Predictive Current Control for Three-Level Four-Leg Indirect Matrix Converter under Unbalanced Input Voltage

In this paper, a robustness evaluation of model predictive current control with instantaneous reactive power minimization for a three-level four-leg indirect matrix converter is presented. Unbalanced voltages can be extremely dangerous, especially for motors and other inductive equipment. Unbalanced voltages can have a detrimental effect on equipment and the power system, which is exacerbated by the fact that a small phase voltage imbalance can result in a disproportionately large phase current imbalance. The robustness test is carried out by considering balance and unbalanced input voltages. The proposed control predicts the behavior of the load current and the instantaneous reactive power for every possible 96 switching states. Subsequently, it selects the optimum switching state which fulfils the objectives of the control without the need of modulators. The cost function has been adequately modified to consider the asymmetrical aspect of the input voltage. Experimental validation using a laboratory prototype was conducted by using FPGA under a wide range of input voltage unbalance. The experimental results show high fidelity load current reference tracking while maintaining relatively low instantaneous reactive power during the transient and steady-state condition. The percentage of reactive power after setting the optimal weighting factor, the average reactive power was found to reduce to approximately 10- 20%

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

An intelligent controlling method for battery lifetime increment using state of charge estimation in PV-battery hybrid system

In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly

Area Optimization for Networks-on-Chip Architectures using Deep Network Partitioning

This paper presents an area optimization for Network-on-Chip (NoC) architecture using deep Network Par- titioning technique. Among the hardest problems in NoC design is customizing the topological structure and application mapping on on-chip network in order to cater for application demand at minimal cost. The area cost of NoC is cut down by utilizing multi- level network partitioning where it partitions large networks into smaller segments. The enhancement in area cost is obtained by reducing both router area and the number of global links. In terms of performance, the multi-level network partitioning offers a better solution by assigning computational cores with heavy inter-core communications into the same segment. Therefore, the average inter-node distances would be minimized. This directly results in better performance due to its shortest path. For verification, the proposed technique has been tested on various System-on-Chip (SoC) applications case studies. The proposed technique results in the reduction of more than 7% router area, 19% global links, and 12% average inter-node distance

MANUAL PENGGUNA MESIN SESAR UNJUR MENGGUNAKAN SISTEM SOLAR PV

Manual ini adalah untuk memberi panduan kepada pengguna mesin salai sesar unjur yang dikuasakan oleh sistem tenaga solar PV. Dalam manual ini, terdapatnya informasi mengenai sistem solar PV dan mesin salai sesar unjur. Dengan adanya manual ini, langkah-langkah keselamatan, dan prosedur untuk maintenance & troubleshooting boleh dirujuk oleh pengguna. Ini bagi memastikan cara kerja penggunaan mesin salai sesar unjur dan sistem solar PV dilaksanakan dengan cara yang teratur dan betul

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

Design of FPGA-based Traffic Light Controller System

This paper proposed a design of a modern FPGA-based Traffic Light Control (TLC) System to manage the road traffic. The approach is by controlling the access to areas shared among multiple intersections and allocating effective time between various users; during peak and off-peak hours. The implementation is based on real location in a city in Malaysia where the existing traffic light controller is a basic fixed-time method. This method is inefficient and almost always leads to traffic congestion during peak hours while drivers are given unnecessary waiting time during off-peak hours. The proposed design is a more universal and intelligent approach to the situation and has been implemented using FPGA. The system is implemented on ALTERA FLEX10K chip and simulation results are proven to be successful. Theoretically the waiting time for drivers during off-peak hours has been reduced further, therefore making the system better than the one being used at the moment. Future improvements include addition of other functions to the proposed design to suit various traffic conditions at different locations