A novel efficient adaptive-neuro fuzzy inference system control based smart grid to enhance power quality (original) (raw)
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International Journal of Science Technology & Engineering
This paper describes and performance an adaptive neuro-fuzzy inference system (ANFIS) based energy management system (EMS) of a grid-connected hybrid system for smart grid application. The hybrid system consists of wind turbine (WT) and solar photovoltaic (PV) panels as a primary energy sources. The rectified wind output and solar panel output is given to LUO converter for boost up the DC voltage in order to connect them to a central DC grid. Then, the power has taken from the DC grid and it is given to the AC smart grid system through H-bridge inverter. The smart grid system consists of new bidirectional intelligent semiconductor transformer (BIST), high frequency ac-dc rectifier and low voltage dc-dc converter hybrid switching dc-ac converter. The smart grid system satisfied the load requirement and in case if the demand is low it will return the excess power to the grid also. On the whole, this proposed system utilizes the best use of solar and wind energy system so that the power can be generated at any time and satisfied the load demand.
2016
Copyright © 2013 Emad M. Natsheh, Alhussein Albarbar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The pro-posed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photo-voltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid s...
Design and Performance Analysis of Grid Connected Solar Power System by Fuzzy Control Algorithm
International Journal of Recent Technology and Engineering
In this paper we propose the fuzzy logic controller based solar fed grid via various loads. Normally present situation solar power play a vital role to meet the load demand. Solar power is the free from pollution and cost free fuel so in this paper I propose the solar based grid integrated framework, it consist of dc-dc boost converter, 3-phase voltage source inverter and fed to grid via various loads. MPPT based fuzzy logic controller is used to obtain the maximum power from the solar. But our proposed solar generation is intermittent in nature so before supplying this power to the load as well as grid we can control and enhance the power quality by utilizing FLC. This FLC control scheme effectively controls the harmonics developed in the grids. Current harmonics and Voltage flickers developed in the PV integrated grid due to non linear loads and critical loads present in the network. The proposed system is verified in MATLAB/SIMLINK.
Comparative Study of ANN-GA and Fuzzy Controller for Photovoltaic System in the Grid Connected Mode
Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. Consequently, it is important to track the generated power of the PV system and utilize the collected solar energy optimally. This paper proposes an integrated offline genetic algorithm (GA) and artificial neural network (ANN) to track the solar power optimally based on various operation conditions due to the uncertain climate change. Data are optimized by GA and then these optimum values are used in neural network training. The obtained results show minimal error of maximum power point (MPP), optimal voltage (Vmpp) and superior capability of the suggested method in the maximum power point tracking (MPPT). The simulation results are presented by using Matlab/Simulink and show that the neural networkGA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the...
Simulation of Solar Based Smart Grid System Using Artificial Neural Network and Fuzzy Controller
IJEER , 2023
To promote the economy and reliability of the energy trading systems, the use of interconnected smart grids is encouraging. A distributed energy management plan for the interconnected operation of the smart grid that maximizes the resident intake of renewable energy is required during operation. On the client side, possibilities and actions are being discussed in the research papers to incorporate the renewable energy sources. In this paper, the use of Artificial Intelligent Techniques to manage energy or power supply to meet the electricity demand of customers is illustrated. Simulation has been done using wind and solar power supply to manage the load demand for the client side in a smart grid system. Smart Grid has been simulated for all these energy sources to be used with the forecasted electric power requirements. All the required energy demand can be identified from the forecast data, allowing smart grids to deliver better results. For this purpose solar panels are first used in smart grids with the help of artificial neural networks and fuzzy controllers so that load shifts can be done easily and efficiently. Simulation work has been done in MATLAB/Simulink.
Contribution to fuzzy logic control of photovoltaic system connected to the electric grid
2016
This research work present a new intelligent control based on the fuzzy logic of the photovoltaic system connected to the electric grid. The aim of the control strategy proposed is to optimize the power produced by the photovoltaic modules and to assure a real time control of the energy injected. The fuzzy logic controller is synthetized to control the conversion power stages (Converter DC-AC and Converter DC-DC) of the PV system under a abrupt variation of the solar irradiation and ambient temperature. The digital simulation is made under the Matlab/Simulink environment and allowed us the validation of the suggested strategy. Actually, the simulation results revealed an improvement of the dynamic performance of the PV system and an efficient real time control of the energy injected. Consequently, the control strategy allows us to reduce the cost and to increase the productivity of the PV system connected to electric grid.
American Journal of Energy Engineering, 2021
Many algorithms have been used to track the MPP in a PV generator. Although these algorithms have proved their worth, the fact remains that they still have limits in terms of stability, response times and significant presence of oscillations, especially for sub-Saharan conditions where the climate variation is very sudden and has a considerable impact on the power delivered at the generator output. In this article, the objective is to develop a maximum power point tracking (MPPT) controller based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve the performance of the Felicity Solar photovoltaic module FL-M-160W submitted to varying environmental conditions. The specifications of the FL-M-160W module are used to analyze and model the PV generator and boost converter located between the panel and the load in Matlab / Simulink. After the experimental tests, a database was set up to develop the neurofuzzy controller. The proposed ANFIS model was tested and validated under the Matlab / Simulink environment and then inserted into the PV system. The optimum voltage Vopt provided by this model is compared to the reference voltage Vpv provided by the PV generator and the error obtained is used to adjust the duty cycle of the DC-DC boost converter. After simulations, the results obtained reveal a good performance of the ANFIS controller compared to conventional P&O, InC and HC controllers in terms of stability, convergence speed, accuracy, robustness, and response time even under unstable environmental conditions with an efficiency of about 98%.
American Journal of Engineering and Applied Sciences, 2023
Rooftop solar panels are a strategy for achieving Indonesia's renewable energy goals, but their non-linear characteristics make them difficult to control, especially in the face of extreme weather changes. An effective controller is needed to optimize the power output of solar panels. This study proposes a Maximum Power Point Tracking (MPPT) controller based on an Adaptive Neural network Fuzzy Inference System (ANFIS) to address this control problem. The capacity of the rooftop solar panels is 3,430-Watt peak (Wp) and they are connected to a 220-Volt (V) grid system. The system is designed, simulated, and analyzed using the Simulink model. The proposed ANFIS MPPT control for rooftop solar panels is compared to Perturb and Observe (P&O) MPPT and no MPPT systems. The simulation results show that in rapid changes in irradiation and extreme temperature, the efficiency of MPPT based on ANFIS is better than P&O MPPT and no MPPT by 0.4523 and 0.1115%, respectively.
Adaptive Fuzzy Controller Design for Solar And Wind Based Hybrid System
International Journal of Engineering & Technology, 2018
Renewable Energy Resources plays an active role in standing against global warming and reduce the use of conventional energy sources. Hybrid systems formed by combining the renewable energy sources are efficient relatively. The intent of this paper is to furnish endurable power for frontier and far-off places with hybrid-system of architecture. The intended system embodying DFIG and solar PV based wind turbine. In solar systems, control mechanism is essential for improving the performance. This paper proposes a method of incremental conductance approach based MPPT Adaptive Fuzzy Logic Controller for grid connected PV system which is composed of a boost converter and a three phase inverter. Adaptive Fuzzy Logic Controller provides fast response and better %THD compared to Fuzzy and PI controllers. In solar system, MPPT will magnify solar output power value. The DFIG has two controllers Grid-Side Control (GSC) and Rotor-Side Control (RSC). The rated rotor speed and DC-link voltage a...
Improve Renewable Energy Output Based on Fuzzy Logic Control
2015
In order to utilize solar energy effectively, it is necessary to study on Maximum Power Point Tracking (MPPT) in photovoltaic power generation system. In this paper, single stage photovoltaic power generation system is studied and the mathematical model of photovoltaic array is established under any arbitrary environment. Due to the nonlinear output characteristic of photovoltaic array, fuzzy control is introduced to realize MPPT. It is presented perturb and observe (P&O) of duty cycle for fuzzy control in MPPT control strategy. The simulation is carried out based on the proposed algorithm. Compared with the conventional duty cycle of P&O method, it can track the maximum power point quickly and accurately [1,3].