Modified particle swarm optimization algorithm based power flow controller for grid-connected microgrids

• Main Author: Khan, Ismail Akbar
• Format: Thesis
• Language: English
• Published: 2018
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/79325/1/IsmailAkbarKhanMFKE2018.pdf; http://eprints.utm.my/id/eprint/79325/

Due to the fast depletion of fossil fuels and environmental concerns, the Microgrids (MGs) have emerged as an alternate source of electrical power generation. Renewable power sources like wind turbines, microturbines, solar Photo-voltaic (PV) and fuel cells connected together in a local grid to form a MG system and provide energy to communities living too far from the utility grid. In spite of the vast benefits of employing MGs in islanding or connecting them with the existing utility grids, they create some serious power quality issues. This is mainly due to the “plug and play” capability of connected DGs and loads within MGs and the use of a non-linear power electronic interface like voltage source inverter or converter used to integrate DGs with the MG. These power quality issues like high harmonic distortion, increased voltage and frequency flickers, high current transients and ineffective active and reactive power regulation limits the wide applicability of these small scale distributed MGs. Therefore, an optimal power control strategy is required to smoothly integrate these DGs within MG and into the main grid with desired active and reactive power sharing ratio and minimized harmonic distortion. This research work is carried out to develop an optimal power controller for the grid connected MGs in order to regulate the active and reactive power flow between the MG and the utility grid according to the desired setpoint with enhanced power quality. Furthermore, in order to improve the performance of the proposed controller under different operating conditions, its gain parameters (Kp and Ki) are optimally selected by using Modified Particle Swarm Optimisation (MPSO) algorithm. Moreover, to validate the effectiveness of the proposed MPSO based controller, its performance is compared with that of the conventional PSO based controller for the same operating conditions. As a result, MPSO provided improvement of 21.6% in overshoot, in 24.8% rise time and 15% in settling time has been obtained. Furthermore, the proposed controller provides an excellent response in regulating active and reactive power along with good power quality, in particular when the high DG penetration is required.