Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Yasoda Kailasa Gounder
Government College of Technology
Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Day-ahead solar irradiance forecast using sequence-to-sequence model with attention mechanism Sowkarthika Subramanian; Yasoda Kailasa Gounder; Sumathi Lingana
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp900-909

Abstract

The increasing integration of distributed energy resources (DERs) into power grid makes it significant to forecast solar irradiance for power system planning. With the advent of deep learning techniques, it is possible to forecast solar irradiance accurately for a longer time. In this paper, day-ahead solar irradiance is forecasted using encoder-decoder sequence-to-sequence models with attention mechanism. This study formulates the problem as structured multivariate forecasting and comprehensive experiments are made with the data collected from National Solar Radiation Database (NSRDB). Two error metrics are adopted to measure the errors of encoder-decoder sequence-to-sequence model and compared with smart persistence (SP), back propagation neural network (BPNN), recurrent neural network (RNN), long short term memory (LSTM) and encoder-decoder sequence-to-sequence LSTM with attention mechanism (Enc-Dec-LSTM). Compared with SP, BPNN and RNN, Enc-Dec-LSTM is more accurate and has reduced forecast error of 31.1%, 19.3% and 8.5% respectively for day-ahead solar irradiance forecast with 31.07% as forecast skill.
Application of machine learning controller in matrix converter based on model predictive control algorithm Yasoda Kailasa Gounder; Sowkarthika Subramanian
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1489-1496

Abstract

Finite control set model predictive control (FCS-MPC) algorithms are famous in power converter for its easy implementation of constraints with cost function than classical control algortihms. However computation complexity increases when swicthing state is high for converters such as matrix converter, multilevel converters and this impose a serious drawback to compute multi-step prediction horizon MPC algorithm which further increases the computation. To overcome the above said difficulty, machine learning based artificial neural network (ANN) controller for matrix converter is proposed. The training data for ANN controller is derived from MPC algorithm and trained offline with an accuracy of 70.3%. The proposed ANN controller shows a similar and better performance than MPC controller in terms of total harmonic distortion (THD), peak overshoot during dynamic change in reference current and dynamic change in load parameter and less computation with less execution time. Further, ANN controller for matrix converter is tested in OPAL-RT using hardware in-loop (HIL) simulation and showed that it outperforms MPC controller.
Co-Authors Sowkarthika Subramanian Sumathi Lingana