http://210.212.227.212:8080/xmlui/handle/123456789/152 2026-05-27T20:51:37Z http://210.212.227.212:8080/xmlui/handle/123456789/197 Title: A NOVEL CONTROL SCHEME FOR HARMONIC COMPENSATION IN RENEWABLE ENERGY INTEGRATED SYSTEM Authors: Sreelekshmi, S; Thasneem, A Abstract: Non-linear loads are frequently used in both domestic and industrial applications. A non-linear load connected to the grid produces currents with harmonic content. The voltage and current of the grid are lowered in quality by these harmonics. A Proportional Integral-modified reduced order generalized integrator-based frequency-locked loop (PI+MROGI-FLL) is developed for controlling the interfacing inverter to mitigate the harmonics. The PI+MROGI-FLL is designed to evaluate the three-phase reference currents by extracting the fundamental constituents from the load currents. It offers many benefits, including improved harmonic mitigation, adaptive frequency and phase, grid synchronisation, and minimal computational burden. The suggested controller is modelled in MATLAB/Simulink using both a PV system and a hybrid system. The proposed controller’s performance is compared with that of existing conventional controllers. In comparison to other controllers, the PI+MROGI controller exhibits higher harmonic mitigation capability 2022-07-01T00:00:00Z http://210.212.227.212:8080/xmlui/handle/123456789/196 Title: CELSM MAGNETIC LEVITATION SYSTEM CONTROL USING FSMC-PID Authors: Sneha, Sajan; Sumayya, Jaleel Abstract: In a magnetic levitated system, the object is suspended without any kind of contact other than magnetic fields. Main application of this magnetic levitation is seen in maglev trains. Nowa days, all focus in this field is for creating a high speed, smoother and quieter vehicle system. Maglev trains benefits more to industry than conventional trains such as low maintenance cost, high speed, less affected by weather, increased power efficiency, less noise and many more. The significant area to be checked is the control part of a maglev system so that it can provide more efficient results. The fuzzy based SMC control of the MAGLEV system of the controlled excitation linear synchronous motor (CELSM) with PID as current controller is presented in this work. The simulation model is created and compared to other control methods. The results show that better maintenance of the magnetic levitation air gap is obtained under FSMC-PID control strategy, as well as the ability to deal with changes in the magnetic levitation system. The feasibility and superiority of the modified control method have also been demonstrated. 2022-07-01T00:00:00Z http://210.212.227.212:8080/xmlui/handle/123456789/195 Title: FUZZY SLIDING MODE SPEED CONTROLLER WITH ACTIVE DAMPING CONTROL FOR DUAL-PMSM Authors: Shahina, S; Farsana, Muhammed Abstract: Permanent magnet synchronous motors (PMSM) are extensively used in many industrial appli cations such as electric train tractions, industrial robots, etc. due to their high torque to weight ratio, high power density, high efficiency, reliability, and ease of maintenance. When compared to single PMSM, the dual PMSM is more cost effective. In the proposed system the two mo tors are operated in a master/slave control scheme. When the load torque is varied, there is an oscillation occured in the slave motor. In this work, active damping control is designed to suppress this unwanted oscillations. From the analysis of the system model, it is inferred that a small variation in motor parameters leads to a large variation in system performance. So various speed control techniques with active damping control are designed by using different controllers (PI, FUZZY, SMC, FUZZY-SMC) to improve the system performance. A comparison of the performance of different controllers with and without parameter variations is evaluated in MAT LAB/Simulink. By comparing the results, it is inferred that the Fuzzy Sliding Mode Controller gives the best performance among PI, FUZZY and SMC 2022-07-01T00:00:00Z http://210.212.227.212:8080/xmlui/handle/123456789/194 Title: SHORT-TERM RESIDENTIAL LOAD FORECASTING USING DEEP LEARNING TECHNIQUES Authors: Sanjay, Steephen; Sheeba, R Abstract: Smart energy management systems have become more popular as the consumption of energy is increasing rapidly. So, in order to monitor the daily energy consumptions in real time smart meters are used. Load forecasting is very important for power system management as it helps in maximum utilization of power generation plants, reliable and efficient operation of the system. In smart homes, the smart meter data are used to forecast the load and it can be even used for a neighborhood. F orecasting of electrical loads can be done using different deep learning techniques and can be used for demand management. Different methods employed includes Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU). The proposed method for forecasting of energy consumption consists of data preprocessing, model generation and validation. Performance of the models are validated using evaluation metrics like R-squared (R2 ), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The error metrics are then compared to find out the accurate model. The main advantage of load forecasting is that we can reduce the energy wastage and increase the efficiency of energy usage 2022-07-01T00:00:00Z