Abstract—Nowadays, technologies have been developed rapidly globally to serve the growth of industries, the economy, and society. At the same time, the power energy industry has applied smart technologies widely to protect against failures in operational processes. Machine learning is one of the key intelligent techniques that can solve potential failures in this industry. This research presents an Artificial Neural Network (ANN) of Machine Learning (ML) for predictive maintenance of production wells that require maintenance at a suitable time at the Fang Geothermal Power Plant in Thailand. The raw data covers a period of 48 months (between 2018 to 2021). The data is gathered from log sheets and historical records, covering 1460 instances. Then, this raw data is calculated in the Thermodynamic and Ratio Power Equation. For the ANN model, the dataset has been separated into two sections for the training set and the testing set, including 664 instances in total. In general, there are two types of ANN models, including Classification Algorithms and Regression Algorithms. This study applies the ANN Classification Algorithms for simulating ANN models. The manual classification technique and the K-mean clustering algorithms are applied for determining the targets of the ANN model. In the simulation of the model, the K-mean clustering algorithms produced the best result, with 99.83% accuracy. The experiment demonstrates that the predictive maintenance could predict accurately, under established criteria, and consistent with the previous maintenance schedules. Therefore, the ANN model will assist operators in assessing and monitoring the system to prevent loss of power generation capacity. This means the model can support the maintenance activities and optimize the operation of the Fang Geothermal Power Plant.
Index Terms—Machine learning, production wells, predictive maintenance, K-mean clustering algorithms.
Sitthilith Chanthamaly and Anucha Promwungkwa are with the Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand (email: email@example.com, Anucha@eng.cmu.ac.th) Fig. 1. The project location in Kanchit Ngamsanroaj is with the Electricity Generating Authority of Thailand (EGAT), Nontheburi, Thailand (email: Kanchit.firstname.lastname@example.org).
Cite: Sitthilith Chanthamaly*, Anucha Promwungkwa, and Kanchit Ngamsanroaj, "Optimal Operation of Geothermal Power Plant by Artificial Neural Network," International Journal of Machine Learning vol. 13, no. 1, pp. 48-54, 2023.Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).