Ilya Grinberg

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 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

Comparison between particle swarm optimization and Cuckoo Search method for optimization in unbalanced active distribution system

Recently, the integration of distributed generation (DG) units to distribution networks has grown significantly. This integration provides an opportunity to control the power flow, resulting in the optimal power flow (OPF) at the distribution level. OPF can reduce system losses and decrease the DG generation costs. Additionally, it can improve the voltage profile. Applying OPF to distribution networks is a challenging task since the nature of distribution networks makes the OPF a nonlinear problem. In this paper, a multi-objective function is used to define the nonlinear power flow problem. To solve the OPF problem, the Particle Swarm Optimization (PSO) and Cuckoo Search (CS) algorithms are applied. These approaches are investigated utilizing IEEE 37 nodes test case. Comparing the results of the two methods shows that the CS algorithm performs better than PSO. The advantages of the CS algorithm, including fewer initial solutions, strong optimization searching ability, and fast convergence speed, make it an effective tool for solving the nonlinear optimization problem.