CN112085321A - Station area state evaluation method based on edge calculation - Google Patents

Station area state evaluation method based on edge calculation Download PDF

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CN112085321A
CN112085321A CN202010751974.9A CN202010751974A CN112085321A CN 112085321 A CN112085321 A CN 112085321A CN 202010751974 A CN202010751974 A CN 202010751974A CN 112085321 A CN112085321 A CN 112085321A
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宋耐超
闫明
史雷敏
郝雁翔
惠杰
井泉
和合
张维
黄宜昕
刘哲
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State Grid Corp of China SGCC
Xuchang Power Supply Co of Henan Electric Power Co
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Abstract

The invention aims to provide a platform area state evaluation method based on edge computing, which is used for reducing data processing pressure of a cloud center and a main station and obtaining more reasonable evaluation results and comprises the following steps: 1. installing an edge calculating device in the transformer area, acquiring electrical characteristic parameters of different transformer areas at different moments in a certain historical time period by using the edge calculating device, and calculating running state parameters of the transformer area by using an edge calculating module; 2. preprocessing the running state parameters of the transformer area; 3. obtaining objective weight through an entropy weight method, and obtaining subjective weight through an analytic hierarchy process; combining the subjective weight and the objective weight to obtain a comprehensive weight, constructing a state evaluation function, and evaluating the state of the transformer area; 4. the collected historical data of the transformer area are used for training the prediction model on the cloud platform, and the prediction model of the edge device is updated regularly.

Description

Station area state evaluation method based on edge calculation
Technical Field
The invention belongs to the technical field of low-voltage distribution room operation management evaluation, and particularly relates to a distribution room state evaluation method based on edge calculation
Background
With the high-efficiency development and strategic decision transformation of national energy, the electric power inevitably becomes the energy basis of future economic development of residents, the last kilometer of power is supplied to a low-voltage distribution area, and the health of the low-voltage distribution area directly influences the power utilization quality and satisfaction degree of users. The method has the advantages that the healthy transformer area is created, the power supply quality can be improved, the loss is reduced, complaints are reduced, the core of scientific management, energy conservation and consumption reduction of power supply enterprises is realized, and the comprehensive management capability of the power supply enterprises is reflected by the concentration of the management level of the transformer area. The method for enhancing the health management of the transformer area is an effective way for power supply enterprises to fulfill social responsibility and improve enterprise benefits through internal mining.
The traditional transformer area state evaluation method mainly has three problems: (1) the method lacks big data application means, over emphasizes professional division of labor, and artificially causes professional barriers and data segmentation in the platform area management work; (2) the method has the advantages that the method has multiple and wide platform areas, numerous equipment, complex problems and extensive field management, and each level of management layer lacks visual platform area equipment health information, so that the method lacks accurate and efficient data support in aspects of platform area investment decision, operation and maintenance service and the like; (3) high complaints, high tripping and high line loss are the first problems troubling a manager of a transformer area, and index information of equipment in the transformer area is dispersed, and only the anomaly analysis of a single index is focused on 'treating symptoms and not treating the root causes'.
However, the edge computing technology can effectively solve the above problems, the edge computing mode extends the computing and analyzing function to the platform area edge computing device, loads the trained model into the edge computing device, and periodically updates the trained model, thereby sharing the burden of the data center, having the characteristics of low time delay, supporting physical distribution computation, being suitable for real-time analysis and optimization decision and the like, and being capable of efficiently utilizing the platform area data, reasonably evaluating the platform area state, finding out abnormal platform areas in time, and facilitating the local management of the platform areas by operation and maintenance personnel.
Disclosure of Invention
The invention aims to provide a platform area state evaluation method based on edge computing, which is used for reducing data processing pressure of a cloud center and a main station and enabling an obtained evaluation result to be more reasonable.
The technical scheme of the invention is as follows: a station area state evaluation method based on edge calculation comprises the following steps:
s1: installing edge computing devices in different transformer areas, collecting electrical characteristic parameters of different transformer areas at different moments in a certain historical time period by using the edge computing devices, and calculating the running state parameters of the collected transformer areas by using an edge computing module of the edge computing devices;
s2: constructing a platform area state evaluation model in the cloud platform, and preprocessing the running state parameters of the platform area;
s3: processing the operating state parameters of the transformer area by an entropy weight method, and solving the weight of each state parameter to obtain objective weight;
s4: processing the operating state parameters of the transformer area by an analytic hierarchy process, and calculating the weight of each state parameter to obtain a subjective weight;
s5: combining the subjective weight and the objective weight to obtain a comprehensive weight, constructing a state evaluation function, and evaluating the station area state by referring to a state evaluation reference result table;
s6: deep training is carried out on the prediction model on the cloud platform through the collected historical data of the platform area, the prediction model of the edge equipment is updated regularly, and the accuracy of the prediction model of the edge computing equipment is guaranteed.
The operating state parameter of the distribution room in the step S1 is the power supply radius X1Economic deviation ratio X of cable wire diameter2Comprehensive line loss rate X3Transformer load factor X4User voltage qualification rate X5Three-phase load unbalance degree X6；
The power supply radius X1The distance between the transformer and the user terminal is one of important parameters for evaluating whether the running state of the transformer area is reasonable. The smaller the power supply radius is, the better the running state of the low-voltage distribution network is;
the economic deviation ratio X of the cable wire diameter2The economic sectional area A corresponding to the average power of the lead flowing through the low-voltage distribution network areasDeviation from its actual cross-sectional area A and AsThe smaller the economic deviation rate of the cable diameter is, the better the running state of the platform area is, and the calculation formula is as follows:
economic deviation rate of cable wire diameter
Figure BDA0002610326840000031
In the formula: pavTransmitting average active power, U, for the linenThe rated voltage of the line is used, and rho is the economic current density of the lead;
the comprehensive line loss rate X3The ratio of the line loss electric energy of the low-voltage distribution network station area to the total power supply quantity of the station area is used for reflecting whether the operation of the station area is economical or not, the smaller the comprehensive line loss rate is, the better the operation state of the station area is, and the calculation formula is as follows:
Figure BDA0002610326840000032
in the formula: p is a radical of1For supplying power to the distribution network areas of low voltage, p2The total electric quantity data of the users in the low-voltage distribution network are obtained;
the load factor X of the transformer4The ratio of the average active power of all loads in the transformer area to the active power of the whole transformer area can reflect the load condition of a transformer in the transformer area, the smaller the load rate of the transformer is in the economic operation range of the transformer, the better the operation state of the transformer area of the low-voltage distribution network is, and the calculation formula is as follows:
Figure BDA0002610326840000041
in the formula: w is a1The power supply load is supplied to the station area in the time period T, S is the capacity of the transformer,
Figure BDA0002610326840000042
loading a power factor for the distribution room;
the user voltage qualification rate X5The ratio of the number of users with qualified voltage in a low-voltage distribution network area to the number of users in the whole area is shown, and the higher the qualified rate of the user voltage is, the better the running state of the low-voltage distribution network area is;
the three-phase load unbalance degree X6The ratio of the deviation of the maximum load and the three-phase average load in A, B, C three phases at the low-voltage side outlet end of the distribution transformer to the three-phase average load is indicated, the smaller the unbalanced degree of the three-phase load is, the better the running state of the transformer area is, and the calculation formula is as follows:
Figure BDA0002610326840000043
in the formula: pA，PB，PCThe loads of the phases A, B and C of the low-voltage side outgoing line end of the distribution transformer are respectively.
In step S2, the operation state parameters of the distribution room are standardized by classifying the operation state parameters into 3 classes, which are respectively a forward index, a reverse index, and an interval index;
the forward indexes are as follows: user voltage qualification rate X5；
The reverse indexes are as follows: radius of power supply X1Economic deviation ratio X of cable wire diameter2Comprehensive line loss rate X3Three-phase load unbalance degree X6；
The interval indexes are as follows: transformer load factor X4；
For the reverse index, carrying out pretreatment based on an extreme value treatment method, wherein the pretreatment formula of the reverse index is as follows:
Figure BDA0002610326840000051
in the formula Ximax、XiminRespectively is the index X in a plurality of transformer areas i1, 2, … …, n;
for the forward index, the forward index is converted into a dimensionless reverse index, and the preprocessing formula of the forward index is as follows:
Figure BDA0002610326840000052
in the formula Ximax、XiminRespectively is the index X in a plurality of transformer areas i1, 2, … …, n;
for interval type indexes, the load rate of the transformer is a dimensionless index and is converted into a reverse index, and the interval type index preprocessing formula is as follows:
Figure BDA0002610326840000053
in the formula: ximidIs the index XiI-1, 2, … …, n.
The entropy weight method in step S3 is an objective assignment method. In the specific using process, the entropy weight method calculates the entropy weight of each index by using the information entropy according to the variation degree of each index, and then corrects the weight of each index through the entropy weight, so that objective index weight is obtained. Generally, if the entropy weight method of the information entropy index weight determination method of a certain index is smaller, the larger the variation degree of the index value is, the more information is provided, the larger the function in the comprehensive evaluation is, and the larger the weight is. Conversely, if the larger the entropy weight method of the information entropy index weight determination method of a certain index, the smaller the degree of variation of the index value is, the smaller the amount of information provided, the smaller the role played in the comprehensive evaluation is, and the smaller the weight thereof is.
The step S3 includes the following steps,
s3.1: calculating the proportion P of the ith station area in the jth indexij：
Figure BDA0002610326840000061
S3.2: calculating entropy E of j indexj：
Figure BDA0002610326840000062
If P isijWhen 0, then
Figure BDA0002610326840000064
S3.3: calculating the weight of each index:
according to the calculation formula of the information entropy, calculating the information entropy of each index to be E1，E2，…，EkCalculating objective weight of each index through information entropy:
Figure BDA0002610326840000063
in the formula 1-EjRedundancy for information entropy.
In the step S4, the subjective weight is determined by using an analytic hierarchy process, which decomposes elements related to evaluation into a target, a criterion, and the like, and performs qualitative and quantitative analysis and evaluation based on the hierarchical analysis.
The step S4 includes the steps of,
s4.1: constructing a comparison matrix, comparing the importance degrees of each index by adopting a 1-9 scale method,
a scale value of 1 represents that both are equally important;
a scale value of 3 indicates that the former is slightly more important than the latter;
a scale value of 5 represents that the former is significantly more important than the latter;
a scale value of 7 represents that the former is very important compared to the latter;
a scale value of 9 represents that the former is extremely important compared to the latter;
the scale values are 2, 4, 6 and 8 to represent intermediate values of adjacent judgment; wherein a scale value of 2 indicates that the former is between equally and slightly important as compared to the latter, a scale value of 4 indicates that the former is between slightly and significantly important as compared to the latter, a scale value of 6 indicates that the former is between significantly and very important as compared to the latter, and a scale value of 8 indicates that the former is between very and extremely important as compared to the latter;
the reciprocal represents the inverse comparison;
comparing the importance of each index and establishing a judgment comparison matrix:
Figure BDA0002610326840000071
in the formula bijThe importance degree of the ith index to the jth index is shown.
Wherein, the judgment criteria of importance are: user voltage qualification rate X5More than three-phase load unbalance X6Comprehensive line loss rate X3Load factor X of transformer4Economic deviation ratio X of cable wire diameter2Power supply radius of platform area X1；
Wherein power supply radius X of platform area1The economic deviation ratio X of the wire diameter of the cable is the 1 st index2Is the 2 nd index, the comprehensive line loss rate X3As the 3 rd index, the transformer load factor X4As the 4 th index, the user voltage qualification rate X5As the 5 th index, three-phase load unbalance degree X6Is the 6 th index;
and the 2 nd index is considered to be 2 in the scale value of the importance degree of the 1 st index;
the scale value of the importance degree of the 3 rd index to the 1 st index is 4;
the scale value of the importance degree of the 3 rd index to the 2 nd index is 3;
the scale value of the importance degree of the 4 th index to the 1 st index is 3;
the scale value of the importance degree of the 4 th index to the 2 nd index is 2;
the scale value of the importance degree of the 4 th index to the 3 rd index is 1/2;
the scale value of the importance degree of the 5 th index to the 1 st index is 6;
the scale value of the importance degree of the 5 th index to the 2 nd index is 5;
the scale value of the importance degree of the 5 th index to the 3 rd index is 3;
the scale value of the importance degree of the 5 th index to the 4 th index is 3;
the scale value of the importance degree of the 6 th index to the 1 st index is 5;
the scale value of the importance degree of the 6 th index to the 2 nd index is 4;
the scale value of the importance degree of the 6 th index to the 3 rd index is 2;
the scale value of the importance degree of the 6 th index to the 4 th index is 2;
the scale value of the importance degree of the 6 th index to the 5 th index is 1/2;
the scale value of the degree of importance of the self-comparison is 1;
the judgment comparison matrix constructed according to the judgment standard is as follows:
Figure BDA0002610326840000081
s4.2: calculating the order of the importance degree of the indexes, and recording the maximum characteristic value of the judgment comparison matrix as lambdamaxCalculating λmaxNormalizing the corresponding characteristic vector to obtain a subjective weight vector alpha, and recording the subjective weight vector alpha as wj。
S4.3, in order to avoid one side brought by subjectivity in the weight calculation method, the consistency of the result is checked, and the formula is as follows:
CR＝CI/RI
Figure BDA0002610326840000091
in the formula: cRJudging the random consistency ratio of the comparison matrix; cIIs a general consistency index; rIIs a random average consistency index;
n and RIThe value relationship is as follows: when n is 1, RI＝0；
When n is 2, RI＝0；
When n is 3, RI＝0.52；
When n is 4, RI＝0.89；
When n is 5, RI＝1.12；
When n is 6, RI＝1.24；
When n is 7, RI＝1.36；
When the test result CRIf the judgment matrix is less than 0.1, the consistency of the judgment matrix can be considered to be acceptable, otherwise, the judgment matrix needs to be corrected.
The objective weight obtained by the entropy weight method in the step S5 is denoted as wiSubjective weight w obtained by analytic hierarchy processjDetermining the integrated weight based onOptimizing the comprehensive weight by using Lagrange multiplier method according to minimum information entropy principle to obtain comprehensive weight WkThe calculation formula is as follows:
Figure BDA0002610326840000092
the state evaluation function is:
Y＝XW
Figure BDA0002610326840000101
in the formula: xmnThe nth index of the mth station area; y ═ Y1，Y2，…，Ym)T，YmIs the m-th station area state evaluation function; w ═ W1，W2，…，Wn)T，WnIs the integrated weight of the nth index.
The evaluation criteria of the station area state evaluation result reference table are as follows:
when the evaluation function value is less than 0.25, the station area state is a good operation state;
when the evaluation function value is [0.25, 0.5], the station area state is a good operation state;
when the evaluation function value is (0.5, 0.75), the station area state is a running state difference;
when the evaluation function value is larger than 0.75, the station area state is a very poor operation state.
The invention has the beneficial effects that: the operation state of the transformer area is evaluated by adopting an edge computing technology, so that the data processing pressure of a cloud center and a main station is reduced, operation and maintenance personnel can conveniently carry out local management on the transformer area, and a large amount of manpower, material resources and financial resources are saved;
according to the method, the subjective weight and the objective weight are combined, the problems that the subjectivity of the subjective weight is large and the objective weight is possibly inconsistent with the actual importance degree are avoided, the obtained combined weight takes the expert experience and the characteristics of data into account, and the defects of an analytic hierarchy process and an entropy weight process are avoided, so that the combined weight is more reasonable, and the obtained evaluation result is more accurate;
the operation state of the platform area is evaluated by adopting an edge computing technology, and an evaluation model in the edge computing device is updated and optimized periodically, so that the accuracy of an evaluation result is improved.
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FIG. 1 is a flow chart of the present invention.
FIG. 2 is a flow chart of model optimization for an edge computing device
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention comprises the steps of:
s1: installing edge computing devices in different transformer areas, collecting electrical characteristic parameters of different transformer areas at different moments in a certain historical time period by using the edge computing devices, and calculating the running state parameters of the collected transformer areas by using an edge computing module of the edge computing devices;
s2: constructing a platform area state evaluation model in the cloud platform, and preprocessing the running state parameters of the platform area;
s3: processing the operating state parameters of the transformer area by an entropy weight method, and solving the weight of each state parameter to obtain objective weight;
s4: processing the operating state parameters of the transformer area by an analytic hierarchy process, and calculating the weight of each state parameter to obtain a subjective weight;
s5: combining the subjective weight and the objective weight to obtain a comprehensive weight, constructing a state evaluation function, and evaluating the station area state by referring to a state evaluation reference result table;
s6: and training the prediction model on the cloud platform by using the collected historical data of the platform area, and regularly updating the prediction model of the edge equipment to ensure the accuracy of the prediction model of the edge computing equipment.
The operating state parameter of the distribution room in the step S1 is the power supply radius X1Economic deviation ratio X of cable wire diameter2Comprehensive line loss rate X3Transformer load factor X4User voltage qualification rate X5Three-phase load unbalance degree X6；
The power supply radius X1The distance between the transformer and the user terminal is one of important parameters for evaluating whether the running state of the transformer area is reasonable. The smaller the power supply radius is, the better the running state of the low-voltage distribution network is;
the economic deviation ratio X of the cable wire diameter2The economic sectional area A corresponding to the average power of the lead flowing through the low-voltage distribution network areasDeviation from its actual cross-sectional area A and AsThe smaller the economic deviation rate of the cable diameter is, the better the running state of the platform area is, and the calculation formula is as follows:
Figure BDA0002610326840000121
in the formula: pavTransmitting average active power, U, for the linenThe rated voltage of the line is used, and rho is the economic current density of the lead;
the comprehensive line loss rate X3The ratio of the line loss electric energy of the low-voltage distribution network station area to the total power supply quantity of the station area is used for reflecting whether the operation of the station area is economical or not, the smaller the comprehensive line loss rate is, the better the operation state of the station area is, and the calculation formula is as follows:
Figure BDA0002610326840000122
in the formula: p is a radical of1For supplying power to the distribution network areas of low voltage, p2The total electric quantity data of the users in the low-voltage distribution network are obtained;
the load factor X of the transformer4The ratio of the average active power of all the loads in the transformer area to the active power of the whole transformer area can reflect the load condition of the transformer in the transformer area,in the economic operation range of the transformer, the smaller the load rate of the transformer is, the better the operation state of a low-voltage distribution network area is, and the calculation formula is as follows:
Figure BDA0002610326840000131
in the formula: w is a1The power supply load is supplied to the station area in the time period T, S is the capacity of the transformer,
Figure BDA0002610326840000132
loading a power factor for the distribution room;
the user voltage qualification rate X5The ratio of the number of users with qualified voltage in a low-voltage distribution network area to the number of users in the whole area is shown, and the higher the qualified rate of the user voltage is, the better the running state of the low-voltage distribution network area is;
the three-phase load unbalance degree X6The ratio of the deviation of the maximum load and the three-phase average load in A, B, C three phases at the low-voltage side outlet end of the distribution transformer to the three-phase average load is indicated, the smaller the unbalanced degree of the three-phase load is, the better the running state of the transformer area is, and the calculation formula is as follows:
Figure BDA0002610326840000133
in the formula: pA，PB，PCThe loads of the phases A, B and C of the low-voltage side outgoing line end of the distribution transformer are respectively.
In step S2, the operation state parameters of the distribution room are standardized by classifying the operation state parameters into 3 classes, which are respectively a forward index, a reverse index, and an interval index;
the forward indexes are as follows: user voltage qualification rate X5；
The reverse indexes are as follows: radius of power supply X1Economic deviation ratio X of cable wire diameter2Comprehensive line loss rate X3Three-phase load unbalance degree X6；
The interval indexes are as follows: load factor of transformerX4；
For the reverse index, carrying out pretreatment based on an extreme value treatment method, wherein the pretreatment formula of the reverse index is as follows:
Figure BDA0002610326840000141
in the formula Ximax、XiminRespectively is the index X in a plurality of transformer areas i1, 2, … …, n;
for the forward index, the forward index is converted into a dimensionless reverse index, and the preprocessing formula of the forward index is as follows:
Figure BDA0002610326840000142
in the formula Ximax、XiminRespectively is the index X in a plurality of transformer areas i1, 2, … …, n;
for interval type indexes, the load rate of the transformer is a dimensionless index and is converted into a reverse index, and the interval type index preprocessing formula is as follows:
Figure BDA0002610326840000143
in the formula: ximidIs the index XiI-1, 2, … …, n.
The entropy weight method in step S3 is an objective assignment method. In the specific using process, the entropy weight method calculates the entropy weight of each index by using the information entropy according to the variation degree of each index, and then corrects the weight of each index through the entropy weight, so that objective index weight is obtained. Generally, if the entropy weight method of the information entropy index weight determination method of a certain index is smaller, the larger the variation degree of the index value is, the more information is provided, the larger the function in the comprehensive evaluation is, and the larger the weight is. Conversely, if the larger the entropy weight method of the information entropy index weight determination method of a certain index, the smaller the degree of variation of the index value is, the smaller the amount of information provided, the smaller the role played in the comprehensive evaluation is, and the smaller the weight thereof is.
The step S3 includes the following steps,
s3.1: calculating the proportion P of the ith station area in the jth indexij：
Figure BDA0002610326840000151
S3.2: calculating entropy E of j indexj：
Figure BDA0002610326840000152
If P isijWhen 0, then
Figure BDA0002610326840000154
S3.3: calculating the weight of each index:
according to the calculation formula of the information entropy, calculating the information entropy of each index to be E1，E2，…，EkCalculating objective weight of each index through information entropy:
Figure BDA0002610326840000153
in the formula 1-EjRedundancy for information entropy.
In the step S4, the subjective weight is determined by using an analytic hierarchy process, which decomposes elements related to evaluation into a target, a criterion, and the like, and performs qualitative and quantitative analysis and evaluation based on the hierarchical analysis.
The step S4 includes the steps of,
s4.1: constructing a comparison matrix, comparing the importance degrees of each index by adopting a 1-9 scale method,
a scale value of 1 represents that both are equally important;
a scale value of 3 indicates that the former is slightly more important than the latter;
a scale value of 5 represents that the former is significantly more important than the latter;
a scale value of 7 represents that the former is very important compared to the latter;
a scale value of 9 represents that the former is extremely important compared to the latter;
the scale values of 2, 4, 6 and 8 represent intermediate values of adjacent judgments, wherein the scale value of 2 represents that the former is between equal importance and slight importance compared with the latter, the scale value of 4 represents that the former is between slight importance and obvious importance compared with the latter, the scale value of 6 represents that the former is between obvious importance and extreme importance compared with the latter, and the scale value of 8 represents that the former is between extreme importance and extreme importance compared with the latter;
the reciprocal represents the inverse comparison;
as shown in table 1.
Scale Means of
1 Both are of equal importance
3 The former being slightly more important than the latter
5 The former being significantly more important than the latter
7 The former is very important compared with the latter
9 The former being extremely important in comparison with the latter
2，4，6，8 Intermediate values representing adjacent decisions
Reciprocal of the Inverse comparison
Comparing the importance of each index and establishing a judgment comparison matrix:
Figure BDA0002610326840000161
in the formula bijThe importance degree of the ith index to the jth index is shown.
Wherein, the judgment criteria of importance are: user voltage qualification rate X5More than three-phase load unbalance X6Comprehensive line loss rate X3Load factor X of transformer4Economic deviation ratio X of cable wire diameter2Power supply radius of platform area X1；
Wherein power supply radius X of platform area1The economic deviation ratio X of the wire diameter of the cable is the 1 st index2Is the 2 nd index, the comprehensive line loss rate X3As the 3 rd index, the transformer load factor X4As the 4 th index, the user voltage qualification rate X5As the 5 th index, three-phase load unbalance degree X6Is the 6 th index;
and the 2 nd index is considered to be 2 in the scale value of the importance degree of the 1 st index;
the scale value of the importance degree of the 3 rd index to the 1 st index is 4;
the scale value of the importance degree of the 3 rd index to the 2 nd index is 3;
the scale value of the importance degree of the 4 th index to the 1 st index is 3;
the scale value of the importance degree of the 4 th index to the 2 nd index is 2;
the scale value of the importance degree of the 4 th index to the 3 rd index is 1/2;
the scale value of the importance degree of the 5 th index to the 1 st index is 6;
the scale value of the importance degree of the 5 th index to the 2 nd index is 5;
the scale value of the importance degree of the 5 th index to the 3 rd index is 3;
the scale value of the importance degree of the 5 th index to the 4 th index is 3;
the scale value of the importance degree of the 6 th index to the 1 st index is 5;
the scale value of the importance degree of the 6 th index to the 2 nd index is 4;
the scale value of the importance degree of the 6 th index to the 3 rd index is 2;
the scale value of the importance degree of the 6 th index to the 4 th index is 2;
the scale value of the importance degree of the 6 th index to the 5 th index is 1/2;
the scale value of the degree of importance of the self-comparison is 1;
the judgment comparison matrix constructed according to the judgment standard is as follows:
Figure BDA0002610326840000171
s4.2: calculating the order of the importance degree of the indexes, and recording the maximum characteristic value of the judgment comparison matrix as lambdamaxCalculating λmaxNormalizing the corresponding characteristic vector to obtain a subjective weight vector alpha, and recording the subjective weight vector alpha as wj。
S4.3, in order to avoid one side brought by subjectivity in the weight calculation method, the consistency of the result is checked, and the formula is as follows:
CR＝CI/RI
Figure BDA0002610326840000181
in the formula: cRJudging the random consistency ratio of the comparison matrix; cIIs a general consistency index; rIIs a random average consistency index;
n and RIThe value relationship is as follows: when n is 1, RI＝0；
When n is 2, RI＝0；
When n is 3, RI＝0.52；
When n is 4, RI＝0.89；
When n is 5, RI＝1.12；
When n is 6, RI＝1.24；
When n is 7, RI＝1.36；
As shown in table 2.
n RI n RI
1 0 5 1.12
2 0 6 1.24
3 0.52 7 1.36
4 0.89
When the test result CRIf the judgment matrix is less than 0.1, the consistency of the judgment matrix can be considered to be acceptable, otherwise, the judgment matrix needs to be corrected. Test results C of the method of the inventionRIs 0.0179, so the consistency of the judgment matrix is acceptable.
The objective weight obtained by the entropy weight method in the step S5 is denoted as wiSubjective weight w obtained by analytic hierarchy processjDetermining comprehensive weight, and optimizing the comprehensive weight by Lagrange multiplier method according to the minimum information entropy principle to obtain comprehensive weight WkThe calculation formula is as follows:
Figure BDA0002610326840000191
the state evaluation function is:
Y＝XW
Figure BDA0002610326840000192
in the formula: xmnThe nth index of the mth station area; y ═ Y1，Y2，…，Ym)T，YmIs the m-th station area state evaluation function; w ═ W1，W2，…，Wn)T，WnIs the integrated weight of the nth index.
The evaluation criteria of the station area state evaluation result reference table are as follows:
when the evaluation function value is less than 0.25, the station area state is a good operation state;
when the evaluation function value is [0.25, 0.5], the station area state is a good operation state;
when the evaluation function value is (0.5, 0.75), the station area state is a running state difference;
when the evaluation function value is larger than 0.75, the station area state is a very poor operation state.
The method is described below with reference to specific examples.
Step 1: installing edge computing devices in different transformer areas, collecting electrical characteristic parameters of different transformer areas at different moments in a certain historical time period by using the edge computing devices, and calculating the running state parameters of the collected transformer areas by using an edge computing module of the edge computing devices;
the operating state parameters of 8 zones are shown in table 1:
table 1:
Figure BDA0002610326840000201
step 2: constructing a platform area state evaluation model in the cloud platform, and preprocessing the running state parameters of the platform area;
the operation state parameters of the transformer area are standardized by dividing the operation state parameters into 3 types, namely a forward index, a reverse index and an interval index;
the forward indexes are as follows: user voltage qualification rate X5；
The reverse indexes are as follows: radius of power supply X1Economic deviation ratio X of cable wire diameter2Comprehensive line loss rate X3Three-phase load unbalance degree X6；
The interval indexes are as follows: transformer load factor X4；
For the reverse index, carrying out pretreatment based on an extreme value treatment method, wherein the pretreatment formula of the reverse index is as follows:
Figure BDA0002610326840000202
in the formula Ximax、XiminRespectively is the index X in a plurality of transformer areas i1, 2, … …, n;
for the forward index, the forward index is converted into a dimensionless reverse index, and the preprocessing formula of the forward index is as follows:
Figure BDA0002610326840000211
in the formula Ximax、XiminRespectively is the index X in a plurality of transformer areas i1, 2, … …, n;
for interval type indexes, the load rate of the transformer is a dimensionless index and is converted into a reverse index, and the interval type index preprocessing formula is as follows:
Figure BDA0002610326840000212
in the formula: ximidIs the index XiI-1, 2, … …, n.
The results of the pretreatment of the operating state parameters of the distribution room are shown in table 2:
TABLE 2
Figure BDA0002610326840000213
And step 3: and processing the running state parameters of the transformer area by an entropy weight method, and calculating the weight of each state parameter to obtain the objective weight. Processing the operating state parameters of the transformer area by an analytic hierarchy process, and solving the weight of each state parameter to obtain a subjective weight; combining the subjective weight and the objective weight to obtain a comprehensive weight, wherein the weight result is shown in a table 3, constructing a state evaluation function, and evaluating the station area state by referring to a state evaluation reference result table, wherein the evaluation result is shown in a table 4.
TABLE 3
Index (I) X1 X2 X3 X4 X5 X6
Objective weight 0.1603 0.1850 0.1710 0.1196 0.1830 0.1811
Subjective weighting 0.0434 0.0652 0.1648 0.1148 0.3724 0.2393
Composite weight 0.0880 0.1159 0.1772 0.1237 0.2755 0.2197
TABLE 4
Platform area Evaluation value Evaluation results
1 0.3207 Good running state
2 0.3239 Good running state
3 0.2944 Good running state
4 0.7502 The operating condition is extremely poor
5 0.3497 Good running state
6 0.4384 Good running state
7 0.8341 The operating condition is extremely poor
8 0.5552 Poor running state
The results show that the operation states of the transformer areas 1, 2, 3, 5 and 6 are normal, and all indexes are basically reasonable; the operation states of the transformer areas 4, 7 and 8 are poor, the three-phase load unbalance degree of the transformer area 4 is large according to various indexes, the line loss rate of the transformer area 8 is large, the operation state of the transformer area 7 is worst, and the transformer area needs to be optimized and modified to improve the state characteristics. The evaluation model in the edge calculation device is periodically optimized and updated, as shown in fig. 2, the objective weight determined by the entropy weight method is periodically updated, and the accuracy of the evaluation model is improved.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:
the operation state of the transformer area is evaluated by adopting an edge computing technology, so that the data processing pressure of a cloud center and a main station is reduced, operation and maintenance personnel can conveniently carry out local management on the transformer area, and a large amount of manpower, material resources and financial resources are saved;
according to the method, the subjective weight and the objective weight are combined, the problems that the subjectivity of the subjective weight is large and the objective weight is possibly inconsistent with the actual importance degree are avoided, the obtained combined weight takes the expert experience and the characteristics of data into account, and the defects of an analytic hierarchy process and an entropy weight process are avoided, so that the combined weight is more reasonable, and the obtained evaluation result is more reasonable;
the operation state of the platform area is evaluated by adopting an edge computing technology, and an evaluation model in the edge computing device is updated and optimized periodically, so that the accuracy of an evaluation result is improved.

Claims (6)

1. A station area state evaluation method based on edge calculation comprises the following steps:
s1: installing edge computing devices in different transformer areas, collecting electrical characteristic parameters of different transformer areas at different moments in a certain historical time period by using the edge computing devices, and calculating the running state parameters of the collected transformer areas by using an edge computing module of the edge computing devices;
s2: constructing a platform area state evaluation model in the cloud platform, and preprocessing the running state parameters of the platform area;
s3: processing the operating state parameters of the transformer area by an entropy weight method, and solving the weight of each state parameter to obtain objective weight;
s4: processing the operating state parameters of the transformer area by an analytic hierarchy process, and calculating the weight of each state parameter to obtain a subjective weight;
s5: combining the subjective weight and the objective weight to obtain a comprehensive weight, constructing a state evaluation function, and evaluating the station area state by referring to a state evaluation reference result table;
s6: deep training is carried out on the prediction model on the cloud platform through the collected historical data of the platform area, the prediction model of the edge equipment is updated regularly, and the accuracy of the prediction model of the edge computing equipment is guaranteed.
2. The method for evaluating a station area state based on edge calculation according to claim 1, wherein: the operating state parameter of the distribution room in the step S1 is the power supply radiusX1Economic deviation ratio X of cable wire diameter2Comprehensive line loss rate X3Transformer load factor X4User voltage qualification rate X5Three-phase load unbalance degree X6；
The power supply radius X1The distance from the transformer to the user side;
the economic deviation ratio X of the cable wire diameter2The economic sectional area A corresponding to the average power of the lead flowing through the low-voltage distribution network areasDeviation from its actual cross-sectional area A and AsThe calculation formula of the ratio of (A) is as follows:
economic deviation rate of cable wire diameter
Figure FDA0002610326830000021
In the formula: pavTransmitting average active power, U, for the linenThe rated voltage of the line is used, and rho is the economic current density of the lead;
the comprehensive line loss rate X3The ratio of the power loss of the line of the low-voltage distribution network station area to the total power supply quantity of the station area is as follows:
comprehensive line loss rate
Figure FDA0002610326830000022
In the formula: p is a radical of1For supplying power to the distribution network areas of low voltage, p2The total electric quantity data of the users in the low-voltage distribution network are obtained;
the load factor X of the transformer4The ratio of the average active power of all loads in the platform area to the active power of the whole platform area is calculated by the following formula:
load factor of transformer
Figure FDA0002610326830000023
In the formula: w is a1The power supply load is supplied to the station area in the time period T, S is the capacity of the transformer,
Figure FDA0002610326830000024
loading a power factor for the distribution room;
the user voltage qualification rate X5The ratio of the number of users with qualified voltage in a low-voltage distribution network area to the number of users in the whole area is indicated;
the three-phase load unbalance degree X6The ratio of the deviation of the maximum load in the three phases of the A, B, C low-voltage side outlet terminal of the distribution transformer to the three-phase average load is calculated according to the following formula:
three-phase load unbalance
Figure FDA0002610326830000031
In the formula: pA，PB，PCThe loads of the phases A, B and C of the low-voltage side outgoing line end of the distribution transformer are respectively.
3. The method for evaluating a station area state based on edge calculation according to claim 2, wherein: in step S2, the operation state parameters of the distribution room are standardized by classifying the operation state parameters into 3 classes, which are respectively a forward index, a reverse index, and an interval index;
the forward indexes are as follows: user voltage qualification rate X5；
The reverse indexes are as follows: radius of power supply X1Economic deviation ratio X of cable wire diameter2Comprehensive line loss rate X3Three-phase load unbalance degree X6；
The interval indexes are as follows: transformer load factor X4；
For the reverse index, carrying out pretreatment based on an extreme value treatment method, wherein the pretreatment formula of the reverse index is as follows:
Figure FDA0002610326830000032
in the formula Ximax、XiminRespectively is the index X in a plurality of transformer areasiMaximum and minimum values of，i＝1，2，……，n；
For the forward index, the forward index is converted into a dimensionless reverse index, and the preprocessing formula of the forward index is as follows:
Figure FDA0002610326830000033
in the formula Ximax、XiminRespectively is the index X in a plurality of transformer areasi1, 2, … …, n;
for interval type indexes, the load rate of the transformer is a dimensionless index and is converted into a reverse index, and the interval type index preprocessing formula is as follows:
Figure FDA0002610326830000041
in the formula: ximidIs the index XiI-1, 2, … …, n.
4. The method for evaluating a station area state based on edge calculation according to claim 3, wherein: the step S3 includes the following steps,
s3.1: calculating the proportion P of the ith station area in the jth indexij：
Figure FDA0002610326830000042
S3.2: calculating entropy E of j indexj：
Figure FDA0002610326830000043
If P isijWhen 0, then
Figure FDA0002610326830000045
S3.3: calculating the weight of each index:
according to the calculation formula of the information entropy, calculating the information entropy of each index to be E1，E2，…，EkCalculating objective weight of each index through information entropy:
Figure FDA0002610326830000044
in the formula 1-EjRedundancy for information entropy.
5. The method for evaluating a station area state based on edge calculation according to claim 4, wherein: the step S4 includes the steps of,
s4.1: constructing a comparison matrix, comparing the importance degrees of each index by adopting a 1-9 scale method,
a scale value of 1 represents that both are equally important;
a scale value of 3 indicates that the former is slightly more important than the latter;
a scale value of 5 represents that the former is significantly more important than the latter;
a scale value of 7 represents that the former is very important compared to the latter;
a scale value of 9 represents that the former is extremely important compared to the latter;
the scale values of 2, 4, 6 and 8 represent intermediate values of adjacent judgments, wherein the scale value of 2 represents that the former is between equal importance and slight importance compared with the latter, the scale value of 4 represents that the former is between slight importance and obvious importance compared with the latter, the scale value of 6 represents that the former is between obvious importance and extreme importance compared with the latter, and the scale value of 8 represents that the former is between extreme importance and extreme importance compared with the latter;
the reciprocal represents the inverse comparison;
comparing the importance of each index and establishing a judgment comparison matrix:
Figure FDA0002610326830000051
in the formula bijThe importance degree of the ith index to the jth index is shown.
Wherein, the judgment criteria of importance are: user voltage qualification rate X5>Three-phase load unbalance degree X6>Comprehensive line loss rate X3>Transformer load factor X4>Economic deviation ratio X of cable wire diameter2>Power supply radius X of distribution room1；
Wherein power supply radius X of platform area1The economic deviation ratio X of the wire diameter of the cable is the 1 st index2Is the 2 nd index, the comprehensive line loss rate X3As the 3 rd index, the transformer load factor X4As the 4 th index, the user voltage qualification rate X5As the 5 th index, three-phase load unbalance degree X6Is the 6 th index;
and the 2 nd index is considered to be 2 in the scale value of the importance degree of the 1 st index;
the scale value of the importance degree of the 3 rd index to the 1 st index is 4;
the scale value of the importance degree of the 3 rd index to the 2 nd index is 3;
the scale value of the importance degree of the 4 th index to the 1 st index is 3;
the scale value of the importance degree of the 4 th index to the 2 nd index is 2;
the scale value of the importance degree of the 4 th index to the 3 rd index is 1/2;
the scale value of the importance degree of the 5 th index to the 1 st index is 6;
the scale value of the importance degree of the 5 th index to the 2 nd index is 5;
the scale value of the importance degree of the 5 th index to the 3 rd index is 3;
the scale value of the importance degree of the 5 th index to the 4 th index is 3;
the scale value of the importance degree of the 6 th index to the 1 st index is 5;
the scale value of the importance degree of the 6 th index to the 2 nd index is 4;
the scale value of the importance degree of the 6 th index to the 3 rd index is 2;
the scale value of the importance degree of the 6 th index to the 4 th index is 2;
the scale value of the importance degree of the 6 th index to the 5 th index is 1/2;
the scale value of the degree of importance of the self-comparison is 1;
the judgment comparison matrix constructed according to the judgment standard is as follows:
Figure FDA0002610326830000061
s4.2: calculating the order of the importance degree of the indexes, and recording the maximum characteristic value of the judgment comparison matrix as lambdamaxCalculating λmaxNormalizing the corresponding characteristic vector to obtain a subjective weight vector alpha, and recording the subjective weight vector alpha as wj。
S4.3, in order to avoid one side brought by subjectivity in the weight calculation method, the consistency of the result is checked, and the formula is as follows:
CR＝CI/RI
Figure FDA0002610326830000071
in the formula: cRJudging the random consistency ratio of the comparison matrix; cIIs a general consistency index; rIIs a random average consistency index;
n and RIThe value relationship is as follows: when n is 1, RI＝0；
When n is 2, RI＝0；
When n is 3, RI＝0.52；
When n is 4, RI＝0.89；
When n is 5, RI＝1.12；
When n is 6, RI＝1.24；
When n is 7, RI＝1.36；
When the test result CRIf the comparison result is less than 0.1, the consistency of the judgment comparison matrix can be accepted, otherwise, the judgment comparison matrix needs to be corrected.
6. The method for evaluating a station area state based on edge calculation according to claim 5, wherein: the objective weight obtained by the entropy weight method in the step S5 is denoted as wiSubjective weight w obtained by analytic hierarchy processjDetermining comprehensive weight, and optimizing the comprehensive weight by Lagrange multiplier method according to the minimum information entropy principle to obtain comprehensive weight WkThe calculation formula is as follows:
composite weight
Figure FDA0002610326830000081
The state evaluation function is:
Y＝XW
Figure FDA0002610326830000082
in the formula: xmnThe nth index of the mth station area; y ═ Y1，Y2，…，Ym)T，YmIs the m-th station area state evaluation function; w ═ W1，W2，…，Wn)T，WnIs the integrated weight of the nth index.
The evaluation criteria of the station area state evaluation result reference table are as follows:
when the evaluation function value is less than 0.25, the station area state is a good operation state;
when the evaluation function value is [0.25, 0.5], the station area state is a good operation state;
when the evaluation function value is (0.5, 0.75), the station area state is a running state difference;
when the evaluation function value is larger than 0.75, the station area state is a very poor operation state.
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Application publication date: 20201215