An improved optimization technique for estimation of solar photovoltaic parameters

The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV

Implementation of a Novel Tabu Search Optimization Algorithm to Extract Parasitic Parameters of Solar Panel

The aging of PV cells reduces their electrical performance i.e., the parasitic parameters are introduced in the solar panel. The shunt resistance (RSh), series resistance (RS), photo current (IPh), diode current (Id), and diffusion constant (a1) are known as parasitic or extraction parameters. Cracks and hotspots reduce the performance of PV cells and result in poor V–I characteristics. Certain tests are carried out over a long period of time to determine the quality of solar cells; for example, 1000 h of testing is comparable to 20 years of operation. The extraction of solar parameters is important for PV modules. The Tabu Search Optimization (TSO) algorithm is a robust meta-heuristic algorithm that was employed in this study for the extraction of parasitic parameters. Particle Swarm Optimization (PSO) and a Genetic lgorithm (GA), as well as other well-known optimization methods, were used to test the proposed method's correctness. The other approaches included the lightning search algorithm (LSA), gravitational search algorithm (GSA), and pattern search (PS). It can be concluded that the TSO approach extracts all six parameters in a reasonably short period of time. The work presented in this paper was developed and analyzed using a MATLAB-Simulink software environment.publishedVersio

Optimal Extraction of Photovoltaic Model Parameters Using Gravitational Search Algorithm Approach

Abstract Extraction of accurate Photo Voltaic (PV) model parameters is a challenging task for PV simulator developers. To mitigate this challenging task a novel approach using Gravitational Search Algorithm (GSA) for accurate extraction of PV model parameters is proposed in this paper. GSA is a population based heuristic optimization method which depends on the law of gravity and mass interactions. In this optimization method, the searcher agents are collection of masses which interact with each other using laws of gravity and motion of Newton. The developed PV model utilizes mathematical equations and is described through an equivalent circuit model comprising of a current source, a diode, a series resistor and a shunt resistor including the effect of changes in solar irradiation and ambient temperature. The optimal values of photo-current, diode ideality factor, series resistance and shunt resistance of the developed PV model are obtained by using GSA. The simulations of the characteristic curves of PV modules (SM55, ST36 and ST40) are carried out using MATLAB/ Simulink environment. Results obtained using GSA are compared with Differential Evolution (DE), which shows that GSA based parameters are better optimal when compared to DE

Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

Wind Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters

In order to increase the efficiency of the solar photovoltaic (PV) system, accurate electrical modeling of the system under different environmental conditions is necessary. The double diode electrical model of solar PV is known to be more accurate than its single diode model counterpart since it takes into account the effect of recombination. However, because of its nonlinear characteristics, the parameters of the double diode model have to be identified using ptimization algorithms. In this paper, the Wind Driven Optimization (WDO) algorithm is proposed as a potential new method for identifying the parameters of a twelveparameter double diode model (12p-DDM) of the solar PV. The accuracy and flexibility of the proposed method are verified using three different sets of data: (i) experimental data at the controlled environmental condition, (ii) data sheet values of different solar PV modules and (iii) real-time experimental data at the uncontrolled environmental condition. Additionally, the performance of the WDO is compared to other well-known existing optimization techniques. The obtained results show that the WDO algorithm can provide optimized values with reduced Mean Absolute Error in Power (MAEP) and reduced Root Mean Square Error (RMSE) for different types of solar PV modules at different environmental conditions. We show that the WDO can be confidently recommended as a reliable optimization algorithm for parameter estimation of solar PV model

Can Parallel Gravitational Search Algorithm Effectively Choose Parameters for Photovoltaic Cell Current Voltage Characteristics?

This study asks the question “Can parallel Gravitational Search Algorithm (GSA) effectively choose parameters for photovoltaic cell current voltage characteristics?” These parameters will be plugged into the Single Diode Model to create the IV curve. It will also investigate Particle Swarm Optimization (PSO) and a population based random search (PBRS) to see if GSA performs the search better and or more quickly than alternative algorithm

Parameter estimation of electric power transformers using Coyote Optimization Algorithm with experimental verification

In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms

Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions

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