Reinforcement Learning Based Recloser Control for Distribution Cables With Degraded Insulation Level

Utilities continuously observe cable failures on aged cables that have an unknown degraded basic insulation level (BIL). One of the root causes is the transient overvoltage (TOV) associated with circuit breaker reclosing. To solve this problem, researchers propose a series of controlled switching methods, most of which belong to deterministic control. However, in power systems, especially in distribution networks, the switching transient is buffeted by stochasticity. Since it is hard to model transient overvoltage due to its complexity, we propose a model-free stochastic control method for reclosers under the existence of uncertainty and noise. Concretely, to capture high-dimensional dynamics patterns, we formulate the recloser control problem by incorporating the temporal sequence reward mechanism into a deep Q-network (DQN). Meanwhile, we embed our physical understanding of the problem into the action probability allocation and develop an infeasible-action-space-elimination algorithm. Through PSCAD simulation, we first reveal the impact of load types on cables' TOVs. Then, to reduce the training burden for the proposed reinforcement learning (RL) control method in different applications, we establish a post-learning knowledge transfer method. After the validation with our industrial partner, we exhibit several learning curves to show the enhanced performance. The learning efficiency is proved to be outstanding due to the proposed time sequence reward mechanism and infeasible action elimination method. Moreover, the results on knowledge transfer demonstrate the capability of method generalization. Finally, a comparison with conventional methods is conducted. It illustrates the proposed method is most effective in mitigating the TOV phenomenon among three methods.