Experimental analysis of genetic algorithm-enhanced PI controller for power optimization in multi-rotor variable-speed wind turbine systems.

The direct power control (DPC) algorithm is one of the most popular linear techniques used to implement notable controllers, known for their simplicity and fast dynamic response. However, this approach has drawbacks that cause a decrease in the current quality and disturbances in the network. Therefore, this experimental work presents a simple and efficient solution that uses a proportional-integral regulator based on a genetic algorithm to regulate the power quality.

Arithmetic optimization based MPPT for photovoltaic systems operating under nonuniform situations.

Photovoltaic (PV) modules may encounter nonuniform situations that reduce their useable power volume, causing ineffective maximum power point tracking (MPPT). Moreover, due to the incorporation of bypass diodes, power-voltage (P-V) graph has multi-peaks when each component of the module receives different solar irradiation. This paper proposes a solution to this problem using an arithmetic optimization algorithm (AOA) for MPPT in PV systems operating in nonuniform situations.

Empowering data-driven load forecasting by leveraging long short-term memory recurrent neural networks.

The integration of renewable energy sources has resulted in an increasing intricacy in the functioning and organization of power systems. Accurate load forecasting, particularly taking into account dynamic factors like as climatic and socioeconomic impacts, is essential for effective management. Conventional statistical analysis and machine learning methods struggle with accurately capturing the intricate temporal relationships present in load data.

A comprehensive analysis of advanced solar panel productivity and efficiency through numerical models and emotional neural networks.

This study presents an in-depth analysis and evaluation of the performance of a standard 200 W solar cell, focusing on the energy and exergy aspects. A significant research gap exists in the comprehensive integration of numerical models with advanced machine-learning approaches, specifically emotional artificial neural networks (EANN), to simulate and optimize the electrical characteristics and efficiency of solar panels. To address this gap, a numerical model alongside a novel EANN was employed to simulate the system's electrical characteristics, including open-circuit voltage, short-circuit current, system resistances, maximum power point characteristics, and characteristic curves.

Enhanced direct torque control based on intelligent approach for doubly-fed induction machine fed by three-level inverter.

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) based on 24 sectors direct torque command (DTC) for a doubly-fed induction machine (DFIM) by using a 3-level neutral point clamped inverter. The DTC approach is used in this paper with 24 sectors based on the ANFIS regulator to minimize the torque fluctuations, flux fluctuations, and stator stream THD (Total Harmonic Distortion) of the DFIM drive. The composed technique is accomplished by replacing the hysteresis controllers of the flux and torque with the ANFIS controller.