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A hybrid technique is proposed in this manuscript for the optimal design of an induction motor (IM) drive for the dynamic load profiles during torque and flux control. The proposed hybrid method combines a Ladder-Spherical-Evolution-Search-Algorithm (LSE) and a recalling-enhanced recurrent-neural network (RERNN), which is called an LSE-RERNN technique. The major objective of the proposed method is to minimize IM losses while maintaining control over speed and torque. The proposed method effectively tunes the gain parameter of the PI controller for flux and torque regulation. The LSE methodgenerates a set of gain parameters optimally predicted by RERNN. The method reduces losses without prior knowledge of load profiles, achieving energy savings for steady-state optimum flux. The performance of the proposed technique is done in the MATLAB and is compared with different existing techniques. The value of the proposed method for the mean is 0.328, the standard deviation (SD) is 0.00334, and the median is 0.4173. The loss of the proposed method is much less than 0.3 W while compared to different existing approaches. Moreover, the computation time of the proposed approach is lesser than the existing techniques., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 ISA. Published by Elsevier Ltd. All rights reserved.)

This study presents the efficiency of the wind-driven optimisation (WDO) approach in solving non-convex economic dispatch problems with point-valve effect. The best economic dispatch for a power system is one wherein the system can generate energy at a low cost. The calculation of the generating cost is subject to a number of constraints, such as the power demand for the entire system and the generation limit for each generator unit in the system. In addition, the system should also produce low power loss. The WDO optimisation technique is developed based on the concept of natural wind movement, which serves as a stabiliser to equalise the inequality of air pressure in the atmosphere. One major advantage of WDO over other techniques is its search accuracy. The proposed algorithm has been implemented in two systems, namely, the 10-generator and 40-generator systems. Both systems were tested in a Matlab environment. To highlight the capabilities of WDO, the results using this proposed technique are compared with the results obtained using flower pollination algorithm, moth flame optimisation, particle swarm optimisation and evolutionary programming techniques to determine the efficiency of the proposed approach in solving economic dispatch. The simulation results show the capability of WDO in determining the optimal power generation value with minimum generation cost and low rate of power loss., (© The Korean Institute of Electrical Engineers 2021.)