A Novel Energy Management Scheme using ANFIS for Independent Microgrid (original) (raw)
Related papers
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.
Power Management Strategy Based on Adaptive Neuro Fuzzy Inference System for Ac Microgrid
International journal of multidisciplinary advanced scientific research and innovation, 2021
A power management strategy based on an adaptive neuro-fuzzy inference system is proposed to enhance the fuel economy of fuel cell-battery hybrid vehicle and increase the mileage of continuation of journey. The model of hybrid vehicle for fuel cell-battery structure is developed by electric vehicle simulation software ADVISOR. The simulation results demonstrate that the proposed strategy can satisfy the power requirement of four standard drive cycles and achieve the power distribution between fuel cell system and battery. The comprehensive comparisons with a power tracking control strategy which is widely adopted in ADVISOR verify that the proposed strategy has better validity in terms of fuel economy in four standard drive cycles. Hence, the proposed strategy will take important effect for designing advanced power management system of hybrid vehicle. V
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...
Energy Management Strategy Using ANFIS Approach for Hybrid Power System
Tehnicki vjesnik - Technical Gazette, 2020
Renewable Energy Sources are the promising hopes of upcoming years as they are abundant in nature and available free of cost. In addition to this, these sources are pollution-free which makes them a perfect substitute for fossil fuels. A Hybrid Power System (HPS) is one that has multiple power generating sources like Photo Voltaic (PV) system, Wind turbine, Fuel cell, etc. interconnected to supply electric power for varying demand requirements with / without energy storage backup. This paper concentrates on the automation for control and integration of Renewable energy systems Viz. PV system, Solid Oxide Fuel Cell (SOFC) with Nickel-Metal-Hydride (Ni-MH) battery together with a variable load. The Proposed HPS mainly focuses on the use of PV which is 100% clean in nature with no toxic emissions on power generation. Here, the solar photovoltaic system with power extracting maximum by algorithm used as the major supply contributor in the HPS to meet with variable load demands. If there is a deficit of power supply from PV, the power from the Ni-MH battery / SOFC is utilized to meet the varying load demands. On the other hand, if there is excess supply from PV system, the excess energy will be stored in the Ni-MH battery. For efficient supply-demand balance, the HPS makes use of various control strategies namely Proportional Integral (PI) and Adaptive Neuro Fuzzy Inference System (ANFIS).
International Journal of Advances in Applied Sciences (IJAAS), 2024
This study proposes intelligent control strategies for optimizing the grid integration of photovoltaic (PV) and wind energy in hybrid systems using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS control aims to enhance grid stability, improve power management, and maximize renewable energy (RE) utilization. The hybrid system's performance is evaluated through simulations, considering various environmental conditions and load demands. Results demonstrate the effectiveness of the proposed ANFIS-based control in dynamically adjusting the power output from PV and wind sources, ensuring efficient grid-connected operation. The findings underscore the potential of intelligent control strategies to contribute to the reliable and sustainable integration of RE into the grid.
International Journal of Electrical and Computer Engineering (IJECE), 2022
A novel adaptive-neuro fuzzy inference system (ANFIS) control algorithm-based smart grid to solve power quality issues is investigated in this paper. To improve the steady-state and transient response of the solar-wind and grid integrated system proposed ANFIS controller works very well. Fuzzy maximum power point tracking (MPPT) algorithm-based DC-DC converters are utilized to extract maximum power from solar. A permanent magnet synchronous generator (PMSG) is employed to get maximum power from wind. To maximize both power generations, back-to-back voltage source converters (VSC) are operated with an intelligent ANFIS controller. Optimal power converters are adopted this proposed methodology and improved the overall performance of the system to an acceptable limit. The simulation results are obtained for a different mode of smart grid and non-linear fault conditions and the proven proposed control algorithm works well.
Electrical Engineering & Electromechanics, 2023
Purpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result of rising demand for electricity, rapid fossil fuel exhaustion and the threat of global warming. However, due to their inherent intermittency, these sources offer low system reliability. So, a hybrid energy system that encompasses wind/photovoltaic/battery is implemented in order to obtain a stable and reliable microgrid. Both solar and wind energy is easily accessible with huge untapped potential and together they account for more than 60 % of yearly net new electricity generation capacity additions around the world. Novelty. A KY converter is adopted here for enhancing the output of the photovoltaic system and its operation is controlled with the help of a cascaded an adaptive neuro fuzzy interface system controller. Originality. Increase of the overall system stability and reliability using hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Practical value. A proportional integral controller is used in the doubly fed induction generator based wind energy conversion system for controlling the operation of the pulse width modulation rectifier in order to deliver a controlled DC output voltage. A battery energy storage system, which uses a battery converter to be connected to the DC link, stores the excess power generated from the renewable energy sources. Based on the battery's state of charge, its charging and discharging operation is controlled using a proportional integral controller. The controlled DC link voltage is fed to the three phase voltage source inverter for effective DC to AC voltage conversion. The inverter is connected to the three phase grid via an LC filter for effective harmonics mitigation. A proportional integral controller is used for achieving effective grid voltage synchronization. Results. The proposed model is simulated using MATLAB/Simulink, and from the obtained outcomes, it is noted that the cascaded adaptive neuro fuzzy interface system controller assisted KY converter is capable of maintaining the stable operation of the microgrid with an excellent efficiency of 93 %. References 21, table 1, figures 20.
Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic
Smart Grid and Renewable Energy, 2013
This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed 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 photovoltaic (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 system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.
Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid
IEEE Transactions on Smart Grid, 2016
In this paper, the behavior of a grid-connected hybrid AC/DC Microgrid has been investigated. Different Renewable Energy Sourcesphotovoltaics modules and a wind turbine generator-have been considered together with a Solid Oxide Fuel Cell and a Battery Energy Storage System. The main contribute of this work is the design and the validation of an innovative online-trained artificial neural network based control system for a hybrid microgrid. Adaptive Neural Networks are used to track the Maximum Power Point of renewable energy generators and to control the power exchanged between the Front-End Converter and the electrical grid. Moreover, a fuzzy logic based Power Management System is proposed in order to minimize the energy purchased from the electrical grid. The operation of the hybrid microgrid has been tested in the Matlab/Simulink environment under different operating conditions. The obtained results demonstrate the effectiveness, the high robustness and the self-adaptation ability of the proposed control system.
Optimization of Hybrid Power Systems Performance Based on Adaptive Neuro-Fuzzy Inference System
Journal of Science and Technology, 2016
Hybrid Power Systems (HPSs) is a promising solution for the shortages of electricity in several situations. However, HPSs are still facing several problems. These problems are the cost of electrical kilowatt-hour and repetitive breaking in the utility grid with existence varying loads. Besides the problem of non-optimal utilization of available renewable energy resources and the problems associated with the operation of large generators along small loads, which are the high cost of generation and the minimize in lifetime of the generator. This paper presents study and analyze the load profile and power system generation for a selected case. A fuzzy control system based on ANFIS has been proposed to optimize the performance of the HPS. The proposed system has ten ANFIS models, which linked to the outputs of the proposed control system. All models have been trained to achieve the minimum root mean square error (RMSE). The proposed system has been built and simulated using MATLAB.