A fuzzy sliding mode controller for power quality improvement of solar PV interleaved parallel inverters in a micro-grid

Srinivasan, Sivaranjani; Arivukkannu, Ezhilarasi; Ramaswamy, M.

doi:10.1080/01430750.2022.2095531

Cited by 1 publication

(1 citation statement)

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“…Furthermore, the system is not limited to a single operating point. This condition allows for a wide control range in AC systems, where the signals of the current and voltages feature positive and negative values in the time function [3][4][5][6][7][8][9]. SMC research efforts are focused on DC/DC converters, where references and signals are constant over time, or AC systems that are linearized around an operating point.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sliding-Mode Control of Semi-Bridgeless Boost Converters Considering Power Factor Corrections

Ortiz-Castrillón,

Saldarriaga-Zuluaga,

Muñoz-Galeano

et al. 2023

Energies

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Sliding-mode control (SMC) is a robust technique used in power electronics (PE) for controlling the behavior of power converters. This paper presents simulations and experimental results of an optimal SMC strategy applied to Semi-Bridgeless Boost Converters (SBBC), which includes Power Factor Correction (PFC). As the main contribution, the optimal coefficients of the SMC strategy are obtained using two metaheuristic approaches, namely the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The main objective is to obtain the sliding coefficients that ensure the best converter response in terms of the input current and output voltage, both during start-up and under disturbances (including changes in load, source, and references). The fitness function to be minimized includes two coefficients, namely the Integrative Absolute Error (IAE) and the Integral Time Absolute Error (ITAE), for both the input current and output voltage. These coefficients measure the converter’s effort to follow the control references. The IAE penalizes errors during start-up, whereas the ITAE penalizes errors in the steady state. The tests carried out demonstrated the effectiveness of the GA and PSO techniques in the optimization process; nonetheless, the GA outperformed the PSO approach, providing sliding coefficients that allowed for a reduction in the input current overshoot during start-up of up to 24.15% and a reduction in the setting time of the output voltage of up to 99%. The experimental results were very similar when tuning with the GA and PSO techniques; nevertheless, tuning with the GA technique produced a better response in the face of disturbances compared to the PSO technique.

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