Most countries over the past few decades have modernized their economies and become more reliant on electricity to run, so the electrical power system has also expanded greatly. Optimal Reactive Power Dispatch (ORPD) has a big influence on the reliability, security, and economic operation of the power system. Another thing to note is that ORPD has a few major targets and objectives which are to reduce the active or real power losses, to improve the voltage profile, to reduce transmission costs, and to increase system stability. Non-convex, non-linear, and multimodal problems make the development of intelligent algorithms to solve the reactive power dispatch problem highly relevant. Some researchers chose to compare and contrast optimization techniques from the past with each other in order to answer some remaining uncertainties such as the effectiveness and complexity of the technique toward the chosen objective function(s). Thus, this paper proposed applying the Multistage Artificial Immune System (MAIS) optimization method for solving the ORPD problem with the objective of reducing the power system losses. This algorithm was made by modifying and upgrading the classical AIS optimization method. Instead of only going through the process one time in the classical AIS algorithm, this MAIS method going through the processes more than one time in multiple stages of the same processes. This process includes cloning and mutation as well as selection. These modifications also aid in the development of new and unique solutions, as opposed to the classical AIS optimization process. Therefore, these enhancements could lead to a rise in the accuracy of the results' because there have been increased comparisons. This study confirms that MAIS optimization can deliver superior results in less time than AIS. Keywords—Optimal reactive power dispatch, computational intelligence, multistage artificial immune system, loss minimization.
Transport modeling: An artificial immune system approach