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Boost converters often face challenges such as sluggish dynamic behavior, inadequate voltage regulation, and variations in input voltage and load current. These issues necessitate the need for closed-loop operation. Nature-inspired optimization algorithms (NIOA) have demonstrated their effectiveness in delivering enhanced solutions for various engineering problems. Several studies have been conducted on the use of proportional-integral-derivative (PID) controllers for controlling boost converters, as documented in the literature. Some studies have shown that using fractional order PID (FO-PID) controllers can lead to better performance than traditional PID controllers. Nevertheless, implementing FO-PID can be quite complex. Considering the widespread use of commercial PID controllers in industrial settings, this study focuses on finding the best tuning for these controllers in DC-DC boost converters. The approach used is particle swarm optimization (PSO) based on integral performance criteria. Simulation results indicate that the proposed controller achieves superior performance, evidenced by the lowest settling time, overshoot, integral absolute error (IAE), and integral squared error (ISE) values under varying input voltage and load current conditions, compared to both PID and FO-PID controllers. These findings have been confirmed through hardware implementation, which demonstrates the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

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.

In this paper a new approach has been used for finding the optimal location for placing the Interline Power Flow Controller (IPFC). The IPFC is used to reduce the system loss and power flow in the heavily loaded lines and improve stability of the system. Here a new method, the Disparity Line Utilization Factor (DLUF) is used for determining the optimal placement of the (IPFC) to control the congestion in transmission lines. The DLUF ranks the transmission lines in terms of line congestion. The IPFC is accordingly placed in between the most congested and the least congested line connected to the same bus. The Galaxy-Based Search Algorithm (GBSA) is used for the optimal sizing of the IPFC. The results obtained by the proposed algorithm have been compared with that of the Genetic Algorithm (GA) to ascertain the effectiveness of the proposed method on the power system performance. The proposed method is tested on an IEEE 30 bus test system. It has been shown that by using the GBSA, the tuning of the IPFC further reduces congestion in the system by about 15%. [ABSTRACT FROM AUTHOR]