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Discrete Time Modeling And Control Of DC/DC Switching Converter For Solar Energy Systems. Shaghayegh Kazemlou Advisor : Dr. Shahab Mehraeen Louisiana State University. Advanced Mechanical Design December 2008. Outline. Part I: Grid-connected Renewable System

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discrete time modeling and control of dc dc switching converter for solar energy systems

Discrete Time Modeling And Control Of DC/DC Switching Converter For Solar Energy Systems

ShaghayeghKazemlou

Advisor: Dr. ShahabMehraeen

Louisiana State University

Advanced Mechanical Design

December 2008

outline
Outline
  • Part I: Grid-connected Renewable System
  • Part II: Converter Discrete-Time Model
  • Part III: Converter Discrete-time Control Design
  • Part IV: Simulation Results
  • Part V: Summary and Future Works
grid connected renewable system

Part I

Grid-connected Renewable System

Advanced Mechanical Design

December 2008

solar system model
Solar System Model
  • Solar panels
  • DC-DC buck converter
  • Grid-tie inverter (GTI)

Solar power generation system

  • stabilizing the inverter DC-link capacitor
  • Omitting solar power oscillations

Objective

grid tie inverter
Grid-Tie Inverter

Solar power generation system

controller

dynamics

Synchronous Generator (SG)

grid tie inverter model observer
Grid-Tie Inverter Model/Observer

SG dynamical equations

Inverter dynamical equations

( )

  • Inverter gain ( ) and ac voltage angle ( ) are the control inputs
converter discrete time model

Part II

Converter Discrete-Time Model

Advanced Mechanical Design

December 2008

dc dc buck converter
DC-DC Buck Converter

dc-dc buck converter control system

  • Objective: Maintaining the solar power constant by adjusting duty cycle d
converter discrete time model1
Converter Discrete-Time Model
  • Converter discrete-time equations
  • Photovoltaic array output current is a nonlinear function of
converter discrete time control design

Part III

Converter Discrete-time Control Design

Advanced Mechanical Design

December 2008

state feedback controller
State Feedback Controller

Input:

  • Tracking error :

stable

neural network function approximation
Neural Network function approximation

: activation function

  • Weight estimation error :
  • NN weight update law :

: positive design constant

simulation results

Part IV

Simulation Results

Advanced Mechanical Design

December 2008

simulation results1
Simulation Results
  • System parameters
  • AVR+PSS mechanism for inverter
  • operational frequency of the converter : 10 kHz
  • three-phase resistive load with on each phase
  • Disturbance : load change from to at
  • solar module maximum power :
  • solar module maximum power point voltage :
simulation results2
Simulation Results
  • Solar Voltage Less than MPP Voltage:

Converter input power

Converter input voltage

  • Disturbance between t=1.4s to t=1.6s
simulation results3
Simulation Results
  • Solar Voltage Less than MPP Voltage:

Converter output voltage

Converter inductance current

Converter input current

simulation results4
Simulation Results
  • Solar Voltage higher than MPP Voltage:

Converter input power

Converter input voltage

Converter output voltage

simulation results5
Simulation Results
  • Input Voltage Adjustment to Load Change:

Converter input power

Converter input voltage

Converter output voltage

summary
Summary
  • The inverter is controlled bya novel stabilizer similar to power system stabilizer (PSS).
  • The interaction of the solar array dc-dc converter with the GTI is addressed.
  • A nonlinear discrete-time model of a photovoltaic-connected buck converter was presented.
  • Adaptive neural network (NN) controller is employed to enhance stability of dc-dc converter connected to grid-tie inverter (GTI) in the presence of power system disturbances.
  • Simulation results of the controller imply that the converter input voltage and power as well as the inductor current are stabilized which verifies the accuracy of the converter discrete-time model and the effectiveness of the proposed discrete-time controller.
recommendations for future works
Recommendations for Future Works
  • Improve the efficiency and effectiveness of discrete-time adaptive neural network in the power system stability and control
  • The system model can be developed to a more general distributed generation system where other renewable generators or synchronous generators all are interconnected. In this case each system is influenced by other subsystem’s states and a more general control method is necessary.
  • The solar system connected dc-dc converter can be modeled in a dc distribution system with interconnected subsystems working in high penetration of renewable generation.