Development of Nonlinear Fault Prediction Models for Smart Grid Maintenance: Applications in Biotech Facility Operations and Infrastructure

Abstract

Reliable operational infrastructure is critical for the biotechnology industry, where power disruptions can significantly impact sensitive processes and equipment. This study explores the application of nonlinear fault prediction models in smart grid maintenance, tailored to the needs of biotech facility operations. Using nonlinear fault prediction theory, the research constructs a Model Prediction (MP) and Adaptive Inference Network (AIN) framework, leveraging the NSET prediction model to enhance fault diagnostics and prevention in biotechnology-reliant environments. The Model Prediction component utilizes historical power system data to forecast fault-free grid operations. Predicted data is compared with real-time grid performance, and discrepancies serve as inputs for the diagnostic system. The Adaptive Inference Network addresses the complex relationships between predicted operational data and potential fault scenarios, enabling the construction of a robust fault diagnosis system. Together, these components provide an integrated approach to locate faults before protection devices and circuit breakers are triggered, offering advanced fault warning capabilities. Simulation results demonstrate that the nonlinear fault prediction model significantly outperforms traditional fault maintenance methods. It achieves more than a 1% improvement in diagnostic accuracy, operates with lower resource requirements, and delivers faster analysis speeds. These advantages make the proposed model particularly valuable for biotech facilities, where uninterrupted power supply and rapid fault intervention are critical to maintaining operational continuity and product integrity. The findings highlight the importance of advanced fault prediction and diagnostic systems in ensuring the stability and efficiency of smart grid operations in biotechnology. By enabling proactive fault detection and early intervention, this study provides a scalable and practical solution for minimizing risks and ensuring the reliable operation of biotech infrastructure. These advancements contribute to optimizing facility management and safeguarding critical processes in the biotechnology industry.