Prediction model of insulator contamination degree based on adaptive mutation particle swarm optimisation and general regression neural network
Prediction model of insulator contamination degree based on adaptive mutation particle swarm optimisation and general regression neural network
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DOI：
10.1049/joe.2018.8669
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发表时间：
2018-12
影响因子：
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通讯作者：
Rumeng Wang;Ming Zhang;Yunpeng Jiang;Yong Yang
中科院分区：
文献类型：
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作者：
Rumeng Wang;Ming Zhang;Yunpeng Jiang;Yong Yang
输电线路绝缘子表面积聚的污染物主要来自空气中的悬浮颗粒。因此，在绝缘子污秽程度预测中，有必要考虑气象因素和环境因素。鉴于广义回归神经网络（GRNN）在容错性和鲁棒性方面的优势，本研究将其用于预测等效盐存款密度（ESDD）。在此基础上，提出了自适应变异粒子群优化GRNN预测模型。根据自适应算法和变异算法，动态调整粒子的惯性权值和加速因子，达到搜索全局最优平滑因子的目的。该优化方法能有效避免粒子群优化算法（PSO）的早熟收敛，解决PSO容易陷入局部最优值的缺点。结果表明，本文提出的预测模型可以有效地预测绝缘子的ESDD，预测误差小于GRNN和PSO-GRNN模型。该研究可为制定更加科学合理的检修计划提供指导，实现对线路污染物的有效控制。
: The contaminants accumulated on the surface of transmission line insulator mainly come from the suspended particles in the air. Therefore, it is necessary to consider meteorological factors and environmental factors in the prediction of insulator contamination degree. In view of the advantages of generalised regression neural network (GRNN) in the aspects of fault tolerance and robustness, this study uses it to predict equivalent salt deposit density (ESDD). Furthermore, the adaptive mutation particle swarm optimisation and GRNN prediction model is proposed in this study. According to adaptive algorithm and mutation algorithm, the inertia weight and acceleration factor of particles are dynamically adjusted to achieve the purpose of searching global optimal smoothing factor. The optimisation method can effectively avoid the premature convergence of particle swarm optimisation (PSO) and solve the drawback that PSO is easy to fall into the local optimal value. The results show that the prediction model proposed in this study can effectively predict the insulators ESDD, and the prediction error is less than the GRNN and PSO–GRNN models. The research can provide guidance for the development of a more scientific and rational maintenance plan to achieve effective control of the contaminants of the line.