Digital Twins of the Water Cooling System in a Power Plant Based on Fuzzy Logic

• Published in: Sensors (Basel, Switzerland) Switzerland), 2021-10, Vol.21 (20), p.6737
• Main Authors: Alves de Araujo Junior, Carlos Antonio, Mauricio Villanueva, Juan Moises, Almeida, Rodrigo José Silva de, Azevedo de Medeiros, Isaac Emmanuel
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Automation; Big Data; Cooling; cooling system; Cooling systems; Data analysis; Decision making; Digital twins; Downtime; Energy consumption; Energy efficiency; Energy industry; Fuzzy logic; Fuzzy sets; Gas turbine engines; Genetic algorithms; Liquid cooling; Optimization techniques; Power plants; Process controls; Sensors; Supervisory control and data acquisition; Temperature; Temperature control; Water temperature
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21206737

In the search for increased productivity and efficiency in the industrial sector, a new industrial revolution, called Industry 4.0, was promoted. In the electric sector, power plants seek to adapt these new concepts to optimize electric power generation processes, as well as to reduce operating costs and unscheduled downtime intervals. In these plants, PID control strategies are commonly used in water cooling systems, which use fans to perform the thermal exchange between water and the ambient air. However, as the nonlinearities of these systems affect the performance of the drivers, sometimes a greater number of fans than necessary are activated to ensure water temperature control which, consequently, increases energy expenditure. In this work, our objective is to develop digital twins for a water cooling system with auxiliary equipment, in terms of the decision making of the operator, to determine the correct number of fans. This model was developed based on the algorithm of automatic extraction of fuzzy rules, derived from the SCADA of a power plant located in the capital of Paraíba, Brazil. The digital twins can update the fuzzy rules in the case of new events, such as steady-state operation, starting and stopping ramps, and instability. The results from experimental tests using data from 11 h of plant operations demonstrate the robustness of the proposed digital twin model. Furthermore, in all scenarios, the average percentage error was less than 5% and the average absolute temperature error was below 3 °C.