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This paper presents the design and development of Dynamic Adaptability (DA) feature, added to a multi-objective Model Predictive Control (MPC) of a basic grid-tied inverter with a secondary function for switching frequency reduction. The classical MPC is enhanced with dynamically varying secondary function weightage in accordance with the demand of the operating condition for self-reliant change-over across grid-tied mode and grid-forming modes in an Inverter Dominated Microgrids (IDM) environment. A customized DA database has been developed through prior investigation on the IDM, considering possible operating conditions with diverse combinations of the sampling frequency and the secondary function weight. Investigations unveiled that there is a unique value of secondary weight for every operating condition, beyond which the reference tracking is lost. This necessitated the dynamic assignment of the weight to achieve a desirable inverter performance. The dynamic weightage assignment is accomplished by accessing the DA database through the priority signals for harmonic standards, inverter losses and reference tracking, respectively. The DA imparted MPC is tested for its mode transfer capability with desired performance under all operating modes of IDM in simulations with a 15 kVA grid-tied/forming inverter and the results are presented. Further, the experimental investigations are conducted with a 10 kVA inverter on a 5-bus laboratory scale IDM emulator along with hardware-in-loop. Both inter-mode and intra-mode transitions were initiated in the IDM and the inverter performance are presented here. Both the simulation and the hardware results yielded competent values of reference tracking accuracy and mode transition times.

This paper presents the design and development of Dynamic Adaptability (DA) feature, added to a multi-objective Model Predictive Control (MPC) of a basic grid-tied inverter with a secondary function for switching frequency reduction. The classical MPC is enhanced with dynamically varying secondary function weightage in accordance with the demand of the operating condition for self-reliant change-over across grid-tied mode and grid-forming modes in an Inverter Dominated Microgrids (IDM) environment. A customized DA database has been developed through prior investigation on the IDM, considering possible operating conditions with diverse combinations of the sampling frequency and the secondary function weight. Investigations unveiled that there is a unique value of secondary weight for every operating condition, beyond which the reference tracking is lost. This necessitated the dynamic assignment of the weight to achieve a desirable inverter performance. The dynamic weightage assignment is accomplished by accessing the DA database through the priority signals for harmonic standards, inverter losses and reference tracking, respectively. The DA imparted MPC is tested for its mode transfer capability with desired performance under all operating modes of IDM in simulations with a 15 kVA grid-tied/forming inverter and the results are presented. Further, the experimental investigations are conducted with a 10 kVA inverter on a 5-bus laboratory scale IDM emulator along with hardware-in-loop. Both inter-mode and intra-mode transitions were initiated in the IDM and the inverter performance are presented here. Both the simulation and the hardware results yielded competent values of reference tracking accuracy and mode transition times. [ABSTRACT FROM AUTHOR]

Renewable energy sources are an environmentally attractive idea, but they require a proper control scheme to guarantee optimal operation. In this work, we tune different controllers for an Interleaved Boost Converter (IBC) powered by a photovoltaic array using three metaheuristics: Genetic Algorithm, Particle Swarm Optimization, and Gray Wolf Optimization. We also develop several controllers for a second simulated scenario where the IBC is plugged into an existing microgrid (MG) as this can provide relevant data for real-life applications. In both cases, we consider hybrid controllers based on a Linear Quadratic Regulator (LQR). However, we hybridize it with an Integral action (I-LQR) in the first scenario to compare our data against previously published controllers. In the second one, we add a Proportional-Integral technique (PI-LQR) as we do not have previous data to compare against to provide a more robust controller than I-LQR. To validate our approach, we run extensive simulations with each metaheuristic and compare the resulting data. We focus on two fronts: the performance of the controllers and the computing cost of the solvers when facing practical issues. Our results demonstrate that the approach proposed for tuning controllers is a feasible strategy. The controllers tuned with the metaheuristics outperformed previously proposed strategies, yielding solutions thrice faster with virtually no overshoot and a voltage ripple seven times smaller. Not only this, but our controllers could correct some issues liaised to the IBC when it is plugged into an MG. We are confident that these insights can help migrate this approach to a more diverse set of MGs with different renewable sources and escalate it to real-life experiments.

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.

The distributed generation (DG) should supply continuous power to the local critical load, even in the condition of utility outage. And many efforts have been made for the three-phase inverter in the DG to transfer between the islanding mode and the grid-tied mode seamlessly. This paper proposes a transfer strategy based on indirect current control for the three-phase inverter in the DG, which is composed mainly of two cascaded feedback loops, i.e., capacitor voltage loop and external grid current loop, in synchronous reference frame. In the grid-tied mode, the grid current is controlled by regulating the capacitor voltage. When islanding happens, the reference of the voltage loop is fixed by the limiter. As the local load is located in parallel with the filter capacitor in the proposed strategy, the load voltage is always controlled by the capacitor voltage loop directly, and the quality of the load voltage can be guaranteed. Besides, the performance of the capacitor voltage loop is further improved by introducing an inner capacitor current loop. Moreover, the steady state and transient state of the system, including the output of the limiter, are comprehensively analyzed, and then the compensators and limiters are designed. Finally, the proposed control strategy is verified by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

Renewable energy sources are an environmentally attractive idea, but they require a proper control scheme to guarantee optimal operation. In this work, we tune different controllers for an Interleaved Boost Converter (IBC) powered by a photovoltaic array using three metaheuristics: Genetic Algorithm, Particle Swarm Optimization, and Gray Wolf Optimization. We also develop several controllers for a second simulated scenario where the IBC is plugged into an existing microgrid (MG) as this can provide relevant data for real-life applications. In both cases, we consider hybrid controllers based on a Linear Quadratic Regulator (LQR). However, we hybridize it with an Integral action (I-LQR) in the first scenario to compare our data against previously published controllers. In the second one, we add a Proportional-Integral technique (PI-LQR) as we do not have previous data to compare against to provide a more robust controller than I-LQR. To validate our approach, we run extensive simulations with each metaheuristic and compare the resulting data. We focus on two fronts: the performance of the controllers and the computing cost of the solvers when facing practical issues. Our results demonstrate that the approach proposed for tuning controllers is a feasible strategy. The controllers tuned with the metaheuristics outperformed previously proposed strategies, yielding solutions thrice faster with virtually no overshoot and a voltage ripple seven times smaller. Not only this, but our controllers could correct some issues liaised to the IBC when it is plugged into an MG. We are confident that these insights can help migrate this approach to a more diverse set of MGs with different renewable sources and escalate it to real-life experiments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]