CN113552528A - An abnormal analysis method of substation bus unbalance rate based on improved genetic algorithm - Google Patents
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Abstract
一种基于改进遗传算法的变电站母线不平衡率异常分析方法，属检测领域。包括根据SCADA系统提供的实际运行数据，推导计算电能表日电量值；根据发生故障时电采系统提供电量数据与真实电量的对应关系、SCADA系统计算得出电量值与真实电量的对应关系，建立不同故障类型在不同数据来源下的日电量对比表；基于电采系统母线平衡报表提供的电量数据，建立母线不平衡率异常分析模型；以改进遗传算法求解母线不平衡率异常分析模型，通过优化结果判断故障电能表及故障类型。能够快速准确地判断出造成母线不平衡率异常的线路电能表，对提升现场电能表监控率具有重要意义。适用于变电站母线不平衡率异常分析及电能表二次回路排故领域。
The invention relates to an abnormal analysis method for the unbalance rate of a substation busbar based on an improved genetic algorithm, which belongs to the field of detection. Including the deduction and calculation of the daily electricity value of the electric energy meter according to the actual operation data provided by the SCADA system; according to the corresponding relationship between the electric energy data provided by the electric power acquisition system and the real electric energy when a fault occurs, and the corresponding relationship between the electric energy value and the real electric energy calculated by the SCADA system, establish The daily electricity comparison table of different fault types under different data sources; based on the electricity data provided by the bus balance report of the electric power mining system, the abnormal analysis model of the unbalance rate of the bus is established; the abnormal analysis model of the unbalance rate of the bus is solved by the improved genetic algorithm. As a result, the fault energy meter and the fault type were judged. It is of great significance to improve the monitoring rate of on-site electric energy meters that can quickly and accurately determine the line electric energy meter that causes the abnormal bus unbalance rate. It is suitable for the abnormal analysis of the unbalance rate of the substation bus and the troubleshooting of the secondary circuit of the electric energy meter.
Description
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:
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;
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:
specifically, in the step (3), the bus imbalance abnormality analysis model is:
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:
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).
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;
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
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)|
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:
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 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
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
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:
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:
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;
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.
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