Hybrid Islanding Detection in Microgrid With Multiple Connection Points to Smart Grids Using Fuzzy-Neural Network

• Published in: IEEE transactions on power systems 2017-07, Vol.32 (4), p.2640-2651
• Main Authors: Kermany, Saman Darvish, Joorabian, Mahmood, Deilami, Sara, Masoum, Mohammad A. S.
• Format: Article
• Language: English
• Subjects: Adaptive systems; Amplitudes; Artificial neural networks; Circuit breakers; Computer simulation; Discrete Wavelet Transform; Distributed generation; Electric power grids; Fuzzy logic; Fuzzy systems; Hybrid islanding detection; Inference; Islanding; Islanding technique; Learning theory; Mathematical analysis; microgrid; Microgrids; Monitoring; multiple connection points; Neural networks; Performance evaluation; Power system reliability; probability of islanding; Reliability; smart grid; Voltage measurement; Wavelet transforms
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2016.2617344

This paper presents a new hybrid islanding detection approach for microgrids (MGs) with multiple connection points to smart grids (SGs) which is based on the probability of islanding (PoI) calculated at the SG side and sent to the central control for microgrid (CCMG). The PoI values are determined using a combination of passive, active, and communication islanding detection approaches based on the utility signals measured at the SGs sides which are processed by discrete wavelet transform using an artificial neural network (ANN). If PoIANN is larger than the threshold value (indicating high possibility of islanding) then a more accurate approach based on fuzzy network is used to recompute it (PoI FU Z ZY ) where the fuzzy parameters are determined by an adaptive neuro-fuzzy inference system. In the proposed technique, an active islanding is only performed when PoI is high and the amplitudes of the disturb signals are proportional to PoI FUZZY . Furthermore, if the PoI is not correctly received by CCMG, two auxiliary tests will be performed in the MG side to detect islanding. These tests include an intentional passive islanding detection in a short preset time and an active islanding detection with disturb signals proportional to the calculated PoI. Detailed simulations are performed and analyzed to evaluate the performance of the proposed method.