Measurement-Based Load Modelling of Oil Drilling Rig Microgrid Using Machine Learning

In remote oil drilling applications, the efficient operation of their associated islanded microgrids is critical to ensuring uninterrupted energy supply, optimizing resource utilization, and reducing operational costs. The load profile needed by an oil well drilling rig fluctuates wildly especially during lifting or lowering the heavy drill string by the draw-works resulting in more power demand. This fluctuation in power demand can cause the diesel engine that powers the drilling rig to operate inefficiently. Hence, accurate load modelling and prediction for this pulsating load are essential for optimizing the microgrid performance of the oil drilling rig and facilitating effective energy management. This research paper presents a comprehensive study on load modeling and predictive analytics for microgrids in the context of oil drilling rigs. The measurement-based data and advanced machine learning techniques are used to develop an accurate load model. The proposed model can be utilized for studies of dynamic stability, energy saving solutions and creating a new adaptive protection approach. The simulation results have shown that the exponential GPR regression model proves to be the most accurate, one out of the 18 regression methods studied, with an error rate of 3.09%.