In recent times, there has been considerable interest in fault detection within electrical power systems, garnering attention from both academic researchers and industry professionals. Despite the development of numerous fault detection methods and their adaptations over the past decade, their practical application remains highly challenging. Given the probabilistic nature of fault occurrences and parameters, certain decision-making tasks could be approached from a probabilistic standpoint. Protective systems are tasked with the detection, classification, and localization of faulty voltage and current line magnitudes, culminating in the activation of circuit breakers to isolate the faulty line. An essential aspect of designing effective fault detection systems lies in obtaining reliable data for training and testing, which is often scarce. Leveraging deep learning techniques, particularly the powerful capabilities of pattern classifiers in learning, generalizing, and parallel processing, offers promising avenues for intelligent fault detection. To address this, our paper proposes an anomaly-based approach for fault detection in electrical power systems, employing deep autoencoders. Additionally, we utilize Convolutional Autoencoders (CAE) for dimensionality reduction, which, due to its fewer parameters, requires less training time compared to conventional autoencoders. The proposed method demonstrates superior performance and accuracy compared to alternative detection approaches by achieving an accuracy of 97.62% and 99.92% on simulated and publicly available datasets.
The electric power grid plays a vital role in modern society, reliably supplying electricity to residential, commercial, and industrial sectors. As our dependence on electricity grows, there is a corresponding increase in the need for robust and effective electrical distribution systems (EDSs). Guaranteeing the safety and dependability of these systems requires mitigating risks and ensuring uninterrupted power delivery. [1,2] Therefore, the adoption of sophisticated methods for detecting and classifying faults is crucial to optimize the performance of EDSs. [3,4] These techniques serve as a critical tool in identifying and managing faults, optimizing maintenance efforts, and ultimately strengthening the overall resilience of the grid [3,5]. The electrical power system, comprising various dynamic elements, is susceptible to disturbances and faults, necessitating swift fault detection and protection operation to maintain stability [6]. It is important to swiftly detect and classify faults on transmission lines, with protection systems initiating relays to prevent outages [6]. Effective fault detection and classification, ensuring rapid restoration of the power system, are imperative for service reliability and minimizing outages [7]. Protection schemes must promptly detect and remove affected segments during a fault incident to minimize its impact [8]. However, the expansion of modern power networks poses challenges for protection systems, requiring integrated schemes capable of monitoring different grid layers. Wide Area Protection (WAP) using phasor measurements from Phasor Measurement Units (PMUs) has been proposed, yet challenges remain in interpreting data and identifying faulty components [9].
Existing fault detection algorithms for transmission networks often rely on iterative solutions or require numerous PMUs, while distribution networks face issues due to distributed generation impacting fault levels and relay operation [10,11]. Synchrophasor measurements offer a more reliable alternative but are currently limited to distribution networks, highlighting the need for an integrated scheme applicable to both distribution and transmission networks [9].
Fault diagnosis is categorized into two main types: model-based and process history-based approaches. Model-based methods involve analyzing faults by representing a system or process using either quantitative or qualitative models. On the other hand, process history-based techniques rely on empirical data gathered from the process, establishing connections between inputs and desired outputs without prior mathematical modeling. Feature extraction is crucial in process history-based methods as it helps capture essential information from empirical data for pattern recognition. With advancements in signal processing and a deeper understanding of power systems, various techniques have emerged for direct measurement and transformation, enabling the extraction of inherent fault characteristics. Commonly utilized methods for feature extraction in the literature include Wavelet and Fourier transforms, which effectively isolate fault-related characteristics with robustness and precision. [12][13][14][15]. However, these classical methods may yield inaccurate results due to assumptions about line parameters. Artificial neural networks (ANNs) and support vector machines (SVMs) are robust pattern recognition methodologies capable of efficiently generalizing dynamic parameters using both supervised and unsupervised learning approaches. [16]. Recently, machine learning algorithms have been widely used by combining signal processing approaches to rapidly and accurately identify faults [17][18][19]. Signal processing techniques extract the features from initially captured voltage and current signals to determine fault occurrence and their types [20]. Unfortunately, the selection of faulted features across different frequency ranges is often arbitrary, leading to inconsistency in results. Therefore, enhancing the fault detection accuracy for EDSs has emerged as a significant research focus. Also, existing fault detection techniques are supervised approaches, which poses challenges for real-time applications due to the requirement of prior labeling, and achieving online fault detection and clustering with a high degree of accuracy remains elusive. Recently autoencoders have emerged as an interesting option for anomaly detection in a time series because it needs to be trained only on one type of data that is normal data. In [21] authors have used deep autoencoders for anomaly detection in wireless communication networks. Similarly [22] have used autoencoders for anomaly detection in videos.
In our proposed method we have used a deep convolutional autoencoder model to detect faults in the distribution and transmission system. At first, the model is trained on normal time-series data of current which has no fault. During training the autoencoder learns to reconstruct the nor
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