Enhanced Anomaly-Based Fault Detection System in Electrical Power Grids

Wisam Elmasry,Mohammed Wadi,Muhammad Mansoor Alam

doi:10.1155/2022/1870136

Wisam Elmasry, Mohammed Wadi + Show 1 more

Open Access

https://doi.org/10.1155/2022/1870136

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Abstract

Early and accurate fault detection in electrical power grids is a very essential research area because of its positive influence on network stability and customer satisfaction. Although many electrical fault detection techniques have been introduced during the past decade, the existence of an effective and robust fault detection system is still rare in real-world applications. Moreover, one of the main challenges that delays the progress in this direction is the severe lack of reliable data for system validation. Therefore, this paper proposes a novel anomaly-based electrical fault detection system which is consistent with the concept of faults in the electrical power grids. It benefits from two phases prior to training phase, namely, data preprocessing and pretraining. While the data preprocessing phase executes all elementary operations on the raw data, the pretraining phase selects the optimal hyperparameters of the model using a particle swarm optimization (PSO)-based algorithm. Furthermore, the one-class support vector machines (OC-SVMs) and the principal component analysis (PCA) anomaly-based detection models are exploited to validate the proposed system on the VSB dataset which is a modern and realistic electrical fault detection dataset. Finally, the results are thoroughly discussed using several quantitative and statistical analyses. The experimental results confirm the effectiveness of the proposed system in improving the detection of electrical faults.

Highlights

Nowadays, there is a rapid growth in the electrical power grids in terms of size and complexity [1]. is growth includes all sectors of electrical power industry starting from generation to transmission and distribution [2]
Evaluation Metrics. e outcome of the testing phase is merely a binary classification process indicating whether a sample is “normal” or “faulty.” a confusion matrix will be constructed after the testing phase is finished. is confusion matrix has four cells which contain the following measures: true positive (TP), true negative (TN), false positive (FP), and false negative (FN). ese measures are required to compute eight commonly used evaluation metrics, as follows
Effective fault detection in electrical power industry will lead to maximizing service quality as well as minimizing economic losses. erefore, an anomaly-based fault detection system is proposed to cope with drawbacks and limitations of the existing electrical fault detection systems. e proposed system consists of four phases, namely, data preprocessing, pretraining, training, and testing

Summary

Introduction

There is a rapid growth in the electrical power grids in terms of size and complexity [1]. is growth includes all sectors of electrical power industry starting from generation to transmission and distribution [2]. Is growth includes all sectors of electrical power industry starting from generation to transmission and distribution [2]. Is deviation of voltage and current from nominal states is caused by human errors, environmental conditions, and equipment failures [4]. When an electrical fault occurs, it imposes excessively high current to flow across the network that may cause damage to devices and equipment [5]. Erefore, an early and accurate fault detection is pivotal to prevent equipment damage, service interruption, and loss of human and animal lives [6]. Electrical fault detection systems based on binary classification have been extensively researched during the last decade [7], it was reported that there is a research gap in this domain including the automation and validation of the system [8]. There is a dire need for an intelligent system that acts efficiently in the real-world power systems

Methods

Results

Conclusion