An improved hybrid computational method for the two-diode model in photovoltaic simulation applications

• Main Author: Chin, Vun Jack
• Format: Thesis
• Language: English
• Published: 2018
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/80932/1/ChinVunJackPFKE2018.pdf; http://eprints.utm.my/id/eprint/80932/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:118928
• Institution: Universiti Teknologi Malaysia

For a successful realization of the solar photovoltaic (PV) system, the availability of an accurate, fast and reliable computer simulation tool is indispensable. The most crucial component that directly affects the accuracy of the simulator is the model of the PV cell (or module) itself. As an improvement over its single diode counterpart, the two-diode model exhibits superior accuracy for wide variations of irradiance and temperature. However, due to the limited number of information that are available on the manufacturer datasheet, the determination of all seven parameters of the two-diode model is very challenging. This thesis proposes a new hybrid method to improve the computation of the two-diode model. Unlike other existing hybrid methods, the proposed method retains the computation speed of the analytical approach and utilizes only standard datasheet information. Furthermore, it does not employ any simplification in the computation of the model parameters. Four parameters are determined analytically, while the remaining three are optimized by using differential evolution. The speed is improved significantly because the parameters are optimized only once, at standard test condition, while the values at other conditions are computed using the analytical equations. Additionally, a procedure to guide the initial conditions of the Newton-Raphson iteration is introduced. For validation, the algorithm is implemented in MATLAB software and its performance is compared with other established computational methods for mono-crystalline, poly-crystalline and thin film modules. When evaluated against the experimental data extracted from the datasheets, the mean absolute error is improved by 10 times, while the speed is increased by approximately three times. The standard deviation of the decision parameters over 100 independent runs is less than 0.1, which suggests that the optimization process is very consistent. Lastly, to prove the applicability of the proposed method in simulation applications, the algorithm is implemented into an in-house PV array simulator and its performance is validated using field data obtained from a PV monitoring station.