Brain-Inspired Deep Meta-Reinforcement Learning for Active Coordinated Fault-Tolerant Load Frequency Control of Multi-Area Grids

This paper proposes an active coordinated fault tolerance load frequency control (AFCT-LFC) method, which effectively prevents sudden frequency changes caused by unit actuator failures or unplanned decommissioning in a multi-area interconnected grid subject to the performance-based frequency regulation market mechanism. It can also reduce regulation mileage payments and achieve multi-objective active fault-tolerant control. In addition, this paper proposes a brain-Inspired deep meta-deterministic policy gradient algorithm (BIMA-DMDPG), which adopts multi-agent centralized training, equates the controller of each area as an agent capable of independent decision making, and implements distributed training by dividing the environment into multiple environments. In addition, meta-reinforcement learning is employed to realize multi-task collaborative learning. The optimal policy is actively selected under different fault conditions to achieve active fault-tolerant control. The superior performance of the method is verified in a four-area LFC model of the China Southern Grid (CSG), in which it is tested alongside a selection of existing algorithms. Note to Practitioners-AFCT-LFC is based on advanced artificial intelligence algorithm, which can effectively identify any fault in multi-area grids and make rapid response to achieve active fault-tolerant control. Compared with the existing model-based fault-tolerant control methods, the BIMA-DMDPG algorithm proposed in this paper does not need to rely on accurate mathematical models, and can be applied to practice through simple training, which is very suitable for practical applications. Therefore, AFCT-LFC is an advanced adaptive active fault-tolerant control method that can be truly applied in practice because of its fast-decision-making ability and performance.