CN109190872A - A kind of power distribution network Supply Security integrated evaluating method - Google Patents
A kind of power distribution network Supply Security integrated evaluating method Download PDFInfo
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Abstract
The invention discloses a kind of power distribution network Supply Security integrated evaluating methods, it is characterized in that, the method includes the steps: S1: power distribution network power supply safety appraisement system is established based on analytic hierarchy process (AHP), the power distribution network power supply safety appraisement system of foundation includes several evaluation layers, and each layer of evaluating includes several power distribution network power supply safety project indicators；S2: weight of each power distribution network power supply safety project indicator in power distribution network power supply safety appraisement system in each evaluation layer established in S1 is determined using artificial neural network method；S3: according to weight of each power distribution network power supply safety project indicator obtained in S2 in power distribution network power supply safety appraisement system, power distribution network power supply safety system score is calculated.The present invention has stronger network fault tolerance ability, by guaranteeing basic data observability, establishing power distribution network power supply safety appraisement system, evaluation result can accurately reflect the power supply safety operation conditions of power distribution network to each single-phase performance analysis of power distribution network comprehensively.
Description
Technical field
The present invention relates to safe distribution technique fields, and in particular to a kind of power distribution network Supply Security integrated evaluating method.
Background technique
Important link of the power distribution network as connection power transmission network and user, the quality of operating status directly affect user's use
The reliability and power quality of electricity, compared with power transmission network, the structure of power distribution network is more complicated, and the number of devices for including is huger
Greatly, automatization level is relatively low, and consequently leads to the difficulty of power distribution network operation data acquisition, and increases distribution operation
The difficulty of status assessment.
Distribution system Supply Security analysis as assessment with Running State important means, the practicability of function and
Precision of analysis is most important.The safety of distribution system refers to when breaking down in interconnection system operation, guarantees
To the ability of based model for load duration power supply.Relative to power transmission network, the research of power distribution network safety analysis is also immature, and current power distribution network supplies
Electric safety analysis proposes more practical and effectively evaluating index not yet.
The most safety assessment system referring to power transmission network of power distribution network Supply Security analysis at present, but due to distribution Netcom
Often with there is the characteristics of closed loop design, open loop operation, determine that the evaluation index of power distribution network safety analysis cannot indiscriminately imitate power transmission network
Criterion.Power distribution network Supply Security analysis method has entropy assessment, Fuzzy Evaluation Method etc., using entropy assessment to distribution network electric energy quality
It is assessed, this method determines weight from the dispersion degree of data itself, but does not consider the physical meaning of data itself,
Less meet reality；Fuzzy Evaluation Method uses the ambiguity of subjection degree description indexes, preferably solves the uncertain of data
Property, but in complication system, relative defects weight coefficient error is larger, so that assessment result is inaccurate.
In conclusion defect existing for existing power distribution network Safety Assessment System is summarized are as follows:
(1) foundation of appraisement system is not bound with power distribution network current development level, does not account for obtaining for basic data
Property, accuracy, cause evaluation result to be difficult to reflect the practical operation situation of power grid；
(2) appraisement system is single, cannot reflect the operation level of entire power distribution network comprehensively；
(3) evaluation index, evaluation weight are formulated with subjectivity and randomness.
Summary of the invention
Based on the deficiencies of the prior art, it is an object of the invention to provide a kind of power distribution network Supply Security overall merit
Method guarantees the validity and practicability of weight, Jin Erti for solving the subjectivity and randomness of previous Weight Determination
The reliability and accuracy of high power distribution network Supply Security evaluation.
To achieve the above object, the technical solution of the present invention is as follows:
A kind of power distribution network Supply Security integrated evaluating method, which is characterized in that the method includes the steps:
S1: power distribution network power supply safety appraisement system, the power distribution network power supply safety evaluation of foundation are established based on analytic hierarchy process (AHP)
System includes several evaluation layers, and each layer of evaluating includes several power distribution network power supply safety project indicators；
S2: determine that each power distribution network power supply safety project in each evaluation layer established in S1 refers to using artificial neural network method
The weight being marked in power distribution network power supply safety appraisement system；
S3: according to each power distribution network power supply safety project indicator obtained in S2 in power distribution network power supply safety appraisement system
Weight calculates power distribution network power supply safety system score.
Further, the evaluation layer of the appraisement system of power distribution network power supply safety described in S1 include major heading layer, rule layer and
Indicator layer.
Further, each power distribution network power supply in each evaluation layer established in S1 is determined in S2 using artificial neural network method
The method of weight of the item security index in power distribution network power supply safety appraisement system includes:
S21: establishing the neural network structure of assessment indicator system, according to the layer in power distribution network Supply Security appraisement system
The several and project indicator establishes corresponding neural net layer and number of nodes；
S22: initialization network initializes sample data by the method for establishing evaluation index subordinating degree function,
And determine learning procedure N and study precision E；
S23: neural network is trained by the method for inputting different time sections power distribution network history data, is obtained
Weight of each power distribution network power supply safety project indicator in power distribution network power supply safety appraisement system.
Further, neural net layer includes input layer, hidden layer and output layer in S21, wherein the number of nodes of input layer
R corresponds to the power distribution network power supply safety project indicator number of indicator layer, and the number of nodes n of output layer corresponds to of the major heading layer
The number of nodes m of number, hidden layer is obtained according to m2 >=r.
Further, include: to the training process of neural network in S23
J) one group of learning sample is inputted.It include input vector Xi and desired output Ok, sample data to every group of learning sample
Take different time sections power distribution network history data；
K) with input vector Xi connection input layer to the weight Wij and threshold θ j between hidden layer, each mind of hidden layer is calculated
It calculates hidden layer each unit by Sigmoid function through unit activating value and exports Zj；
L) Zj, connection weight hidden layer to the weight Vjk and threshold value between output layer are exported with hidden layer each unit
Each neural unit activation value of output layer is calculated, by Sigmoid function, output layer each unit is calculated and exports Yk；
M) desired output Ok and output layer reality output Yk is used, each unit is calculated and corrects error, obtain output layer weight tune
Whole amount Δ Vjk and adjusting thresholds amount
N) it calculates hidden layer and corrects error, obtain hidden layer weighed value adjusting amount Δ Wij and hidden layer threshold value adjustment amount Δ θ j；
O) new weight Vjk (N+1) and the new threshold value between hidden layer and output layer are calculated
P) new weight Wij (N+1) and the new threshold θ k (N+1) between input layer and hidden layer are calculated；
Q) after training complete P sample group, if judging, global error E is less than set required precision or the number of iterations is big
I is gone to step in setting the number of iterations, otherwise the number of iterations t=t+1, goes to step a；
R) global error E1 is recorded, the implicit number of plies is set as m-1, m+1, by step a to h, calculates separately global error
E2, E3 compare global error.Until the corresponding global error minimum of intermediate quantity m, i.e. E1 < E2, and E1 < E3, then m is optimal hidden
Number containing node layer.
The invention has the benefit that
The weight of each index of evaluation system is determined based on BP neural network algorithm, there is stronger network fault tolerance ability, is led to
Single-phase performance analysis each to power distribution network is crossed, guarantees basic data observability, establishes power distribution network power supply safety appraisement system,
Evaluation result can accurately reflect the power supply safety operation conditions of power distribution network comprehensively；
By network training, adjustment to network weight, so that indices weight is more rationally and effectively；
By comprehensive score, can be for different assessment results to power distribution automation main station system power supply safety evaluation
Power distribution network lean O&M provides data supporting.
Detailed description of the invention
Fig. 1 is the method flow diagram of the specific embodiment of the invention；
Fig. 2 is the analytic hierarchy process (AHP) structural map of the specific embodiment of the invention；
Fig. 3 is the design parameter structural map of the neural network corresponding with Fig. 2 of the specific embodiment of the invention.
Specific embodiment
It is carried out below with reference to technical effect of the embodiment and attached drawing to design of the invention, specific structure and generation clear
Chu is fully described by, to be completely understood by the purpose of the present invention, feature and effect.Obviously, described embodiment is this hair
Bright a part of the embodiment, rather than whole embodiments, those skilled in the art is without creative efforts
Other embodiments obtained, belong to the scope of protection of the invention.
The present embodiment chooses the index for being able to reflect power distribution network Supply Security, analytic hierarchy process (AHP) based on analytic hierarchy process (AHP)
(AHP) challenge is decomposed, by analyzing challenge, by total PROBLEM DECOMPOSITION at many subproblems, then will
Subproblem resolves into smaller problem, to set up well-bedded index system to describe complicated problem.The present embodiment
Relatively reasonable evaluation index system is established from distribution network systems layer and controller switching equipment layer respectively.System layer is mainly powered from power distribution network
From the aspect of ability, power quality, reliability.Power supply capacity mainly considers system N-1 percent of pass, heave-load device accounting；Electric energy
Quality mainly considers the three-phase imbalance rate of equipment；Power supply is it is contemplated that the main failure rate for considering system equipment, customer power supply electricity
Source state etc..The main assessment equipment communication conditions of mechanical floor, equipment remote signalling accuracy, equipment operation defect, equipment failure rate etc..
After the Supply Security analysis Establishing of power distribution network, each index is determined using BP artificial neural network algorithm
Weight.Each nerve net unit number that neural network is determined according to appraisement system, select true power distribution network history data as
Sample data carries out network training, so that it is determined that reasonable weight index.After each evaluation index weight determines, further according to weight
Index carries out the overall evaluation to the Supply Security of power distribution network, and different evaluation results corresponds to the different power supply safety etc. of power distribution network
Grade.
Specifically, as shown in Figure 1, a kind of power distribution network Supply Security integrated evaluating method, the method includes the steps:
S1: power distribution network power supply safety appraisement system, the power distribution network power supply safety evaluation of foundation are established based on analytic hierarchy process (AHP)
System includes several evaluation layers, and each layer of evaluating includes several power distribution network power supply safety project indicators；
S2: determine that each power distribution network power supply safety project in each evaluation layer established in S1 refers to using artificial neural network method
The weight being marked in power distribution network power supply safety appraisement system；
S3: according to each power distribution network power supply safety project indicator obtained in S2 in power distribution network power supply safety appraisement system
Weight calculates power distribution network power supply safety system score.
The embodiment of step S1 specifically: the foundation of power distribution network power supply safety appraisement system is based on power distribution automation main station
System is commented in conjunction with operation condition in site and the critical issue of influence power supply reliability based on analytic hierarchy process (AHP) construction as shown in Figure 2
Valence system:
For power distribution network power supply safety appraisement system as major heading layer, rule layer includes Distribution Network Equipment operating status, power supply
6 aspects such as safety N-1 verification, user's operation conditions, controller switching equipment operational defect, system application index, three-phase imbalance,
Indicator layer includes 17 bottom single indexs, and all indexs all have a measurability, i.e., index value can directly measure to obtain or
It is obtained according to the basic data measured is for statistical analysis, guarantees the accuracy and practicability of evaluation result.
The specific embodiment of step S2 includes realizing that the following are BP neural network concrete implementations based on BP neural network
Step:
1) neural network structure of assessment indicator system is established
Various neural network unit numbers are determined first, according to the appraisement system of foundation, determine the design parameter of neural network
As shown in figure 3, using an input layer, node r=17；One node in hidden layer is m=12, an output layer, node n
=1.Wherein the selection of hidden node is obtained according to m2 >=r empirical equation, then is determined by plots changes and error precision, choosing
Take the more few corresponding Hidden nodes of the smaller frequency of training of error precision.
2) network is initialized
The random number of the weight and threshold value of neural network between [0,1] is initialized, the present invention is by establishing evaluation index
The method of subordinating degree function initializes sample data, and determines learning procedure N and study precision E；
3) neural metwork training process, the specific steps are as follows:
A) one group of learning sample is inputted.It include input vector Xi and desired output Ok, sample data to every group of learning sample
Take different time sections power distribution network history data；
B) with input vector Xi connection input layer to the weight Wij and threshold θ j between hidden layer, each mind of hidden layer is calculated
It calculates hidden layer each unit by Sigmoid function through unit activating value and exports Zj；
C) Zj, connection weight hidden layer to the weight Vjk and threshold value between output layer are exported with hidden layer each unit
Each neural unit activation value of output layer is calculated, by Sigmoid function, output layer each unit is calculated and exports Yk；
D) desired output Ok and output layer reality output Yk is used, each unit is calculated and corrects error, obtain output layer weight tune
Whole amount Δ Vjk and adjusting thresholds amount
E) it calculates hidden layer and corrects error, obtain hidden layer weighed value adjusting amount Δ Wij and hidden layer threshold value adjustment amount Δ θ j；
F) new weight Vjk (N+1) and the new threshold value between hidden layer and output layer are calculated
G) new weight Wij (N+1) and the new threshold θ k (N+1) between input layer and hidden layer are calculated；
H) after training complete P sample group, if judging, global error E is less than set required precision or the number of iterations is big
I is gone to step in setting the number of iterations, otherwise the number of iterations t=t+1, goes to step a；
I) global error E1 is recorded, the implicit number of plies is set as m-1, m+1, by step a to h, calculates separately global error
E2, E3 compare global error.Until the corresponding global error minimum of intermediate quantity m, i.e. E1 < E2, and E1 < E3, then m is optimal hidden
Number containing node layer.
S3 to Supply Security overall assessment,
By each evaluation criterion weight, power distribution network overall power security system score is calculated.The total score that such as scores is 10 points, will
The different safety class that different score values correspond to power distribution network Supply Security is as follows:
The present embodiment determines the weight of each index of evaluation system based on BP neural network algorithm, has stronger network fault tolerance
Ability, by guaranteeing basic data observability, establishing power distribution network power supply safety and comment to each single-phase performance analysis of power distribution network
Valence system, evaluation result can accurately reflect the power supply safety operation conditions of power distribution network comprehensively；
By network training, adjustment to network weight, so that indices weight is more rationally and effectively；
By comprehensive score, can be for different assessment results to power distribution automation main station system power supply safety evaluation
Power distribution network lean O&M provides data supporting.
It should be noted that described above is presently preferred embodiments of the present invention, the invention is not limited to above-mentioned
Embodiment all should belong to protection scope of the present invention as long as it reaches technical effect of the invention with identical means.
Claims (5)
1. a kind of power distribution network Supply Security integrated evaluating method, which is characterized in that the method includes the steps:
S1: power distribution network power supply safety appraisement system, the power distribution network power supply safety appraisement system of foundation are established based on analytic hierarchy process (AHP)
Layer is evaluated including several, each layer of evaluating includes several power distribution network power supply safety project indicators；
S2: determine that each power distribution network power supply safety project indicator in each evaluation layer established in S1 exists using artificial neural network method
Weight in power distribution network power supply safety appraisement system；
S3: according to power of each power distribution network power supply safety project indicator obtained in S2 in power distribution network power supply safety appraisement system
Weight, calculates power distribution network power supply safety system score.
2. power distribution network Supply Security integrated evaluating method as described in claim 1, it is characterised in that: power distribution network described in S1
The evaluation layer of power supply safety appraisement system includes major heading layer, rule layer and indicator layer.
3. power distribution network Supply Security integrated evaluating method as claimed in claim 2, which is characterized in that utilize artificial mind in S2
Determine that each power distribution network power supply safety project indicator in each evaluation layer established in S1 is commented in power distribution network power supply safety through network technique
The method of weight in valence system includes:
S21: establishing the neural network structure of assessment indicator system, according in power distribution network Supply Security appraisement system the number of plies and
The project indicator establishes corresponding neural net layer and number of nodes；
S22: initialization network initializes sample data by the method for establishing evaluation index subordinating degree function, and really
Determine learning procedure N and study precision E；
S23: neural network is trained by the method for inputting different time sections power distribution network history data, obtains and respectively matches
Weight of the power grid power supply safety project indicator in power distribution network power supply safety appraisement system.
4. power distribution network Supply Security integrated evaluating method as claimed in claim 3, which is characterized in that neural network in S21
Layer includes input layer, hidden layer and output layer, wherein the number of nodes r of input layer corresponds to the power distribution network power supply safety of indicator layer
Project indicator number, the number of nodes n of output layer correspond to the number of the major heading layer, and the number of nodes m of hidden layer is obtained according to m2 >=r
?.
5. power distribution network Supply Security integrated evaluating method as claimed in claim 4, which is characterized in that application net in S23
The training process of network includes:
A) one group of learning sample is inputted.To every group of learning sample, taken not comprising input vector Xi and desired output Ok, sample data
With period power distribution network history data；
B) with input vector Xi connection input layer to the weight Wij and threshold θ j between hidden layer, it is single to calculate each nerve of hidden layer
First activation value calculates hidden layer each unit and exports Zj by Sigmoid function；
C) Zj, connection weight hidden layer to the weight Vjk and threshold value between output layer are exported with hidden layer each unitIt calculates defeated
Each neural unit activation value of layer out calculates output layer each unit and exports Yk by Sigmoid function；
D) desired output Ok and output layer reality output Yk is used, each unit is calculated and corrects error, obtain output layer weighed value adjusting amount
Δ Vjk and adjusting thresholds amount
E) it calculates hidden layer and corrects error, obtain hidden layer weighed value adjusting amount Δ Wij and hidden layer threshold value adjustment amount Δ θ j；
F) new weight Vjk (N+1) and the new threshold value between hidden layer and output layer are calculated
G) new weight Wij (N+1) and the new threshold θ k (N+1) between input layer and hidden layer are calculated；
H) it after training complete P sample group, is set if judging that global error E is greater than less than set required precision or the number of iterations
Determine the number of iterations and go to step i, otherwise the number of iterations t=t+1, goes to step a；
I) record global error E1, set and imply the number of plies as m-1, m+1, by step a to h, calculate separately global error E2,
E3 compares global error.Until the corresponding global error minimum of intermediate quantity m, i.e. E1 < E2, and E1 < E3, then m is optimal hidden layer
Node number.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111582634A (en) * | 2020-03-26 | 2020-08-25 | 西南交通大学 | Multi-factor safety grading method and system for underground large-space construction |
| CN112907087A (en) * | 2021-03-03 | 2021-06-04 | 中国人民解放军国防科技大学 | Grid power efficiency evaluation model optimization method based on numerical inverse problem |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103678952A (en) * | 2013-11-14 | 2014-03-26 | 昆明理工大学 | Elevator risk evaluation method |
| CN104700321A (en) * | 2015-03-16 | 2015-06-10 | 国家电网公司 | Analytical method of state running tendency of transmission and distribution equipment |
| CN106022583A (en) * | 2016-05-12 | 2016-10-12 | 中国电力科学研究院 | Electric power communication service risk calculation method and system based on fuzzy decision tree |
-
2018
- 2018-07-05 CN CN201810731757.6A patent/CN109190872A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103678952A (en) * | 2013-11-14 | 2014-03-26 | 昆明理工大学 | Elevator risk evaluation method |
| CN104700321A (en) * | 2015-03-16 | 2015-06-10 | 国家电网公司 | Analytical method of state running tendency of transmission and distribution equipment |
| CN106022583A (en) * | 2016-05-12 | 2016-10-12 | 中国电力科学研究院 | Electric power communication service risk calculation method and system based on fuzzy decision tree |
Non-Patent Citations (1)
| Title |
|---|
| 谈元鹏 等: "基于属性偏好学习的配电网综合评价方法", 《计算机应用研究》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111582634A (en) * | 2020-03-26 | 2020-08-25 | 西南交通大学 | Multi-factor safety grading method and system for underground large-space construction |
| CN111582634B (en) * | 2020-03-26 | 2024-02-23 | 西南交通大学 | Multi-factor safety grading method and system for underground large-space construction |
| CN112907087A (en) * | 2021-03-03 | 2021-06-04 | 中国人民解放军国防科技大学 | Grid power efficiency evaluation model optimization method based on numerical inverse problem |
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