Support Vector Machine for Reliable Voltage Fluctuation Detection and Compensation in Industrial IoT Networks

Modern industrial operations rely on Industrial Internet of Things (IIoT) networks, which are susceptible to voltage fluctuations may interrupt activities and result in significant expenses. To identify and compensate for voltage fluctuations in IIoT networks, this research provides a technique based on Support Vector Machine (SVM). When dealing with high-dimensional feature spaces, the SVM method shines due to how well it can detect patterns in the data. Utilizing SVM, create a detection model, it can properly spot voltage changes happen in real time. In addition, compensating systems are included to lessen the effect of identified variations, providing industrial operations will remain stable and uninterrupted. Extensive tests and simulations show the proposed SVM-based strategy improves the IIoT networks resilience and dependability against voltage variations. This technique shows promise to optimize resource consumption and keep processes running quickly in industrial settings. This study helps to enhance reliable and durable IIoT systems by addressing the difficulty of voltage fluctuations. These systems are important for current industrial processes to continue.