配电网保护装置自适应迭代学习控制技术

Adaptive Iterative Learning Control Technology for Distribution Network Protection Devices

针对复杂的配电网保护装置结构,现有的控制技术在控制结果收敛和误差控制方面存在限制的问题,提出了一种配电网保护装置自适应迭代学习控制技术。使用图论原理对配电网络进行分析和优化设计,通过分解复杂的配电网结构,将其简化为更简单的形式。结合迭代学习控制和自适应控制理论,对配电网保护装置进行改进和优化。利用k-means聚类方法将具有相似最优补偿能力的数据进行划分和整合,并进行补偿容量调整,以满足最大投切次数约束。根据自适应迭代学习控制收敛性证明函数构建控制模型,实现配电网保护装置的渐进跟踪。实验结果表明,该技术误差小,能够对加密功能、保护功能和拓扑自适应测点进行有效迭代学习控制。

In response to the complex structure of distribution network protection devices,existing control technologies have limitations in convergence of control results and error control.A self-adaptive iterative learning control technology for distribution network protection devices is proposed.Graph theory principles are used to analyze and optimize the design of distribution networks,by decomposing complex distribution network structures and simplifying them into simpler forms.Iterative learning control and adaptive iterative learning control theory are combined to improve and optimize the protection device of the distribution network.The k-means clustering method is used to partition and integrate data with similar optimal compensation capabilities,and the compensation capacity was adjusted to meet the maximum number of switching constraints.A control model based on the convergence proof function of adaptive iterative learning control is constructed to achieve progressive tracking of distribution network protection devices.The experimental results show that this technology has small error and can effectively iterate learning control for encryption function,protection function,and topology adaptive measurement points.