A review on condition monitoring of GIS

Condition monitoring of high voltage apparatus is of much importance for the maintenance of electric power systems. Whether it is detecting faults or partial discharges that take place in high voltage equipment, or detecting contamination and degradation of outdoor insulators, deep learning which is a branch of machine learning has been extensively investigated. Instead of using hand-crafted manual features as an input for the traditional machine learning algorithms, deep learning algorithms use raw data as the input where the feature extraction stage is integrated in the learning stage, resulting in a more automated process. This is the main advantage of using deep learning instead of traditional machine learning techniques. This paper presents a review of the recent literature on the application of deep learning techniques in monitoring high voltage apparatus such as GIS, transformers, cables, rotating machines, and outdoor insulators.

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“…Gas insulated switchgear (GIS) is widely used in the industry [76]. A GIS platform is shown in Figure 8.…”

Section: Gas Insulated Switchgearmentioning

confidence: 99%

Deep Learning in High Voltage Engineering: A Literature Review

et al. 2022

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“…foundation and core components of the power system [3]. However, during prolonged operation, various factors such as harsh environmental conditions and fluctuating loads can lead to various faults in power equipment, including electrical faults [4], mechanical faults [5], thermal faults [6].…”

Section: Introductionmentioning

confidence: 99%

Gas-insulated switch-gear mechanical fault detection based on acoustic feature analysis using a multi-state pre-trained neural network

Wang,

Zhang,

Shao

et al. 2024

Meas. Sci. Technol.

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The acoustic-based approach is a prevalent way for non-contact fault diagnosis on Gas-Insulated Switch-gear (GIS). GIS always works under different voltages causing great diversity in acoustic frequency. However, based on the frequency principle, neural networks always focus on a specific frequency, which challenges robust fault detection on GIS. This paper introduces a novel multi-stage training method to improve the robustness of fault detection on GIS. The proposed method consists of three components: a Multi-Channel Based Frequency Regressor (MCBFR), an Audio Spectrogram Transformer Auto-Encoder (AST-AE), and a Feature Interaction Module (FIM). MCBFR and AST-AE are optimised to extract specific features from acoustics during the pre-training stage. The FIM fuses components extracted by MCBFR and AST when training the model that can indicate the final result. Also, we apply a multi-stage training strategy during the training stage to reduce the cost of potential model retraining. The efficacy of the proposed method was validated using experimental data from a real GIS, and it shows competitive performance in fault detection compared to existing methods.

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“…The usage of SF 6 can reduce the size and cost of GIS and improve the operation and maintenance of GIS. However, any abnormal condition on the insulation in GIS can cause partial discharge (PD), which could lead to insulation breakdown [2,6,10]. Hence, detection of PD within switchgears is important.…”

Section: Introductionmentioning

confidence: 99%

Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence

et al. 2021

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In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.

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A review on condition monitoring of GIS

Cited by 10 publications

References 12 publications

Deep Learning in High Voltage Engineering: A Literature Review

Deep Learning in High Voltage Engineering: A Literature Review

Gas-insulated switch-gear mechanical fault detection based on acoustic feature analysis using a multi-state pre-trained neural network

Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence

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