
The existing literature analysis methods are realized based on a complex calculation process, and can also be used for a transformer substation with a simple loop; once too many lines are arranged in the transformer substation, the calculated amount is greatly increased, and the troubleshooting is also very difficult.
The invention content is as follows:
in order to overcome the defects of the prior art, the invention provides a transformer substation bus unbalance rate abnormity analysis method based on an improved genetic algorithm. Establishing a bus unbalance rate abnormity analysis model by establishing a daily electric quantity comparison table of different fault types under different data sources; and solving the abnormal analysis model of the unbalance rate of the bus by using an improved genetic algorithm, and judging the fault electric energy meter and the fault type according to the optimization result. The method can quickly and accurately judge the line electric energy meter causing the abnormal unbalance rate of the bus, and has important significance for promoting loss-reducing energy-saving work and improving the monitoring rate of the on-site electric energy meter.
The technical scheme of the invention is as follows: a transformer substation bus unbalance rate abnormity analysis method based on an improved genetic algorithm specifically comprises the following steps:
(1) deducing and calculating the daily electric quantity value of the electric energy meter according to actual operation data provided by the SCADA system;
(2) establishing a daily electric quantity comparison table of different fault types under different data sources according to the corresponding relation between the electric quantity data provided by the electric collection system and the real electric quantity when a fault occurs and the corresponding relation between the electric quantity value calculated by the SCADA system and the real electric quantity;
(3) establishing a bus unbalance rate abnormity analysis model based on the electric quantity data provided by the bus balance report of the electric power acquisition system;
(4) and solving the abnormal analysis model of the unbalance rate of the bus by using an improved genetic algorithm, and judging the fault electric energy meter and the fault type according to the optimization result.
Specifically, in the step (1), the calculation formula of the daily electric quantity value of the electric energy meter is as follows:
Figure BDA0003137501050000021
in the formula, W is the daily electric quantity of an electric energy meter arranged on a line; u shapebusThe average value of the voltage of the 10kV bus on the day is provided for the SCADA system; i is the average value of the current of the line in the day provided by the SCADA system;
Figure BDA0003137501050000022
for power factor, generally 0.95 is taken; t is time, and is taken for 24 hours.
Specifically, in the step (2), the daily electricity quantity comparison table of different fault types under different data sources comprises the data of the electricity meter type, the fault type, the electric power collection system and the SCADA system, and the fault types are classified.
Further, the format and content of the daily electric quantity comparison table of different fault types under different data sources are as follows:
Figure BDA0003137501050000023
Figure BDA0003137501050000031
specifically, in the step (3), the bus imbalance abnormality analysis model is:
Figure BDA0003137501050000032
wherein C is an objective function; f. ofBIRFor 10kV bus of transformer substationThe balance rate; w is ainputiThe ith input electric quantity; n is the total number of input lines; w is aoutputjThe j output electric quantity; m is the total number of output lines; epsilon is a reasonable value of the unbalance rate of the bus. The smaller the difference value between the total input electric quantity and the total output electric quantity is, the better the bus unbalance rate index is.
Further, in the step (4), the improved method for solving the bus imbalance rate anomaly analysis model by the improved genetic algorithm comprises the following steps: introducing a deviation coefficient alpha into the chromosome coding rule, wherein the calculation formula of the deviation coefficient alpha is as follows:
Figure BDA0003137501050000033
in the formula, WSCADAThe electric quantity value is calculated according to an electric energy meter daily electric quantity value calculation formula; wPECAn electric quantity value provided for the electric power production system; and alpha is the percentage of the electric quantity of the electric production system deviating from the electric quantity of the SCADA system. When the alpha of a certain line electric energy meter exceeds a certain limit lambda, the line electric energy meter is considered as an electric energy meter with possible abnormal electric quantity.
The limit λ is preferably 10%.
Specifically, the initial population generation simulates the initial population with the abnormal electric quantity condition of the single-line electric energy meter. If there are n electrical quantities in the W table, the initial population will consist of n chromosomes.
Further, the initial population generating step is as follows:
step 1: determining all the line electric energy meters with possible abnormal electric quantity by using a calculation formula of the deviation coefficient alpha, determining the number n of the groups, and forming a W meter;
step 2: setting the number i of the groups to be 1;
and step 3: w of the ith position in the W tablePECIs replaced by a corresponding WSCADAForm WiTable, representing one chromosome;
and 4, step 4: and judging the relation between i and n. If i<n, i + +, go to step 3; otherwise, the abnormal electric quantity representing all the line electric energy meters is replaced, and the initial group is generated, namely WiTable (i ═ 1,2, …, n).
Compared with the prior art, the invention has the advantages that:
1. the daily electric quantity comparison meter is used for comparing the theoretical difference of the daily electric quantity values of the electric mining system and the SCADA system, so that the fault type can be reflected quickly and accurately;
2. the improved algorithm is adopted to solve instead of manual calculation, so that the workload is greatly reduced and the working efficiency is improved for troubleshooting of a plurality of transformer substation faults of the electric energy meter;
3. the method can accurately pre-judge the fault position and the fault type, and is suitable for the conditions of ratio error, partial voltage loss, current loss or faults of a plurality of electric energy meters and the like without an effective identification method.
Description of the drawings:
FIG. 1 is a flow chart of initial population generation for an improved genetic algorithm.
FIG. 2 is an overall flow chart of an improved genetic algorithm for solving a bus imbalance rate anomaly analysis model.
Fig. 3 is a work flow block diagram of the transformer substation bus imbalance rate anomaly analysis method based on the improved genetic algorithm.
The specific implementation mode is as follows:
in order to make the technical means, the creation features, the achievement purposes and the effects of the invention easy to understand, the invention is further explained with the accompanying drawings and the embodiments.
First, a daily electric quantity value is calculated by using the following formula according to actual operation Data provided by an SCADA (Supervisory Control And Data Acquisition, i.e., a power Data Acquisition And monitoring Control system).
Figure BDA0003137501050000041
In the formula, W is the daily electric quantity of an electric energy meter arranged on a line; u shapebusThe average value of the voltage of the 10kV bus on the day is provided for the SCADA system; i is the average value of the current of the line in the day provided by the SCADA system;
Figure BDA0003137501050000042
for power factor, generally 0.95 is taken; t is time, and is taken for 24 hours.
If the electric energy meter displays that the electric quantity in the electric acquisition system and the SCADA system is P under the normal condition, the data of the electric acquisition system can deviate from the normal value under the fault condition, and the SCADA system still displays the normal value (0 when the phase B loses current), the data of the two systems are different. The comparison table of the daily electric quantity of different fault types under different data sources is shown in table 1.
TABLE 1 daily electric quantity comparison table for different fault types under different data sources
Figure BDA0003137501050000051
Secondly, based on the electric quantity data provided by the bus balance report of the electric power acquisition system, establishing a bus unbalance rate abnormity analysis model:
min C＝|fBIR(winput1,winput2,...,winputn,woutput1,woutput2,...,woutputm)|
Figure BDA0003137501050000052
i＝1,...,n,j＝1,...,m
0≤C≤ε
wherein C is an objective function; f. ofBIRThe unbalance rate of a 10kV bus of the transformer substation is obtained; w is ainputiThe ith input electric quantity; n is the total number of input lines; w is aoutputjThe j output electric quantity; m is the total number of output lines; epsilon is a reasonable value of the unbalance rate of the bus. The smaller the difference value between the total input electric quantity and the total output electric quantity is, the better the bus unbalance rate index is.
The model is then solved with a modified genetic algorithm.
The method is characterized in that a traditional genetic algorithm generally comprises elements such as chromosome coding, initial population generation, genetic operation, fitness function and termination condition, and the chromosome coding rule and the initial population generation method are improved on the basis of the traditional genetic algorithm, so that the method is more suitable for solving a bus imbalance rate anomaly analysis model.
The invention introduces a deviation coefficient alpha into a chromosome coding rule, and the calculation formula of the deviation coefficient alpha is as follows:
Figure BDA0003137501050000053
in the formula, WSCADAAn electric quantity value calculated according to actual operation data provided by the SCADA system; wPECAn electric quantity value provided for the electric power production system; and alpha is the percentage of the electric quantity of the electric production system deviating from the electric quantity of the SCADA system.
When the alpha of a certain line electric energy meter exceeds a certain limit lambda (10% is obtained through program verification), the line electric energy meter is considered as an electric energy meter with possible abnormal electric quantity.
And generating an initial population by simulating the condition of abnormal electric quantity of the single-line electric energy meter. If there are n electrical quantities in the W table, the initial population will consist of n chromosomes. The initial population generation is shown in fig. 1, and the specific steps are as follows:
step 1: determining all the line electric energy meters with possible abnormal electric quantity by using a formula (3) (namely a calculation formula of a deviation coefficient alpha), determining the number n of the groups, and forming a W meter;
step 2: setting the number i of the groups to be 1;
and step 3: w of the ith position in the W tablePECIs replaced by a corresponding WSCADAForm WiTable, representing one chromosome;
and 4, step 4: and judging the relation between i and n. If i<n, i + +, go to step 3; otherwise, the abnormal electric quantity representing all the line electric energy meters is replaced, and the initial group is generated, namely WiTable (i ═ 1,2, …, n).
The overall flow for solving the model using the improved genetic algorithm is shown in fig. 2.
In summary, the method for analyzing the unbalance rate abnormality of the substation bus according to the present technical solution is shown in fig. 3, and includes the following steps:
step 1, deducing and calculating a daily electric quantity value of an electric energy meter according to actual operation data provided by an SCADA system;
step 2, establishing a daily electric quantity comparison table of different fault types under different data sources according to the corresponding relation between the electric quantity data provided by the electric collection system and the real electric quantity when a fault occurs and the corresponding relation between the electric quantity value calculated by the SCADA system and the real electric quantity;
step 3, establishing a bus unbalance rate abnormity analysis model based on the electric quantity data provided by the bus balance report of the electric power acquisition system;
and 4, solving a bus unbalance rate abnormity analysis model by using an improved genetic algorithm, and judging the fault electric energy meter and the fault type according to an optimization result.
Example (b):
take a transformer substation in a certain city with abnormal bus imbalance as an example. The data of the electric quantity of the transformer substation in the electric power acquisition system is shown in a table 2, and the data of the current and the voltage in the SCADA system is shown in a table 3.
Table 2 electric quantity data of transformer substation in electric collecting system
Figure BDA0003137501050000061
Figure BDA0003137501050000071
TABLE 3 electric quantity data of transformer substation in SCADA system
Metering point name current/A voltage/kV Electric quantity/kWh
No. 2 main transformer 348.461 10.44 143660.5
No. 3 main transformer 314.752 10.41 129390.4
De 21 67.46 10.44 27811.83
De 22 16.89 10.44 6963.264
De 23 38.29 10.44 15785.87
De 24. de 49.23 10.44 20296.12
De 25 48.74 10.44 20094.11
German patent No. 26 44.78 10.44 18461.51
De 27 79.01 10.44 32573.57
De 28 8.826 10.44 3638.708
De 31 78.6 10.41 32311.42
De 32 33.83 10.41 13907.06
De 33 58.22 10.41 23933.47
German 34 30.91 10.41 12706.69
De 35. step C 17.91 10.41 7362.563
De 36. German patent application 9.78 10.41 4020.428
De 37 30.22 10.41 12423.04
De 38 59.335 10.41 24391.83
And calculating the daily electric quantity value of the electric energy meter according to current and voltage data provided by the SCADA system, and listing the daily electric quantity value calculated according to a formula as the rightmost column of the table 3.
The α values calculated from the deviation coefficient formula are shown in table 4.
TABLE 4 alpha values
Figure BDA0003137501050000072
Figure BDA0003137501050000081
The overall flow of solving the bus imbalance abnormality analysis model by using the improved genetic algorithm is shown in fig. 2, and the obtained optimization results are shown in table 5.
TABLE 5 optimization results
Abnormal measuring point Fitness value Corrected busbar imbalance/%)
De 35. step C 0.9939 0.61
And performing field investigation according to an optimization result, and finding that the transformer substation 35 is a three-phase three-wire system wiring, and the phase A is cracked due to the flow change, so that the phase A loses current.
In addition, when the method provided by the technical scheme is used for troubleshooting more than 10 transformer substations, the fault positions can be accurately positioned, and the pre-judged fault types completely meet the requirements, so that the working efficiency of field workers is effectively improved.
The technical scheme is based on an electric production system and an SCADA system, the difference of two system data when the bus unbalance rate is abnormal is analyzed, a bus unbalance rate abnormality analysis model is constructed, an improved genetic algorithm suitable for an optimization solution model is further provided, and finally verification is performed by using an actual example, so that the following conclusion is obtained:
1. when the bus unbalance rate is abnormal, the data of the electric power acquisition system and the data of the SCADA system are different, and the reason of the abnormality can be effectively analyzed by utilizing the difference.
2. The deduced daily electric quantity calculation formula of the electric energy meter and the daily electric quantity comparison meter are accurate, and the relationship between the electric quantity displayed by different source systems and the real electric quantity can be intuitively reflected.
3. The established bus unbalance rate abnormity analysis model is effective, manual calculation is replaced by an improved algorithm, the workload is greatly reduced for troubleshooting of a plurality of transformer substation faults of the electric energy meter, and the working efficiency is improved.
4. The transformer substation bus unbalance rate abnormity analysis method based on the improved genetic algorithm can accurately pre-judge the fault position and the fault type, and is suitable for the situations that no effective identification method exists yet, such as transformation ratio error, partial voltage loss, current loss or faults of a plurality of electric energy meters, and the like.
In summary, according to the technical scheme provided by the invention, the daily electric quantity relational expressions respectively corresponding to different fault types under different data sources are established by comparing the electric quantity data provided by the bus balance report in the electric power acquisition system with the daily electric quantity value of the SCADA system, and then a bus unbalance rate abnormity analysis model is established based on the electric quantity data provided by the bus balance report of the electric power acquisition system, so that a genetic algorithm is improved to solve the bus unbalance rate abnormity analysis model, and the fault electric energy meter and the fault type are judged through an optimization result. The method can quickly and accurately judge the line electric energy meter causing the abnormal bus unbalance rate, pre-judge the fault type, solve the problem of large workload of on-site troubleshooting, is suitable for the condition that no effective identification method exists for the faults of ratio change errors, partial voltage loss, current loss or a plurality of electric energy meters and the like, and has important significance for promoting loss-reducing and energy-saving work and improving the monitoring rate of the on-site electric energy meter.
The technical scheme of the invention can be widely applied to the field of unbalance rate abnormity analysis of the transformer substation bus and fault elimination of the secondary circuit of the electric energy meter.

Claims (8)

1. A transformer substation bus unbalance rate abnormity analysis method based on an improved genetic algorithm is characterized by comprising the following steps:
(1) deducing and calculating the daily electric quantity value of the electric energy meter according to actual operation data provided by the SCADA system;
(2) establishing a daily electric quantity comparison table of different fault types under different data sources according to the corresponding relation between the electric quantity data provided by the electric collection system and the real electric quantity when a fault occurs and the corresponding relation between the electric quantity value calculated by the SCADA system and the real electric quantity;
(3) establishing a bus unbalance rate abnormity analysis model based on the electric quantity data provided by the bus balance report of the electric power acquisition system;
(4) and solving the abnormal analysis model of the unbalance rate of the bus by using an improved genetic algorithm, and judging the fault electric energy meter and the fault type according to the optimization result.
2. The improved genetic algorithm-based substation bus imbalance rate anomaly analysis method according to claim 1, wherein in the step (2), the daily electricity quantity comparison table of different fault types under different data sources comprises data of an electricity meter type, a fault type, an electricity collection system and a SCADA system, and the data are classified according to the fault types.
3. The substation bus imbalance rate abnormality analysis method based on the improved genetic algorithm of claim 1, wherein in the step (3), the bus imbalance rate abnormality analysis model is as follows:
Figure RE-FDA0003241824350000011
wherein C is an objective function; f. ofBIRThe unbalance rate of a 10kV bus of the transformer substation is obtained; w is ainputiThe ith input electric quantity; n is the total number of input lines; w is aoutputjThe j output electric quantity; m is the total number of output lines; epsilon is a reasonable value of the unbalance rate of the bus.
4. The substation bus imbalance rate anomaly analysis method based on the improved genetic algorithm according to claim 1, wherein in the step (4), the improved method for solving the bus imbalance rate anomaly analysis model by the improved genetic algorithm is as follows: introducing a deviation coefficient alpha into the chromosome coding rule, wherein the calculation formula of the deviation coefficient alpha is as follows:
Figure RE-FDA0003241824350000021
in the formula, WSCADAIs an electric quantity value calculated according to a SCADA system;WPECan electric quantity value provided for the electric power production system; and alpha is the percentage of the electric quantity of the electric production system deviating from the electric quantity of the SCADA system. When the alpha of a certain line electric energy meter exceeds a certain limit lambda, the line electric energy meter is considered as an electric energy meter with possible abnormal electric quantity.
5. The substation bus imbalance rate anomaly analysis method based on the improved genetic algorithm is characterized in that the limit value lambda is preferably 10%.
6. The transformer substation bus imbalance rate abnormality analysis method based on the improved genetic algorithm is characterized in that the initial population is generated to simulate the initial population by the condition that the electric energy of the single-line electric energy meter is abnormal; if there are n electrical quantities in the W table, the initial population will consist of n chromosomes;
the generation steps of the initial population are as follows:
step 1: determining all the line electric energy meters with possible abnormal electric quantity by using the following formula, determining the number n of the groups, and forming a W meter;
Figure RE-FDA0003241824350000022
in the formula, WSCADAIs an electric quantity value calculated according to a SCADA system; wPECAn electric quantity value provided for the electric power production system; and alpha is the percentage of the electric quantity of the electric production system deviating from the electric quantity of the SCADA system.
Step 2: setting the number i of the groups to be 1;
and step 3: w of the ith position in the W tablePECIs replaced by a corresponding WSCADAForm WiTable, representing one chromosome;
and 4, step 4: and judging the relation between i and n. If i<n, i + +, go to step 3; otherwise, the abnormal electric quantity representing all the line electric energy meters is replaced, and the initial group is generated, namely WiTable (i ═ 1,2, …, n).
7. The method for analyzing the unbalance rate abnormality of the substation bus based on the improved genetic algorithm as claimed in claim 1, wherein the method for analyzing the unbalance rate abnormality of the substation bus based on the improved genetic algorithm can pre-judge possible fault types by comparing a bus balance report of an electric power generation system with actual operation data of a SCADA system, and provides a new idea for removing faults of an electric energy meter.
8. The transformer substation bus imbalance rate abnormality analysis method based on the improved genetic algorithm is characterized in that a bus imbalance rate abnormality analysis model is established based on electric quantity data of a bus balance report form of an electric power acquisition system so as to improve the genetic algorithm to solve the model; the optimized result is used for judging the electric energy meter with faults, so that the on-site investigation is facilitated, the workload is reduced, and the working efficiency is improved.
CN202110725432.9A 2021-06-29 2021-06-29 Transformer substation bus unbalance rate abnormity analysis method based on improved genetic algorithm Pending CN113552528A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110725432.9A CN113552528A (en) 2021-06-29 2021-06-29 Transformer substation bus unbalance rate abnormity analysis method based on improved genetic algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110725432.9A CN113552528A (en) 2021-06-29 2021-06-29 Transformer substation bus unbalance rate abnormity analysis method based on improved genetic algorithm

Publications (1)

Publication Number Publication Date
CN113552528A true CN113552528A (en) 2021-10-26

Family

ID=78102455

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110725432.9A Pending CN113552528A (en) 2021-06-29 2021-06-29 Transformer substation bus unbalance rate abnormity analysis method based on improved genetic algorithm

Country Status (1)

Country Link
CN (1) CN113552528A (en)

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104466976A (en) * 2014-11-04 2015-03-25 中国南方电网有限责任公司超高压输电公司南宁局 Series capacitance compensation device unbalanced current optimizing method based on genetic algorithm
CN105573997A (en) * 2014-10-09 2016-05-11 普华讯光（北京）科技有限公司 Method and device for determining electric larceny suspect user
CN106780125A (en) * 2016-12-08 2017-05-31 国网浙江杭州市富阳区供电公司 A kind of acquisition abnormity urgency level computational methods based on monthly power consumption
CN106970347A (en) * 2017-05-31 2017-07-21 国网上海市电力公司 A kind of method of utilization bus unbalance factor investigation transformer station kilowatt-hour meter secondary circuit failure
CN107220906A (en) * 2017-05-31 2017-09-29 国网上海市电力公司 Multiple Time Scales multiplexing electric abnormality analysis method based on electricity consumption acquisition system
CN107340492A (en) * 2017-05-31 2017-11-10 国网上海市电力公司 Electric power meter failure analysis methods with scene anticipation are excavated based on big data
CN108828494A (en) * 2018-04-18 2018-11-16 电子科技大学 Intelligent electric energy meter function calibration method based on genetic algorithm
CN108876036A (en) * 2018-06-14 2018-11-23 国网福建省电力有限公司 A kind of regulation optimization method about transmission line of electricity separated time critical point electricity abnormal problem
CN112003308A (en) * 2020-08-24 2020-11-27 国网江苏省电力有限公司淮安供电分公司 Platform area load balance control method based on genetic algorithm
CN112485514A (en) * 2020-11-14 2021-03-12 国网江苏省电力有限公司营销服务中心 Method for evaluating uncertainty of power consumption of electric balance

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105573997A (en) * 2014-10-09 2016-05-11 普华讯光（北京）科技有限公司 Method and device for determining electric larceny suspect user
CN104466976A (en) * 2014-11-04 2015-03-25 中国南方电网有限责任公司超高压输电公司南宁局 Series capacitance compensation device unbalanced current optimizing method based on genetic algorithm
CN106780125A (en) * 2016-12-08 2017-05-31 国网浙江杭州市富阳区供电公司 A kind of acquisition abnormity urgency level computational methods based on monthly power consumption
CN106970347A (en) * 2017-05-31 2017-07-21 国网上海市电力公司 A kind of method of utilization bus unbalance factor investigation transformer station kilowatt-hour meter secondary circuit failure
CN107220906A (en) * 2017-05-31 2017-09-29 国网上海市电力公司 Multiple Time Scales multiplexing electric abnormality analysis method based on electricity consumption acquisition system
CN107340492A (en) * 2017-05-31 2017-11-10 国网上海市电力公司 Electric power meter failure analysis methods with scene anticipation are excavated based on big data
CN108828494A (en) * 2018-04-18 2018-11-16 电子科技大学 Intelligent electric energy meter function calibration method based on genetic algorithm
CN108876036A (en) * 2018-06-14 2018-11-23 国网福建省电力有限公司 A kind of regulation optimization method about transmission line of electricity separated time critical point electricity abnormal problem
CN112003308A (en) * 2020-08-24 2020-11-27 国网江苏省电力有限公司淮安供电分公司 Platform area load balance control method based on genetic algorithm
CN112485514A (en) * 2020-11-14 2021-03-12 国网江苏省电力有限公司营销服务中心 Method for evaluating uncertainty of power consumption of electric balance

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
史媛 等: "基于SCADA在线数据计算的母线不平衡率异常分析平台", 《电力信息与通信技术》 *

Similar Documents

Publication Publication Date Title
CN103489035A (en) Power grid electric energy quality comprehensive evaluation method based on gray weighting correlation analysis algorithm
CN104462779B (en) Using rapid modeling and the theory wire loss on-line calculation method of Data Quality Analysis
CN104281737A (en) Method for selecting type of topological structure of offshore wind power plant current collecting system
CN108629520B (en) Method for evaluating running state of high-voltage transmission line in microclimate environment
CN102522756B (en) Inductive reactive compensation method for power grid for avoiding voltage off-normal risks
CN111257820B (en) Three-phase intelligent electric meter wiring remote detection method
CN109473996A (en) The method and system of transmission cross-section stability margin under a kind of acquisition planned manner
CN113655425B (en) Metering point operation error monitoring method and system suitable for 10KV wiring line
CN111724060A (en) Integrated analysis monitoring system and analysis method for transformer area line loss
CN102590652B (en) Electric-information-based equipment performance evaluation system and method
CN112288293A (en) Comprehensive evaluation method for electric energy quality of large charging station
CN110687494A (en) Method and system for monitoring faults of remote gateway electric energy meter
CN112485514A (en) Method for evaluating uncertainty of power consumption of electric balance
CN106970347A (en) A kind of method of utilization bus unbalance factor investigation transformer station kilowatt-hour meter secondary circuit failure
CN110058189B (en) Electric energy meter misconnection judgment method based on phase angle analysis
CN111342454A (en) Method and system for analyzing big data of low voltage cause at platform area outlet
CN110687385A (en) Grid-connected unit remote frequency disturbance testing method based on load interval dynamic adjustment
CN114765451A (en) Photovoltaic power station fault detection method based on fault knowledge graph
CN113126017A (en) Fault diagnosis method for data acquisition failure in electric energy acquisition system
CN113552528A (en) Transformer substation bus unbalance rate abnormity analysis method based on improved genetic algorithm
CN111384714B (en) Multi-factor state distribution-based low-voltage transformer area line loss problem searching method
CN110896217B (en) Network source coordination control method and device for improving voltage stability of receiving-end power grid
CN113376553A (en) Intelligent discrimination method and system for three-phase four-wire metering string current loop wiring
Li Abnormal energy consumption analysis based on big data mining technology
CN110991825A (en) Line loss judgment method based on big data

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