Photovoltaic parameters estimation of poly-crystalline and mono-crystalline modules using an improved population dynamic differential evolution algorithm

Photovoltaic (PV) parameters estimation from the experimental current and voltage data of PV modules is vital for monitoring and evaluating the performance of PV power generation systems. Moreover, the PV parameters can be used to predict current-voltage (I-V) behavior to control the power output of the PV modules. This paper aimed to propose an improved differential evolution (DE) integrated with a dynamic population sizing strategy to estimate the PV module model parameters accurately. This study used two popular PV module technologies, i.e., poly-crystalline and mono-crystalline. The optimized PV parameters were validated with the measured data and compared with other recent meta-heuristic algorithms. The proposed population dynamic differential evolution (PDDE) algorithm demonstrated high accuracy in estimating PV parameters and provided perfect approximations of the measured I-V and power-voltage (P-V) data from real PV modules. The PDDE obtained the best and the mean RMSE value of 2.4251E-03 on the poly-crystalline Photowatt-PWP201, while the best and the mean RMSE value on the mono-crystalline STM6-40/36 was 1.7298E-03. The PDDE algorithm showed outstanding accuracy performance and was competitive with the conventional DE and the existing algorithms in the literature

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

Rethinking solar photovoltaic parameter estimation: global optimality analysis and a simple efficient differential evolution method

Accurate, fast, and reliable parameter estimation is crucial for modeling, control, and optimization of solar photovoltaic (PV) systems. In this paper, we focus on the two most widely used benchmark datasets and try to answer (i) whether the global minimum in terms of root mean square error (RMSE) has already been reached; and (ii) whether a significantly simpler metaheuristic, in contrast to currently sophisticated ones, is capable of identifying PV parameters with comparable performance, e.g., attaining the same RMSE. We address the former using an interval analysis based branch and bound algorithm and certify the global minimum rigorously for the single diode model (SDM) as well as locating a fairly tight upper bound for the double diode model (DDM) on both datasets. These obtained values will serve as useful references for metaheuristic methods, since none of them can guarantee or recognize the global minimum even if they have literally discovered it. However, this algorithm is excessively slow and unsuitable for time-sensitive applications (despite the great insights on RMSE that it yields). Regarding the second question, extensive examination and comparison reveal that, perhaps surprisingly, a classic and remarkably simple differential evolution (DE) algorithm can consistently achieve the certified global minimum for the SDM and obtain the best known result for the DDM on both datasets. Thanks to its extreme simplicity, the DE algorithm takes only a fraction of the running time required by other contemporary metaheuristics and is thus preferable in real-time scenarios. This unusual (and certainly notable) finding also indicates that the employment of increasingly complicated metaheuristics might possibly be somewhat overkill for regular PV parameter estimation. Finally, we discuss the implications of these results and suggest promising directions for future development.Comment: v2, see source code at https://github.com/ShuhuaGao/rePVes

A scaling law for monocrystalline PV/T modules with CCPC and comparison with triple junction PV cells

This is the final version of the article. Available from Elsevier via the DOI in this record.Scaling laws serve as a tool to convert the five parameters in a lumped one-diode electrical model of a photovoltaic (PV) cell/module/panel under indoor standard test conditions (STC) into the parameters under any outdoor conditions. By using the transformed parameters, a current-voltage curve can be established under any outdoor conditions to predict the PV cell/module/panel performance. A scaling law is developed for PV modules with and without crossed compound parabolic concentrator (CCPC) based on the experimental current-voltage curves of six flat monocrystalline PV modules collected from literature at variable irradiances and cell temperatures by using nonlinear least squares method. Experiments are performed to validate the model and method on a monocrystalline PV cell at various irradiances and cell temperatures. The proposed scaling law is compared with the existing one, and the former exhibits a much better accuracy when the cell temperature is higher than 40 °C. The scaling law of a triple junction flat PV cell is also compared with that of the monocrystalline cell and the CCPC effects on the scaling law are investigated with the monocrystalline PV cell. It is identified that the CCPCs impose a more significant influence on the scaling law for the monocrystalline PV cell in comparison with the triple junction PV cell. The proposed scaling law is applied to predict the electrical performance of PV/thermal modules with CCPC.The authors gratefully acknowledge the EPSRC Solar Challenge project SUNTRAP (EP/K022156/1) and Sȇr Cymru National Research Network grant 152 for financial support in the UK

A scaling law for monocrystalline PV/T modules with CCPC and comparison with triple junction PV cells

Scaling laws serve as a tool to convert the five parameters in a lumped one-diode electrical model of a photovoltaic (PV) cell/module/panel under indoor standard test conditions (STC) into the parameters under any outdoor conditions. By using the transformed parameters, a current-voltage curve can be established under any outdoor conditions to predict the PV cell/module/panel performance. A scaling law is developed for PV modules with and without crossed compound parabolic concentrator (CCPC) based on the experimental current-voltage curves of six flat monocrystalline PV modules collected from literature at variable irradiances and cell temperatures by using nonlinear least squares method. Experiments are performed to validate the model and method on a monocrystalline PV cell at various irradiances and cell temperatures. The proposed scaling law is compared with the existing one, and the former exhibits a much better accuracy when the cell temperature is higher than 40 °C. The scaling law of a triple junction flat PV cell is also compared with that of the monocrystalline cell and the CCPC effects on the scaling law are investigated with the monocrystalline PV cell. It is identified that the CCPCs impose a more significant influence on the scaling law for the monocrystalline PV cell in comparison with the triple junction PV cell. The proposed scaling law is applied to predict the electrical performance of PV/thermal modules with CCPC

Six-parameter electrical model for photovoltaic cell/module with compound parabolic concentrator

It is known that compound parabolic concentrators (CPCs) can improve electrical performance of a photovoltaic (PV) flat-plate system. However, a lumped electrical model of a PV cell/module with CPC for assessing performance under different operating conditions is unavailable. In this paper, a six-parameter based model is developed and applied to a PV cell, two PV models with CPC, and a PV module with 2D asymmetric CPC (trough). For validation, CPC with a single PV cell and two CPC modules with 2 × 2 and 9 × 9 PV cells are fabricated and measured in an indoor laboratory under standard test conditions. Results show that the optimised algorithm precisely predicts the six model parameters. A sensitivity analysis is performed to identify the importance of each parameter in the model. Ideality factor, circuit current and reverse saturation current are found to be the most dominant factor, while shunt resistance is the least important with CPC gain coefficient and series resistance are in between. Transient performance of a PV cell with CPC under variable outdoor climate conditions is also examined by coupling optical, thermal and electrical effects

EXTRACTION OF DOUBLE-DIODE PHOTOVOLTAIC MODULE MODEL’S PARAMETERS USING HYBRID OPTIMIZATION ALGORITHM

This paper presents seven parameters of double diode model of the photovoltaic module under different weather conditions are extracted using differential development with an integrated mutation per iteration (DEIM) algorithm. The algorithm is produced by integrating of two other algorithms namely, electromagnetism like (EML) and differential evolution (DE) algorithms. DEIM enhances the mutation step of the original DE by using the attraction-repulsion principle found in the EML algorithm. Meanwhile, a novel strategy based on adjusting mutation and crossover rate factors for each iteration is adopted in this paper. The implemented scheme's success is confirmed by comparing its results with those obtained by techniques cited in the literature. Along with the results, the DEIM suggests high closeness with the experimental I–V characteristic. For the proposed algorithm the average Root Mean Square Error ( MSE), Absolute Error (AE ), Mean Bias Error ( MBE), and execution time values were 0.0608, 0.044, 0.0053, and 23.333, respectively. The comparisons and evaluation results proved that the DEIM is better in terms of precision and rapid convergence. Furthermore, fewer control parameters are needed in comparison to EML and DE algorithms

Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database