Electric mobility has become an essential part of the future of transportation. Detection, diagnosis and prognosis of fault in electric drives are improving the reliability, of electric vehicles (EV). Permanent magnet synchronous motor (PMSM) drives are used in a large variety of applications due to their dynamic performances, higher power density and higher efficiency. In this study, health monitoring and prognosis of PMSM is developed by creating intelligent digital twin (i-DT) in MATLAB/ Simulink. An artificial neural network (ANN) and fuzzy logic are used for mapping inputs distance, time of travel of EV and outputs casing temperature, winding temperature, time to refill the bearing lubricant, percentage deterioration of magnetic flux to compute remaining useful life (RUL) of permanent magnet (PM). Health monitoring and prognosis of EV motor using i-DT is developed with two approaches. Firstly, in-house health monitoring and prognosis is developed to monitor the performance of the motor in-house. Secondly, Remote Health Monitoring and Prognosis Centre (RHMPC) is developed to monitor the performance of the motor remotely using cloud communication by the service provider of the EV. The simulation results prove that the RUL of PM and time to refill the bearing lubricant obtained by i-DT twins theoretical results.

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Utilisation of renewable energy sources (RES) is increasing day by day to reduce greenhouse emissions. The toxic emission from ship is the main concern in marine sector. Here, utilisation of renewable energy for propulsion and electrification of accessories in a ship are proposed. Microgrid with AC and DC bus is developed using solar panels, wind mills, fuel cell, diesel generator, and energy storage devices. Energy management system with two fuzzy logic controllers (FLCs) is used to select and manage energy in the microgrid. Selection of source is decided by FLC1 based on the availability of RES. Generation of control pulses for inter-linking converters is decided by FLC2 based on variation in solar irradiance and wind velocity. The microgrid with RES is simulated using MATLAB/SIMULINK. The results show that uncertainty in RES can be handled by FLCs to provide a continuous power supply for transportation of ship and its accessories. 2 Description of the system 2.1 Implementation of EMS The proposed system consists of EMS, microgrid with AC and DC bus, solar power generation, wind power generation, ESD, diesel

This paper presents the modeling of intelligent energy control center (ECC) controlling distributed generators (DGs) using multi-agent system. Multi-agent system has been proposed to provide intelligent energy control and management in grids because of their benefits of extensibility, autonomy, reduced maintenance, etc. The multi-agent system constituting the smart grid and agents such as user agent, control agent, database agent, distributed energy resources (DER) agent work in collaboration to perform assigned tasks. The wind power generator connected with local load, the solar power connected with local load and the ECC controlled by fuzzy logic controller (FLC) are simulated in MATLAB/SIMULINK. The DER model is created in client and ECC is created in server. Communication between the server and the client is established using transmission control protocol/internet protocol (TCP/IP). The results indicate that the controlling of DER agent can be achieved both from server and client. Index Terms-Distributed energy resources (DER) and transmission control protocol/internet protocol (TCP/IP), distributed generators (DGs), energy control center (ECC), fuzzy logic controller (FLC).

SUMMARYThis paper presents an application of Fuzzy Logic Controller (FLC) with triangular and gauss membership functions to study Automatic Generation Control (AGC) of a four-area interconnected power system. Out of the four areas three have steam turbines and one has hydro turbine. All steam turbines in the system contain the reheat effect of non-linearity and hydro turbine contains upper and lower constraints for generation rate. The result proves that the performance of FLC with Gaussian membership function is better than that of conventional controller.