Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function

Hindawi International Journal of Photoenergy Volume 2017, Article ID 8387909, 15 pages https://doi.org/10.1155/2017/8387909 Research Article Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function Faa-Jeng Lin, Su-Ying Lu, Jo-Yu Chao, and Jin-Kuan Chang Department of Electrical Engineering, National Central University, Chungli 320, Taiwan Correspondence should be addressed to Faa-Jeng Lin; Received 26 May 2017; Accepted 8 August 2017; Published 14 November 2017 Academic Editor: Francesco Riganti Fulginei Copyright © 2017 Faa-Jeng Lin et al. 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. An intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF) is proposed in this study. First, a photovoltaic (PV) power plant with a battery energy storage system (BESS) is introduced. The BESS consisted of a bidirectional DC/AC 3-phase inverter and LiFePO4 batteries. Then, the difference of the actual PV power and smoothed power is supplied by the BESS. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. Furthermore, the three-phase output currents of the PV power plant are converted to the dq-axis current components. The resulted q-axis current is the input of the PFNN-AMF power smoothing control, and the output is a smoothing PV power curve to achieve the effect of PV power smoothing. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the PV power smoothing control using PFNN-AMF. In addition, a personal computer- (PC-) based PV power plant emulator and BESS are built for the experimentation. From the experimental results of various irradiance variation conditions, the effectiveness of the proposed intelligent PV power smoothing control can be verified. 1. Introduction Since the environmental pollution problems of the world are deteriorated in recent years, developing clean energy sources and protecting the environment become the major issues of the modern world. Thus, the development and application of clean renewable energy sources, such as solar, wind, tides, fuel cell, and geothermal, are getting more and more worldwide attention. Among these renewable energy sources, solar power will gradually be dominant due to its availability and reliability. Owing to the price of the photovoltaic (PV) system declines of around 75% in less than 10 years has made the solar power more cost competitive in various countries and market segments; the cumulative installed capacity of the PV in the world has been reached to 178 GW in the end of 2014 [1]. European Photovoltaic Industry Association (EPIA) predicts that the worldwide total installed capacity of the PV system in 2019 could reach between 396 and 540 GW with the highest probability scenario being around 450 GW. Meanwhile, the government of Taiwan has decided to raise the official PV installation target from 13 GW to 20 GW in 2025. In other words, the global cumulative PV capacity will have explosive growth in the next decade. Furthermore, the renewable energy source- (RES-) based distributed energy sources are normally connected to the grid using power electronics. Therefore, the development of a grid-connected PV system including DC/DC converter and AC/DC inverter considering the ancillary service of power quality is important for solving the issues of environmental protection [2]. The intermittent nature of the output power from RESs becomes a serious concern for the stability of the grid particularly with increasing RES penetration and a high percentage of instantaneous demand being supplied by RESs. In Germany, 80% of instantaneous demand was supplied by RESs on Aug. 23, 2015. Therefore, significant operating reserves are required to meet the demand in case of a sudden decrease in the output of RESs, thus causing an increase in the operational cost of the power system [3]. Moreover, additional regulations and standards are expected to be imposed on large PV power plants owing to their potential adverse impacts on reliability and stability of the power system. A 2 possible solution for regulating the intermittent output power of a PV power plant is to integrate a battery energy storage system (BESS) [4]. The BESS can provide flexible energy management solutions that can improve the power quality of renewable-energy power generation systems. Several control strategies and configurations for hybrid BESSs, such as combining the BESS with superconducting magnetic energy system, flywheel energy system, and energy capacitor system, have been proposed [5]. The BESSs have a response time in the range of milliseconds and are able to compensate in real time the high intermittency of the RESs by operating the BESSs in charging/discharging mode in order to keep the output power ramp rate of the power plant inside admissible values [6]. In Lin et al. [7], an intelligent wind power smoothing control using recurrent fuzzy neural network was developed. The difference of the actual wind power and smoothed power is supplied by the BESS. On the other hand, the state of charge (SOC) of a battery, which is used to describe its remaining capacity, is a very important control parameter for the battery usage. As the SOC is an important parameter, accurate estimation of the SOC can protect battery, prevent over discharge, improve the battery life, and facilitate the development of control strategies to save energy [8]. There are many methods to estimate the SOC of a battery including inverse mapping using the SOC-to-open circuit voltage characterization curves, Coulomb counting, impedance measurements, and algorithms using extended Kalman filter and particle filter [9, 10]. Since the neural networks possess the characteristics of fault tolerance, parallelism, and online learning, many researches of neural network modeling and control for renewable energy applications have been proposed [11–13]. In Liu et al. [11], a complex-valued recurrent neural network was proposed to predict the total output of the wind power plant based on historical data of wind speed and wind direction. Moreover, an improved differential evolution algorithm-based Elman neural network controller was proposed to control a squirrel-cage induction generator system for grid-connected wind power applications [12]. In Urias et al. [13], a prediction model that utilizes a layer recurrent neural network technique for estimating the wind power output of the turbine was proposed based on a multilayer neural network with back propagation training. In addition, a fuzzy neural network (FNN) is capable of fuzzy reasoning in handling uncertain information and artificial neura (...truncated)