The stations use large compressors, up to 50 megawatts (MW), to compress natural gas for travel through hundreds of miles of pipeline. Historically gas turbines powered the compressors. Today, more efficient large synchronous electric motors are used. The motors are controlled by VSDs, such as ABB’s largest drive, the MEGADRIVE-LCI.
When power supply disruptions happen, protection systems quickly shut down the compressors. This halts the supply of gas. Restarting compressors lasts from a few hours to days. The resulting interruption in the gas supply can mean financial losses that range into the hundreds of millions of USD per year for large facilities.
Installed in the MEGADRIVE-LCI, the MPTC uses a control algorithm based on Model Predictive Control that ensures the operation of the drive during power and grid disturbances in order to provide the compressor with partial torque, preventing the compressor from going into surge.
The MEGADRIVE-LCI application is the first where MPTC is used to control a commercial multi-megawatt drive system with much faster dynamic behaviour, where the underlying optimisation problems need to be solved in less than a millisecond.
Live testing of the software was done over the winter at two Statoil gas facilities in Norway. It was installed on ABB MEGADRIVE-LCIs used to power two 42.2 MW compressors at a gas processing plant in Kollsnes, and three 7.5 MW booster compressors in Kårstø. The control software successfully protected the compressor operation during voltage dips.
A pioneer in electric drive technology, ABB has installed about 1,000 MEGADRIVE-LCIs globally and brings the decade-long experience in high-power LCI applications to make significant productivity contributions to key facilities in the natural gas supply chain.
*Model Predictive Control is a control algorithm that has its roots in the process industries and that has been in use in chemical plants and oil refineries since the 1980s. In comparison with traditional control techniques, MPC intelligently predicts the future behaviour of the system to be controlled via a mathematical model, and solves an optimization problem to compute the best control action with respect to given criteria and limits of operations.