R. Arunkrishna

Bio: R. Arunkrishna is an academic researcher from VIT University. The author has contributed to research in topics: AC power & Inverter. The author has an hindex of 1, co-authored 1 publications receiving 8 citations.

Comparison Between PI Controller and Fuzzy Logic-Based Control Strategies for Harmonic Reduction in Grid-Integrated Wind Energy Conversion System

Abstract: In this paper, a novel fuzzy logic-based control (FLC) strategy is developed to perform multi-function strategy for smooth and controlled operation of three-phase renewable energy system (RES)-based wind energy conversion system (WECS) with grid integration. The inverter acts as an converter to infuse the power obtained from the wind energy and as a active power filter to compensate reactive power demand and load current harmonics. The control strategies in accordance with 3-phase 4-wire unbalanced load tend to appear as a balanced linear load system at grid. The control strategy is developed and validated using MATLAB/Simulink. The proposed controller is compared with PI-based controller and validate that the proposed FLC provide better efficiency by reducing harmonics.

Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

Abstract: The introduction of electric vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric vehicles and the power grid has spurred the emergence of a smart grid technology, denoted as vehicle-to grid-technology. Vehicle-to-grid technology manages the energy exchange between a large fleet of electric vehicles and the power grid to accomplish shared advantages for the vehicle owners and the power utility. This paper presents an optimal scheduling of vehicle-to-grid using the genetic algorithm to minimize the power grid load variance. This is achieved by allowing electric vehicles charging (grid-to-vehicle) whenever the actual power grid loading is lower than the target loading, while conducting electric vehicle discharging (vehicle-to-grid) whenever the actual power grid loading is higher than the target loading. The vehicle-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance of the proposed algorithm under various target load and electric vehicles’ state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric vehicle historical data.

Investigation on the Development of a Sliding Mode Controller for Constant Power Loads in Microgrids

Abstract: To implement renewable energy resources, microgrid systems have been adopted and developed into the technology of choice to assure mass electrification in the next decade Microgrid systems have a number of advantages over conventional utility grid systems, however, they face severe instability issues due to the continually increasing constant power loads To improve the stability of the entire system, the load side compensation technique is chosen because of its robustness and cost effectiveness In this particular occasion, a sliding mode controller is developed for a microgrid system in the presence of constant power loads to assure a certain control objective of keeping the output voltage constant at 480 V After that, a robustness analysis of the sliding mode controller against parametric uncertainties was performed and the sliding mode controller’s robustness against parametric uncertainties, frequency variations, and additive white Gaussian noise (AWGN) are presented Later, the performance of the proportional integral derivative (PID) and sliding mode controller are compared in the case of nonlinearity, parameter uncertainties, and noise rejection to justify the selection of the sliding mode controller over the PID controller All the necessary calculations are reckoned mathematically and results are verified in a virtual platform such as MATLAB/Simulink with a positive outcome

Sliding Mode Controller and Lyapunov Redesign Controller to Improve Microgrid Stability: A Comparative Analysis with CPL Power Variation

Abstract: To mitigate the microgrid instability despite the presence of dense Constant Power Load (CPL) loads in the system, a number of compensation techniques have already been gone through extensive research, proposed, and implemented around the world. In this paper, a storage based load side compensation technique is used to enhance stability of microgrids. Besides adopting this technique here, Sliding Mode Controller (SMC) and Lyapunov Redesign Controller (LRC), two of the most prominent nonlinear control techniques, are individually implemented to control microgrid system stability with desired robustness. CPL power is then varied to compare robustness of these two control techniques. This investigation revealed the better performance of the LRC system compared to SMC to retain stability in microgrid with dense CPL load. All the necessary results are simulated in Matlab/Simulink platform for authentic verification. Reasons behind inferior SMC performance and ways to mitigate that are also discussed. Finally, the effectiveness of SMC and LRC systems to attain stability in real microgrids is verified by numerical analysis.

Hybrid Model Predictive Control of DC–DC Boost Converters With Constant Power Load

Abstract: This article presents a hybrid model predictive controller to ensure dc microgrid stability and enhance the performance of dc-dc boost converters interfaced with constant power loads (CPLs) in a hybrid system. Hybrid systems are dynamic systems with both continuous current mode and discontinuous current mode states. The main purpose in this article is to develop an advanced control technique for voltage regulation and stabilization of the converters in the presence of CPLs due to serious stability concerns, without considering the accurate modelling information of the system. In this regard, an automatic model, considering different modes of operation induced by semiconductor switches in dc-dc boost converters and highly non-linear nature of CPL is employed to design the proposed control approach. The non-linear CPL connected directly to a dc-dc boost converter is utilized to define an optimal tracking control problem by minimizing a finite-prediction horizon cost function, which is known as a finite control set MPC. The proposed controller, which is implemented in both continuous and discontinuous current modes, accounts for the regulation of output voltage within the predefined range. The effectiveness of the proposed hybrid model predictive control is verified using a comparative evaluation with discrete-time averaged model predictive control, continuous control set MPC, and the conventional PI control under experimental conditions. The results authenticate an improved dynamic performance, which can be applied to practical dc microgrids with CPLs.

Maximum Power Point Tracking Implementation by Dspace Controller Integrated Through Z-Source Inverter Using Particle Swarm Optimization Technique for Photovoltaic Applications

Abstract: Maximum Power Point Tracking (MPPT) technique is used to extract maximum power from the photovoltaic system. This paper involves working on an enhanced Particle Swarm Optimization (PSO) based MPPT method for the photovoltaic (PV) system integrated through Z-Source inverter. The main benefit of the proposed method is the diminishing of the steady-state oscillation when the maximum power point (MPP) is located. Additionally, during an extreme environmental condition, such as partial shading and large fluctuations of irradiance and temperature, the proposed method has the capability to track the MPP. This algorithm is implemented in dspace 1104 controller. MATLAB simulations are carried out under varying irradiance and temperature conditions to evaluate its effectiveness. Its performance is compared with a conventional method like Perturb and observe (P&O) method.