A model for booster station matching of gas turbine and gas compressor power under different ambient conditions

Equipment degradation is inevitable during operation. The breakdown of equipment is costly due to replacement expenses and may cause a facility to shut down, leading to higher costs and production losses. Hence, equipment maintenance is essential to ensure a satisfactory level of reliability during its lifecycle. Maintenance strategies may adopt a reactive approach, in which maintenance happens after failure and should be avoided, or a preventive approach, in which maintenance happens regardless of the state of the system. This work aims at addressing the challenges posed by system deterioration and costly downtime through the development and implementation of a condition monitoring solution for a centrifugal compressor situated in an offshore platform. To achieve this objective, a comprehensive methodology was developed containing two modules: fault detection and prognostics. In the fault detection module, a hybrid approach was adopted by combining measured and simulated data generated using physics-based models, where a machine learning model is trained to detect anomalies and generate alarms. In the prognostics module, a degradation state variable is introduced to predict the time to failure of the centrifugal compressor. By leveraging process simulation to construct a physics-based model, the system can track changes in design polytropic efficiency using real-time sensor data to make predictions of time to failure using a Kalman filter algorithm. We demonstrate the methodology through examples using actual centrifugal compressor measurements collected from Valhall public industrial data. The fully automated solution provides real-time insights on the operational state of the centrifugal compressor, enabling proactive maintenance strategies. The analysis of centrifugal compressor sensor data is streamlined to detect anomalies and trigger alarms for different sensors, enabling prediction of time to failure within a few days before the compressor shutdown happens. The approach presented in this work integrates the strengths of both physics-based and data-driven methods. The methodology exploits the alarm generation and complements it with a time-to-failure prediction using a Kalman filter algorithm. The presented solution contains a real-world application of machine learning in condition monitoring and has the potential to enhance predictive maintenance planning and prevent costs associated with downtime.

show abstract

Modeling of thermal power plants in the study of reliability of power supply and energy security

Krupenev,

Piskunova,

Galfinger

2023

«Izvestiya vysshikh uchebnykh zavedenii. PROBLEMY ENERGETIKI»

View full text Add to dashboard Cite

RELEVANCE. Maintaining the required level of energy security (ES) and reliability of fuel and energy supply is one of the priority tasks in the management of the energy sector. In the functioning of the fuel and energy complex various threats are possible, which can lead to the violation of the reliability of fuel and energy supply and the emergence of shortages ofvarious types of energy resources. To assess the level of energy security, approaches are considered to form a mathematical model of the FEC, which combines all sectors of the energy sector. Despite the trends of transition to low-carbon energy sources associated with energy transformation, thermal power plants remain one of the main sources of heat and electricity.Due to the ability to generate large amounts of heat only thermal power plants can be a source of centralized energy supply of large territories. In this regard, when modeling the fuel and energy complex, it is necessary to develop mathematical models of TPPs, which will correctly reflect the technological processes that affect the reliability of energy supply. OBJECT. Development of mathematical models of interaction of electric power, heat supply and fuel systems within the framework of thermal power plants designed to study energy security and reliability of fuel and energy supply. METHODS. Mathematical modeling of fuel consumption dependences on electrical and thermal load at thermal power plants is used as methods. To obtain analytical dependencies, the least squares approximation of the consumption characteristics of thermal power plants was carried out. RESULTS. As part of the work, analytical dependences of fuel consumption on thermal and electrical load for various types of thermal units were obtained. For all considered steam turbines, linear dependences of theamount of heat supplied to the turbine on its load (electrical and / or heating depending on the type of turbine) were obtained. For boilers, an approximation of the dependence of efficiency on thermal load was carried out. According to the results of the approximation, a linear dependence is assumed. For gas turbine installations, an approximation of the dependence of the efficiency on the load was carried out, according to the results of which a second-order polynomial was obtained. CONCLUSION. The paper investigates the problem of mathematical modeling of the interconnected operation of fuel, heat and power and electric power systems within the framework of the functioning of the energy sector. Analytical dependences are obtained for different types of generating equipment, including boilers, steam turbines and gas turbine installations. The obtained dependences are necessary for the analysis of energy security and reliability offuel and energy supply. The models of thermal power plants presented in the paper are the most suitable for the analysis of energy security and reliability of fuel and power supply, as they provide the necessary accuracy of calculations and take into account the specifics of various generating equipment, without dropping to the level of microparameters.

show abstract

A model for booster station matching of gas turbine and gas compressor power under different ambient conditions

Cited by 3 publications

References 19 publications

Gas turbine off-design behavior modelling and operation windows analysis under different ambient conditions

Gas turbine off-design behavior modelling and operation windows analysis under different ambient conditions

An Automated Workflow for Condition Monitoring of Centrifugal Compressors Using a Combined Data-Driven and Physics-Based Approach

Modeling of thermal power plants in the study of reliability of power supply and energy security

Contact Info

Product

Resources

About