Optimizing Energy Efficiency in Three-Phase Induction Motors via GWO-Tuned PID Control

Induction motors consume a significant share of industrial electricity, making their efficiency a crucial aspect of sustainable energy management. Traditional Proportional–Integral–Derivative (PID) controllers are commonly used to regulate motor performance; however, their fixed parameters often fail to maintain optimal control under varying load conditions. Although optimization methods, such as Genetic Algorithms and Particle Swarm Optimization, have been introduced to enhance PID tuning, they often encounter challenges, including premature convergence and limited adaptability. This creates a clear need for an optimization strategy that is both robust and dynamically responsive to ensure energy-efficient motor operation. To address this gap, this study introduces a Grey Wolf Optimization (GWO)-based PID tuning strategy that distinguishes itself from existing methods by achieving a superior adaptive balance between exploration and exploitation. This characteristic enables the controller to maintain stable, responsive performance even under fluctuating load conditions. Experimental results confirm that the proposed GWO-PID controller successfully reduces motor current from 285.21 A to 164.2 A and lowers power consumption from 150 kW to 84.5 kW, achieving a 42.4% reduction in current and a 44.7% improvement in energy efficiency. Additionally, electricity costs decrease by 43.5%, demonstrating strong economic potential. The novelty of this research lies in integrating GWO’s adaptive intelligence with PID control, yielding a more effective, reliable, and energy-efficient solution than existing optimization-based controllers for industrial induction motor systems. ABSTRAK: Motor aruhan menggunakan sebahagian besar tenaga elektrik industri, menjadikan kecekapan operasi satu aspek penting dalam pengurusan tenaga mampan. Pengawal konvensional Kadar-Integral-Pembezaan (PID) lazim digunakan bagi mengawal prestasi motor; namun, parameter tetapnya sering gagal mengekalkan kawalan optimum di bawah keadaan beban berubah. Walaupun kaedah pengoptimuman seperti Algoritma Genetik dan Pengoptimuman Kawanan Partikel telah diperkenalkan bagi menambah baik talaan PID, pendekatan ini sering menghadapi masalah penumpuan awal dan keupayaan adaptasi yang terhad. Hal ini memerlukan satu strategi pengoptimuman yang lebih mantap dan responsif secara dinamik bagi memastikan operasi motor cekap tenaga. Bagi menangani masalah ini, kajian ini memperkenalkan satu strategi talaan PID berasaskan Pengoptimuman Serigala Kelabu (Grey Wolf Optimization, GWO) yang menonjol dari kaedah sedia ada melalui keseimbangan adaptifnya yang unggul pada penerokaan dan pengeksploitasian. Ciri ini membolehkan pengawal mengekalkan prestasi stabil dan responsif walaupun berkeadaan beban yang berubah. Dapatan kajian melalui eksperimen menunjukkan bahawa pengawal GWO–PID yang dicadangkan ini berjaya mengurangkan arus motor daripada 285.21 A kepada 164.2 A dan menurunkan penggunaan kuasa daripada 150 kW kepada 84.5 kW—mencapai pengurangan arus sebanyak 42.4% dan peningkatan kecekapan tenaga sebanyak 44.7%. Selain itu, kos elektrik turut menurun sebanyak 43.5%, sekaligus membuktikan potensi ekonomi yang kukuh. Keunikan kajian ini terletak pada integrasi kecerdasan adaptif GWO dengan kawalan PID, menawarkan penyelesaian lebih berkesan, boleh dipercayai, dan cekap tenaga berbanding pengawal berasas pengoptimuman lain bagi sistem motor aruhan industri.