Design of a non-linear controller to track de maximum power point of photovoltaic systems in electrical power systems with distributed generation

  1. Aránzazu Delgado Martín
Supervised by:
  1. Jesús Rodríguez Vázquez Director

Defence university: Universidad de Huelva

Fecha de defensa: 26 January 2016

  1. María Reyes Sánchez Herrera Chair
  2. María Isabel Milanés Montero Secretary
  3. Engin Karatepe Committee member

Type: Thesis


This Doctoral thesis work is focused on the non-linear backstepping control of a buck-boost power converter and DC/AC power converter to track the maximum power point in PV systems and transfer the power to the electrical network. First, a backstepping control has been implemented to regulate the PV array output voltage in simulation to achieve the maximum power point. Forthat, a grid-connected PV system that consists of a PV array, a buck-boost converter, a DC/AC converter and a load has been modeled in Matlab-Simulink. Then, the designed backstepping controller is implemented in the system. The backstepping control is based on Lyapunov functions guaranteeing the locally stability of the system. This control is robust and tests have been carried out to validate its performance. Once the proposed control is verified in simulation, the method has been proved in an experimental platform. In this case, the experimental platform consists of a commercial PV module, a built buck-boost converter and a DC load to test the backstepping controller in the DC/DC converter. The experiments carried out validate the performance of the proposed control. The voltage that provides the maximum power point is always achieved under changeable environmental conditions, testing the robustness of the control. Finally, the non-linear backstepping controller is proposed to control the DC/AC power converter in an experimental platform, including the connection to the grid. Thus, backstepping controllers are obtained for distributed hybrid photovoltaic (PV) power supplies of telecommunication equipment. The grid-connected PV system contains the PV array, the built DC-DC buck-boost converters linked to single-phase inverters and telecom equipment as loads. The backstepping approach is robust and able to cope with the grid non-linearity 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 set-up controlled by one low- cost dsPIC. 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. A modification of the backstepping control has been also proposed, an adaptive backstepping controller. This non-linear control also tracks the maximum power point regulating the buck-boost converter input voltage regardless of the parameter values of the DC/DC converter. Apart from the proposed algorithms, other MPPT algorithms have been implemented in order to compare the results of different techniques. A neuro-fuzzy system with fuzzy logic MPPT control is designed and then it is compared with the P&O algorithm, the PI control and the proposed backstepping control. Finally, the research work about the PV system control under partial shading conditions using artificial vision with backstepping control is sent to a paper, being in revision at this moment. Additional system performance related with power quality has been proposed. A PV active power line conditioner is designed to transfer the maximum power to the electrical network and to compensate the reactive power and the non-linear loads. Besides, the use of switching output reactances is proposed to improve the compensation of a shunt active power filter. Finally, two power indexes have been tested in a distributed network, the Load Characterization Index (LCI) that identifies linear and non-linear loads in the power systems and the Unbalance Current Ratio (UCR) that assigns the responsibility for system unbalance to load and source sides.