【摘要】 针对电力变压器状态评价过程中存在的不确定性，提出一种基于云相似度和证据融合的电力变压器状态评价方法。首先，考虑到各指标在状态等级边界处的随机性与模糊性问题，采用云模型来建立状态评价的基本框架。其次，为兼顾状态等级划分的严明性和模糊性，采用改进的云熵优化算法确定云模型的熵值。然后，考虑到指标数据本身的不确定性，利用正向云发生器和云合成算法生成各试验项目待识别云和标尺云，并采用模糊贴近度计算二者间的相似度作为证据源的基本概率分配。最后，采用考虑证据可信度和不确定度的冲突证据修正方法修正证据源，并融合不同证据以判断电力变压器的最终状态。经实例验证，相较于传统方法，所提方法能够有效处理状态评价过程中的不确定性，评价结果符合电力变压器的真实情况，对电力设备状态评价有一定参考价值。更多还原

【Abstract】 There is uncertainty in power transformer condition evaluation, and so an evaluation based on cloud similarity and evidence fusion is proposed. First, considering the randomness and fuzziness of each indicator at the state-level boundary of the power transformer, a cloud model is used to establish the basic framework of condition evaluation.Second, considering the rigor and fuzziness of state-level classification, an improved cloud entropy optimization algorithm is used to determine the cloud entropy of the cloud model. Then, given the uncertainty of the index data itself, a forward cloud generator and cloud synthesis algorithm are used to generate the identification and scale clouds for each test item, and the similarity between both is computed using fuzzy closeness as the basic probability assignment of the evidence sources. Finally, conflicting evidence correction methods that consider the credibility and uncertainty of the evidence are used to correct the evidence sources and blend different evidence to determine the final state of the power transformer. It is verified by examples that, compared with the traditional method, the method can effectively deal with the uncertainty in the condition evaluation process, and the evaluation results are in accord with the actual situation of the power transformer. This has a certain reference value for condition evaluation.更多还原