Parameters Extraction of a Photovoltaic Cell Model Using a Co-evolutionary Heterogeneous Hybrid Algorithm

© 2019 IEEE. This paper proposes a new hybrid algorithm with a combination between the wind driven optimization (WDO) algorithm and the differential evolution with integrated mutation per iteration (DEIM) algorithm. The proposed algorithm, a wind driven optimization based on differential evolution with integrated mutation per iteration (WDO-based on DEIM) algorithm, is utilized to extract the unknown parameters in both of a single-diode photovoltaic (PV) cell model and a double-diode PV cell model. To show the effectiveness of the proposed model, its performance is validated internally by comparing the generated current-voltage (I-V) characteristic curves by the proposed algorithm with the actual I-V characteristic curves, and externally with those obtained by the WDO and DEIM algorithms. The results show the superiority of the proposed model. According to the normalized-root-mean-square error (nRMSE), the mean absolute percentage error (MAPE) and the coefficient of determination (R^{2}) of the achieved results, the proposed WDO-based on DEIM algorithm outperforms the aforementioned algorithms. Finally, the average efficiency of the WDO-based on DEIM algorithm is 95.31%, while it is 81.08% for the WDO algorithm and 88.37% for DEIM algorithm in the single-diode PV cell model. While, it is 96.78% based on WDO-based on DEIM algorithm and it is 92.30% for the WDO algorithm and 91.42% for DEIM algorithm in the double-diode PV cell model

An improved wind driven optimization algorithm for parameters identification of a triple-diode photovoltaic cell model

© 2020 Elsevier Ltd The double-diode photovoltaic cell model is insufficient to accurately characterize the different current components of a photovoltaic cell. Therefore, the triple-diode model of a photovoltaic cell is considered to model its complicated physical characteristics by clearly defining the different current components of the photovoltaic cell. The identification of its unknown parameters is a complex, multi-modal and multi-variable optimization problem. An improved wind driven optimization algorithm is proposed in this paper to identify its nine unknown parameters. The proposed method is a combination of the mutation strategy of the differential evolution algorithm and the covariance matrix adaptation evolution strategy of the wind driven optimization algorithm. The mutation strategy aims to bolster the exploration ability of the improved wind driven optimization algorithm, while the covariance matrix adaptation evolution strategy based on wind driven optimization algorithm aims to improve the searching of the classical wind driven optimization algorithm. Therefore, improved wind driven optimization algorithm is more accurate and faster than the classical wind driven optimization algorithm in finding the global optimum and balancing exploration and exploitation. The proposed model has been utilized on 15-minute interval data to identify the unknown parameters of three commercial photovoltaic technologies, namely, mono-crystalline, poly-crystalline and thin-film. To show the effectiveness of the proposed model, its performance is validated by comparing it with that obtained by the classical wind driven optimization, the adaptive wind driven optimization, moth-flame optimizer, sunflower optimization and the improved opposition-based whale optimization algorithms. The results demonstrate that improved wind driven optimization outperforms the aforementioned models in accuracy, convergence speed and feasibility. In addition, improved wind driven optimization more clearly defined different current components and generated any current-voltage curve under any operating condition

Lattice strain causes non-radiative losses in halide perovskites

Halide perovskites are found to exhibit strain patterns over large areas, which influences the lifetimes of charge carriers.EPSR

An optimized offline random forests-based model for ultra-short-term prediction of PV characteristics

© 2005-2012 IEEE. The fluctuation of meteorological data causes random changes in photovoltaic (PV) performance, which may negatively affect the stability and reliability of the electrical grid. This paper proposes a new ultra-short-term offline hybrid prediction model for PV I-V characteristic curves based on the dynamic characteristics of the meteorological data on a 15-min basis. The proposed hybrid prediction model is a combination of the random forests (RFs) prediction technique and the ant-lion optimizer (ALO). ALO is used to optimize the hyper-parameters of the RFs model, which aims to improve its performance in terms of accuracy and computational time. The performance of the proposed hybrid prediction model is compared with that of conventional RFs, RFs-iteration, generalized regression neural network (GRNN), GRNN-iteration, GRNN-ALO, a cascade-forward neural network (CFNN), CFNN-iteration, CFNN-ALO, feed-forward neural network (FFNN), FFNN-iteration, and FFNN-ALO models. The result shows that the I-V characteristic-curve prediction accuracy, in terms of the root-mean-squared error, mean bias error, and mean absolute percentage error of the proposed model are 0.0091 A, 0.0028 A, and 0.1392%, respectively, with an accuracy of 99.86%. Moreover, the optimization, training, and testing times are 162.15, 10.1919, and 0.1237 s, respectively. Therefore, the proposed model performs better than the aforementioned models and the other existing models in the literature. Accordingly, the proposed hybrid (RFs-ALO) offline model can significantly improve the accuracy of PV performance prediction, especially in grid-connected PV system applications

Parameters Extraction of a Photovoltaic Cell Model Using a Co-evolutionary Heterogeneous Hybrid Algorithm

© 2019 IEEE. This paper proposes a new hybrid algorithm with a combination between the wind driven optimization (WDO) algorithm and the differential evolution with integrated mutation per iteration (DEIM) algorithm. The proposed algorithm, a wind driven optimization based on differential evolution with integrated mutation per iteration (WDO-based on DEIM) algorithm, is utilized to extract the unknown parameters in both of a single-diode photovoltaic (PV) cell model and a double-diode PV cell model. To show the effectiveness of the proposed model, its performance is validated internally by comparing the generated current-voltage (I-V) characteristic curves by the proposed algorithm with the actual I-V characteristic curves, and externally with those obtained by the WDO and DEIM algorithms. The results show the superiority of the proposed model. According to the normalized-root-mean-square error (nRMSE), the mean absolute percentage error (MAPE) and the coefficient of determination (R^{2}) of the achieved results, the proposed WDO-based on DEIM algorithm outperforms the aforementioned algorithms. Finally, the average efficiency of the WDO-based on DEIM algorithm is 95.31%, while it is 81.08% for the WDO algorithm and 88.37% for DEIM algorithm in the single-diode PV cell model. While, it is 96.78% based on WDO-based on DEIM algorithm and it is 92.30% for the WDO algorithm and 91.42% for DEIM algorithm in the double-diode PV cell model

An adaptive wind-driven optimization algorithm for extracting the parameters of a single-diode PV cell model

© 2010-2012 IEEE. This paper presents a new methodology to extract the unknown parameters of a single-diode photovoltaic (PV) cell model. The first contribution of this paper is the development and implementation of a new version of the wind-driven optimization algorithm, called an adaptive wind-driven optimization (AWDO) algorithm. The advantages of the AWDO algorithm are: 1) accurate extraction of the global values of the optimized PV parameters in changing weather conditions, which is achieved by building solutions from random operations; and 2) capability of handling the given complex multi-modal and multi-dimensional optimization problems. The second contribution is the identification of a generalization model to generalize the extracted parameters of a single-diode PV cell model. That provides an ability of the proposed methodology to work with any I-V characteristic curve of PV cells and at any weather condition on a 15-min basis. To validate the proposed methodology, it has been tested for 1307 I-V characteristic curves of a PV module at various weather conditions on a 15-min basis. Additionally, its accuracy and computational efficiency are verified and compared with five well-known existing extraction methods: Villalva's model, particle swarm optimization, biogeography-based optimization, Gang's model, and bacterial foraging optimization by both simulation and outdoor measurements. The results show that the AWDO algorithm can provide the extracted five parameters with an acceptable range of accuracy and faster than the aforementioned models. Therefore, the proposed methodology (AWDO based on Chenlo's model) can be confidently recommended as a reliable, feasible, valuable, and fast optimization algorithm for parameter extraction of a single-diode PV cell model

Lattice strain causes non-radiative losses in halide perovskites

Halide perovskites are found to exhibit strain patterns over large areas, which influences the lifetimes of charge carriers.EPSR

Lattice strain causes non-radiative losses in halide perovskites

Halide perovskites are promising semiconductors for inexpensive, high-performance optoelectronics. Despite a remarkable defect tolerance compared to conventional semiconductors, perovskite thin films still show substantial microscale heterogeneity in key properties such as luminescence efficiency and device performance. However, the origin of the variations remains a topic of debate, and a precise understanding is critical to the rational design of defect management strategies. Through a multi-scale investigation-combining correlative synchrotron scanning X-ray diffraction and time-resolved photoluminescence measurements on the same scan area-we reveal that lattice strain is directly associated with enhanced defect concentrations and non-radiative recombination. The strain patterns have a complex heterogeneity across multiple length scales. We propose that strain arises during the film growth and crystallization and provides a driving force for defect formation. Our work sheds new light on the presence and influence of structural defects in halide perovskites, revealing new pathways to manage defects and eliminate losses