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A Novel Approach for an MPPT Controller Based on the ADALINE Network Trained with the RTRL Algorithm

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A Novel Approach for an MPPT Controller Based on the ADALINE Network Trained with the RTRL Algorithm energies Article A Novel Approach for an MPPT Controller Based on the ADALINE Network Trained with the RTRL Algorithm Julie Viloria-Porto *, Carlos Robles-Algarín * and Diego Restrepo-Leal * Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470003, Colombia * Correspondence: [email protected] (J.V.-P.); [email protected] (C.R.-A.); [email protected] (D.R.-L.); Tel.: +57-5-421-7940 (C.R.-A.) Received: 9 November 2018; Accepted: 27 November 2018; Published: 5 December 2018 Abstract: The Real-Time Recurrent Learning Gradient (RTRL) algorithm is characterized by being an online learning method for training dynamic recurrent neural networks, which makes it ideal for working with non-linear control systems. For this reason, this paper presents the design of a novel Maximum Power Point Tracking (MPPT) controller with an artificial neural network type Adaptive Linear Neuron (ADALINE), with Finite Impulse Response (FIR) architecture, trained with the RTRL algorithm. With this same network architecture, the Least Mean Square (LMS) algorithm was developed to evaluate the results obtained with the RTRL controller and then make comparisons with the Perturb and Observe (P&O) algorithm. This control method receives as input signals the current and voltage of a photovoltaic module under sudden changes in operating conditions. Additionally, the efficiency of the controllers was appraised with a fuzzy controller and a Nonlinear Autoregressive Network with Exogenous Inputs (NARX) controller, which were developed in previous investigations. It was concluded that the RTRL controller with adaptive training has better results, a faster response, and fewer bifurcations due to sudden changes in the input signals, being the ideal control method for systems that require a real-time response. Keywords: RTRL algorithm; MPPT controller; PV module; dynamic neural network controller 1. Introduction Due to the negative impact caused by the use of conventional energy sources, environmental problems such as pollution, and poor quality in the provision of primary energy service, renewable energy is presented as an excellent alternative to reduce dependence on conventional electrical network. The notable increase in the use of renewable energies as an alternative means of generating and storing clean energy is considered worldwide as one of the technological solutions to mitigate climate change. Internationally it is recommended that in places with high solar density, the use of photovoltaic (PV) energy sources be chosen, since this type of energy provides an inexhaustible energy resource [1]. In the global report presented in 2017 by the International Agency for Renewable Energy (IRENA), it is evident that solar energy is a leader in power generation capacity due to its competitive costs in many emerging markets of the world [2]. In order to improve competitiveness, increase demand, and obtain the highest performance, the elements of a PV system must be optimized. There are several techniques to increase the performance of PV systems, among which solar trackers [3–7], hybrid systems [8,9], and controllers for the maximum power point tracking (MPPT) stand out. With the MPPT controllers, better performance of the PV systems is obtained, since they take advantage of all the current generated by the solar panels independently of the voltage. These controllers are more expensive than Pulse-Width Modulation Energies 2018, 11, 3407; doi:10.3390/en11123407 www.mdpi.com/journal/energies Energies 2018, 11, 3407 2 of 17 (PWM) controllers, but due to their high efficiency they are ideal for working in medium and large solar power plants [10]. In these controllers, the perturb and observe (P&O) algorithm has traditionally been used. This algorithm is characterized by its low efficiency and presenting oscillations around the operating point. The presence of oscillations has contributed to the researchers focusing their efforts on designing MPPT controllers [10] with better performance. In this way, investigations have been carried out with genetic algorithms [11], fuzzy logic [12–17], golden section [18,19], simulated annealing [20], artificial bee colony [21], adaptive control [22], glowworm swarm optimization [23], ant colony optimization [24], artificial neural networks [25–30], and numerical methods [31]. Taking into account the previous context, this paper proposes as a novelty the design of an MPPT neuro-controller based on an adaptive neural network (ADALINE) with Real-Time Recurrent Learning (RTRL) [32]. We decided to explore the application of this method in order to design a robust controller that adapts to sudden climatic changes to which a PV module will be exposed. Specifically, we highlight as a contribution the application of the RTRL algorithm in an MPPT controller, which was developed with a single neuron and without using the bias. In addition, this training algorithm was programmed online with the Simulink function blocks without using the MATLAB Neural Network Toolbox (R2017b Version 9.3). In this way, it was possible to develop an efficient controller with excellent computing times. The results obtained show the advantage of the use of an online learning method in an artificial neural network (ANN), with which the volume of data to be acquired and the times of capture and conditioning of the data are reduced. In addition, the controller is more flexible since it has the ability to learn when there are sudden changes in the operating conditions of the PV system. It is noteworthy that the neurocontroller developed has a single neuron, which is simpler to implement compared to controllers based on nonlinear autoregressive network with exogenous inputs (NARX) [25], since these networks have at least two layers and a feedback between the output layer and the input layer. Additionally, the neuro-controller proposed in this paper is more efficient than those implemented with multilayer perceptron (MLP) [33]. This is one of the most popular architectures of neural networks and usually has at least one hidden layer. In order to evaluate the robustness of the controller implemented with the RTRL algorithm and compare it with other methods, the algorithms least mean square (LMS) [34] (standard and iterative version) and the traditional P&O were implemented. Additionally, the results were compared with previous results of the Magma Ingeniería research group with a fuzzy controller [12,35] and a controller based on a NARX network [25]. For this, modeling and simulation of the PV module, the DC-DC converter, and the MPPT algorithms was carried out in MATLAB/Simulink. It should be noted that this research is part of a set of intelligent control techniques developed in the Magma Ingeniería research group, with the aim of implementing a cost-effective MPPT controller with high efficiency, precision, and low computational cost; that can initially be used in the photovoltaic systems of the Universidad del Magdalena in Santa Marta, Colombia. The modeling of the PV system is presented in Section2. Section3 presents the simulation results with the MPPT controllers. Finally, the conclusions are presented in Section4. 2. Modeling of the PV System 2.1. FV Module To simulate the PV module, the mathematical model proposed in [36,37] was used. From this model, it is possible to find the curve fitting parameter (b), directly from the I-V ratio of the PV module shown in Equation (1). Ix h ( V − 1 )i I(V) = 1 − e bVx b (1) ( −1 ) 1 − e b The variables Vx and Ix must be obtained from Equations (2) and (3). Energies 2018, 11, 3407 3 of 17 EnergiesEnergies 20182018,, 1111,, xx FORFOR PEERPEER REVIEWREVIEW 33 ofof 1717 E퐸 푉 −푉 E 퐸 (( 퐸i푖 ln|j 푉V푚푎푥max−−V푉oc표푐 j|)) i 퐸푖 (E퐸 푖 ln|푉Vmax푚푎푥−푉V 표푐 |) Vx 푉==s 푠 TC푖 푇퐶(T(푇−−T푇 ))++sV푠푉max −−s푠((V푉max − V푉 )푒e 퐸iN푖푁 푉푚푎푥 −푉min푚푖푛 ((2)2) 푉푥 = 푠 푇퐶V 푉 (푇 − 푇N푁 ) + 푠푉푚푎푥 − 푠(푉푚푎푥 − 푉푚푖푛min )푒 푖푁 푚푎푥 푚푖푛 (2) 푥 EiN퐸푖푁 푉 푁 푚푎푥 푚푎푥 푚푖푛 퐸푖푁 Ei퐸푖 Ix = p 퐸[푖Isc[ + TCi(T − TN)]] (3) 퐼퐼푥 == 푝푝 [퐼퐼푠푐 ++푇퐶푇퐶푖((푇푇 −−푇푇푁))] ((33)) 푥 EiN퐸푖푁 푠푐 푖 푁 퐸푖푁 2 where,where, Ei is thethe solarsolar irradiance,irradiance, EEiNiN is anan irradianceirradiance constantconstant ofof 10001000 W/mW/m2,, Iscsc isis the the short circuit where, Ei is the solar irradiance, EiN is an irradiance constant of 1000 W/m2, Isc is the short circuit current, p is the number of PV modules in parallel, s is the number of PV modules in series, T is the current,current, pp isis thethe numbernumber ofof PVPV modulesmodules inin parallel,parallel, ss isis thethe numbernumber ofof PVPV modulesmodules inin series,series, TT isis thethe operating temperature, TCii and TCvv areare the current and voltage temperature coefficients,coefficients, TN is the operating temperature, TC◦i and TCv are the current and voltage temperature coefficients, TN is the temperature constantconstant ofof 2525 °C,C, VVococ isis thethe openopen circuitcircuit voltage,voltage,V Vmaxmax isis 103%103% ofof VVococ, and Vmin isis the the 85% 85% temperature constant of 25 °C, Voc is the open circuit voltage, Vmax is 103% of Voc, and Vmin is the 85% of Vococ. of Voc. The electrical parameters were taken from the data sheet of the 65 W PV module (Yingli Solar TheThe electricalelectrical parametersparameters werewere takentaken fromfrom thethe datadata sheetsheet ofof thethe 6565 WW PVPV modulemodule (Yingli(Yingli SolarSolar JSJS JS 65). From these data, the value of b = 0.0737561 was obtained [25]. The mathematical modeling of 65).65). FromFrom thesethese data,data, thethe valuevalue ofof bb == 00..07375610737561 waswas obtainedobtained [[2525].]. TheThe mathematmathematicalical modelingmodeling ofof thethe the PV module was carried out in the MATLAB/Simulink software. PVPV modulemodule waswas carriedcarried outout inin thethe MATLAB/SimulinkMATLAB/Simulink software.software. 2.2. DC-DC Buck Converter 2.2.2.2. DCDC--DCDC BuckBuck ConverterConverter As a control device, a DC-DC Buck converter was designed, in which the output voltage V is As a control device, a DC-DC Buck converter was designed, in which the output voltage Vo is As a control device, a DC-DC Buck converter was designed, in which the output voltage Vo is controlled with a PWM signal.
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