# Online Identification of Photovoltaic Source Parameters by Using a Genetic Algorithm
## 1 Introduction
This information allows simplifying the parameters' calculation since at such points, the PV voltage and current are equal to zero; thus, some equations can be simplified. On the other hand, in order to maximize the energy production, the PV system is always controlled to work as close as possible to its maximum power point. Therefore, the measurement of \(I_{sc}\) and \(V_{oc}\) is undesirable, since in such points, the PV source delivers zero power. Moreover, the power converter used to regulate the PV source is often not properly designed to work in the short circuit or open circuit points. Therefore, additional devices and complex procedures should be introduced to perform those measurements. For these reasons, all the SDM parameter identification procedures requiring the \(I_{sc}\) and \(V_{oc}\) values are mainly effective when run offline, on the basis of previously-acquired sets of measurements. In recent years, some authors have proposed computational intelligence-based methods for the PV source model parameter identification. The proposed methods range from genetic algorithms (GAs) to differential evolution, and examples of applications to the identification of the five-parameter SDM are given in the literature [12; 13]. In the case of GAs, the core idea is to define a population of individuals where each individual is a set of parameter values and then to select the best-fitted individuals as the base for generating a new population, by minimizing an error function. The main advantage of this approach is that it does not require the use of complex equations to evaluate the model parameters. As well as in any other numerical approaches, although the initial values could be generated randomly, providing a good guess solution significantly helps the algorithm convergence and improves the execution time. On the other hand, an inappropriate selection of the initial values will result in unacceptable parameter values or in non-convergence of the algorithm [14]. Computational intelligence-based algorithms usually require powerful computing platforms to exhibit a reasonable execution time; for this reason, up to now, the embedded implementation of such techniques has been critical, sometimes forcing designers to simplify the objective function or to discard the algorithm. However, powerful embedded platforms have recently been made available on the market, for example field programmable system-on-chip (FPSoC ) and microcontrollers based on 32-bit ARM processor cores, such as the STM32 family. As for FPSoC, a technical contribution has demonstrated that it is possible to achieve a performance comparable to that of a desktop computer when running a particle swarm optimization (PSO) algorithm [15]. As for the other platform, an STM32 microcontroller has been used for the implementation of a fixed low-order controller [16]; however, to the best of the authors' knowledge, no implementation of complex optimization algorithms on such a device family has been proposed, yet. In this paper, a novel approach for the online SDM five-parameter identification, requiring only some measured points close to the MPP, is proposed. In particular, the values of \(I_{sc}\) and \(V_{oc}\) are properly estimated so as to avoid the loss of power deriving from their measurements. Then, the problem of the appropriate determination of the guess solution is solved by using a set of suitable explicit formulas [10]. Finally, the exact solution is obtained by running a GA. The proposed method has been implemented on a very low-cost (EUR 20.00), high-performance board, namely the NUCLEO-F429 ZI, which is based on an STM32 microcontroller by STMicroelectronics (Geneva, Switzerland). The experimental results demonstrate the validity of the proposed approach. \[I_{pv}=f(V_{pv},I_{pv})=I_{ph}-I_{s}[e^{\frac{(V_{pv}+I_{pv}R_{s})}{\eta_{t} }}-1]-\frac{V_{pv}+I_{pv}R_{s}}{R_{h}} \tag{1}\]