Islanding detection in a distributed generation integrated power system using phase space technique and probabilistic neural network

• Published in: Neurocomputing (Amsterdam) 2015-01, Vol.148, p.587-599
• Main Authors: Khamis, Aziah, Shareef, Hussain, Mohamed, Azah, Bizkevelci, Erdal
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Artificial neural network; Computer science; control theory; systems; Connectionism. Neural networks; Data processing. List processing. Character string processing; Distributed generation; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Islanding detection; Memory organisation. Data processing; Non-detection zone; Operation. Load control. Reliability; Phase space; Power networks and lines; Simulation; Software; Wavelet transform
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2014.07.004

The high penetration level of distributed generation (DG) provides numerous potential environmental benefits, such as high reliability, efficiency, and low carbon emissions. However, the effective detection of islanding and rapid DG disconnection is essential to avoid safety problems and equipment damage caused by the island mode operations of DGs. The common islanding protection technology is based on passive techniques that do not perturb the system but have large non-detection zones. This study attempts to develop a simple and effective passive islanding detection method with reference to a probabilistic neural network-based classifier, as well as utilizes the features extracted from three phase voltages seen at the DG terminal. This approach enables initial features to be obtained using the phase-space technique. This technique analyzes the time series in a higher dimensional space, revealing several hidden features of the original signal. Intensive simulations were conducted using the DigSilent Power Factory® software. Results show that the proposed islanding detection method using probabilistic neural network and phase-space technique is robust and capable of sensing the difference between the islanding condition and other system disturbances. •A new method to extract the hidden features in power system voltage signals is introduced using phase space technique.•Phase space features and probabilistic neural network is proposed to intelligently detect islanding condition.•Results show phase space based PNN can provide significant improvement in performance with respect to conventional islanding detection algorithms.