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Time Series-Based Fault Detection and Classification in IEEE 9-Bus Transmission Lines Using Deep Learning
(2025-01-01)
Jiriwibhakorn, Somchat
;
Kanwal, Shazia
Transmission line faults present a significant threat to the stability of power systems, potentially causing widespread outages. Timely detection of these faults is essential to prevent substantial disruptions in the power supply. This paper explores a time series-based deep learning technique for fault detection and classification in the IEEE 9-bus system. Post asymmetrical fault current and voltage time series data have been used to train a convolutional neural network (CNN), representing normal and faulty conditions, with convolutional and ReLU layers. A fully connected layer is used to detect features without missing critical information of the signal, achieving MSE as zero for fault detection and 0.0149 for fault classification. This demonstrates the effectiveness of CNNs for real-time fault detection and classification in complex power grids. The robustness of the CNN model indicates its potential for deployment in practical applications, enhancing the reliability and resilience of the transmission network. Using deep learning techniques opens opportunities for further improvements in fault detection and location strategies within the power grid.
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Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods
(2024-01-01)
Kanwal, Shazia
;
Jiriwibhakorn, Somchat
Electric systems are getting more complex with time, and primitive protection methods such as traveling wave and impedance-based methods face limitations and shortcomings. This paper incorporates and presents the applications of an adaptive neuro-fuzzy inference system and compares it with a back propagation neural network, self-organizing map, and hybrid method of discrete wavelet with adaptive neuro-fuzzy inference system for fault detections, classification, and localization in transmission lines. These methods, in comparison with primitive methods, could be capable of detecting, identifying, and predicting the location of the faults more accurately. The IEEE 9-bus system is utilized to obtain data from one end of the transmission line to develop an ANFIS-based model. This system is simulated in MATLAB/Simulink for different fault cases at various locations. The three-phase voltage and current at one end of IEEE 9-bus number seven are taken for training. Three ANFIS models are developed for fault detection, classification, and localization and compared with other models. For verification of the models, mean square error, mean absolute error, and regression analysis have been computed and compared for all the models. All four techniques have performed well for fault classification, detection, and location. However, the percentage error for the ANFIS-based fault model is less compared to backpropagation, self-organizing map, and discrete wavelet transform with ANFIS. Therefore, the proposed ANFIS models can be implemented for deploying in real-time-based protection systems.
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Fault Detection in Transmission Lines Using CNN
(2024-01-01)
Kanwal, Shazia
;
Jiriwibhakorn, Somchat
Transmission line faults pose a significant risk to power systems, potentially leading to widespread outages. Detecting these faults using advanced algorithms is crucial for preventing major disruptions in power supply. In this paper, we work on a fault detection technique for the IEEE 9-bus system based on deep learning. By training a Convolutional Neural Network (CNN) on features extracted from both normal and faulty conditions, we achieve an accuracy of 86%. This high accuracy underscores the potential of CNNs for real-world implementation in fault detection systems. The robustness of the CNN approach suggests its viability for deployment in complex, real-time systems, offering improved reliability and resilience against transmission line faults. Additionally, utilizing deep learning techniques opens avenues for further refinement and optimization of fault detection strategies in the power grid.
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Artificial Intelligence based Faults Identification, Classification, and Localization Techniques in Transmission Lines-A Review
(2023-12-01)
Kanwal, Shazia
;
Jiriwibhakorn, Somchat
An overview of the many methods used for fault detection, classification and location in the power system, particularly in transmission lines, is provided in this review, it also includes an experimental result of adaptive neuro-fuzzy inference system -based fault detection , fault classification and fault location. Being in operation outdoor environment, transmission lines are more vulnerable to various faults which may lead to system collapse in severe cases. Therefore, to ensure the reliable and safe operation of power system it is imperative to critically monitor the faults in transmission lines. In this regard, researchers around the globe have developed several techniques and constantly putting efforts to further improve the protection efficacy. The brief yet thorough analysis and comparison of the artificial intelligence-based techniques, hybrid methodologies and most recent approaches in the context of power system faults have been discussed and presented. In addition, the research work and the experimental results of an adaptive neuro-fuzzy inference system-based techniques have also been discussed for IEEE-9 bus system. The mean square error for testing data of ANFIS-based fault detection, classification, is zero and for fault location Mean square error is 5.32km. This piece of work could be helpful in the development of a comprehensive understanding of various artificial intelligence-based techniques within the realm of fault detection, classification and localization in transmission lines.

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