International Journal of Photoenergy

Volume 2015, Article ID 413654, 10 pages

http://dx.doi.org/10.1155/2015/413654

## Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy

Received 19 November 2014; Revised 15 January 2015; Accepted 17 January 2015

Academic Editor: Cheuk-Lam Ho

Copyright © 2015 Antonino Laudani 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.

#### Abstract

A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.

#### 1. Introduction

Nowadays, the photovoltaic (PV) based generation systems are extremely common and you can view both small plants (some kWpp) on the roof top or larger plants (MWpp) usually in rural or industrial environments. From the point of view of the technology and materials employed, several steps have been done in order to improve the efficiency and the performances [1]. On the other hand, from the point of view of the system, the major study has concerned the aspect of both characterization and energy conversion, giving to the designers some advanced tools for the setup of the generation system. High level performances are usually achieved by using suitable conversion systems, which try to make work the system in the maximum power point condition [2]. Although these conversion systems consist, in some case, in extremely complex processes and even in artificial intelligence based algorithms, there is a lack in studying of the effects of environmental conditions and in ageing of the parameters characterizing the PV modules; consequently, considering the PV system as a whole, some additional components would be needed which allow both the monitoring of each PV module and the intelligent management of the PV system aimed at the optimization of the performances. In particular, there is a scarcity of embedded systems able to characterize in real time the PV arrays during their normal working in order to update the parameters of the PV model for a better estimation of generated power. The reason of this lack is essentially due to two issues: (i) the requirement of several sensors for the continuous monitoring of the PV plants and (ii) the difficulty of identifying in real time the PV model, since this requires the solution of a transcendental (nonlinear) problem, the five-parameter model, which is really hard to solve without the use of suitable computing environment such as Matlab, Mathematica, and Maple. In this work, by following our previous successful experiences in the application of neural networks (NNs) to the PV field, we propose a solution of the identification problem for the five-parameter model starting from few information, thanks to the synergy between a neural network and an analytical approach, the so-called reduced forms of the one-diode model. The proposed procedure can be easily implemented in embedded algorithms based on low cost microcontroller, allowing the development of a new strategy for the intelligent management of PV plants. This work is structured as follows: in Section 2 a brief review of the application of soft computing to PV field is presented; the one-diode model and the problems linked to its identification are illustrated in Section 3, together with the innovative method based on reduced form; the proposed neural solution with the issues of its setup and training is described in Section 4; results and validations on real data are reported in Section 5; authors’ conclusions follow in Section 6.

#### 2. The Application of Soft Computing Techniques to PV System

The soft computing techniques have been currently applied in several works in the literature for the solution of different issues regarding PV systems. One of the most successful applications is the offline identification of the PV model of a PV system from measurements; almost all the soft computing based optimization techniques have been adopted to address this problem: simulated annealing [3], genetic algorithm [4], differential evolution [5, 6], evolutionary algorithm [7, 8], artificial bee swarm optimization [9], bacterial foraging algorithm [10], semianalytical/deterministic approach, and so on [11–13] (see also the references within the above cited papers). On the other hand, the same optimization techniques have been less used to face other kinds of problems regarding PV systems because of their high computational costs, which make impossible their use in real time and embedded application. Only recently, some attempts have been successfully done to PV array reconfiguration [14] and maximum power point tracking [15, 16]. Among the soft computing techniques, neural networks and fuzzy logic, thanks to their intrinsic nature and their online low computational cost, have been successfully utilized in the PV field, often by hybrid configurations [17]. For example, controllers based on fuzzy logic have been used to face the maximum power point tracking (MPPT) [18], to manage a storage system [19], to predict daily irradiation [20], and for the PV array reconfiguration [21]. The neural networks (NNs) have been widely used in the field of renewable energy since more than 20 years (a review of year 2001 can be found in [22]) and their application to PV field still arouses interest as testified by recent papers on MPPT algorithms [2, 23–26] and on the energy production estimation and forecasting (a review was published by [27]). In the last year, we have used NNs also for the development of some components of the PV system (irradiance sensor and maximum power point tracking), implementing them in a low cost microcontroller based environment: indeed, one of the main advantages in using NNs with respect to other soft computing techniques is their easy implementation and good performance in terms of both computational costs and memory consumption [28]. However, although NNs are often applied in the literature to efficiently solve mathematical problems [29, 30], in the PV module modeling they are only used to interpolate experimental data rather than to identify a real model. In the opinion of the authors, this fact may be due to the difficulty in obtaining a suitable training set able to effectively represent the identification problem, but this is not the only problem affecting the neural approach, as we will show in this paper: indeed, the output parameters (in particular the shunt resistance) often present variations with respect to input data that are very difficult to establish, and no neural approach is able to perform an acceptable identification, at least with a limited number of neurons and reasonable computational costs. Nevertheless, as we will demonstrate, the use of reduced form of one-diode model helps us to overcome this problem and makes the proposed approach feasible and efficient.

#### 3. The One-Diode Model: Identification Problem and Its Reduced Form

The five-parameter model, also known as “one-diode model,” for the electrical representation of a solar panel is widely used in solar power industry and is generally recognized as design tool [31]. This model was originally formulated for a PV cell, but under some conditions it has been demonstrated valid for a module composed by an arbitrary number of cells and also for a generic PV array composed by series and parallel connected modules. The importance of the one-diode model and its success is due to this generalization capability: it is enough accurate under the hypothesis that all the cells/modules work in the same conditions of irradiance and temperature, hypothesis usually verified with a good accuracy in a not shaded module/array. Among the different versions [32] of the five-parameter model, in this work we use the one proposed and validated by de Soto et al. [33], which is also the most adopted one. The original model is based on the circuit representation of a photovoltaic device by means of an independent current source, an antiparallel diode, and two output resistances, one in parallel with the diode and the other one in series with the output branch (see Figure 1). The model current-voltage relation is expressed by (1)