State evaluation method of communication network of intelligent substation based on clustering and neural network
A technology of intelligent substation and communication network, which is applied in the field of intelligent substation communication network status evaluation based on clustering and neural network, can solve the problems of inconvenient daily maintenance of staff and incomplete reflection of information, achieve real-time and accurate evaluation results, improve The effect of stability
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Embodiment 1
[0037] 1. Principle of fuzzy neural network
[0038] T-S fuzzy system is a kind of fuzzy system with strong self-adaptive ability. The model can not only automatically update, but also continuously revise the membership function of fuzzy subsets. The T-S fuzzy system is defined by the following "if-then" rule form, where the rule is R i In the case of , the fuzzy reasoning is as follows:
[0039]
[0040] in, is the fuzzy set of the fuzzy system; is the fuzzy system parameter; y i For the output obtained according to the fuzzy rules, the input part (that is, the if part) is fuzzy, and the output part (that is, the then part) is definite, and the fuzzy reasoning indicates that the output is a linear combination of inputs.
[0041] Assume that for the input quantity x=[x 1 ,x 2 ,...,x k ], first calculate each input variable x according to fuzzy rules j degree of membership:
[0042]
[0043] In the formula, Respectively, the center and width of the membership...
Embodiment 2
[0122] In this embodiment, the communication system of a substation in a power supply bureau is taken as an example, and the state evaluation model based on the fuzzy neural network obtained in the above-mentioned embodiment 1 is used to evaluate the online monitoring index data of the operation status of the communication network of the substation. Select nine indicators such as "availability", "response time" and "packet loss rate" in Table 1 to form an evaluation index set, and invite experts to score the importance of each factor, and use the analytic hierarchy process to establish a judgment matrix for each level of factors. The weight vector of each element is obtained by finding the eigenvector corresponding to the largest eigenvalue under the consistency test condition. The specific parameters and weight calculation results are shown in Table 4 below:
[0123] Table 4
[0124] Characteristic Parameters
Weights
measured value
Availability x1
...
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