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Photovoltaic modules parametric identification is often performed based on data available in the data sheet and referred to standard test conditions. On-line identification is trickier when irradiance and temperature sensors are not available. In this paper, an identification procedure is presented aiming to identify some module fault conditions that often occur during its lifetime. The proposed approach is applied to a large database of current vs voltage curves that are experimentally measured on a real photovoltaic plant.

This is the version of the paper distributed at the IEEE ICCEP 2019 Conference that has taken place in Otranto (Italy) from 2 to 4 July 2019.

The single diode equivalent circuit allows describing photovoltaic panels behavior quite accurately. The values of the five parameters appearing in this model are usually identified in Standard Test Conditions, at which irradiance and temperature values are given. Instead, when this identification has to be performed on field, suitable environmental sensors are required. In this paper, a method is presented for identifying the single diode model parameters as well as the irradiance and temperature conditions at which the panel is actually working. The method has the measured panel current vs voltage curve as the only input and it is suitable for being implemented on an embedded system for field applications. The method is validated through a large database of experimental results.

This is the version of the paper distributed at the IEEE ICCEP 2019 Conference that has taken place in Otranto (Italy) from 2 to 4 July 2019.

The single diode equivalent circuit allows describing photovoltaic panels behavior quite accurately. The values of the five parameters appearing in this model are usually identified in Standard Test Conditions, at which irradiance and temperature values are given. Instead, when this identification has to be performed on field, suitable environmental sensors are required. In this paper, a method is presented for identifying the single diode model parameters as well as the irradiance and temperature conditions at which the panel is actually working. The method has the measured panel current vs voltage curve as the only input and it is suitable for being implemented on an embedded system for field applications. The method is validated through a large database of experimental results.

In this paper, the use of the Dual Kalman Filter for the identification of photovoltaic system parameters is presented. The system includes the photovoltaic source, the dc/dc converter and the battery/dc bus and both its states and parameters in the actual operating conditions are identified. In particular, the proposed approach gives the confidence interval for the system settling time, which is used for the real-time optimization of the perturbative maximum power point tracking algorithm. The proposed technique is implemented by using a Field-Programmable Gate Array and it is validated by means of both simulation and experimental results.