Fault Detection Method Using ANN for Power Transmission Line

Leh, Nor Adni Mat; Zain, Fatihah Mohd; Muhammad, Zuraida; Hamid, Shabinar Abd; Rosli, Anis Diyana

doi:10.1109/iccsce50387.2020.9204921

Cited by 21 publications

(4 citation statements)

References 5 publications

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“…ANN is a computational system inspired by the functioning of biological neural systems (Leh et al , 2020). This complex network is organized into several layers.…”

Section: Methodsmentioning

confidence: 99%

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

Assadi

Slimane

Hanene

et al. 2023

COMPEL

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Purpose This study aims to focus on an adaptive method for fault detection and classification of fault types that trigger in three-phase transmission lines using artificial neural networks (ANNs). The proposed scheme can detect and classify several types of faults, including line-to-ground, line-to-line, double-line-to-ground, triple-line and triple-line-to-ground faults. Design/methodology/approach The fundamental components of three-phase current and voltage were used as inputs in the ANNs. An analysis of the impact of variations in the fault resistance, fault type and fault inception time was conducted to evaluate the ANNs performance. The survey compares the performance of the multi-layer perceptron neural network (MLPNN) and Elman recurrent neural network trained with the backpropagation learning technique to improve each of the three phases of the fault detection and classification process. A detailed analysis validates the choice of the ANNs architecture based on the variation in the number of hidden neurons in each step. Findings The mean square error, root mean square error, mean absolute error and linear regression are measured to improve the efficiency of the ANN models for both fault detection and classification. The results indicate that the MLPNN can detect and classify faults with a satisfactory performance. Originality/value The smart adaptive scheme is fast and accurate for fault detection and classification in a single circuit transmission line when faced with different conditions and can be useful for transmission line protection schemes.

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“…ANN is a computational system inspired by the functioning of biological neural systems (Leh et al , 2020). This complex network is organized into several layers.…”

Section: Methodsmentioning

confidence: 99%

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

Assadi

Slimane

Hanene

et al. 2023

COMPEL

View full text Add to dashboard Cite

show abstract

“…5a and 5b, respectively. Correct weight tuning in BPNN reduces the error rate [32]. Weights are selected at random.…”

Section: Layermentioning

confidence: 99%

Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods

Kanwal,

Jiriwibhakorn

2024

IEEE Access

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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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“…The results showed an efficient surface roughness prediction with a 9-4-1 MLP architecture. Similarly, Leh et al [15] developed a model with EBP to detect failures in a power transmission line. The results show a tolerable accuracy in failure detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Approach for Degradation Path Prediction Using Different Models and Architectures of Artificial Neural Networks

Shaheen

Németh

2022

Period. Polytech. Mech. Eng.

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Degradation and failure prediction has become more and more crucial for maintenance planning and scheduling, the decision-making process, and many other areas of manufacturing systems. This paper presents an approach where different artificial neural network models were developed to predict the degradation path of a machine component using different architectures, including fully connected networks (FCN) and arbitrarily connected networks (ACN). These models were trained using the Neuron-by-Neuron (NBN) training algorithm with forward-backward computations, where NBN is an improved form of the Levenberg-Marquardt (LM) algorithm, combined with FCN and ACN architectures, which can be trained efficiently, it can give more accurate predictions with a fewer number of neurons used. The developed models were evaluated using the statistical performance measure of the sum of squared error (SSE). The results show that the used networks are successfully able to predict the degradation path; the 8-neurons model of FCN architecture and the 3-neurons model of ACN architecture with tanh (mbib) hidden layers activation function and linear function (mlin) of the outputs have the lowest prediction error (SSE) among all the developed models. The use of such architectures combined with NBN training algorithm can easily model manufacturing systems with complex component structures that provide a vast amount of data.

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Fault Detection Method Using ANN for Power Transmission Line

Cited by 21 publications

References 5 publications

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods

Machine Learning Approach for Degradation Path Prediction Using Different Models and Architectures of Artificial Neural Networks

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