Intelligent Power Control of Three-Phase Grid-Connected PV System during Grid Faults(2/3)

Project Details

Description

The purpose of this project is to develop a PC-based intelligent control of three-phase grid-connected photovoltaic (PV) system for active and reactive power control during grid faults. In the first year, the maximum power point tracking (MPPT) method of PV system, the analysis of grid fault types, the detective method of the grid faults, the design of phase-locked loop (PLL), the analysis of active power and reactive power during grid faults, and the development of the experimental system are all developed. Moreover, the three-phase grid-connected PV system, which includes PV panel, boost converter, three-phase inverter and grid, can be regarded as a nonlinear system with uncertainties. There are some other problems of the three-phase grid-connected PV system, such as the change of temperature and irradiance alters system operating point. Thus, it is very difficult to develop a physical nonlinear model for the system. Hence, the traditional controller such as proportional-integral (PI) controller is hard to achieve the desired control performance in the presence of plant parameter variations and unknown external disturbance. Since the designs of intelligent controllers do not require mathematical models, the aforementioned problems can be solved. Therefore, the development of the intelligent control of active and reactive power for the three-phase grid-connected PV system during grid faults has become an important issue. In the second year of this project, a probabilistic wavelet fuzzy neural network (PWFNN) controller is proposed to achieve the low voltage ride through (LVRT) requirement and improve the control performance of active and reactive power of the three-phase grid-connected PV system during grid faults, in which the learning rates of PWFNN are tuned online. Furthermore, a Takagi-Sugeno-Kang probabilistic fuzzy neural network with asymmetric membership function (TSKPFNN-AMF) controller is proposed to achieve the LVRT requirement and improve the control performance of active and reactive power of the three-phase grid-connected PV system during grid faults in the last year of this project.In terms of the realization of software, the proposed control algorithms are realized in the PC by using “C” language and combined with the Simulink functions to achieve the overall control function of the three-phase grid-connected PV system. Therefore, the PC-based active and reactive power control of the three-phase grid-connected PV system can be achieved.
StatusFinished
Effective start/end date1/08/1631/07/17

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 7 - Affordable and Clean Energy
  • SDG 11 - Sustainable Cities and Communities
  • SDG 17 - Partnerships for the Goals

Keywords

  • Grid faults
  • photovoltaic (PV) system
  • low voltage ride through (LVRT)
  • reactive power control
  • probabilistic wavelet fuzzy neural network (PWFNN)
  • Takagi-Sugeno-Kang probabilistic fuzzy neural network with asymmetric membership function (TSKPFNN-AMF)

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.