Machine learning predictor for micro gas turbine performance evaluation

Abstract

Due to the increased trends and innovation with reliable IoT sensors, high-speed networks, and supercomputers the amount of data solving has improved in predicting performance with greater accuracy. Machine learning-based diagnostics and health monitoring for large gas turbines are available but lack application in Micro gas turbine component design and performance prediction. In this paper to predict the optimized net efficiency using a machine learning-based model was developed. Micro gas turbine power plant mathematical model (MGTPPMM) was developed that could generate an electric power of 6kW which was validated using the operational conditions of Jet Cat PHT3. The resulting data was used to optimize and predict the net efficiency of gas turbines and also its compressor and turbine characteristics using a Machine learning algorithm model (MLAM). The MGTPPMM has an accuracy of 92% in simulating various operating conditions while two MLAM was developed Predictor 1 and Predictor 2. Predictor 1 has an accuracy of 76% and Predictor 2 has an accuracy of just less than1 % error at sea level operating conditions. However at higher altitudes greater than 17 Km operating condition the accuracy of the MGTPPMM is 88% while that of the MLAM - Predictor 2 has an error of less than 6% this is due to the lack of sufficient validated results data required for training sets. The proposed models may help to design more efficient micro gas turbines at the component level and also could predict performance characteristics to monitor the health of the system virtually by implementing a degradation monitor system.