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Renewable energy, particularly wind energy, is expected to contribute significantly to the overall power generation. Induction machines are extensively used as generators in wind power generation because of their multiple benefits, such as robustness, reliability, and low cost and maintenance. However, due to the reactive power demand from the system to which they are connected, this type of generator brings new problems related to power quality, generally consisting of voltage regulation and reactive power compensation. These problems may cause voltage drops and dynamic instability. This study presents a metaheuristic method to attain a microgrid system with an optimal distribution based on its different constraints. The numerical model of an induction generator constructed in MATLAB/Simulink was used, and the simulation results obtained demonstrate the efficacy of the proposed metaheuristic technique. [ABSTRACT FROM AUTHOR]

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems. [ABSTRACT FROM AUTHOR]

Mini Hydropower Plants (MHPPs) are increasingly popular for rural electrification in developing nations due to their ecologically friendly operation. However, constant load fluctuation in these facilities poses a speed control issue. The mechanical, hydraulic governor, commonly used to face this challenge, cannot provide the best speed control due to its mechanical component system. Thus, an electrohydraulic PIDbased governor is proposed to control the frequency and speed of MHPPs in a distribution network. This governor's suitability for regulating the system's frequency in response to significant load variations within the distribution network is going to be determined in this study. The small hydropower plant and distribution system are modeled using the PSCAD software. A comparison between the mechanical hydraulic governor and the electro-PID governor was conducted by analyzing load fluctuations between 5% and 20%. The electro-PID governor responded faster and more actively to load connections and disconnections than the mechanical hydraulic governor, as the latter reduces large overshoots and undershoots, which can be dangerous and damaging to equipment. The electro-PID governor also helps to maintain a stable frequency within acceptable limits, ensuring smooth operation and minimizing the risk of system failures or disruptions. [ABSTRACT FROM AUTHOR]

Wind generation system is becoming increasingly important as renewable energy sources due to its advantages such as low maintenance requirement and mainly it does not cause environmental contamination. This paper presents the improvement procedure of the transient state and the regulation of the output frequency by adjusting the terminal capacitor. The aim is to provide frequency control of a self-excited induction generator in remote site using different strategies which are based on the adjustment of the reactive power at the outputs of a three-phase self-excited induction generator. A thyristor controlled reactor and a switched resistive load will be used to control reactive power. The proposed particle swarm optimization algorithm technique, location of the thyristor controlled reactor device, and parameter value are optimized simultaneously. The results obtained by this strategy will be compared with those provided by the use of Fuzzy Logic Controller. This study will be conducted through the analysis of the frequency in the steady state and transient case using a developed induction generator numerical model built using MATLAB/Simulink. Simulation and experimental results will be exposed and analyzed considering a resistive inductive load on a laboratory test bench.

This document is a list of academic research papers related to optimal power flow (OPF) in power systems. The papers cover various topics, including the use of different optimization algorithms, the integration of renewable energy sources, and the application of flexible AC transmission system (FACTS) devices. The papers come from a diverse range of authors and institutions, including researchers from Pakistan, China, Saudi Arabia, and other countries. The document provides a valuable resource for library patrons conducting research on OPF and related topics in power systems. [Extracted from the article]

The increasing use of wind energy generators in mountainous areas is an important necessity in establishing and enhancing renewable energy. This procedure has created a need for reliable and efficient methods for detecting anomalies remotely basing on computational sensor nodes optimization.These methods are useful for detecting deviations in the generator's behavior that may indicate a potential failure, allowing for prompt maintenance, minimizing the intervention costs, and ultimately reducing downtime and improving the generator's overall reliability compared to previous techniques. In this context, this paper presents the principal smart anomaly detection methods applied to wind energy generators. It covers different techniques, including machine learning algorithms, statistical approaches, and deep learning models. Furthermore, this paper aims to perform the optimization of computational sensor nodes (CSN) as new strategy, instead of basic wireless sensor network, in smart fault detection. This paradigm is growing very rapidly in varied fields. One of the promising application fields of CSN is wind energy generators, where this technology offers important support. This technology will allow for precise resource management. The experimental results show that the proposed approach can effectively detect anomalies in wind turbine data, and it outperforms traditional statistical methods with 99 % recognition rate against 93 % in the best way.. [ABSTRACT FROM AUTHOR]