基于深度学习的继电保护自适应优化算法研究  

Research on Adaptive Optimization Algorithm of Relay Protection Based on Depth Learning

作　　者：吴春旭 WU Chun-xu(Yangzhou Sanxin Power Supply Service Co.Ltd.,Gaoyou 225600,China)

机构地区：[1]扬州三新供电服务有限公司高邮分公司,江苏高邮225600

出　　处：《电气开关》2025年第1期32-34,39,共4页Electric Switchgear

摘　　要：在电力系统继电保护领域,传统保护策略多依赖固定参数和规则,难以灵活应对系统环境的复杂变化。为解决此问题,提出一种基于深度学习的自适应优化算法。通过引入深度学习技术,实现对电力系统运行状态的精准识别与预测,进而推动继电保护的智能化与自适应优化。该算法可根据不同工况自动调整保护定值,从而提高保护的准确性和及时性。实验结果显示,与传统固定参数保护策略相比,这一基于深度学习的自适应优化算法在问题识别和故障定位方面展现显著优势。通过实时调整保护策略,此算法有效提升电力系统的安全可靠性,降低了误动作和漏动作的风险。In the field of power system relay protection,traditional protection strategies rely heavily on fixed parameters and rules,making it difficult to flexibly respond to complex changes in the system environment.To address this issue,a deep learning based adaptive optimization algorithm is proposed.By introducing depth learning technology,accurate recognition and prediction of the operating status of the power system can be achieved,thereby promoting the intelligence and adaptive optimization of relay protection.This algorithm can automatically adjust the protection settings according to different working conditions,thereby improving the accuracy and timeliness of protection.The experimental results show that compared with traditional fixed parameter protection strategies,this depth learning based adaptive optimization algorithm exhibits significant advantages in problem identification and fault localization.By adjusting the protection strategy in real-time,this algorithm effectively improves the safety and reliability of the power system,reducing the risk of misoperation and leakage.

关 键 词：深度学习 继电保护 自适应优化 故障定位 智能化 

分 类 号：TM71[电气工程—电力系统及自动化]