The way of implementing an adaptive maximum power point tracking algorithm for photovoltaic (PV) applications in a field programmable gate array (FPGA) is described in this paper. A dual Kalman filter allows estimating the settling time of the whole system, including the PV source and the dc/dc converter controlling the operating point thereof, so that the tracking algorithm self adapts its parameters to the actual weather conditions. The real-Time identification need of this application requires an FPGA platform, so that the intrinsic algorithm parallelism is exploited and the execution time is reduced. The tradeoff solutions proposed in this paper, accounting for the algorithm complexity and the limited FPGA hardware, as well as some solutions for optimizing the implementation are described. The proposed adaptive algorithm is implemented in a low-cost Xilinx Spartan-6 FPGA and it is validated through experimental tests.@en

The maximum power point (MPP) tracking function allows one to extract the maximum power from the photovoltaic (PV) system in any operating condition. The perturb and observe technique is one of the mainly used algorithms aimed at this function. Its performances depend on the values of the two design parameters, which are the amplitude and the frequency of the perturbations that it applies in identifying the position of the MPP. In this paper, a method for the online optimization of the perturbation period is presented. It is based on the identification of the whole system, including the PV source and the dc/dc converter controlling it. The system impulse and frequency responses are evaluated by using the cross-correlation method. Such a technique, as well as the tracking algorithm, is effectively implemented by using a field-programmable gate array, and it is validated by means of simulation and experimental results.@en

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.@en

One of the main functions any photovoltaic system is equipped with is the maximum power point tracking. The perturb and observe technique is one of the mainly used algorithms for tracking the optimal operating point of the photovoltaic source. Its performances depend on the values of the two design parameters, that are the amplitude and the frequency of the perturbations it applies for identifying the position of the maximum power point. In this paper, two methods for an on-line optimization of the perturbation period are presented. They are based on the identification of the dc/dc converter, that controls the photovoltaic source, by means of its impulse and frequency response, both evaluated by using the cross-correlation method. Such methods, as well as the tracking algorithm, are effectively implemented by using a Field Programmable Gate Array. The feasibility of the proposed methods has been confirmed by Matlab simulations.@en

The aim of this paper is to present a Field Programmable Gate Array (FPGA) implementation of an adaptive perturb and observe (P&O) maximum power point tracking controller for photovoltaic application. The implemented controller is able to adapt the perturbation period of the P&O algorithm in order to face the changing environment conditions as well the aging of the photovoltaic system. The proposed implementation allows significantly improving the timing performances and reducing the FPGA resources. A complete time/area performance analysis and the experimental results confirm the feasibility and the efficiency of the proposed controller implementation.@en