# Neural Network Approach to MPPT Control and Irradiance Estimation
## 1 Introduction  
Solar energy plays a significant role in electricity production nowadays, due to its sustainability and abundance, simple installation of photovoltaic (PV) systems, reliable non-polluting power generation, etc. Accurate modeling of the PV cell/module is extremely important for optimal design and evaluation of PV systems. Various equivalent electrical circuits for photovoltaic cell/module modeling have been presented in the literature. Among them, a five-parameter single-diode model is commonly used, due to its low complexity and satisfactory accuracy [1]. Hence, many techniques have been proposed for the extraction of those five parameters: some perform the parameter extraction based on the manufacturer datasheet information [2; 3; 4], while the others obtain the parameters using a set of experimental current-voltage (\(I\)-\(V\)) points [5; 6; 7]. Moreover, the methodologies that utilize both the datasheet and experimental data to extract the unknown parameters are also available in the literature [8]. The equivalent PV circuit is described by a set of transcendental equations, which are solved by iterative numerical procedures [9]. The closed-form solution of \(I\)-\(V\) characteristics can be obtained by utilizing the Lambert \(W\) function [9; 10]. Besides the electrical equivalent, the data-driven PV cell modeling approaches have also been suggested by several authors. Those techniques perform PV model identification directly from the measured data and usually have higher accuracy [11; 12]. In [13], the PV cell is modeled by using the Multilayer Neural Networks, while in [14; 15] authors use the generalized regression and radial basis function neural networks. Recently, a one-dimensional deep residual network framework has been applied to the PV modeling problem [16]. A data-driven method based on the simple linear regressionand smoothing spline has been proposed in [17], while a hybrid approach, which combines the benefits of neural networks and fuzzy logic, is given in [18]. PV cells require maximum power point tracking (MPPT) algorithms to ensure that the amount of power extracted is maximized. The maximum power point (MPP) changes with irradiance and temperature variations, and, therefore, it is important to develop the MPPT algorithms that will be fast enough to track these changes. Since, in general, the MPPT algorithms can be classified in different ways, in this paper we will divide them into two categories: model-free and model-based methods. The only prerequisites for implementing model-free algorithms are current and voltage measurements, without compulsory information of the PV model. The implementation of these algorithms is cost-effective; however, they have disadvantages such as oscillations around the MPP, slow convergence speed, and poor performance under the rapid change of the irradiance. Among these methods, the most popular are the Perturb and Observe (P&O) [19; 20; 21; 22] and Incremental Conductance algorithm [23; 24]. Recently, many algorithms that use different optimization techniques have been proposed to solve the MPPT problem under non-uniform irradiance conditions [25; 26; 27]. The model-based MPPT algorithms utilize the photovoltaic model to determine the MPP position. These methods exhibit fast tracking speed and high efficiency in the steady-state, but usually have greater complexity. In [28], the MPPT algorithm that uses the polynomial approximation of the equivalent circuit equations has been proposed, while the algorithm that relies on the exact closed-form solution based on Lambert \(W\)-function can be found in [29]. Many model-based MPPT algorithms that use neural networks have also been proposed in the literature [30]. The neural network (NN)-based algorithms provide the MPP voltage at the output, while their inputs can be datasheet information [31; 32; 33], irradiance and temperature [34; 35; 36], voltage and current measurements [37; 38].