Distributed Systems for Simulation Analysis of Motor Drive Systems using Adaptive Algorithms

High processing needs and latency make it difficult to simulate motor drive systems in distributed environments, which affect accuracy and real-time performance. Optimizing motor control and cutting energy use require effective modeling. Conventional simulation techniques have trouble scaling up and down, frequently demanding large amounts of resources and being unable to adjust to changing load circumstances, which leads to sluggish or imprecise simulations. This method improves scalability and lowers latency by dynamically adjusting computing loads in a distributed system through the use of adaptive algorithms. It improves the accuracy and efficiency of simulation by utilizing real-time adaption and parallel processing. The purpose of this work was to suggest distributed systems for motor drive system simulation analysis utilizing adaptive algorithms. Initially, the dataset was gathered from a test bench-mounted momentum permanent magnet synchronous motor (PMSM) in a three-phase system motor vehicle. The exponentially weighted moving standard deviation (EWMS) utilized in standardized data process representations for training. We proposed the Adaptive Controller with dynamic fuzzy system ensemble (AC-DMFSE) for distributed systems for simulation analysis of motor drive systems. To optimize motor performance in dynamic situations, adaptive techniques are used, such as fuzzy logic-based optimization and model predictive control. Our test findings show that the suggested distributed technique reduced simulation times and MSE while enhancing the accuracy of system performance evaluation. The foundation for scalable and effective motor drive system simulations is laid by this work, which also offers insightful information for improving the systems’ performance in practical applications.