Marketing Service Center, State Grid Tianjin Electric Power Company, Tianjin 300120, China.
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Sensors (Basel). 2022 Nov 6;22(21):8543. doi: 10.3390/s22218543.
The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper proposes an abnormal data detection network based on Deep Reinforcement Learning, which includes a main network and a target network composed of deep learning networks. This work uses the greedy policy algorithm to find the action of the maximum value of based on the -learning method to obtain the optimal calculation policy. It also uses the reward value and discount factor to optimize the target value. In particular, this study uses the fuzzy c-means method to predict the future state information value, which improves the computational accuracy of the Deep Reinforcement Learning model. The experimental results show that compared with the traditional smart meter data anomaly detection method, the proposed model improves the accuracy of meter data anomaly detection.
智能电网中快速增长的电力数据给安全管理带来了困难。使用人工智能方法处理大规模电力数据已成为热点研究课题。考虑到智能电表的预警检测问题，本文提出了一种基于深度强化学习的异常数据检测网络，该网络包括一个由深度学习网络组成的主网络和一个目标网络。本工作使用贪婪策略算法根据 -学习方法找到值的最大值的动作，以获得最佳计算策略。它还使用奖励值和折扣因子来优化目标值。特别是，本研究使用模糊 c-均值方法来预测未来状态信息值，从而提高了深度强化学习模型的计算准确性。实验结果表明，与传统的智能电表数据异常检测方法相比，所提出的模型提高了电表数据异常检测的准确性。
