Application of ANN to power system fault analysis

Lazim, Fadila; Hamzah, N.; Arsad, Pauziah Mohd

doi:10.1109/scored.2002.1033109

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…The outputs of the nodes of each layer affect only the outputs of the next layer. In back propagation stage, the difference (errors) between real output values (target) and actual output values and sum of their square [4,5]. The use of the bias adjust in the ANN S is optional, but the results may be enhanced by it, A multi-layer network with one hidden layer is shown in fig.…”

Section: The Back-propagation Methodsmentioning

confidence: 99%

“…Artificial Neural Network (ANN) is a comprehensive multi-paradigm prototyping and developed that can be used to solve complex problem [4].…”

Section: Artificial Neural Network Techniquementioning

confidence: 99%

“…Table (3) shows the results the measured impedance, fault location, and percentage error, when the effect of the angle of an impedance deviation vector is compensated by (ANN) for different fault location. The percentage error in an impedance measurement of the distance relay reach is calculated as follows: Table (4,5,6) repeat to procedure in same situation as a mentioned in above for fault resistance (R F =20 ) and with various fault location. Fig.…”

Section: Compensation Of Fault Resistance Influencementioning

confidence: 99%

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Fault Location Estimation Algorithm for Digital Distance Protection Relay Based on Artificial Neural Network

Al-Badrani¹

2009

(AREJ)

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This paper investigates study the influence of a fault resistance on the performance of a digital distance protection installed on a transmission lines, it is well known that augment of fault resistance can cause seriously overreach or underreach to distance relay depending on the different operational situations of power system, To compensate the magnitude and phase error of the apparent impedance, This algorithm uses the angle of an impedance deviation v e c t o r (). The impedance correction algorithm for ground faults has been employed by the Fault Resistance Compensation Block (FRCB) to compensate fault resistance effect. Artificial neural network technique using Matlab/Simulink software is trained to determine the suitable value of an impedance deviation angle for the FRCB, In order to minimize the estimated impedance error of a distance relay. The results of simulation showed that compensated scheme relay performance are acceptably improve and the impedance estimated by relay is close to the actual value via FRCB with ANN S. So the performance of distance relay is much more reliable and accuracy.

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Section: The Back-propagation Methodsmentioning

confidence: 99%

“…Artificial Neural Network (ANN) is a comprehensive multi-paradigm prototyping and developed that can be used to solve complex problem [4].…”

Section: Artificial Neural Network Techniquementioning

confidence: 99%

Section: Compensation Of Fault Resistance Influencementioning

confidence: 99%

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Fault Location Estimation Algorithm for Digital Distance Protection Relay Based on Artificial Neural Network

Al-Badrani¹

2009

(AREJ)

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“…Aggarwal et al [3] combined empirical mode decomposition (EMD) with the probabilistic neural network (PNN), studied the fault classification of transmission lines, used MATLAB/SIMULINK to carry out experiments in 500 kV, 300 km transmission lines, and verified the excellent learning speed and classification accuracy of this method. Lazim et al [4] designed the fault diagnosis method of a transmission line through an artificial neural network (ANN) by using two factors, bus voltage and line fault current, and then simulated it in MATLAB 6.0. Kari et al [5] designed a method based on an adaptive neural fuzzy inference system (ANFIS) and the Dempster-Shafer theory (DST) on the basis of dissolved gas in oil (DGA), compared it with an ordinary ANFIS through experiments, and verified the effectiveness of the method.…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

Qian,

Zhu,

et al. 2020

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Power big data contains a lot of information related to equipment fault. The analysis and processing of power big data can realize fault diagnosis. This study mainly analyzed the application of association rules in power big data processing. Firstly, the association rules and the Apriori algorithm were introduced. Then, aiming at the shortage of the Apriori algorithm, an IM-Apriori algorithm was designed, and a simulation experiment was carried out. The results showed that the IM-Apriori algorithm had a significant advantage over the Apriori algorithm in the running time. When the number of transactions was 100 000, the running of the IM-Apriori algorithm was 38.42% faster than that of the Apriori algorithm. The IM-Apriori algorithm was little affected by the value of support min . Compared with the Extreme Learning Machine (ELM), the IM-Apriori algorithm had better accuracy. The experimental results show the effectiveness of the IM-Apriori algorithm in fault diagnosis, and it can be further promoted and applied in power grid equipment.

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Distance protection schemes in distributed generation connected Transmission system combined with machine learning

Prabhakar

2023

2023 IEEE 3rd International Conference on Smart Technologies for Power, Energy and Control (STPEC)

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Application of ANN to power system fault analysis

Cited by 4 publications

References 3 publications

Fault Location Estimation Algorithm for Digital Distance Protection Relay Based on Artificial Neural Network

Fault Location Estimation Algorithm for Digital Distance Protection Relay Based on Artificial Neural Network

Archives of Electrical Engineering

Distance protection schemes in distributed generation connected Transmission system combined with machine learning

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