Search

Power loss and malfunctioning of equipment due to current and voltage harmonics produced by nonlinear loads are major concerns for both power suppliers and consumers. This paper employs DSTATCOM to mitigate these harmonic distortions in distribution systems. DSTATCOM is a current controlled voltage source PWM converter with passive components. It supplies compensating current that eliminates current harmonics and provides reactive power compensation thereby providing a smooth source current. The performance of DSTATCOM is controlled using proportional integral and fuzzy logic–based control (FLC) schemes to improve power quality at dynamic loading. As the penetration of photovoltaic (PV) systems in energy production is significant, the power quality issues for PV-integrated systems have also been investigated. The maximum power point for a PV array is tracked using perturb and observe and FLC methods. The complete system is simulated in MATLAB and extensively investigated for a large range of balanced and unbalanced nonlinear load conditions. The simulation results are validated in real time through the hardware in loop (HIL) test bench using Opal-RT controller. The proposed PV-integrated DSTATCOM with FLC yields better performance in terms of DC link voltage settling time, total harmonics distortion, and smooth source current as compared to other schemes. [ABSTRACT FROM AUTHOR]

This paper presents the design, modeling, and implementation of a photovoltaic (PV) integrated shunt active power filter (SAPF) for clean power generation along with power quality improvement. The system uses fuzzy logic control (FLC) for maximum power point tracking (MPPT) and SAPF control. The proposed system implements synchronous reference frame theory based direct power control strategy using hysteresis control. The system performance is evaluated in real-time under-balanced/unbalanced and nonlinear load conditions using field-programmable gate array (FPGA) based computational engine with different controllers. The performance is observed to be better with FLC as compared to traditional controllers. [ABSTRACT FROM AUTHOR]

This paper focuses on optimal energy sharing between the two sources i.e., the internal combustion engine and the battery-powered electric motor in a hybrid electric vehicle (HEV). It is necessary that these sources operate in their efficient operating region while fulfilling the energy demanded by the vehicle to obtain the maximum fuel economy. As both of these sources have different operating characteristic and vehicle running conditions, the situation requires a smart controller to address this problem appropriately. In this work, fuzzy logic and Elman neural network-based adaptive energy management strategies (EMS) in an HEV are designed and implemented. The input parameters to these EMS are torque demand, battery state of charge, and regenerative braking. The proposed strategy aims to maximise the fuel economy while maintaining the battery health. A power-split HEV along with EMS is designed, modelled and simulated in MATLAB/Simulink first and then the whole system is validated in real-time using controller hardware in the loop testing platform (CHIL). The FPGA based MicroLabBox CHIL has been employed to test the system behaviour in real-time. The proposed EMS have been compared with conventional strategies and the comparison reveals that the Elman neural network-based method results in higher fuel economy, faster response, and minimal mismatch between desired and attained vehicle speeds. • Design of Fuzzy logic & Elman neural network based energy management strategy (EMS). • The Brakes, torque, and battery SoC are considered in formulating the EMS. • Higher fuel economy and lesser SoC degradation is obtained. • Almost no mismatch is scene between demanded speed and the achieved speed. • Real-time validation of the proposed EMS on CHIL testing platform. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the design and implementation of a photovoltaic‐integrated shunt active power filter (SAPF) to improve the power quality and to generate clean power. The system uses adaptive neuro‐fuzzy inference system (ANFIS)‐based maximum power point tracking and control of synchronous reference frame theory–based SAPF. Various control schemes are implemented in MATLAB and then validated in real‐time using FPGA‐based computation engine of OPAL‐RT 4510. Control techniques built around the artificial neural network, fuzzy logic control, and ANFIS are compared for balanced and unbalanced loads on parameters like total losses with/without compensation, voltage drop, power factor, and total harmonic distortion. [ABSTRACT FROM AUTHOR]