An Improvement of Load Flow Solution for Power System Networks using Evolutionary-Swarm Intelligence Optimizers

Load flow report which reveals the existing state of the power system network under steady operating conditions, subject to certain constraints is being bedeviled by issues of accuracy and convergence. In this research, five AI-based load flow solutions classified under evolutionary-swarm intelligence optimizers are deployed for power flow studies in the 330kV, 34-bus, 38-branch section of the Nigerian transmission grid. The evolutionary-swarm optimizers used in this research consist of one evolutionary algorithm and four swarm intelligence algorithms namely; biogeography-based optimization (BBO), particle swarm optimization (PSO), spider monkey optimization (SMO), artificial bee colony optimization (ABCO) and ant colony optimization (ACO). BBO as a sole evolutionary algorithm is being configured alongside four swarm intelligence optimizers for an optimal power flow solution with the aim of performance evaluation through physical and statistical means. Assessment report upon application of these standalone algorithms on the 330kV Nigerian grid under two (accuracy and convergence) metrics produced PSO and ACO as the best-performed algorithms. Three test cases (scenarios) were adopted based on the number of iterations (100, 500, and 1000) for proper assessment of the algorithms and the results produced were validated using mean average percentage error (MAPE) with values of voltage profile created by each solution algorithm in line with the IEEE voltage regulatory standards. All algorithms proved to be good load flow solvers with distinct levels of precision and speed. While PSO and SMO produced the best and worst results for accuracy with MAPE values of 3.11% and 36.62%, ACO and PSO produced the best and worst results for convergence (computational speed) after 65 and 530 average number of iterations. Since accuracy supersedes speed from scientific considerations, PSO is the overall winner and should be cascaded with ACO for an automated hybrid swarm intelligence load flow model in future studies. Future research should consider hybridizing ACO and PSO for a more computationally efficient solution model

Low-cost Car Battery Security Alert System

This is a technical article that showcases a low-cost car battery security alert system that utilizes a multi-vibrator circuit into a dual-tone multi-frequency (DTMF) output (loud beep sound) alarm to monitor and safeguard the car battery from local theft. The entire circuitry is a simple one and low-cost in production. The incessant cases in which car batteries are stolen especially in developing countries is on the high side. And the cost of replacing car batteries is on the increase daily. Therefore, one needs to secure her car battery from street theft. Using both mechanical fastening and electronic security-based systems one could sleep with two eyes closed. The device serves as an electronic watchdog on the car battery in the car while it is parked outside the owner’s residence or elsewhere. The security system is provided with an internal rechargeable battery energizing the alarm circuitry, having a single-pole double-throw (SPDT) relay, and connected cables, with an output sound capable of alerting the neighbourhood. Whenever the car battery is disconnecting from the terminal heads or the loop cable is broken the connected alarm will be triggered and this will call the attention of the neighbourhood and the owner thereby deterring the intruder. The entire system was simulated using Circuit Wizard software with good results. The system was fabricated using discrete semiconductor devices that are relatively simple and available for operation and maintenance, packaged, and tested. The circuit voltage is 11.52 volts and draws a current of 3.79A resulting in a wattage of 44 watts. The device is affordable