KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Kanwal, ShaziaSubject: search.filters.subject.transmission line

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.