International Journal of Photoenergy

Volume 2016, Article ID 5214061, 16 pages

http://dx.doi.org/10.1155/2016/5214061

## Sliding Mode Real-Time Control of Photovoltaic Systems Using Neural Estimators

^{1}Electrical Engineering Department, Faculty of Engineering Vitoria-Gasteiz, University of the Basque Country, Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain^{2}Systems Engineering and Automatic Control Department, Faculty of Engineering Vitoria-Gasteiz, University of the Basque Country, Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain^{3}Electrical Engineering Department, Faculty of Engineering, University of the Basque Country, Alameda Urquijo, s/n, 48013 Bilbao, Spain^{4}Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Sheikh Saqr Bin Khalid Rd., Ras Al Khaimah, UAE

Received 15 April 2016; Revised 13 June 2016; Accepted 28 June 2016

Academic Editor: Prakash Basnyat

Copyright © 2016 J. A. Ramos-Hernanz 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

The maximum power point tracking (MPPT) problem has attracted the attention of many researchers, because it is convenient to obtain the maximum power of a photovoltaic module regardless of the weather conditions and the load. In this paper, a novel control for a boost DC/DC converter has been introduced. It is based on a sliding mode controller (SMC) that takes a current signal as reference instead of a voltage, which is generated by a neuronal reference current generator. That reference current indicates the current () at the maximum power point (MPP) for given weather conditions. In order to test the designed control system, a photovoltaic module model based on a second artificial neuronal network (ANN) has been obtained from experimental data gathered during 18 months in the Faculty of Engineering Vitoria-Gasteiz (Spain). We have analyzed the performance of such model and we found that it is very accurate (MSE = 0.062 A and = 0.991 with test dataset). We also have tested the performance of the overall SMC design with both simulated and real tests, concluding that it guarantees that the power in the output of the converter is very close to the power of the photovoltaic module output.

#### 1. Introduction

In the recent Paris Conference [1], we can find important initiatives for supporting green energies. With regard to photovoltaic energy, two main initiatives stand out: the former, the creation of the “Alliance for Solar Energy” promoted by the Government of India and signed by 120 countries, and the latter, the creation of the “Global Solar Council,” mainly composed by associations devoted to renewable and solar energy from the five continents. The council was created with the aim of promoting photovoltaic energy worldwide through a unique interlocutor of the sector with the international organizations, increasing collaboration between different countries and supporting emerging solar markets.

Due to its simplicity of operation, robustness, and cheapness, photovoltaic solar energy is a very appropriate source of energy, especially for emerging countries, where the construction of large electrical infrastructures is infeasible in some areas. Moreover, in recent years, great progress in this area, better efficiency, and improved performance of photovoltaic modules have been achieved. These factors, along with the advancement of electronic technology, make this type of energy even more valued.

The optimal operation of a photovoltaic system depends on two types of variables; the first type is those that are imposed and depend on weather conditions, that is, irradiance and temperature. The second type of variables is those that can be modified to search for the desired performance of the system, given the weather conditions. This is the case that we are facing in this paper, that is, working at the maximum power point. In order to get it, it is mandatory to get an appropriate performance of the converter.

The search of control algorithms for improving converters performance in photovoltaic systems has a considerable significance for many researchers [2, 3]. Among different control algorithms, the sliding mode control is successful due to its many advantages [4–6], the most outstanding being its easy implementation, simplicity, robustness, and high performance in many fields such as robotics [7] or photovoltaic energy [8].

When a sliding mode controller is tuned by scientists or practitioners, it is desired to have a model of the photovoltaic module to control in order to carry out preliminary analytic or simulated tests instead of using the actual module. In the literature, there are a number of models of different complexities to explain the electrical behavior of photovoltaic modules. In order to clarify such variety, we can make a first division into theoretical and empirical models. Theoretical models use a characteristic equation [9] (see Section 2.1), among them being a number of models which use different degrees of freedom; that is, some of them are very complete but in other cases researchers determine/approximate them in several practical ways in order to get these models being useful.

The most complete model used in the literature is based on a double diode equivalent circuit which leads to a 7-parameter model, that is, , , , , , , and . Two-diode model has been used around the fifties [10, 11]. Later, Gow and Manning, using the same model, adjusted the parameters through Levenberg-Marquardt and Newton-Raphson algorithms [12]. This model is still being used in more recent literature [13, 14].

In [15], the approach used by Villalva et al. is to modify the value of and and adjust them by means of an iterative algorithm to fit the theoretical curve to experimental data in the MPP, implementing the complete model under Simulink-SimPowerSystem assisted by Simulink. De Soto et al. used this 5-parameter approach building three versions: from a basic model with two approximations of five magnitudes to a model where they are calculated [16]. They compared the results generated by this model with experimental data obtained from a facility by other researchers and studied the effect of changing and .

A number of researchers make some approximations of the characteristic parameters. The most usual of them is to assume that , so A and the third term of the characteristic equation is discarded leading to a 4-parameter model. In [17, 18], this simplification is carried out when the model of the exact same module is built following almost the same procedure. These works are quite similar to [19]. However, there are several differences: the first calculates the temperature coefficient used to calculate the short circuit current , while the last one uses the value provided by the manufacturer; and the used numeric value of ideality factor of the diode is different between [17, 18] and [19]. A model where the ideality factor can be varied to fit it to different PV technologies is proposed in [20]. As a last 4-parameter model, Bayrak and Cebeci formulate a distinct model from the previous ones and develop the model of a microgrid facility and hybrid PV and fuel system [21].

Ramos-Hernanz et al. use polynomial interpolation and describe a model which allows obtaining only the and curves and provides further insight into the behavior of the photovoltaic module, but it is suited only for a range of temperatures and irradiances [22].

Another approach to the empirical methods is modeling the behavior of the photovoltaic elements by means of artificial neuronal networks [23]. In [24], an interesting review of different approaches is presented, while [25, 26] developed a practical model, but with very narrow amplitudes for irradiance and temperature. Lopez-Guede et al. are seeking to expand these limits [27], but in spite of expanding the range of magnitudes, they still obtain partial models with data captured during quite small time range (two months). Bonanno et al. use RFB neural networks to generate two models [28]. The former is to obtain from irradiance and voltage and uses 5,600 real samples. The second one is devoted to obtain , again from irradiance and voltage using 4,600 real samples.

Researchers reported a relative MSE of 2% and 1%, respectively, but the test has been done with training data, so in fact they have reported the training accuracy. In this paper, a new sliding mode controller (SMC) for maximum power point tracking in a photovoltaic module is introduced, and its performance is demonstrated in a real installation. Its main characteristic is that as reference generator it uses an artificial neural network which will seek for a reference current corresponding to the maximum power operating point (). Gow and Manning have been developing works related to this paper; for example, in [12], they have used the facilities with a different photovoltaic module to design a control system that uses a voltage reference generator implemented through a polynomic equation.

In order to design the SMC controller, we have carried out several tasks related to the three main elements of the autonomous photovoltaic system (photovoltaic module, converter, and load):(i)A model of the Mitsubishi Electric PV-TD185MF5 185 W photovoltaic module has been developed based on neural networks, using actual measurements taken during 18 months, with an approximate average duration of ten minutes for each measure. A total of 62,912 samples (, , temperature , and irradiance ) have been obtained, covering a broad number of different weather situations.(ii)These real data have been used to draw the characteristic curves of the module and calculate the MPP in each one of them. These points were learned by a second artificial neural network in order to obtain the reference current at which that the module operates at its optimum point, given the power supplied by the module and the temperature. That is, a neuronal reference current generator implementation has been obtained.(iii)A SMC has been designed, so that, given a reference current, the photovoltaic module will work at its point of maximum power.(iv)And finally, the joint behavior of all these components has been validated through simulated and real tests using the installation that is at the Faculty of Engineering Vitoria-Gasteiz (University of the Basque Country, Spain).

The document is structured as follows: in Section 2, we give a short background on some basic concepts used in this paper as photovoltaic generators, characteristic curves of photovoltaic modules, boost converters DC/DC, artificial neural networks, and sliding mode control. Section 3 describes how the model of a photovoltaic module has been obtained using artificial neural networks. In Section 4, we describe the sliding mode control, as well as the modeling process of the reference current generator, while the results obtained are discussed in Section 5. Finally, Section 6 gives our main conclusions.

#### 2. Background

##### 2.1. Photovoltaic Generator

A solar cell can be modeled as a PN semiconductor junction which when exposed to light converts light energy into electrical energy, generating a DC current which will mainly depend on the existing solar irradiance and temperature.

Electric generators are usually classified as current or voltage sources. A photovoltaic device shows a functional mixed behavior, which is a source of current or voltage depending on the operating point in which the device is working.

Most manufacturers provide few experimental data on the performance of solar cells working at ideal operating conditions. These data are typically provided in Standard Test Conditions (STC) (1,000 W/m^{2}, 25° (±2)°C, AM 1.5 according to EN 6090-4). There are manufacturers that also provide these data for other conditions (800 W/m^{2}, Nominal Operating Cell Temperature (NOCT), AM 1.5). Usually the provided data are short circuit current (), open circuit voltage (), current at the maximum power point (), voltage at the maximum power point (), temperature coefficient of open circuit voltage () [%/K], and temperature coefficient of short circuit current () [%/K].

The basic model of a photovoltaic generator is a photovoltaic cell. The cell can be modeled as a diode, usually made of silicon and designed to maximize the absorption of photons and to minimize the reflection, converting a part of the received solar energy to electric energy. The ideal cell model is presented in the dotted area of Figure 1.