2024 : 5 : 19
Mahyar Abasi

Mahyar Abasi

Academic rank: Assistant Professor
ORCID: https://orcid.org/0000-0001-5228-6803
Education: PhD.
ScopusId: AAM-8891-2020
Faculty: Engineering
Address: Arak University
Phone: 08632625099

Research

Title
Design of New Intelligent Islanding Detection Scheme in Multi-Machine Power Systems to Prevent Wide-Area Blackouts
Type
Presentation
Keywords
wide-area measurement, islanding, adaptive neuro-fuzzy interface system, particle swarm optimization
Year
2022
Researchers Hojatollah Makvandi ، Mahyar Abasi ، Mahmood Joorabian ، Sajad Soltani ، Javad Ebrahimi ، Zeinab Sabzian Molaee

Abstract

The present study discusses wide-area instability detection and proper execution time of the controlled islanding process by introducing a novel approach based on an adaptive neuro-fuzzy interface system (ANFIS). Besides that, a particle swarm optimization (PSO) method is also adopted to find the optimal values of parameters of the ANFIS so that accuracy is enhanced regardless of the loading condition or the type of disturbance. Moreover, a parallel ANFIS network (P-ANFIS) has been incorporated to take into account various types of stability margins. In this approach, a unique ANFIS is assigned for each of the two adjacent areas. Several offline studies and tests are also carried out so that the ANFIS is properly trained and is capable of on-time and accurate reactions using the signals received from all over the network. Wide-area measurement system (WAMS) has also been employed to monitor the parameters in real-time. By doing this, the suggested P-ANFIS can perform stability analysis during any unwanted unstable oscillations; thus, the command to start the islanding process is released. To test the proposed method, it is applied to the standard IEEE 39-bus system and the speed and accuracy of the method are demonstrated at different disturbances. According to the obtained results, the ANFIS-based method can suitability detect the proper conditions of islanding, both physically and from an execution time aspect.