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Probabilistic Neural Network-Aided Fast Classification of Transmission Line Faults Using Differencing of Current Signal
Electrical power transmission lines are most vulnerable to different faults due frequent atmospheric hazards. Hence, fault detection and classification are imperative to restrict unwanted power outage by isolation of the faulted line. A probabilistic neural network (PNN)-based fault classification methodology is proposed here. This work uses differencing-based modulated fault signals as input to the PNN architecture to extract fault features in terms of three-phase fault intensity index, which are further analyzed for direct classification of faults aided by a decision tree like study. Simulation of faults has been carried out in practical alike simulation environment with variation of fault location, fault resistance and inherent power line noise, which helps to develop robustness of fault analyzer. Classifier accuracy of 99.33% is achieved here, and more importantly, using only (1/6)th fraction of post fault signals, which is considerably low compared to similar contemporary works. Furthermore, the proposed classifier is equipped with the ability of detecting ground fault without inspecting the neutral current. This method uses only 35.7% of the total fault locations for training, and the rest for validating the model, which is also an above average performance. Finally, the analyzer comprises of simple PNN model as the only classifier, hence possess considerably low computational complexity.
Probabilistic Neural Network-Aided Fast Classification of Transmission Line Faults Using Differencing of Current Signal
Electrical power transmission lines are most vulnerable to different faults due frequent atmospheric hazards. Hence, fault detection and classification are imperative to restrict unwanted power outage by isolation of the faulted line. A probabilistic neural network (PNN)-based fault classification methodology is proposed here. This work uses differencing-based modulated fault signals as input to the PNN architecture to extract fault features in terms of three-phase fault intensity index, which are further analyzed for direct classification of faults aided by a decision tree like study. Simulation of faults has been carried out in practical alike simulation environment with variation of fault location, fault resistance and inherent power line noise, which helps to develop robustness of fault analyzer. Classifier accuracy of 99.33% is achieved here, and more importantly, using only (1/6)th fraction of post fault signals, which is considerably low compared to similar contemporary works. Furthermore, the proposed classifier is equipped with the ability of detecting ground fault without inspecting the neutral current. This method uses only 35.7% of the total fault locations for training, and the rest for validating the model, which is also an above average performance. Finally, the analyzer comprises of simple PNN model as the only classifier, hence possess considerably low computational complexity.
Probabilistic Neural Network-Aided Fast Classification of Transmission Line Faults Using Differencing of Current Signal
J. Inst. Eng. India Ser. B
Mukherjee, Alok (Autor:in) / Chatterjee, Kingshuk (Autor:in) / Kundu, Palash Kumar (Autor:in) / Das, Arabinda (Autor:in)
Journal of The Institution of Engineers (India): Series B ; 102 ; 1019-1032
01.10.2021
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Classification and Fast Detection of Transmission Line Faults Using Signal Entropy
| Springer Verlag | 2021