Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics
Waqar Muhammad Ashraf, Ghulam Moeen Uddin, Muhammad Farooq, Fahid Riaz, Hassan Afroze Ahmad, Ahmad Hassan Kamal, Saqib Anwar, Ahmed M. El‐Sherbeeny, Muhammad Haider Ali Khan, Noman Hafeez, Arman Ali, Abdul Raheem Samee, Muhammad Ahmad Naeem, Ahsaan Jamil, Hafiz Ali Hassan, Muhammad Muneeb, Ijaz Ahmad Chaudhary, Marcin Sosnowski, Jarosław Krzywański
Abstract
Constructing the power curve of a power generation facility integrated with complex and large-scale industrial processes is a difficult task but can be accomplished using Industry 4.0 data analytics tools. This research attempts to construct the data-driven power curve of the generator installed at a 660 MW power plant by incorporating artificial intelligence (AI)-based modeling tools. The power produced from the generator is modeled by an artificial neural network (ANN)—a reliable data analytical technique of deep learning. Similarly, the R2.ai application, which belongs to the automated machine learning (AutoML) platform, is employed to show the alternative modeling methods in using the AI approach. Comparatively, the ANN performed well in the external validation test and was deployed to construct the generator’s power curve. Monte Carlo experiments comprising the power plant’s thermo-electric operating parameters and the Gaussian noise are simulated with the ANN, and thus the power curve of the generator is constructed with a 95% confidence interval. The performance curves of industrial systems and machinery based on their operational data can be constructed using ANNs, and the decisions driven by these performance curves could contribute to the Industry 4.0 vision of effective operation management.