A method for controlling an active power filter using neural networks is presented. Currently, there is an increase of voltage and current harmonics in power systems, caused by nonlinear loads. The active power filters (APFs) are used to compensate the generated harmonics and to correct the load power factor. The proposed control design is a pulse width modulation control (PWM) with two blocks that include neural networks. Adaptive networks estimate the reference compensation currents. On the other hand, a multilayer perceptron feedforward network (trained by a backpropagation algorithm) that works as a hysteresis band comparator is used. Two practical cases with Matlab-Simulink are presented to check the proposed control performance.

Backstepping controllers are obtained for distributed hybrid photovoltaic (PV) power supplies of telecommunication equipment. Grid-connected PV-based power supply units may contain dc-dc buck-boost converters linked to single-phase inverters. This distributed energy resource operated within the self-consumption concept can aid in the peak-shaving strategy of ac smart grids. New backstepping control laws are obtained for the single-phase inverter and for the buck-boost converter feeding a telecom equipment/battery while sourcing the PV excess power to the smart grid or to grid supply the telecom system. The backstepping approach is robust and able to cope with the grid nonlinearity and uncertainties providing dc input current and voltage controllers for the buck-boost converter to track the PV panel maximum power point, regulating the PV output dc voltage to extract maximum power; unity power factor sinusoidal ac smart grid inverter currents and constant dc-link voltages suited for telecom equipment; and inverter bidirectional power transfer. Experimental results are obtained from a lab setup controlled by one inexpensive dsPIC running the sampling, the backstepping and modulator algorithms. Results show the controllers guarantee maximum power transfer to the telecom equipment/ac grid, ensuring steady dc-link voltage while absorbing/injecting low harmonic distortion current into the smart grid.

This paper presents a comparative analysis of control methods to extract the maximum power and to track the maximum power point (MPP) from photovoltaic (PV) systems under changeable environmental conditions. The PV system consists of a solar module and a DC/DC converter, in this case a buck-boost converter, connected to a load. The maximum power point tracking (MPPT) algorithms compared are the perturb and observe (P&O) method, the PI control, a neuro-fuzzy and fuzzy technique and finally a backstepping control. The parameters considered for the comparison are the efficiency of the MPPT algorithm taking into account the extracted power from the PV system, steady and dynamic response of the system under changeable conditions such as the temperature and the irradiance and the signals ripple. The methods have been compared and the algorithm with the best results has been implemented in an experimental platform.

This work presents a new control method to track the maximum power point of a grid-connected photovoltaic (PV) system. A backstepping controller is designed to be applied to a buck-boost DC-DC converter in order to achieve an optimal PV array output voltage. This nonlinear control is based on Lyapunov functions assuring the local stability of the system. Control reference voltages are initially estimated by a regression plane, avoiding local maximum and adjusted with a modified perturb and observe method (P&O). Thus, the maximum power extraction of the generating system is guaranteed. Finally, a DC-AC converter is controlled to supply AC current in the point of common coupling (PCC) of the electrical network. The performance of the developed system has been analyzed by means a simulation platform in Matlab/Simulink helped by SymPowerSystem Blockset. Results testify the validity of the designed control method.Ovaj rad predstavlja novu metodu upravljanja za slije.enje točke maksimalne snage fotonaponskog (PV) sustava. Dana je sinteza backstepping regulatora za primjenu u silazno-uzlaznom DC-DC pretvaraču za postizanje optimalnog izlaznog napona PV-a. Ova je nelinearna metoda upravljanja zasnovana na Ljapunovim funkcijama osiguravajući tako lokalnu stabilnost sustava. Upravljačke reference napona prvo su estimirane korištenjem regresijske ravnine izbjegavajući lokalne maksimume, a zatim podešene tzv. modificiranom perturbiraj i uoči metodom (P&O). Prema tome, zagarantirano je maksimalno izvlačenje energije iz sustava proizvodnje. Naposlijetku, DC-AC pretvaračem upravlja se na način da osigurava željena izmjenična struja u točki zajedničkog spoja (PCC) elektroenergetske mreže. Ponašanje razvijenog sustava analizirano je kroz simulacije provedene u Matlab/Simulink okruženju uz korištenje SymPowerSystem biblioteke