M. Suryakalavathi’s research while affiliated with Department Of Electronics And Information Technology, India and other places

Privatization of the power industry has made proper utilization of the available resources a compulsory requirement. Optimal power flow (OPF) is an ideal solution to the problem. At the same time, stable operation of the power systems in both normal and contingency condition is of vital importance. Use of FACTS devices is a good method to stop further contingencies in the power system. In this paper, a combined index based strategy for the optimal placement of Thyristor Controlled Series Compensator (TCSC) and optimal tuning of generators using Krill Herd Algorithm has been proposed for contingency management. The contingency analysis has been done using a new method, namely, rapid contingency ranking technique (RCRT). The TCSC has been placed on the basis of an index which is a combination of Line Utilization Factor (LUF) and Fast Voltage Stability Index (FVSI). A multi objective function has been chosen for tuning the generators. The multi-objective function includes voltage deviation, active power generation cost and transmission line loss. The proposed method has been tested and implemented on an IEEE 30 bus system.

In this article, space vector based decoupled PWM techniques are proposed for both symmetrical and asymmetrical configuration of open-end winding induction motor drive. These decoupled PWM techniques are generated using instantaneous reference voltages of the two inverters. That means, one inverter reference voltages are phase shifted with respect to other inverter reference phase voltages, So that each inverter operates independently. It does not require any complex computations as in case of conventional PWM techniques. A common-mode voltage is identified in the dual inverter configuration. These decoupled PWM techniques also reduce the common mode voltage by better extent. All the proposed PWM techniques for symmetrical and asymmetrical configurations of open-end winding induction motor are implemented using MATLAB/SIMULINK and the corresponding results are reported and compared.

In the new competitive electric world, it is compulsory for the electrical industry to make effective utilization of the available resources. Optimal tuning of generators and implementation of FACTS devices has been found to be very effective in this regard. In this paper, a combination strategy of optimal tuning of generators using Krill herd (KH) algorithm in the presence of Static VAR Compensator (SVC) has been proposed. A combinatory index (CI), which is a combination of Vi/Vo index and L-index, has been formulated and verified for obtaining the optimal location of SVC. A multi objective function has been formulated for tuning the generators. The results obtained after performing Optimal Power Flow on an IEEE 30 bus system for normal loading and for severe system conditions due to line outage in the presence of SVC using KH has been verified with that of GA, to prove the effectiveness of the chosen methodology.

This Paper presents a decoupled space vector modulation algorithm for a three level open-end winding induction motor drive. In the proposed scheme, switching vectors for the 2- two level inverters are generated separately using the instantaneous reference phase voltages along with the simple digital logic. So that, each inverter can be operated as a two level inverter isolately. This modulation scheme does not require any complex computations. This algorithm generates different pulse width modulation (PWM) schemes by phase shifting one inverter phase voltages with respect to the other inverter. A common- mode voltage is identified in the dual inverter, all the PWM schemes presented in this paper mitigating and also eliminating the common-mode voltage (CMV) in a dual inverter. All the pulse width modulation (PWM) schemes generated in the proposed decoupled algorithm are simulated using MATLAB/SIMULINK environment, and the corresponding results are presented.

In this paper, Galaxy based search algorithm (GbSA) is used to solve multi-objective problem of optimization in power systems. The proposed GbSA resembles the spiral arms of some galaxies to search for the optimal solutions. The GbSA also uses a modified Hill Climbing algorithm as a local search. Simulation results show that the GbSA finds the optimal or very near optimal values in all runs of the algorithm. The weighted sum technique with equal weights has been chosen to solve the multi-objective function. The functions considered are to minimize the power losses in transmission line, cost of the real power generation and voltage deviation. Static VAR Compensator (SVC) is used for the purpose of optimal power flow. L-index is used to identify the optimal location to place SVC. The results have been compared with Genetic algorithm (GA) for IEEE-14 System.

Intelligent drive control methods are very much essential for the efficient use of Induction motors in various industrial production processes. Fault diagnosis of induction motor drives is based on the Voltages, Currents and Speed. In this paper hybrid structures of Neuro-Fuzzy (BPN with Fuzzy logic) are proposed for external fault diagnosis of induction motor drive. The results are analyzed based on the overall improvement and performance of the machine.

This paper presents a nature inspired heuristic optimization algorithm based on lightning progression called the propagation algorithm (PA) to solve optimal reactive power problem. It is from the imitated natural phenomenon of lightning and the procedure of step frontrunner propagation using the theory of fast particles. Three particle kinds are established to distinguish the transition particles that produce the first step frontrunner population, the space particles that attempt to turn out to be the frontrunner, and the prime particle that epitomize the particle thrilled from best positioned step frontrunner. The proposed PA has been tested in standard IEEE 30,57,118 bus test systems and simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limits.