Modeling method research of flexible amorphous silicon solar cell

Abstract

Circuit model of solarcell is the core section of photovoltaic power generation system simulation model. The modeling methods of flexible amorphous silicon solar cell were studied based on improved pho tovoltaic cell circuit model. A group of flexible amorphous silicon solar cell' I-V and P-V data were teste dunder the condition of standard solar light. After that, Quasi Newton method and self organizing migrating algorithm (SOMA) were adopted to calculate the 8 unknown parameters of circuit model, finally establishing the single diode circuit model of this kind of amorphous silicon thin film solar cell. The optimal results cal culated by self organizing migrating algorithm showed that the standard deviation of the calculated current in the circuit model is less than 0.79%, while the sum of squared residuals is only 0.05%. The model laid a theoretical foundation of performance optimization and maximum power tracking control technology devel opment of flexible amorphous silicon solar cells.

Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

“…In order, to compare it with other algorithms, a benchmark commercial solar cell and benchmark photovoltaic module are selected in single diode, double diode and photovoltaic module models are considered. The study test cases are designated as follows: The current-voltage measurements are collected from [29,54] and have been widely used by different papers to test electric circuit models, modelling or translate the current versus voltage and technique for parameter extraction. Furthermore, statistical analyses are carried out to measure the accuracy of the estimated parameters and model suitability.…”

“…The goal is to minimize the multi-objective functions adapted to minimize the absolute errors between experimental and calculated current-voltage data under inequality constraint functions. Three different cases are examined as follows: single and double diode models of a commercial mono-crystalline silicon solar cell (R.T.C France company) at 33 • C, based on current-voltage experimental data recorded in [29]; (ii) flexible dual junction amorphous hydrogenated silicon a-Si:H solar cell under standard sunlight, based on data obtained in a light intensity of 1000 W/m 2 and at a temperature of 300 K [54]; (iii) a Photowatt-PWP 201 photovoltaic module which 36 polycrystalline silicon cells are connected in series and the data is measured at an irradiance of 1000 W/m 2 , and a temperature of 25 • C [29]. To verify the performance of the proposed approach and the quality of the obtained results, statistical analyses are carried out to measure the accuracy of the calculated parameters and model suitability.…”

“…The results obtained are compared with recent techniques such as the Biogeography-Based Optimization algorithm with Mutation strategies (BBO-M) [68], Levenberg-Marquardt algorithm combined with Simulated Annealing (LMSA) [47], Artificial Bee Swarm Optimization algorithm [48], Artificial Bee Colony optimization (ABC) [49], hybrid Nelder-Mead and Modified Particle Swarm Optimization (NM-MPSO) [50], Repaired Adaptive Differential Evolution (RADE) [59], Chaotic Asexual Reproduction Optimization (CARO) [69] for solar cell single and double diodes. For organic flexible hydrogenated amorphous silicon, a-Si:H solar cell will be compared with the Quasi-Newton (Q-N) method and Self-Organizing Migrating Algorithm (SOMA) [54]. The optimal parameters of Photowatt-PWP 201 are compared with the Newton-Raphson [29] Pattern Search (PS) [55], Genetic algorithm (GA) [56] and Simulated Annealing algorithm (SA) [58].…”

“…The current-voltage measurements are collected from [29,54] and have been widely used by different papers to test electric circuit models, modelling or translate the current versus voltage and technique for parameter extraction. Furthermore, statistical analyses are carried out to measure the accuracy of the estimated parameters and model suitability.…”

Research on MPPT Control Algorithm of Flexible Amorphous Silicon Photovoltaic Power Generation System Based on BP Neural Network

“…Flexible amorphous silicon photovoltaic cells. Flexible amorphous silicon photovoltaic cells model is established by physical model, that is, the physical model is determined after basic circuit model expression is built and then quasi-newton method is used to calculate the parameter values [5].…”