基于小波分析的智能电网故障检测与分类

M. Munir, S. Hussain, Ali Al-Alili, Reem Al Ameri, Ehab El-Sadaany
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引用次数: 0

摘要

智能电网的核心特征之一是系统故障的检测和诊断,这对于电网的平稳运行至关重要。对于公用输电网系统,这些故障可以表现为短路故障和开路故障。由于数字时代的到来,传统电网也经历了向能够产生大量数据的数字设备和现代传感器的大规模过渡。挑战在于对这些数据进行预处理,使其能够用于检测瞬态和电网故障。本文提出了结合支持向量机(SVM)和k近邻(KNN)等人工智能技术,在合理的精度范围内检测和综合分类最常见的故障暂态。为了衡量所提出方案的有效性,对改进的IEEE-39总线系统进行了全面的评估研究。总线电压和线路电流测量采取了一系列的故障场景,导致高频暂态信号。利用连续小波变换(CWT)对信号进行分析。然后对测量信号进行离散小波变换(DWT)预处理,采用多比四(Db4)母小波,分解故障信号的高频成分。DWT产生一系列高频和低频细节和近似系数,从中提取一系列统计特征,并将其用作训练和测试分类算法的输入。结果表明,所建立的模型能够成功地用于传动系统的故障检测和分类,并具有可接受的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault Detection and Classification in Smart Grids Using Wavelet Analysis
One of the core features of the smart grid deemed essential for smooth grid operation is the detection and diagnosis of system failures. For a utility transmission grid system, these failures could manifest in the form of short circuit faults and open circuit faults. Due to the advent of the digital age, the traditional grid has also undergone a massive transition to digital equipment and modern sensors which are capable of generating large volumes of data. The challenge is to preprocess this data such that it can be utilized for the detection of transients and grid failures. This paper presents the incorporation of artificial intelligence techniques such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to detect and comprehensively classify the most common fault transients within a reasonable range of accuracy. For gauging the effectiveness of the proposed scheme, a thorough evaluation study is conducted on a modified IEEE-39 bus system. Bus voltage and line current measurements are taken for a range of fault scenarios which result in high-frequency transient signals. These signals are analyzed using continuous wavelet transform (CWT). The measured signals are afterward preprocessed using Discrete Wavelet Transform (DWT) employing Daubechies four (Db4) mother wavelet in order to decompose the high-frequency components of the faulty signals. DWT results in a range of high and low-frequency detail and approximate coefficients, from which a range of statistical features are extracted and used as inputs for training and testing the classification algorithms. The results demonstrate that the trained models can be successfully employed to detect and classify faults on the transmission system with acceptable accuracy.
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