Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

Abstract

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

Fuzzy Controller for a Voltage-Regulated Solar-Powered MPPT System for Hybrid Power System Applications

“…The MPP condition in Equation (7) was used in conjunction with the voltage regulation requirement for the voltage-regulated MPPT design in this study. Compared to the other artificial intelligent MPPT algorithms [22][23][24], the MPP condition of the proposed algorithm is well defined and the range of the MPP evaluation condition is confined to a finite interval (90°, 270°) that simplifies the design for determining the range of the input membership functions. Besides, the process of this algorithm in locating the operating point was more direct and would not require the use of variations of the input variables or search algorithms to predict operating point locations, allowing for a fast tracking speed.…”

“…A comprehensive power management function including the MPPT to manage and control the power flow among the hybrid system is critical for maximizing the efficiency of energy usage. Results using combination of neural network and fuzzy logic algorithms for energy management for hybrid power systems were reported in [22][23][24]. In this study, solar and battery hybrid power systems are considered.…”

“…PV system is the basic power source of the hybrid system. Analysis and design of power management system for hybrid power systems received great attention recently [20][21][22][23][24][25]. A comprehensive power management function including the MPPT to manage and control the power flow among the hybrid system is critical for maximizing the efficiency of energy usage.…”

A comparative study of PI, fuzzy‐PI, and sliding mode control strategy for battery bank SOC control in a standalone hybrid renewable system

“…1,2 Renewable hybrid systems usually are installed for reliability improvement of the customers, supplied from a weak network or electrification of rural or inaccessible areas. [4][5][6][7] When it comes to the storage units, because of the various advantages of Li-ion batteries, such as lower weight and higher energy density than other types, 8 Li-ion batteries are a suitable choice for hybrid renewable systems (HRS). As it is known, unpredictable behavior of PV/WT plants makes the employment of the support systems and storage units necessary.…”

A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables

“…In order to automatically adjust step sizes, variable step sizing algorithms based on adaptive and artificial intelligent techniques such as fuzzy logic and adaptive neuro-fuzzy system were assessed [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Fuzzy logic controllers are characterized by their ability to imitate human thinking.…”