An adaptive approach for optimal tuning of a SMC for an automated voltage regulator system is displayed in this study. The approach is centered on hybrid of the GA and MOPSA. In addition, unique objective functions for the controller's parameter optimization are suggested. The performance of the resulting perfect sliding mode controller is confirmed by comparing it to controllers adjusted using various techniques that have been published in the literature. The simulation outcomes indicate that controllers tuned with the projected MOPSO and GA algorithms outperform controllers tuned with existing methods. In addition, a comparison study is performed to select the best controller for use in AVR systems. The suggested algorithm's major benefit is a considerable boost in convergence speed. With step changes and step load modifications in input wind power, the system model with built-in intelligent controller is generated in MATLAB/SIMULINK. The benefits of the recommended intelligent control algorithm are confirmed by comparing the outcomes of the sliding mode controller and the projected MOPSO self-tuned controller. The findings show that the hybrid Wind/PV system's reactive power adjustment capabilities. When used in conjunction with BES, it is extremely successful in optimising the voltage profile although providing active energy to local load.
This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.
This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.
An advanced model is proposed for grid connectivity of an interconnected network consisting of a charging station for electric automobiles. To automate the discharge procedure of charging/ the battery energy storing system, a wind network, the photovoltaic system, and the battery energy storing system is developed to efficiently increase the consumption degree of solar and wind energy sources and create renewable inner-city capacity. On the basis of DC bus architecture, the power design was planned such that buffered storage systems and renewable energy resources can be incorporated. The proposed optimal control algorithm uses the Swarm Optimization Algorithm consists of Multi-Objective Particle, developed for electric vehicles charging or discharge behaviors to minimize the overall actual energy loss and increase the integration of EVs with power networks due to the efficiency and economy of network activity, taking into account the economic issue and the satisfaction of consumers, the voltage limits and the parking availability pattern. To test the proposed EV charging strategy, simulation studies based on efficiency, and assessed major energy fluxes within the device. Energy management approaches have also been developed to optimize the power requirements and charging times of various electric vehicles. Results suggest that proposed model will substantially reduce the power grid’s operational costs while meeting the charging criteria of the customer. Improved performance on global search capabilities is also checked, as is the desired outcome of enhanced particle swarm optimization algorithm. The findings show that the new approach is in a position to prepare EV charging times optimally, taking into account electronic knowledge and uncertainty.
Alternative renewable energy structures for example hydro turbine generators can be utilised to replace or rise in the efficiency of energy distribution infrastructure in remote communities. A small hydro turbine linked to grid divides the load into current and electricity. The voltage amplitude in the energy distribution structure will be changed as a result of the power quality problem, and this will have a direct influence on the electric load. This study introduces the PV-STATCOM, a novel smart inverter that can be used to control a solar inverter as dynamic reactive power compensator (DRPC). The recommended photovoltaic STATCOM can be utilised to offer voltage regulate for serious structure demands. For the night, the whole inverter capacity is utilised for STATCOM operations. The smart inverter temporarily disables its real power generating function during a large system outage throughout the day and makes its whole inverter capacity accessible for STATCOM operation. This research examines the stability of the voltage control structure`s excitation in the Micro Hydro Power Plant. The MOPSO algorithm may be used to regulate the Permanent Magnet Synchronous Machine and control the voltage on the direct current linked part of system. The system will most probably be unstable if the exact definition of system parameters is uncertain. To present the parameter specifications for the stable structure, the DC-link control system is modelled, theoretically assessed, and simulated. This paper proposes the use of a particle swarm optimization centred SVC controller for reactive power optimization and adjustment in a separated hybrid system with micro hydro and wind diesel. The small linear signal model of the hybrid micro hydro Diesel wind model is investigated under various loading scenarios. The SVC controller is compared to the GA-based controller and optimised using the PSO approach. The purpose of study is to employ STATCOM for reactive power compensation in order to increase energy structure's dependable operating limit. It also tries to reduce voltage variations caused by renewable energy sources' variable nature. To obtain an acceptable outcome, the proper modification of Proportional–Integral parameters in STATCOM is conducted consequentially centred on BFA and GA. The STATCOM control circuit's PI controller's settings have been optimised. This article discusses the optimization and adjustment strategies for PID controllers in a STATCOM based circuit for PV-Micro hydro hybrid system voltage stability.
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