Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine

Abstract

Microgrid is a low voltage electrical network with distributed generations, energy storage devices and controllable loads. This paper utilizes artificial neural network (ANN) to predict the optimum voltages in order to extract the maximum power and increment the efficiency of photovoltaic system. In this regard, the optimum voltages are achieved by the genetic algorithm (GA). Then these optimum values are used in ANN method. The results of ANN-GA is compared with the other methods that verified the proposed method with high accuracy which can track the maximum power point (MPP) under different insolation and temperature circumstances and also, meet the load demand with less fluctuation around the MPP.; also it can increase the convergence speed to achieve the MPP. As well as, the evaluation of fuzzy logic controller (FLC) in comparison with the PI controller in pitch angle of wind turbine (WT) is carried out. In order to control the output power of wind turbine, by implementing the wind speed and active power as inputs of FLC, it has faster responses, smoother power curves, less oscillation than aforementioned methods which lead to improve the dynamic responses of WT. The models are developed and applied in the Matlab/Simulink program.

Power management and control strategies for off-grid hybrid power systems with renewable energies and storage

“…Observe [16]- [18] and Incremental Conductance [19]- [21] are the most commonly used in MPPT algorithms.…”

“…In this paper, the MPPT algorithm is based on FLC which provides a simple design methodology and does not require information about the exact model of the system. Similar FLC-based MPPT controllers have been applied in [16], [21]- [27].…”

Operations of a microgrid with renewable energy integration and line switching

“…Maknouninejad et al [8] proposed a multilevel smartgrid control approach using interconnected microgrids, obtaining optimal operations for the DER to increase the overall smartgrid efficiency. Rezvani et al [19] developed an algorithm that combines Artificial Neural Networks and Genetic Algorithms to find the maximum power point of RES integrated into microgrid. Heymann et al [20] compared interior-point algorithm, a dynamic programming, and mixed integer linear programming (MILP) for islanding mode microgrid management.…”

Enhanced Microgrid Dynamic Performance Using a Modulated Power Filter Based on Enhanced Bacterial Foraging Optimization

“…Recreation analysis approves that the frequency control of the MG framework has been upgraded essentially with the utilization of a FGSPID controller when contrasted with the customary controller. Reference [9] presented an upgrade of MG dynamic reactions under severe conditions utilizing the simulated neural system for quick changes of photovoltaic radiation and FLC for wind turbine. It uses an artificial neural network (ANN) to control the outcomes of DGs, by actualizing FLC; it has quicker reactions, smoother control actions, and less disturbance than previously mentioned techniques which prompt enhancement of the dynamic reactions.…”

“…Enhanced bacterial foraging optimization (EBFO) principally relies upon breeding between PSO with BFO systems. The EBFO strategy assembles powerful PSO with BFO which directs us to the effective seeking method by exceptionally quickened exact joining [9][10][11]. EBFO realizes remarkable impacts, with effective universal property that is independent in solving different numerical optimization problems that lead to positive options of EBFO application and to properly select and control MPF parameters.…”