This article presents an efficient method for power quality events (PQEs) detection and classification based on Curvelet transform (CT) and optimized extreme learning machine (OELM). Initially, various PQEs signal data are extracted even under noisy and simultaneously occurred multi-event conditions to reflect the real-time conditions. Relevant features of all these extracted signals are computed by using a multi-resolution and multidirectional fast discrete CT (FDCT) approach. These features are used to classify the events accurately by the proposed OELM. In this classification strategy, a modified differential evolution (MDE) is introduced to optimize the conventional ELM to enhance its precision rate. The proposed novel approach is tested and analyzed under various noisy conditions with 20 dB, 30 dB, and 50 dB with single and multi PQEs conditions. Comparative results with other recently proposed approaches by various authors reveal that the proposed CT- and OELM-based classifier titled CT-OELM can be considered as a competitive choice for implementing in the real-time monitoring system.

本文提出了一种基于Curvelet变换（CT）和优化的极限学习机（OELM）的电能质量事件（PQE）检测和分类的有效方法。最初，即使在嘈杂并同时发生的多事件条件下也提取各种PQEs信号数据，以反映实时条件。所有这些提取信号的相关特征都是通过使用多分辨率和多方向快速离散CT（FDCT）方法来计算的。这些功能被提议的OELM用于对事件进行准确分类。在这种分类策略中，引入了改进的差分进化（MDE）来优化常规ELM，以提高其准确率。在20 dB，30 dB，在单个和多个PQE条件下为50 dB。各种作者与其他最近提出的方法的比较结果表明，提议的基于CT和OELM的分类器CT-OELM可以被视为在实时监控系统中实施的竞争选择。