Power supply reliability fuzzy comprehensive evaluation method based on improved membership degree function
A technology of fuzzy comprehensive evaluation and membership function, which is applied in data processing applications, instruments, calculations, etc., can solve problems such as consistent evaluation results, and achieve the effect of solving the problems of consistent evaluation results that cannot be distinguished, and fine power supply reliability fuzzy comprehensive evaluation
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Embodiment 1
[0072] In this embodiment, a fuzzy comprehensive evaluation method for power supply reliability based on an improved membership function includes the following steps:
[0073] Step S1, select N grass-roots indicators that can reflect the current situation of power supply reliability of the distribution network to establish an evaluation index system;
[0074] Step S2, dividing the quality grades of the N evaluation indicators into 6 quality grades, which are: "best", "excellent", "good", "medium", "qualified" and "unqualified";
[0075] Step S3, using the improved AHP combined with the variation coefficient method to determine the comprehensive weight of each evaluation index in the evaluation index system;
[0076] Specific steps are as follows:
[0077] Step S3.1, preprocessing the data corresponding to the evaluation index in the evaluation index system, removing the deformed data and using the average value method to fill in the missing data;
[0078] Step S3.2, use the ...
Embodiment 2
[0120] In this embodiment, a fuzzy comprehensive evaluation method for power supply reliability based on an improved membership function includes the following steps:
[0121] First, determine the distribution network power supply reliability evaluation index system, as shown in Table 1
[0122] Table 1 Power supply reliability index of distribution network
[0123]
[0124] Then divide the quality level of the indicators, as shown in Table 2
[0125] Table 2 Power supply reliability evaluation index and its quality level
[0126]
[0127]Afterwards, the comprehensive weight of each indicator is obtained, and the subjective weight is obtained by using the improved analytic hierarchy process to calculate the weight of each indicator. First, a judgment matrix needs to be constructed. Generally, the 0.1-0.9 scaling method is used to construct the original judgment matrix.
[0128] Table 3 Meaning of scale value 0.1-0.9
[0129]
[0130]
[0131] Judgment matrix A=(...
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