http://210.212.227.212:8080/xmlui/handle/123456789/467 2026-05-27T20:57:49Z http://210.212.227.212:8080/xmlui/handle/123456789/475 Title: POWER DEMAND REDUCTION IN ELECTRIC VEHICLE CHARGING Authors: Nowfal, S; Jibi P, Mathew Abstract: Due to rising fossil fuel prices and accelerating carbon dioxide (CO2) emissions, the use of electric vehicles (EVs) has grown over the past few years. At present EV charging stations use the existing utility power grid, and hence there is an increase in load demand at the distribution side and thereby stress on the utility grid. There are different solutions for this problem, mainly PV integration, Power factor correc tion (PFC) in chargers, managed charging, indirect power demand reduction, etc. For a level 1 EV charging, AC to DC conversion and also its power factor correction is necessary. There are multiple problems with the diode bridge rectifier used in typical chargers, notably conduction loss and nonlinear characteristics. Bridge rec tifiers with input diodes operate poorly, are inefficient, and also have a low power factor. This project focuses on important EV power demand reduction strategies like PFC, charging with on-site renewable energy, and indirect power demand re duction. This project make use of a novel Cuk-SEPIC converter with fuzzy logic control for PFC and calculating its power factor.This topology is compared with two other converter topologies (Cuk-push-pull, and Cuk-flyback). All these con verters are designed to work in discontinuous conduction mode. The Cuk-SEPIC converter is the integration of both Cuk and SEPIC converters in which the Cuk converter works in the positive half and the SEPIC converter works in the negative half cycle. The next objective is the integration of PV in level 2 charging along with its design, cost analysis, and simulations for reducing power demand on the grid 2023-04-01T00:00:00Z http://210.212.227.212:8080/xmlui/handle/123456789/474 Title: SWITCHED INDUCTOR DOUBLE SWITCHED DC-DC CONVERTER FOR SOLAR HOT PLATE COOKING Authors: Jayakrishnan, K; Jibi P, Mathew Abstract: Conventional DC/DC converters provide limited practical gains at high duty cycles and the passive elements required is higher which reduces the compactness, con sequently, increases the cost of the system. Such trend is not suitable for various high gain applications. To overcome the above mentioned issue, a new switched in ductor arrangement is proposed which is named as switched inductor double switch DC/DC converter (SL-DS-DC) and P&O MPPT technique will be implemented which provides better maximum power extraction and finally this circuit is used for solar hot plate cooking application. The major advantages of the proposed method is that it requires less number of passive elements eventually reduces the size. An other advantage of this converter is it provides lower voltage stress as compared to various other converters. The suggested high gain DC-DC converter, which can run a 240W solar hot plate cooking equipment, has been designed and simulated 2023-04-01T00:00:00Z http://210.212.227.212:8080/xmlui/handle/123456789/473 Title: SHORT-TERM LOAD FORECASTING IN POWER SYSTEMS USING DEEP LEARNING ALGORITHMS Authors: Asish, Johnson; Jibi P, Mathew Abstract: Short-Term Load Forecasting (STLF) plays a crucial role in power system planning and operation, as it helps utilities to efficiently allocate their resources and ensure reliable service to customer. In this project the performance of different forecasting algorithms such as Long Short-Term Memory (LSTM), Particle Swarm Optimization-Gated Recurrent Unit (PSO GRU), Multivariate LSTM, and 1-Dimensional Convolution Neural Network-Long Short Term Memory (1-D CNN LSTM) are evaluated. A widely used benchmark dataset namely Global Energy Forecasting Competition (GEFCOM) dataset is used in this work for training and performance validation. The performance of different load forecasting models is compared using performance indices like accuracy and Mean Absolute Percentage Error (MAPE). Among the different models used for short-term load forecasting, Multivariate LSTM model is found to be more accurate than other models. The results indicate that Multivariate LSTM is a promising approach for STLF, and its superior performance is attributed to its ability to handle multiple input variables. The study highlights the importance of model selection in accurate load forecasting and demonstrates the potential of Multivariate LSTM for STLF. The findings can help power system planners and operators to choose an appropriate STLF algorithm based on their specific needs and requirements 2023-04-01T00:00:00Z http://210.212.227.212:8080/xmlui/handle/123456789/472 Title: A SUSTAINABLE AND ECONOMICAL MICROGRID DESIGN FOR ELECTRIC POWER DISTRIBUTION OF TKMCE CAMPUS Authors: Ashik, Y; Jibi P, Mathew Abstract: The electric power demand and electricity prices are increasing day by day. The increase in EVs, new automatic machinery and increased load demand puts a high burden on the utility. A localised solution incorporating the areas’ renewable sources is needed to accommodate future demands. The rising demand of electricity in the TKMCE campus and increasing tariffs are a major concern. The aim of this project is to design a Hybrid microgrid architecture using a PV system as renewable resource, incorporate other generation and loads for the TKMCE campus to reduce the future electricity cost. The project cost analysis is also done to analyse the economic viability. Different cases and scenarios are proposed and analysed and study on the existing system, protection, primary control method and the PV system design is done. A droop control method as part of the primary control of the hierarchical control, to share the power between the inverter-based sources and to regulate the voltage and frequency at the PCC bus is designed and simulated. A concept of “Zero electricity bill” and the modifications that needs to be introduced to attain this concept is also proposed. 2023-04-01T00:00:00Z