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

• Khaoula Assadi ^[2] ; Jihane Ben Slimane ^[1] ; Hanene Chalandi ^[2] ; Salah Salhi ^[2]
  1. [1] Northern Border University

     Northern Border University

     Arabia Saudí

     
  2. [2] National Engineering School of Tunis, LR11ES20 Analysis, Design and Control of Systems Laboratory, University of Tunis El Manar, Tunis, Tunisia
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 42, Nº 6, 2023, págs. 1518-1530
• Idioma: inglés
• Enlaces
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• Resumen

  • 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.