An ANN fault detection procedure applied in virtual measurement systems case

Postolache, Octavian; Pereira, José Miguel; Cretu, M.; Girão, Pedro Silva

doi:10.1109/imtc.1998.679769

Cited by 14 publications

(2 citation statements)

References 3 publications

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“…Several machine learning (ML) models like support vector machine (SVM), K-nearest neighbour (k-nearest neighbours (kNN)), fuzzy logic, artificial neural network (ANN), Deep neural network (DNN), and Graph neural network have been utilised for FL identification. Among the knowledge-based methods, ANN has been widely used in power system applications like event detection [14], fault applications [15], data integrity check, and distribution management [16]. Feedforward neural network combined with SVM to locate different types of faults in a practical DS using relay and breaker measurements at substations is presented in ref.…”

Section: Introductionmentioning

confidence: 99%

Ensemble voting‐based fault classification and location identification for a distribution system with microgrids using smart meter measurements

et al. 2022

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This study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)-based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event-driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k-nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE-123 bus system modified with added PV-based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real-time simulation-based testbed using a state-of-the-art digital real-time simulator, industry standard DNP3 communication protocol and a cpu-based control centre running the FL algorithm.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Ensemble voting‐based fault classification and location identification for a distribution system with microgrids using smart meter measurements

et al. 2022

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“…Typical examples of KB methods are artificial neural networks (ANNs), support vector machines (SVMs), fuzzy logic, genetic algorithm etc. Among KB methods, ANN is the most common method used in power systems in the areas of event detection [11], fault location identification (FLI) [12], bad data detection, and distribution management [13]. ANN and SVM are combined to locate different types of faults in radial DN utilising measurements at substations, relays, and circuit breakers [14].…”

Section: Introductionmentioning

confidence: 99%