| 配电网设备利用率的组合智能评价方法 |
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| 引用本文： | 尹卿宇,徐启峰,周洁.配电网设备利用率的组合智能评价方法[J].陕西电力,2020,0(11):55-61. |
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| 作者姓名： | 尹卿宇 徐启峰 周洁 |
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| 作者单位： | 福州大学 电气工程与自动化学院，福建 福州 350106 |
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| 摘 要： | 针对配电网设备利用率的评价方法在主观性，不能辨识需要整改的指标问题，提出了一种智能评价方法。首先，区分定性指标和定量指标，利
用R聚类筛选出信息量大的定量指标。然后，建立正态云模型提取定量指标的等级隶属度，兼顾评价等级界限的模糊性和随机性；构造数据云模型对定
量指标进行修正，体现时间特征对评价结果的影响。最后，结合层次分析法和熵权法对指标权重赋值，使用拉格朗日乘子法得到最接近主客观权重的
组合权重，改善权值的灵敏度与指标的主观性；采用D-S证据理论融合原理得到评价结果。通过对某省配电网设备利用率的评价结果验证了所提方法的
有效性。
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| 关 键 词： | 配电网 设备利用率 R聚类 正态云模型 D-S证据理论 |
Combined Intelligent Evaluation Method of Equipment Utilization Rate in Distribution Network |
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| YIN Qingyu,XU Qifeng,ZHOU Jie.Combined Intelligent Evaluation Method of Equipment Utilization Rate in Distribution Network[J].Shanxi Electric Power,2020,0(11):55-61. |
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| Authors: | YIN Qingyu XU Qifeng ZHOU Jie |
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| Affiliation: | College of Electrical Engineering and Automation，Fuzhou University，Fuzhou 350106， China |
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| Abstract: | Targeting the subjectivity of evaluation method for equipment utilization rate in distribution network and the index
needed to be corrected that cannot be identified with the method， the paper proposes an intelligent evaluation method. Firstly，
qualitative indicators and quantitative indicators are given， and quantitative indicators with a large amount of information are
selected by R clustering. Then normal cloud model is established to extract the graded membership of the quantitative indicators，
taking into account the fuzziness and randomness of the evaluation grade boundaries；The data cloud model is constructed to modify
the quantitative indicators， reflecting the influence of time characteristics on the evaluations. Finally，the index weights are
assigned with analytic hierarchy process and entropy method， the combined weight that is closest to the subjective and objective
weights is obtained using Lagrange multiplier method， and the sensitivity of the weights and the subjectivity of the indicators
are improved. While DS evidence theory fusion principle is used to get the evaluations. The evaluations of the equipment
utilization rate in the distribution network of a province verify the effectiveness of the proposed method. |
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| Keywords: | distribution network equipment utilization rate R clustering normal cloud model D-S evidential theory |
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