D. Subbulekshmi

Bio: D. Subbulekshmi is an academic researcher from VIT University. The author has contributed to research in topics: Linearization & PID controller. The author has an hindex of 3, co-authored 11 publications receiving 18 citations. Previous affiliations of D. Subbulekshmi include PSG College of Technology.

Process Identification with Autoregressive Linear Regression Method using Experimental Data: Review

Abstract: Objective: To obtain mathematical model and parameters of poorly understood and imprecisely known plant/process Methods: One solution to this problem is to obtain these using identification techniques Process identification is a technique where a mathematical model of the process under study is build from process input-output data Several autoregressive models are used to estimate the present value of the model using its past values of the process Initially, the data set is generated for the given system and the auto regressive model is fitted to it, for the estimation of the model parameters Residual error for a system is calculated using auto regression model parameters Findings: A mathematical model for plant under study can be formulated with different system identification using Linear Regression methods like Auto Regressive eXogenous variable (ARX), Auto Regressive Moving Average with an eXogenous variable (ARMAX), Output Error (OE) and Box-Jenkins (BJ) For high model order ARX model is preferred and takes low computations but only suitable for white noise The ARMAX model considers disturbance affecting process and provides higher performance index ie fitness which reveals percentage variation in output estimated by respective model The Output-Error (OE) model estimates process model but cannot model disturbance features The residual analysis of Box- Jenkins model shows that the prediction error is not auto-correlated, correlated and is uncorrelated with the input applied to process, thus showing Box- Jenkins model ability to capture noise dynamics of process Applications/Improvements: The process model can be identified for the unknown, poorly known or partially known system and formulated model can be used in model Predictive controller design and Adaptive control techniques

Grid tied PV System using modular multilevel inverter

Abstract: A grid tied photovoltaic system using modular multilevel inverter topology is proposed in this paper. Basic unit structure of modular multilevel inverter used in this system is capable of converting DC power from PV array to AC power for feeding power to the household loads or utility grid. The proposed modular multilevel inverter structure has lesser power electronic devices compared to the existing multilevel inverter topologies. The proposed system generates a nearly sinusoidal signal and achieves better output profile with low total harmonic distortion. Simulation of the proposed system is carried out in MATLAB/Simulink software and the results are presented.

A Multicarrier PWM Technique for Five Level Inverter Connected to the Grid

Abstract: In this paper, a three-phase multilevel cascaded H-bridge inverter is developed for injecting renewable power energy into the grid through a filter. The main contribution of this work is to reduce the total harmonic distortion (THD) by using a passive filter and to enhance the output voltages by adopting a multicarrier pulse width modulation (MCPWM). The simulation results affirm a good performance of the proposed multicarrier PWM control using a three-phase five-level inverter. The proposed inverter is tested as well as the THD and the spectral analysis of the output voltage are calculated using Simulink/Matlab software.

A hybrid reference pulse width modulation technique for binary source multilevel inverter

Abstract: The article presents a seven-level reduced switch asymmetrical multilevel inverter with two different methods of pulse width modulation (PWM) techniques. Phase disposition (PD) PWM and hybrid variable-frequency phase disposition PWM (HVFPD-PWM) are the two different PWM methods for making the quality of output voltage waveform. In the first method, the unipolar sine reference with triangular carriers is used. In the second method, the hybrid unipolar reference (sinusoidal with trapezoidal) is proposed with variable frequency carriers to generate the switching pulses for asymmetric multilevel inverter (MLI). The main objective of this proposed method is to reduce the total harmonic distortion in the output voltage waveforms. A comprehensive comparison of the proposed HVFPD-PWM and the conventional PD-PWM with asymmetrical seven-level inverter is presented to show the enriched performances of the proposed method. The performance and viability of the suggested PWM are evaluated through simulation and experimental results using an asymmetrical seven-level inverter. The total harmonic distortion for the proposed PWM method (16.95%) is significantly reduced as compared with the conventional PWM method (18.01%) at the modulation index of one.

Design and Analysis of ANFIS – Based MPPT Method for Solar Photovoltaic Applications

Abstract: The solar photovoltaic energy is becoming popular in the modern-day distribution networks due to the clean energy factor. The photovoltaic modules exhibit a nonlinearity in the output power concerning the environmental conditions. This work suggests an adaptive neuro-fuzzy inference system- (ANFIS-) based maximal power point tracker (MPPT) for the optimization of the solar photovoltaic system (SPVS). The controller modelled is utilized to optimize the output power of a DC-DC converter connected to a 400 W PV array. The entire model is analysed employing MATLAB/SIMULINK using primary features provided by the technical data. The behavior of the controller modelled is tested for various weather conditions and partial shading conditions. The findings show the controller’s tracking speed effectiveness and dynamic response in PSCs.