基于MI-PSO-BP算法的配电设备状态实时评估方法

为了提高配电设备故障预测水平，提出了一种常规综合评估方法与实时评估方法相结合的配电设备运行状态实时评估方法。给出了两阶段综合状态评估方法的框架体系，通过互信息理论(MI)量化设备各属性与状态的相关关系，消除冗余属性。利用粒子群优化(PSO)算法对BP神经网络权值与阈值进行优化，以提高评估质量。利用该MI-PSO-BP模型对某地区配电变压器实时状态进行评估，评估结果及发展趋势与实际情况相吻合，验证了该评估方法的正确性和有效性。

Real-time condition assessment method based on MI-PSO-BP algorithm for distribution equipment

In order to raise the fault prediction level of distribution equipment, a novel real-time condition assessment method is proposed for distribution equipment, which combines conventional comprehensive assessment method with real-time assessment method. The framework of the two-stage comprehensive assessment method is given. In order to eliminate redundant attributes, the relationship between the attributes and the condition of the equipment is quantified by MI(Mutual Information) theory. The weights and thresholds of BP neural network are optimized by PSO(Particle Swarm Optimization) algorithm to improve the quality of assessment. The real-time condition of the actual distribution transformer is evaluated with the propose MI-PSO-BP assessment model, and the evaluation result and trend are coincident with the fault report, which verifies the correctness and effectiveness of the proposed assessment method.