Abstract
The dynamic performance of smart (micro)grids depends on the proper selection of the controller gains and power-sharing parameters. This manuscript describes the control design to achieve a deadbeat desirable performance in terms of: i) Zero steady-state error. ii) Minimum rise time. iii) Minimum settling time. iv) Less than 2% overshoot/undershoot. This paper considers an Islanded microgrid system composed of two distributed generation (DG) units. Each DG unit includes three-phase pulse width modulation (PWM) inverter. The proposed controllers are proportional- integral (PI) type. The Controllers gains of the inverters and the Phase Locked Loop (PLL) parameters are designed to guarantee deadbeat dynamic performance in terms of minimal overshoot and system stability. The Particle Swarm Optimization (PSO) is used to tune the controller parameters of the current, PQ loops, and the PLL. The proposed controllers are compared with the traditional (Ziegler and Nichols), auto-tuned, and interior-point methods to shows the excellence of the proposed technique. Results authenticate and endorse the effectiveness of the proposed controllers and PLL design technique to achieve the desired deadbeat response of the study microgrid system.
Highlights
The dynamic performance of smartgrids depends on the proper selection of the controller gains and power-sharing parameters
The droop controls that are applied in synchronous generators and reactive power sharing can be used in the application of PWM inverter in distributed generation (DG) [5]-[8]
Remarkable enhancement is achieved by different computational intelligent techniques, as particle swarm optimization (PSO), bacteria foraging optimization (BFO), and ant colony optimization (ACO) [19]-[22]
Summary
Renewable energy systems such as photovoltaic arrays (PVs), fuel cells, microturbines, small hydrogeneration plants and wind energy have vast development in distributed generation (DG) systems. Many research papers show that traditional PI controllers cannot survive against load changes and disturbances in large microgrids [16]-[18] To overcome this problem, remarkable enhancement is achieved by different computational intelligent techniques, as particle swarm optimization (PSO), bacteria foraging optimization (BFO), and ant colony optimization (ACO) [19]-[22]. Remarkable enhancement is achieved by different computational intelligent techniques, as particle swarm optimization (PSO), bacteria foraging optimization (BFO), and ant colony optimization (ACO) [19]-[22] Such heuristic methods facilitate the design of MG control. To achieve good dynamic performance the maximum overshoot performance index is minimized within the tuning procedure This mini-max optimization problem is solved using Particle Swarm Approach. Concluding remarks on the proposed approach for controlling islanded microgrid system are specified
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