• Energy Research

Modeling of synchronous static compensator (STATCOM) of a power system based on the dynamic phasor model to investigate the performance of STATCOM for power quality analysis is described. It is compared with electromagnetic transient program (EMTP) like simulation. The dynamic phasor model and electromagnetic transient (EMT) model of the STATCOM including the power system are implemented in Matlab/Simulink toolbox and PSCAD/EMTDC, respectively. STATCOM dynamic phasor model including switching functions and their control system are presented. A satisfactory solution for power quality problems on typical distribution network is analyzed using the dynamic phasor model and EMTP like PSCAD/EMTDC simulation techniques. The simulation results revealed that the dynamic phasor model of STATCOM is in excellent agreement with the detailed time-domain EMT model of PSCAD/EMTDC simulation. The dynamic behavior of STATCOM using phasor model can be applied for analyzing power quality issues. It is found faster in speed and higher accuracy can be obtained and correlates well with PSCAD/EMTDC simulation results.

This paper presents an adaptive fuzzy logic controller (FLC) design technique for photovoltaic (PV) inverters using differential search algorithm (DSA). This technique avoids the exhaustive traditional trial and error procedure in obtaining membership functions (MFs) used in conventional FLCs. This technique is implemented during the inverter design phase by generating adaptive MFs based on the evaluation results of the objective function formulated by the DSA. In this work, the mean square error (MSE) of the inverter output voltage is used as an objective function. The DSA optimizes the MFs such that the inverter provides the lowest MSE for output voltage and improves the performance of the PV inverter output in terms of amplitude and frequency. The design procedure and accuracy of the optimum FLC are illustrated and investigated using simulations conducted for a 3 kW three-phase inverter in a MATLAB/Simulink environment. Results show that the proposed controller can successfully obtain the desired output when different linear and nonlinear loads are connected to the system. Furthermore, the inverter has reasonably low steady state error and fast response to reference variation.

This paper presents a new energy management system (EMS) for small hybrid electric vehicle (HEV), which utilizes energy from the combination of battery, fuel cell (FC) and super capacitors (SC). These alternative energy sources are excellent potential solution to the ever-rising price of petrol. The major focus of this paper is the energy management system, which consists of a strategy to coordinate the three different sources. This novel strategy for small HEV allowed the model to meet various load demand condition of an electric scooter or any three-wheeled vehicles. The model is simulated in MATLAB SIMULINK and its performance is analyzed by using the European test drive cycle (ECE-47). The simulation model system includes three renewable energy sources, EMS, DC machine, vehicle speed and feedback control system. The comparison result shows that the model capable to perform the proposed driving cycle.

Abstract This paper presents a new method for maximum power point tracking of photovoltaic (PV) energy harvesting system by using the Hopfield neural network (HNN) optimized fuzzy logic controller (FLC). In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. A complete simulation model of a PV system using the MATLAB/Simulink software is developed to validate the HNN optimized FLC. A hardware prototype of the PV maximum power point tracking controller was also implemented using the dSPACE DS1104 controller. Simulation and experimental results show the performance and effectiveness of the HNN optimized FLC. It is proven that the proposed HNN optimized FLC can provide accurate tracking of the PV maximum power point and improve the efficiency of PV systems.

This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algorithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers’ robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN’s performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance of these controllers under varying power system conditions. Additionally, a comparative study is performed to compare their performances with other solutions available in the literature based on several parameters. Results show the superiority of the ANN-based controllers in terms of cost reduction and efficiency.

Abstract Hybrid electric vehicle technologies emerge mainly because of the instability in fossil fuel prices, resources and the terrible impact of global warming. As most transport systems use fossil fuel and emit greenhouse gases, many researchers have studied the potential of fuel-cell hybrid electric vehicles (FCHEVs). FCHEVs are vehicles with zero greenhouse gas emission because they only depend on hydrogen. Numerous studies have proven that fuel cells with energy storage elements can provide sufficient energy required by FCHEVs. However, end users demand FCHEVs that are not only efficient in delivering the energy required but can also optimize hydrogen consumption and prolong battery lifetime to compete with current internal combustion engine vehicles. Therefore, advanced optimization algorithms for an FCHEV energy management system (EMS) must be developed to improve the performance efficiency of FCHEVs. This paper presents a critical review of the different types of FCHEV EMSs and their optimization algorithms to solve existing limitations and enhance the performance of future FCHEVs. Consequently, a comprehensive review on the major categories of FCHEV EMSs, such as proportional–integral–derivative controller, operational or state mode, rule-based or fuzzy logic, and equivalent consumption minimization strategies, are explained. This paper also describes optimization techniques such as linear programming, dynamic programming, Pontryagin’s minimum principle, genetic algorithm, particle swarm optimization and rule-based logic optimization for the EMSs of FCHEVs. Furthermore, it focuses on the various factors and challenges of existing optimization algorithms, hydrogen fuel source, environment and safety, and economical and societal concerns, as well as provides recommendations for designing capable and efficient EMSs for FCHEVs. All the highlighted insights of this review will hopefully lead to increasing efforts toward the development of an advanced optimization algorithm for future FCHEV EMSs.

This paper presents the simulation modeling and prototype development of an inverter controller for photovoltaic (PV) applications using the dSPACE DS1104 controller platform. The controller platform can link the simulation model developed in the MATLAB/SIMULINK environment to its prototype hardware. In the dSPACE controller, the voltage regulator controls the inverter by employing a proportional integral (PI) controller and the Park transformation method to generate sinusoidal pulse-width modulation (SPWM) signals. The SPWM signals switch the insulated gate bipolar transistor (IGBT) to stabilize the 50-Hz sinusoidal AC output voltages of the inverter. The simulation was performed with a DC power supply that acts as the PV generator to supply the power to the inverter. The simulation model was then translated into the inverter prototype using the dSPACE platform and tested in the laboratory to prove the efficacy of the proposed controller. The experimental results for the inverter system demonstrated the feasibility of the proposed control strategy by maintaining the THD of the output voltage at 4.6%.