Deep reinforcement learning framework for adaptive power control in grid-forming inverters: A multi-objective optimization approach基于深度强化学习的构网型逆变器自适应功率控制：多目标优化方法

英文 Abstract

A novel deep reinforcement learning system is introduced, revolutionizing grid-forming inverter control through an attention-based neural architecture with adaptive policy optimization. The system uniquely integrates real-time stability constraints with multi-objective learning, addressing the fundamental challenges of power system control under uncertain conditions. The approach employs a comprehensive state-space representation incorporating grid dynamics and historical information, complemented by an advanced attention mechanism that enables selective feature prioritization across varying operational conditions. The learning architecture combines a hierarchical policy network structure with a prioritized experience replay mechanism, achieving rapid adaptation and stable control performance. The result validation demonstrates improvements over conventional methods, including a 43.75% reduction in harmonic distortion (from 3.2% to 1.8%), a 46.7% faster dynamic response (8 vs 15 ms), and a 50% extension in stable operation range under weak grid conditions (operational down to short circuit ratio, SCR=1.5). The system maintains 96% inference accuracy while executing within 50 μ s, meeting real-time control requirements. Additionally, the system demonstrates superior power decoupling performance, reducing coupling effects by 80% compared to traditional approaches while maintaining stable operation across diverse grid conditions. Learning-based control systems in power electronics demonstrate strong generalization across various operating conditions while ensuring stability. Integrating deep learning with power system constraints opens up new applications for complex real-time control problems that require adaptability and reliability.

中文翻译

本文提出一种新型深度强化学习系统，通过带有自适应策略优化的注意力神经网络改进构网型逆变器控制。该系统将实时稳定性约束与多目标学习结合，用于应对不确定条件下的电力系统控制问题。方法采用包含电网动态和历史信息的完整状态空间表示，并利用注意力机制在不同运行条件下选择性突出关键特征；学习架构结合分层策略网络和优先经验回放，实现快速适应和稳定控制。实验结果显示，与传统方法相比，该方法将谐波失真从 3.2% 降至 1.8%，动态响应时间从 15 ms 缩短至 8 ms，并将弱电网稳定运行范围扩展到短路比 SCR=1.5。系统在 50 μs 内完成推理并保持 96% 的推理准确率，满足实时控制需求；同时，功率解耦性能得到提升，耦合效应降低 80%。研究表明，将深度学习与电力系统约束结合，可为需要适应性与可靠性的复杂实时控制任务拓展新的应用空间。

中文概述

本文研究构网型逆变器在不确定和弱电网条件下的自适应功率控制，提出结合注意力机制、分层策略网络和优先经验回放的深度强化学习框架。原文报告该方法可降低谐波失真、缩短动态响应时间、扩大弱电网稳定运行范围，并满足实时控制所需的推理速度。

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