Mohammed Safiuddin

ANN-based protection system for Controllable Series-Compensated transmission lines

This paper presents a protection system for classifying and locating faults in Thyristor-Controlled Series Compensated (TCSC) transmission lines. The proposed scheme is based on Multi-layer Perceptron Neural Networks (MLPNN). The Levenberg-Marquardt (LM) training algorithm is employed. The LM algorithm appears to be the fastest training algorithm and highly nominated for better generalized models. Three-phase power system currents and voltages at the relay location are used as inputs to MLPNN-based relay. Two neural networks are trained to address fault classification and location. Feasibility and reliability of the proposed scheme are investigated using fault data set of a typical 500 kV power system simulated in EMTPATP software package. Studied system is subjected to all possible faults at different operating conditions, including fault location, fault inception angle and fault resistance. Simulation results demonstrate the robustness and fault tolerant features of proposed protection system.

Design and implementation of Static VAR Compensator for classroom and research applications in Smart Grid laboratory

Smart Grid laboratory (SGL) has been established at Buffalo State College as a collaborative effort of two academic institutions, University at Buffalo and Buffalo State College (UB-BSC). It is equipped with state-of-the-art equipment and serves as a hands-on teaching tool for undergraduates, as well as a research lab for graduate students. This paper discusses the development of a Static VAR Compensator (SVC) for use with the SGL at Buffalo State College. An overview of the UB-BSC SGL is presented first. The SVC circuit and characteristics are discussed in brief. Next, the determination of SVC parameters is discussed, thoroughly. With these parameters, the modes of operation of the SVC have been simulated and the system has been implemented using UB-BSC SGL equipment, the procedures of this experiment are documented. Finally, the hardware results are compared with those of simulated results.

A Dynamic Model for a Gas-Liquid Corona Discharge Using Neural Networks

This paper presents a novel dynamic nonlinear model for pulsed corona discharge using backpropagation neural networks. The Levenberg-Marquardt training algorithm, which is perfectly suitable for fitting functions, is employed. The developed model is based on the voltage-current characteristics of an actual hybrid-series reactor and takes the practical constrains associated with a real system into account. The validity and accuracy of the model have been tested in the Electromagnetic Transients Program, using MODELS language and a TACS-91 time-variant controlled resistor. The results clearly demonstrate that the BPNN-based model is very robust and effective in emulating the chaotic performance for pulsed corona discharge using backpropagation neural networks.

SOFC Emulation Using Computer Controlled DC Motor/Generator Set

A dynamic and steady state model of the solid oxide fuel cell (SOFC) is presented. Simulation results are shown. A laboratory prototype of the fuel cell is built. Both the dynamic and steady state results of the experimental model are verified with those obtained from simulation and show a close agreement with it. The proposed experimental model was designed to be connected to the grid as well as to supply isolated loads. The proposed model uses a DC motor as the prime mover for a separately excited DC generator, which represents the fuel cell. The fuel cell current and voltage are measured using a National Instruments (NT) data acquisition card and the control signal is fed back to change the generators field current.

A Partnership Between the Electrical Power Industry and Academia to Address the Technical Talent Gap

National Grid partnered with the University at Buffalo to develop a Master of Engineering Degree program to provide a professional degree education tailored to the needs of employees working full time. The target audience for the program was new engineers just hired into the company that needed a background in electric power, as well as existing engineers that wanted to earn an advanced degree, obtain PDH credits and/or increase their knowledge of electric power. The program was offered via distance learning to reach a larger audience and the program had participation from utility engineers from National Grid, Energy East, New York Power Authority, and Ontario Hydro. The distance learning using Webcasting tools had the additional benefit of allowing guest speakers and instructors from anywhere with Internet access to do a presentation for one of the classes

Evaluation of performance of an electrical generator with a superconductor element as rotor

This paper presents some results of a study to evaluate energy conversion potential of an innovative electrical generator, employing a YBCO superconductor thin film disk rotor. Creating a rate of change of flux in a magnetic field using the ldquoflux repulsionrdquo property of superconductors, an electrical generator is realized. Using ANSYS simulation and a simplified experimental set up, the feasibility of the design concept of proposed device is evaluated for different magnetic field strengths and at different rotating speeds of the superconductor disk.