An enhanced Harris Hawk optimization algorithm for parameter estimation of single, double and triple diode photovoltaic models

Soft Computing https://doi.org/10.1007/s00500-022-07109-5 (0123456789().,-volV)(0123456789(). ,- volV) OPTIMIZATION An enhanced Harris Hawk optimization algorithm for parameter estimation of single, double and triple diode photovoltaic models Abdelhady Ramadan1 • Salah Kamel1 • Ahmed Korashy1 • Abdulaziz Almalaq2 • Jose Luis Domı́nguez-Garcı́a3 Accepted: 31 March 2022  The Author(s) 2022 Abstract Due to the rapid development of photovoltaic (PV) system and spreading of its application, the accuracy of modeling of solar cells, as the main and basic element of PV systems, is gaining relevance. In this paper, an Enhanced Harris Hawk Optimization Algorithm (EHHO) is proposed and applied for estimating the required parameters of different PV models in an effective and accurate way. Harris Hawk Algorithm (HHO) is based on Hawks ways in hunting and catching their preys. The HHO utilizes two phases including exploration and exploitation. The main purpose of proposed enhancement is to improve the second phase of HHO. This enhancement is performed on the exploration phase by fluctuating toward or outward the best optimal solution using sine and cosine functions. Both conventional and proposed algorithms are applied for single, double and triple diode PV models. In order to test the applicability and robustness of proposed algorithm, it is applied for estimating the parameters of different real PV systems and compared with other recent optimization algorithms. The results show that the proposed EHHO is more accurate than conventional HHO and other algorithms. Keywords Photovoltaic (PV)  Optimization algorithm  Harries hawk and single  Double and triple diode models List of symbols SD DD TD PV HHO Iph Rs Single diode Double diode Triple diode Photo voltaic Harries Hawk optimization Photo generated current source Series resistance & Salah Kamel Abdelhady Ramadan Ahmed Korashy Abdulaziz Almalaq Jose Luis Domı́nguez-Garcı́a 1 Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt 2 Department of Electrical Engineering, Engineering College, University of Hail, Hail 55476, Saudi Arabia 3 Catalonia Institute for Energy Research, IREC, Jardins de le Dones de Negre, s/n, 08930 Barcelona, Spain Rsh It Id1,Isd Id2 Id3 Vt Vtm Itm WCA TSA RMSE SCA TLBO PSO EHHO n, n1 n2 n3 K q T (Ko) X(t ? 1) X(t) Xrabbit(t) Shunt resistance PV module output current First diode current Second diode current Third diode current Terminal voltage PV real voltage PV real current Water Cycle Algorithm Tunicate Swarm Algorithm Root Mean Square Error Sine–Cosine Algorithm Teaching Learning-Based Optimization Particle Swarm Optimization Enhanced Harries Hawk Optimization Diffusion diode ideality Recombination factor Leakage factor = 1.380 9 10-23 (J/Ko) Boltzmann constant 1.602 9 10-19 (C) Coulombs Photocell temperature (Kelvin) Hawk position in next iteration Hawk current position Rabbit current position 123 A. Ramadan et al. SOA MRFO Seagull optimization algorithm Manta Ray Foraging Optimization +V t It Rs 1 Introduction I ph Recently, solar energy became an important source of renewable energy in the world as it is used in different applications such as energy generation, self-sustained systems (e.g., water-pumping) as well as smart homes and water heating (Abbassi et al. 2018; Chen et al. 2019a). The increase in solar energy applications leads the need of obtaining accurate and reliable models to be used for the analysis and development of solar modules and its integrations. In this regard, as the characteristic of PV solar cell is similar to P–N junction characteristics so different types of models have been developed based on the number of diodes in the model (single (SD), double (DD) and triple diodes (TD)). In literature, different algorithms have been applied to estimate the parameters of SD and DD models to develop more and more accurate PN model. A comparative study for the most recent algorithms applied to SD and DD models has been presented in Abbassi et al. (2018). The SD model contains only five parameters. These parameters are two currents (photovoltaic current and diode current) and two resistors (series and shunt resistance) and the diffusion diode ideality factor. The SD model is considered a simple model due to it has a small number of parameters (Oliva et al. 2017; Li et al. 2013; Askarzadeh and Rezazadeh 2012). Although the SD model is simple in parameter estimation, some researchers tend to use the Double diode model. DD model has been developed to overcome the problems in the SD model by taking into consideration the recombination losses (Gupta et al. 2012; Jamadi et al. 2016). DD model represents the recombination losses by adding one diode to the SD model and raise the number of the model parameters to seven parameters instead of five I d1 I d2 I sh R sh I sh R sh Fig. 2 DD model +V t It I ph I d1 I d2 Rs I d3 Fig. 3 TD model mathematical model parameters in the SD model. These two parameters are (second diode current and recombination factor). The accuracy achieved by the DD model is higher than SD, Fig. 1 SD model Rs It +V t I ph 123 I sd I sh R sh An enhanced Harris Hawk optimization algorithm for parameter estimation of... which gave the chance for the researchers to develop a triple diode model. The TD model has nine estimated parameters. In the TD model, one diode is added to the DD model to raise the number of diodes in the model to three. The third diode represents leakage current and grain boundaries. Using DD and TD models in estimating the parameters of solar cells is more complex but they give more accurate results than those obtained-based SD model. In the literature, several optimization algorithms have been applied to estimate the parameters of solar cell-based SD, DD and TD models (Qais et al. 2019; Omnia et al. 2018; Elazab et al. 2020; Allam et al. 2016; Abbassi et al. 2019, 2017; Ramadan et al. 2020). Allam et al. (2016), Moth-Flame Optimization Algorithm has been also used to estimate parameter of SD and DD and TD models. Abbassi et al. (2019), Salp Swarm-inspired algorithm has been adapted for parameter estimation of the DD model. Abbassi et al. (2017), comparative study to improve the SD model using genetic algorithm optimization algorithm has been presented. Ramadan et al. (2020), an enhancement teaching learn optimization algorithm has been developed for estimating the parameter of SD and DD. Many state-of-the-art methods have been developed for PV parameter estimation (Yu et al. 2019; Chen et al. 2019b; Liao et al. 2017; Zhang et al. 2020; Kler et al. 2017). Yu et al. (2019), a performance-guided JAYA (PGJAYA) algorithm has been proposed for extracting parameters of different PV models. Chen et al. (2019b), perturbed stochastic fractal search (PSFS) has been proposed to estimate the PV parameters in an optimization framework. Several hybrid optimization algorithms (...truncated)