Abstract
A hybrid Linear Quadratic Regulator (LQR) and Proportional-Integral (PI) control for a MicroGrid (MG) under unbalanced linear and nonlinear loads was presented and evaluated in this paper. The designed control strategy incorporates the microgrid behavior, low-cost LQR, and error reduction in the stationary state by the PI control, to reduce the overall energetic cost of the classical PI control applied to MGs. A Genetic Algorithm (GA) calculates the parameters of LQR with high-accuracy fitness function to obtain the optimal controller parameters as settling time and overshoot. The gain values of the classical PI controller were determined through the improved LQR values and geometrical root locus. When MG operates in the grid-tied mode under unbalanced conditions, the controller performance of the Current Source Inverter (CSI) of the MG is considerably affected. Consequently, the CSI operates in a negative-sequence mode to compensate for unbalanced current at the Point of Common Coupling (PCC) between the MG and the utility grid. The study cases involved the reduction of the negative-sequence percentage in the current at the PCC, mitigation of harmonics in the current signal injected by the MG, and close related power quality issues. All these cases have been analyzed by implementing an MG connected at the PCC of a low-voltage distribution network. A numerical model of the MG in Matlab/Simulink was implemented to verify the performance of the designed LQR-PI control to mitigate or overcome the power quality concerns. The extensive simulations have permitted verifying the robustness and effectiveness of the proposed strategy.
Highlights
Nowadays, fossil fuels are the primary source of energy worldwide, but the extensive use of this natural resource has caused an increase in the average temperature of the earth
Advances in the technology directed on the energy production area, environmental sustainability, and the appearance of small generation systems have opened new opportunities to research in Distributed Energy Resources (DERs)
K =1 where yk is the actual value and ŷk is the estimated behavior vector response of the proposed controlled model. This modest fitness function is really dependent on highly nonlinear parameters of the control interconnecting the grid, loads, and the power sources, which can be dynamically adjusted with the proposed Genetic Algorithm (GA) optimization algorithm
Summary
Fossil fuels are the primary source of energy worldwide, but the extensive use of this natural resource has caused an increase in the average temperature of the earth. Unbalanced loads connected at the PCC provoke that the Current Source Inverter (CSI) may supply three-phase unbalanced currents with components of a negative-sequence directly towards the MG This effect degrades the CSI performance and the energy quality index due to fluctuations in the inverter’s current and power signals. Such schemes guarantee the current equilibrium at the PCC, and an acceptable energy quality index according to the norm ruling the MGs. The significant contributions of this paper are to propose the GA with an effective and accurate fitness function that helps to calculate the controller design parameters and to hybridize the properties of PI and LQR controllers applied to the MG to provide the demands of energy.
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