Solar PV Modelling and Parameter Extraction Using Artificial Immune System

Jacob, Basil; Balasubramanian, Karthik; T, Sudhakar Babu; Azharuddin, S. Mohammed; Rajasekar, N.

doi:10.1016/j.egypro.2015.07.375

Cited by 79 publications

(20 citation statements)

References 14 publications

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“…Step 1: Initialization of parameters: Population size (number of air parcels) = , maximum number of iterations = , dimensions of air parcel (number of parameters) = , dimension limits ( and ) and maximum velocity are initialized. Define the objective function (10). Randomly generate the velocity ( 1 , 2 … , ) and position ( 1 , 2 , … ) for each air parcels.…”

Section: B Wdo Implemented For Parameter Estimation Of Solar Pvmentioning

confidence: 99%

“…Evolutionary algorithm techniques are considered to be excellent in dealing with nonlinear equations. In the recent years, different optimization techniques have been introduced to estimate the parameters of solar PV; namely, the Genetic Algorithm (GA) [8], Pattern Search (PS) optimization [9], Artificial Immune System (AIS) [10], Bacterial Foraging Algorithm (BFA) [11], Simulated Annealing (SA) [12], Harmony Search (HS) algorithm [13], Artificial Bee Colony Optimization (ABSO) [14], Flower Pollination Algorithm (FPA) [15] and Cuckoo Search (CS) [16]. However, these algorithms still need some modifications to find the most optimized parameters of PV modules [15].…”

Section: Introductionmentioning

confidence: 99%

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Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters

Mathew

Rani

Kumar

et al. 2018

IEEE J. Photovoltaics

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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 optimization 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.

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Section: B Wdo Implemented For Parameter Estimation Of Solar Pvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters

Mathew

Rani

Kumar

et al. 2018

IEEE J. Photovoltaics

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“…France) silicon solar cell (under 1000 W/m 2 at 33°C) are used as the benchmark data [7]. This benchmark set has been widely used to evaluate the performance of different optimization algorithms [27,32,34,[37][38][39]. The lower and upper values of the solar cell parameters of both models are given in Table 1.…”

Section: Results On the Solar Cell Parameter Identificationmentioning

confidence: 99%

“…Regarding the popular metaheuristic algorithms, simulated annealing algorithm [12], genetic algorithm [13,14], particle swarm optimization algorithm [15,16], differential evolution algorithm [17][18][19][20], pattern search [21], artificial bee colony algorithm [22] are widely used for the SCPIP. In addition to these well-known heuristic algorithms, there exist several papers in the literature which consider more recent approaches, such as bacterial foraging algorithm [23,24], teaching-learning-based optimization algorithm [25][26][27], biogeography-based optimization algorithm [28], chaos optimization algorithm [29], artificial fish swarm algorithm [30], bird mating optimizer approach [31], artificial immune system [32], evolutionary algorithm [1], cat swarm optimization algorithm [33], moth-flame optimization algorithm [5], JAYA optimization algorithm [34,35], chaotic whale optimization algorithm [36], imperialist competitive algorithm [37], bee pollinator flower pollination algorithm [38], shuffled complex evolution algorithm [39], memetic algorithm [40], interior search algorithm [41], collaborative swarm intelligence approach [42], and cuckoo search algorithm [43]. On the other hand, it has been proven by No-Free-Lunch theorem [44] that none of these algorithms is able to solve all type of optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Küçükoğlu

2019

International Journal of Photoenergy

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Solar cell parameter identification problem (SCPIP) is one of the most studied optimization problems in the field of renewable energy since accurate estimation of model parameters plays an important role to increase their efficiency. The SCPIP is aimed at optimizing the performance of solar cells by estimating the best parameter values of the solar cells that produce an accurate approximation between the current vs. voltage (I−V) measurements. To solve the SCPIP efficiently, this paper introduces an adaptive variant of the electromagnetic field optimization (EFO) algorithm, named adaptive EFO (AEFO). The EFO simulates the attraction-repulsion mechanism between particles of electromagnets having different polarities. The main idea behind the EFO is to guide electromagnetic particles towards global optimum by the attraction-repulsion forces and the golden ratio. Distinct from the EFO, the AEFO searches the solution space with an adaptive search procedure. In the adaptive search strategy, the selection probability of a better solution is increased adaptively whereas the selection probability of worse solutions is reduced throughout the search progress. By employing the adaptive strategy, the AEFO is able to maintain the balance between exploration and exploitation more efficiently. Further, new boundary control and randomization procedures for the candidate electromagnets are presented. To identify the performance of the proposed algorithm, two different benchmark problems are taken into account in the computational studies. First, the AEFO is performed on global optimization benchmark functions and compared to the EFO. The efficiency of the AEFO is identified by statistical significance tests. Then, the AEFO is implemented on a well-known SCPIP benchmark problem set formed as a result of real-life physical experiments based on single- and double-diode models. To validate the performance of the AEFO on the SCPIP, extensive experiments are carried out, where the AEFO is tested against the original EFO, AEFO variants, and novel metaheuristic algorithms. Results of the computational studies reveal that the AEFO exhibits superior performance and outperforms other competitor algorithms.

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“…These methods can be classified into three categories: analytical methods, numerical methods and evolutionary methods. In the analytical method, a set of transcendental equations is solved to extract parameters from solar cell [12]. The main advantage of the analytical method is the speed of calculation and reasonably accurate results.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

Gnetchejo¹,

Essiane²,

Ele³

et al. 2019

JPEE

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To evaluate the performance of a photovoltaic panel, several parameters must be extracted from the photovoltaic. These parameters are very important for the evaluation, monitoring and optimization of photovoltaic. Among the methods developed to extract photovoltaic parameters from current-voltage (I-V) characteristic curve, metaheuristic algorithms are the most used nowadays. A new metaheuristic algorithm namely enhanced vibrating particles system algorithm is presented here to extract the best values of parameters of a photovoltaic cell. Five recent algorithms (grey wolf optimization (GWO), moth-flame optimization algorithm (MFOA), multi-verse optimizer (MVO), whale optimization algorithm (WAO), salp swarm-inspired algorithm (SSA)) are also implemented on the same computer. Enhanced vibrating particles system is inspired by the free vibration of the single degree of freedom systems with viscous damping. To extract the photovoltaic parameters using enhanced vibrating particles system algorithm, the problem can be set as an optimization problem with the objective to minimize the difference between measured and estimated current. Four case studies have been implemented here. The results and comparison with other methods exhibit high accuracy and validity of the proposed enhanced vibrating particles system algorithm to extract parameters of a photovoltaic cell and module.weather conditions change, analytical methods become ineffective [18].Numerical extraction techniques based on some algorithm fit the points on the PV characteristic curve. Compared to the analytical method, an accurate result can be attained since the algorithm tries to consider all points on the characteristic curve [19]. In the literature, the Newton-Raphson method is the most used [19] [20] [21]; In [22], a numerical method is proposed for modeling and the simulation of PV. The method finds the five parameters from the current-voltage characteristic by using three points of the curve (maximum power, open circuit and short circuit). In [23], the Levenberg Marquardt algorithm is

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Solar PV Modelling and Parameter Extraction Using Artificial Immune System

Cited by 79 publications

References 14 publications

Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters

Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters

Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

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