Microgrids islanding detection using Fourier transform and machine learning algorithm

• Published in: Electric power systems research Vol. 196; p. 107224
• Main Authors: Ezzat, Ahmed, Elnaghi, Basem E., Abdelsalam, Abdelazeem A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-07-2021
• Subjects: Feature extraction; Islanding detection; K-nearest neighbor; Machine learning; Microgrids; Feature extraction; Islanding detection; K-nearest neighbor; Machine learning; Microgrids
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2021.107224

•This paper proposes a methodology to identify the decision of microgrid islanding.•The methodology is two stages: feature extraction and classification stages.•Nine extracted features are calculated using DFT and symmetrical components.•KNN algorithm is used to classify the islanding inception.•Simulation and experimental data are used to assess the methodology with comparisons.
This paper proposes a methodology to identify the decision of microgrid islanding. This methodology is two stages: feature extraction and classification stages. In the feature extraction, instantaneous three phase voltage and current measurements are processed using discrete Fourier transform and the symmetrical components of voltage, current and the voltage times current of the second order harmonic are extracted. These nine extracted features are inputs to the second stage that is based on a machine learning algorithm called K-nearest neighbor technique (KNN). In KNN, a small part of the processed data is trained to get the predictor model that is used to the residual data to identify the islanding decision. A MATLAB simulation model is implemented to simulate the intentional and unintentional islanding events and a lab experiment is set up to make these islanding events practically. In addition, the impact of different types of distributed generation are studied to assess their impacts on the performance of the proposed methodology. Simulation and experimental results show that the proposed methodology has the ability to identify the correct islanding decision with the presence of noise with high accuracy and small detection time. In addition, the methodology has the capability to distinguish between islanding and non-islanding. The comparison with other algorithms shows a higher accuracy, dependability, security and a smallest islanding detection time.
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