access icon free Minimum-features-based ANN-PSO approach for islanding detection in distribution system

© The Institution of Engineering and Technology
Received 04/02/2016, Accepted 29/04/2016, Revised 11/04/2016, Published 11/05/2016

Islanding detection is important for the protection of any distribution system connected to distributed energy resources (DER's). This study proposes an intelligent islanding detection technique based on artificial neural network (ANN) that employs minimal features from the power system. The accuracy of the trained ANN is improved by optimising the learning rate, momentum and number of neurons in the hidden layers using evolutionary programming (EP) and particle swarm optimisation (PSO). The performance comparison between stand-alone ANN, ANN-EP and ANN-PSO in the form of regression value is performed to obtain the best feature combination for an efficient islanding detection. The proposed technique is tested on- and off-line for various islanding and non-islanding events. The simulation results indicate that the proposed technique can successfully distinguish islanding from other non-islanding events such as load variation, capacitor switching, faults, induction motor starting and DER tripping.

Inspec keywords: neural nets; regression analysis; power distribution reliability; power generation reliability; power engineering computing; induction motors; power distribution faults; distributed power generation; particle swarm optimisation

Other keywords: artificial neural network; nonislanding event; distributed energy resources; fault detection; induction motor starting; load variation; DER tripping; stand-alone ANN; islanding event; minimum-feature-based ANN-PSO approach; regression value; capacitor switching; particle swarm optimisation; ANN-EP; DER; evolutionary programming; distribution system; intelligent islanding detection technique

Subjects: Distribution networks; Distributed power generation; Other topics in statistics; Optimisation techniques; Power engineering computing; Asynchronous machines; Neural computing techniques; Other topics in statistics; Optimisation techniques; Reliability

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