REAL-TIME REMOTE POWER MONITORING OF SINGLEPHASE SOLAR PV INVERTER

This thesis focuses on the real-time remote monitoring of single-phase solar PV inverter. The PV module converts sunlight into electricity by producing a direct current (DC) electrical power. The solar charge controller regulates the voltage and current from PV module and store it into the battery. The inverter converts the DC electricity into AC for normal home appliances. The PV system produces DC power which fluctuates with the intensity of sunlight which may not stable. It is necessary to monitor the power produced by the solar PV in real time in order to create an efficient performance for the solar power system. The aim of this project is to set up a data acquisition system to monitor the power output of solar PV system. The data acquisition system includes DAQ device, voltage and current sensors. To monitor the solar PV system, LabVIEW software had been used to get the real-time voltage and current signals from the solar PV system. The voltage and current sensors are set up to the PV input, battery and load output. The LabVIEW is programmed to show the waveform and the value of voltage, current, power and THD for the load. So, it is able to monitor the power generated by the PV module, the power absorbed or supply by the battery and the power consumption of the load. Also, the LabVIEW is programmed to control the switch inside the solar PV system by using the relays. Lastly, it is able to access the LabVIEW by using another computer through web-based interface. So, the solar PV system can be monitored or controlled remotely from long distance as long as there is Internet services. Therefore, it is not necessary to be physically at the solar PV system to monitor the power

Predictive-TOPSIS-based MPPT for PEMFC Featuring Switching Frequency Reduction

A maximum power point tracking (MPPT) for a proton exchange membrane fuel cell (PEMFC) using a combination of conventional finite control set model predictive control (FCS-MPC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed in this paper. The key idea is to maximize the power generation from a PEMFC while minimizing the switching frequency of the power converter. The FCS-MPC technique is formulated to track the maximum power of PEMFC highly affected by ever-changing internal parameters. Meanwhile, the TOPSIS algorithm is applied to overcome the potential weaknesses of insulated-gate bipolar transistor (IGBT), which can only withstand a lower switching frequency. In this project, all simulations were run using MATLAB software to display the output power of the PEMFC system. As a result, the proposed predictive-TOPSIS-based MPPT algorithm can track the MPP for various PEMFC parameters within 0.019 s with an excellent accuracy up to 99.11%. The proposed MPPT technique has fast-tracking of the MPP locus, excellent accuracy, and robustness to environmental changes

Predictive-TOPSIS based MPPT for PEMFC Featuring Switching Frequency Reduction

A maximum power point tracking (MPPT) for a proton exchange membrane fuel cell (PEMFC) using a combination of conventional finite control set model predictive control (FCS-MPC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed in this paper. The key idea is to maximize the power generation from a PEMFC while minimizing the switching frequency of the power converter. The FCS-MPC technique is formulated to track the maximum power of PEMFC highly affected by ever-changing internal parameters. Meanwhile, the TOPSIS algorithm is applied to overcome the potential weaknesses of insulated-gate bipolar transistor (IGBT), which can only withstand a lower switching frequency. In this project, all simulations were run using MATLAB software to display the output power of the PEMFC system. As a result, the proposed predictive-TOPSIS-based MPPT algorithm can track the MPP for various PEMFC parameters within 0.019 s with an excellent accuracy up to 99.11%. The proposed MPPT technique has fast-tracking of the MPP locus, excellent accuracy, and robustness to environmental changes

Predictive Maximum Power Point Tracking for Proton Exchange Membrane Fuel Cell System

This project aims to design a predictive maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system (PEMFC). This predictive MPPT includes the predictive control algorithm of a DC-DC boost converter in the fully functional mathematical modeling of the PEMFC system. The DC-DC boost converter is controlled by the MPPT algorithm and regulates the voltage of the PEMFC to extract the maximum output power. All simulations were performed using MATLAB software to show the power characteristics extracted from the PEMFC system. As a result, the newly designed predictive MPPT algorithm has a fast-tracking of maximum power point (MPP) for different fuel cell (FC) parameters. It is confirmed that the proposed MPPT technique exhibits fast tracking of the MPP locus, outstanding accuracy, and robustness with respect to environmental changes. Furthermore, its MPP tracking time is at least five times faster than that of the particle swarm optimizer with the proportional-integral-derivative controller method