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Determining combined effects of solar radiation and panel junction temperature on all model-parameters to forecast peak power and photovoltaic yield of solar panel under non-standard conditions
摘要： In the current study, we introduce two methods to extract model-physical parameters of a solar panel using photovoltaic metrics at key points. We use a single-diode circuit to describe a solar panel working under STC. According to the first method, we derive a new transcendental equation connecting series resistance Rs to quality factor η and photovoltaic metrics at key points. Following the second method, we establish a new analytic expression giving series resistance as a function of quality factor and key points coordinates. For both methods, we express parallel conductance Gp, photo-generation current Iph and leakage current Is in terms of quality factor and all photovoltaic metrics. We use quality factor as variational parameter to minimize RMSE between experimental and optimized characteristics. We borrow temperature coefficients from manufacturer data sheet and determine panel radiation coefficients to derive analytical expressions giving variations of photovoltaic metrics at key points as functions of panel junction temperature T and solar radiation S. To investigate dependencies of all model-physical parameters versus T and S, we consider numerical values of model-physical parameters at STC as initial conditions, and resolve the system of non-linear equations linking panel current to panel voltage at key points. We test numerical models established and mathematical expressions derived by specifying to PV solar panels such as KC130GT and SM55 operating at different environmental conditions of ambient temperature and solar radiation. As a result, for arbitrary environmental conditions, predicted characteristics agree with measured characteristics validating thereby our numerical approach.
关键词： Model-physical parameters extraction,Solar radiation effect,Panel junction temperature coefficients,Solar radiation coefficient,Panel junction temperature effect,Current-voltage characteristics
更新于2025-09-19 17:13:59
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Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach
摘要： The integration of photovoltaic systems (PVSs) in future power systems grows into a more attractive choice. Thus, the studies related to PVSs operation have gained immense interest. Particularly, research in identifying PV cell model parameters remains an agile field because of the non-linearity of PV cell characteristics and its wide dependency on meteorological conditions of irradiation level and temperature. This paper proposes an Opposition-based Learning Modified Salp Swarm Algorithm (OLMSSA) for accurate identification of the two-diode model parameters of the electrical equivalent circuit of the PV cell/module. Six metaheuristic algorithms, including the recently released basic algorithm SSA, used with the benchmark test PV model of the double diode, and a practical PV module, are employed to assess the performance of OLMSSA. The experimental results and the in-depth comparative study clearly demonstrate that OLMSSA is highly competitive and even significantly better than the reported results of the majority of recently-developed parameter identification methods.
关键词： Metaheuristic Optimizer,Two-diode model,I-V characteristics,Parameters extraction,Photovoltaic panels,Salp Swarm Algorithm
更新于2025-09-19 17:13:59
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Brent’s algorithm based new computational approach for accurate determination of single-diode model parameters to simulate solar cells and modules
摘要： Simulated current-voltage (I-V) characteristics of photovoltaic (PV) cells and modules are significant for the performance assessment, design and quality control, which are decided by the accurate determination of the intrinsic parameters of the devices. Commonly, a single-diode model is utilized to extract these parameters such as the ideality factor (n), series resistance (Rs), shunt resistance (Rsh), saturation current (Io) and photo-generated current (Iλ). Driven by this idea, a new mathematical manipulation was performed on the single-diode equation that yielded a non-linear formula of Rs. Later, Brent’s algorithm was used to precisely estimate Rs at every fine-tuned point of n, thereby all other parameters were determined. The set of parameters that provided the lowest root mean square error (RMSE) between the experimental and simulated I-V data were chosen to be optimum. The proposed Brent’s algorithm (BA) was shown outperform several recently reported computational and heuristic algorithms that were exploited to mine the single-diode model parameters for solar cells and modules with varied device temperatures and solar irradiation conditions.
关键词： Parameters extraction,Brent’s algorithm,Solar cells and modules,Sensitivity analysis,I-V simulation
更新于2025-09-19 17:13:59
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Parameter extraction of PV models using an enhanced shuffled complex evolution algorithm improved by opposition-based learning
摘要： Accurate and efficient parameter extraction of PV models from I-V characteristic curves is significant for modeling, evaluation and fault diagnosis of PV modules/arrays. Recently, a large number of algorithms are proposed for this problem, but there are still some issues like premature convergence, low accurate and instability. In this paper, a new improved shuffled complex evolution algorithm enhanced by the opposition-based learning strategy (ESCE-OBL) is proposed. The proposed algorithm improves the quality of the candidate solution by the opposition-based learning strategy. Moreover, the basic SCE algorithm evolves with the traditional competition complex evolution (CCE) strategy, but it converges slowly and is prone to be trapped in local optima. In order to improve the exploration capability, the complex in the basic SCE is evolved by a new enhanced CCE. The ESCE-OBL algorithm is compared with some state-of-the-art algorithms on the single diode model (SDM) and double diode model (DDM) using benchmark I-V curves data. The comparison results demonstrate that the proposed ESCE-OBL algorithm can achieve faster convergence, stronger robustness and higher efficiency.
关键词： Parameters extraction,Enhanced shuffled complex evolution (ESCE),Opposition-based learning (OBL)
更新于2025-09-12 10:27:22
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Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models
摘要： With the increasing demand for solar energy, accurate, reliable, and efficient parameters extraction of photovoltaic models is becoming more significant and difficult. Accordingly, a more accurate and robust algorithm is continuously needed for this problem. To this end, a classified perturbation mutation based particle swarm optimization algorithm is proposed in this paper. During each generation of the proposed algorithm, the performance of each updated personal best position is evaluated and quantified to be a high-quality or low-quality. Then, for the high-quality personal best position, a mutation strategy with smaller perturbation is developed to enhance the local search ability within the promising search area. For the low-quality personal best position, a bigger perturbation mutation strategy is designed to explore different regions for improving the population diversity. Furthermore, the damping bound handling strategy is employed to mitigate the issue of falling into local optimal. The effectiveness of the proposed algorithm is evaluated by extracting parameters of five different photovoltaic models, and also tested on photovoltaic models under different conditions. Experiment results comprehensively demonstrate the superiority of the proposed algorithm compared with other well-established parameters extraction methods in terms of accuracy, stability, and rapidity.
关键词： Perturbation mutation,Photovoltaic models,Particle swarm optimization,Parameters extraction
更新于2025-09-11 14:15:04
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[IEEE 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Chengdu, China (2018.3.26-2018.3.28)] 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Coupling Matrix Extraction for Microwave Filter Design Using Neural Networks
摘要： In this paper, a novel coupling matrix extraction method for microwave ?lters is proposed. The neural networks (NNs) are introduced to extract the coupling matrix from the simulated responses. Compared to the traditional Cauchy method or vector ?tting with complicated derivations, the new method inherits the high speed of the trained NNs and can extract the coupling matrix of target S-parameters with high accuracy simultaneously. Finally, the coupling matrix of a fourth-order bandpass ?lter is extracted by using NNs. The numerical results validate the effectiveness of the proposed method.
关键词： Error back propagation neural networks (BPNNs),parameters extraction,microwave ?lters,coupling matrix
更新于2025-09-10 09:29:36
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An enhanced Dynamic Modeling of PV Module Using Levenberg- Marquardt Algorithm
摘要： An improved dynamic modeling of PV cell/modules based on automatic parameters extraction is proposed in this paper. For the sake of clarity, three models are compared in this study including, Single Diode (SDM), Double Diode (DDM) and the empirical model developed by Sandia National Laboratory (SANDIA). The use of nominal parameters or the values given by manufacturer in both SDM and DDM diode saturation current I0 and photo-generation current Iph equations can engender a significant error depending on the operating conditions and the consumed lifetime. Hence, these values can be handled as model parameters, and can be adjusted using automatic parameters extraction algorithms. Moreover, parameters based on static extraction methods (with fixed irradiation and temperature) namely, Rs, Rsh and n do not give satisfactory results under variable irradiation and temperature, which involve the use of a dynamic adjustment method to improve these parameters. In this way, static parameters extraction using genetic algorithm (GA) is proposed as a first stage for both SDM and DDM. After that, a dynamic parameters extraction based on the Levenberg-Marquardt algorithm (LMA) has been employed in the purpose to adjust some nominal parameters provided by the literature and the manufacturer, and those given by the static method. The idea consists of considering the PV module and the MPPT as a single system with dynamic inputs (irradiation and temperature) and output (Impp, Vmpp and Pmpp) to minimize the error between the measured and the simulated outputs. The validity of the proposed approach is compared with dynamic LMA models, nominal parameters based models, and the models based on static GA extracted parameters under of different weather conditions and out-door measurements. The improved models show promising results in terms of agreement with real data.
关键词： Photovoltaic module,Genetic Algorithm (GA),Dynamic Parameters Extraction,Static Parameters Extraction,Levenberg- Marquardt (LM)
更新于2025-09-04 15:30:14