Sensorless Speed Control of Induction Motors using DWT-Integrated Ultra Local Model based MRAS

An accurate speed estimate is essential for attaining reliable and efficient sensorless control of induction motors, especially in the presence of parameter mismatches and noisy operational environments. This research examines the efficacy of a Discrete Wavelet Transform (DWT)-augmented Ultra Local Model-based Model Reference Adaptive System (MRAS-ULM) for estimating rotor speed. A comparative examination of dynamic response and frequency spectrum is performed between the traditional MRAS-ULM and the proposed DWT-enhanced MRAS-ULM. Results indicate that the incorporation of DWT markedly enhances noise reduction and signal integrity. At rated speeds, the proposed technique diminishes the higher magnitude of harmonic content, while at low speeds, it guarantees reliable tracking by alleviating noise effects. Frequency spectrum analysis verifies significant attenuation of higher magnitude components and diminished harmonic content, underscoring the efficacy of the suggested technique. These findings confirm that the DWT-enhanced MRAS-ULM is suitable for accurate and dependable speed estimates in induction motor control applications.