Power quality disturbance (PQD) is essential for devices consuming electricity and meeting today’s energy trends. This study contains an effective artificial intelligence (AI) framework for analyzing single or composite defects in power quality. A convolutional neural network (CNN) architecture, which has an output powered by a gated recurrent unit (GRU), is designed for this purpose. The proposed framework first obtains a matrix using a short-time Fourier transform (STFT) of PQD signals. This matrix contains the representation of the signal in the time and frequency domains, suitable for CNN input. Features are automatically extracted from these matrices using the proposed CNN architecture without preprocessing. These features are classified using the GRU. The performance of the proposed framework is tested using a dataset containing a total of seven single and composite defects. The amount of noise in these examples varies between 20 and 50 dB. The performance of the proposed method is higher than current state-of-the-art methods. The proposed method obtained 98.44% ACC, 98.45% SEN, 99.74% SPE, 98.45% PRE, 98.45% F1-score, 98.19% MCC, and 93.64% kappa metric. A novel power quality disturbance (PQD) system has been proposed, and its application has been represented in our study. The proposed system could be used in the industry and factory.

中文翻译：

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使用带门控循环单元的卷积神经网络结构自动检测电能质量扰动

电能质量扰动 (PQD) 对于消耗电力和满足当今能源趋势的设备至关重要。本研究包含一个有效的人工智能 (AI) 框架，用于分析电能质量中的单个或复合缺陷。为此目的设计了卷积神经网络 (CNN) 架构，其输出由门控循环单元 (GRU) 驱动。所提出的框架首先使用 PQD 信号的短时傅立叶变换 (STFT) 获得矩阵。该矩阵包含信号在时域和频域中的表示，适用于 CNN 输入。使用建议的 CNN 架构从这些矩阵中自动提取特征，无需预处理。这些特征使用 GRU 进行分类。使用包含总共七个单一和复合缺陷的数据集来测试所提出框架的性能。这些示例中的噪声量在 20 到 50 dB 之间变化。所提出方法的性能高于当前最先进的方法。所提出的方法获得了 98.44% ACC、98.45% SEN、99.74% SPE、98.45% PRE、98.45%F 1 分、98.19% MCC 和 93.64% kappa 度量。提出了一种新型电能质量扰动 (PQD) 系统，其应用已在我们的研究中得到体现。所提议的系统可用于工业和工厂。