The combination of empirical regressions and heuristic methods presented herein achieve automated convergence for 99.87% of the 5487 modules in the CEC database and greatly enhance more » the accuracy and efficiency by which new modules can be characterized and approved for use. Currently, adding new modules to the database requires calculating operational coefficients using a general purpose equation solver - a cumbersome process for the 300+ modules added on average every month. Rebate applications in California require results from the CEC PV model, and thus depend on an up-to-date database of module characteristics. This paper describes an improved algorithm for calculating the six parameters required by the California Energy Commission (CEC) photovoltaic (PV) Calculator module model. We validate the method by estimating model parameters for a module using outdoor test data and predicting the outdoor performance of the module. We verify the accuracy of the method by recov ering known parameter values from simulated I - V curves. We present a parameter estimation method that makes use of a fu ll range of available I - V curves. In contrast, module testing frequently records I - V curves over a wide range more » of irradi ance and temperature conditions which, when available, should also be used to parameterize the performance model. Many available parameter estimation methods use only short circuit, o pen circuit and maximum power points for a single I - V curve at standard test conditions together with temperature coefficients determined separately for individual cells.
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A single diode model requires a number of parameters to be estimated from measured I - V curves. Many popular models for photovoltaic system performance employ a single diode model to compute the I - V curve for a module or string of modules at given irradiance and temperature conditions.