General rules for signal flow graph modeling and analysis of dc-dc converters

Signal flow graph (SFG) nonlinear modeling approach is well known for modeling dc-dc converters. However, all possible SFGs of a given dc-dc converter system will not yield the generalized graph. A systematic procedure and guidelines for developing unified flow graph models of the dc-dc boost converters, from which complete behavior can be determined is presented. Usefulness of the proposed method is demonstrated through examples. As an illustration a 2-cell cascade boost and interleaved boost converter systems are taken as examples. Derivation of large, small-signal and steady-state models from generalized flow graph is also demonstrated. Large-signal model is developed and programmed in TUTSIM simulator. Large-signal, responses against supply and load disturbances are obtained. Experimental observations are provided to validate the proposed algorithm.     

Power tracking for nonlinear PV sources with coupled inductor SEPIC converter

The photovoltaic (PV) generator exhibits a nonlinear i-v characteristic and its maximum power (MP) point varies with solar insolation. In this paper, a V2-based MP point tracking (MPPT) scheme is developed using a buck-boost transformation topology. Although several buck-boost transformation topologies are available we have considered here a coupled inductor SEPIC converter for experimentation. To achieve almost ripple-free array current we have used ripple steering phenomena with the help of integrated inductor. This integrated inductor not only reduces the magnetic core requirements but also improves converter performance. Mathematical models are formulated and tracking algorithm is evolved. A combined PV system simulation model is developed in the SIMULINK. For a given solar insolation, the tracking algorithm changes the duty ratio of the converter such that the solar cell array (SCA) voltage equals the voltage corresponding to the MP point. This is done by the tracking algorithm, which mainly computes the power proportional to square of terminal voltage and changes the duty ratio of the converter so that this power is maximum. The proposed algorithm is implemented in real-time with the help of Analog Device ADMC-401 DSP evaluation module. The tracking program is developed to perform experimental investigations using analog-to-digital converter (ADC) interrupt. Using this processor we are able to track the MP within 200 ms. The proposed peak power tracking effectiveness is demonstrated through simulation and experimental results.     

Voltage-based maximum power point tracking control of PV system

Photovoltaic (PV) generators exhibit nonlinear v-i characteristics and maximum power (MP) points that vary with solar insulation. An intermediate converter can therefore increase efficiency by matching the PV system to the load and by operating the solar cell arrays (SCAs) at their maximum power point. An MP point tracking algorithm is developed using only SCA voltage information thus leading to current sensorless tracking control. The inadequacy of a boost converter for array voltage based MP point control is experimentally verified and an improved converter system is proposed. The proposed converter system results in low ripple content, which improves the array performance and hence a lower value of capacitance is sufficient on the solar array side. Simplified mathematical expressions for a PV source are derived. A signal flow graph is employed for modeling the converter system. Current sensorless peak power tracking effectiveness is demonstrated through simulation results. Experimental results are presented to validate the proposed method     

Feedforward maximum power point tracking of PV systems using fuzzy controller

A feedforward maximum power (MP) point tracking scheme is developed for the interleaved dual boost (IDB) converter fed photovoltaic (PV) system using fuzzy controller. The tracking algorithm changes the duty ratio of the converter such that the solar cell array (SCA) voltage equals the voltage corresponding to the MP point at that solar insolation. This is done by the feedforward loop, which generates an error signal by comparing the instantaneous array voltage and reference voltage. The reference voltage for the feedforward loop, corresponding to the MP point, is obtained by an off-line trained neural network. Experimental data is used for off-line training of the neural network, which employs back-propagation algorithm. The proposed fuzzy feedforward peak power tracking effectiveness is demonstrated through the simulation and experimental results, and compared with the conventional proportional plus integral (PI) controller based system. Finally, a comparative study of interleaved boost and conventional boost converter for the PV applications is given and their suitability is discussed.