In this manuscript, the optimal location and capacity of Unified Power Flow Controller (UPFC) based on chaotic krill herd (CKH) with runner root algorithm (RRA) for dynamic stability improvement in power system is proposed. The proposed technique is the combined execution of CKH and RRA hence it is known as CKHRA technique. Here, CKH is utilized to optimal location of unified power flow controller when occurring generator fault. CKH chooses maximal power loss line as optimal location to keep UPFC according to the objective function. The minimal voltage deviation is improved using runner root algorithm from the unified power flow controller control parameters. The minimal voltage deviation is utilized to determine the optimal unified power flow controller capacity. The CKHRA technique is executed at MATLAB/Simulink and the performance is assessed by comparison with other techniques. The performance of CKHRA technique is linked with IEEE 14 bus, IEEE 30 bus, IEEE 57 bus bench mark system, whereas the efficiency is examined against various generator fault conditions. By then the statistical analysis, result accuracy percentage and loss sensitivity index are analyzed. The comparative results thus proven the greatness of CKHRA approach and corroborate its ability to improve the dynamic stability of power system.

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for their valuable comments and suggestions. I also thank my fellow graduates from Power Electronics and Power Systems stream. My thanks and appreciations also goes to my friends in developing the project and people who have willingly helped me.

In this article, the advantages of both octadecagonal space vector pulsewidth modulation (SVPWM) and hexagonal SVPWM are explored for a pole-phase modulation (PPM) induction machine (IM) drive applications. In the proposed scheme, a hexagonal SVPWM is used in the low-speed region and octadecagonal SVPWM is used during the high-speed region to achieve better performance over the entire speed range. With this proposed operation, the linear modulation region is enhanced by 9.15% as compared to conventional three-phase SVPWM in the highspeed region. The basic two-level nine-leg inverter is used to excite the nine-phase PPM IM. The finite-element model (FEM) of the PPM IM and inverter structure is simulated using ANSYS Maxwell-2D and Simplorer environment, respectively. The proposed method is verified experimentally on the prototype model of 5-hp nine-phase IM.

Renewable energy sources have gained a lot of importance in today's power generation. These sources of energy are pollution free and freely available in nature. Wind is the most prominent energy source among the renewable energy sources. Increased wind penetration into the existing power system will create reliability problems for grid operation and management. Wind speed forecasting is an important issue in wind power grid integration as it is chaotic in nature. This paper presents a new State Estimation based Neural Network (SENN) for day ahead (24 hours ahead) wind speed forecasting and its performance has been compared with traditional Back Propagation Neural Network (BPNN). SENN is a non iterative technique, where the weights between input-hidden and hidden-output layers are estimated using a Weighted Least Square State Estimation (WLSSE) approach. It accounts noise associated with input and output data, giving accurate results without any iteration. This method is quite efficient and faster compared to conventional Back Propagation Neural Network (BPNN).