Optimized Startup and Real-Time Monitoring of DC Emergency Pump Motor in Gas Turbine Generator MLO System Using Model Predictive Control

This paper presents an advanced approach for operating the emergency pump in the gas turbine generator’s mineral lube oil (MLO) system, focusing on reducing inrush current during the startup of the DC emergency pump motor and monitoring armature current and voltage throughout operation. The emergency pump, driven by a DC shunt motor, is critical for maintaining lubrication during emergency shutdowns when the main and auxiliary pumps fail to provide adequate oil pressure. To optimize the motor startup, a strategy is implemented to reduce inrush current, preventing excessive electrical stress and protecting the system. Real-time monitoring provides continuous feedback during operation, enabling the detection of anomalies and ensuring the motor operates within safe parameters. A dynamic model of the system is developed using MATLAB/Simulink to simulate the motor startup and pump performance. The Model Predictive Control (MPC) approach is applied to manage the motor’s startup and operation effectively, ensuring stable lubrication pressures and safeguarding turbine bearings. A Genetic Algorithm (GA) is employed to optimize the proposed objective function, further enhancing the system's performance and efficiency. Simulation results demonstrate the effectiveness of the proposed method in reducing inrush current and maintaining stable system operation during critical shutdowns, enhancing overall turbine safety and reliability.