Journal of Electrical Engineering and Automation
Journal of Electrical Engineering and Automation. 2025; 4: (3) ; 10.12208/j.jeea.20250076 .
总浏览量: 57
南京邮电大学自动化学院、人工智能学院 江苏南京
*通讯作者： 陶锴,单位：南京邮电大学自动化学院、人工智能学院 江苏南京；
电能质量扰动检测对保障分布式新能源接入的电力系统运行稳定性至关重要。为提升电能质量扰动检测精度，本文提出了一种融合NRBO (Newton Raphson Based Optimizer)、BiGRU (Gated Recurrent Unit)及AM (Attention Mechanism)的扰动自适应检测方法。首先，通过NRBO优化模态分解的超参数，并提取电能质量扰动信号的混合多尺度模糊熵(Composite Multiscale Fuzzy Entropy, CMFE)特征向量。提取扰动信号特征，为后续检测提供可靠的数据基础。随后，本文提出BiGRU-AM模型以检测多类型电能质量复合扰动。为验证方法的有效性，本文开展了8种单一扰动和8种复合扰动的检测实验，并与其他6种算法对比。结果表明，该方法显著提升了性能，在扰动特征提取和检测准确性方面有好的表现优异。
Power quality disturbance detection is crucial for ensuring the operational stability of power systems with integrated distributed renewable energy. To enhance detection accuracy, this paper proposes an adaptive disturbance detection method integrating the NRBO, BiGRU-AM. First, the hyperparameters of modal decomposition are optimized via NRBO, and the composite multiscale fuzzy entropy (CMFE) feature vectors of power quality disturbance signals are extracted. This establishes a reliable data foundation for subsequent detection. Subsequently, a BiGRU-AM model is proposed to detect multiple types of composite power quality disturbances. To validate the method's effectiveness, experiments were conducted on eight types of single disturbances and eight types of composite disturbances, with comparisons against six other algorithms. The results demonstrate that the proposed method significantly improves performance, exhibiting excellent accuracy in both disturbance feature extraction and detection.
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