what are the recent applications of AI algorithms on steam turbine

Introduction

The application of artificial intelligence (AI) algorithms in steam turbines has gained significant attention in recent years. These advancements aim to enhance the efficiency, reliability, and operational flexibility of steam turbines, which are critical components in power generation systems. This synthesis explores the recent applications of AI algorithms in steam turbines, focusing on predictive maintenance, operational optimization, fault diagnosis, and performance enhancement.

Key Insights

Predictive Maintenance and Fault Diagnosis
- AI algorithms, particularly artificial neural networks (ANNs), are used to predict the useful life of steam turbine blades and diagnose faults, significantly reducing operational breakdowns and economic losses .
- Improved neural networks and data mining techniques are applied for fault diagnosis, achieving a diagnosis precision of 84%.
Operational Optimization
- ANNs and support vector machines (SVMs) are employed to optimize the isentropic efficiency of high-pressure steam turbines, leading to significant improvements in energy efficiency and reductions in CO2 and other emissions.
- AI models, such as ANNs, are used to control the steam temperature at the turbine inlet, ensuring safe and efficient start-ups of steam turbines under varying weather conditions.
Vibration Reduction
- AI-based models, including ANNs and SVMs, are developed to reduce the relative vibration of steam turbine shaft bearings, enhancing the reliability and safety of power generation.
Performance Enhancement
- ANNs are applied to control critical variables such as pressure, temperature, speed, and humidity in steam turbines, resulting in enhanced performance and operational efficiency.
- AI techniques, including NARMA controllers, are used to improve the performance of steam turbines by optimizing control models and electric circuit designs.
Design and Flow Optimization
- ANNs are utilized in the design process of turbine flow parts, significantly reducing the time required for design optimization and improving the spatial distribution of flow parameters.

Conclusion

The integration of AI algorithms in steam turbines has led to substantial advancements in predictive maintenance, operational optimization, fault diagnosis, vibration reduction, and performance enhancement. These applications not only improve the efficiency and reliability of steam turbines but also contribute to the broader goals of energy efficiency and emission reduction in the power generation sector.

What are the recent applications of AI algorithms on steam turbine

Summary

Introduction

Key Insights

Conclusion

Sources and full results

An empirical analysis of applications of artificial intelligence algorithms in wind power technology innovation during 1980–2017

AI algorithms in wind power technology have evolved over time, accelerating the automation of wind power systems and increasing efficiency in generation, operation, and maintenance from 1980 to 2017.

Using the artificial neural network to control the steam turbine heating process

The inverse neural network control algorithm effectively controls steam turbine heating, ensuring start-up rate and safety in power engineering machinery under variable weather conditions.

The use of artificial neural network (ANN) for modeling the useful life of the failure assessment in blades of steam turbines

Artificial neural network modeling effectively predicts useful life of steam turbine blades, with resonance stress being the most influential parameter and frequency ratio being second.

Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing

The artificial neural network (ANN) process model effectively predicts and reduces vibrations in steam turbine shaft bearings, improving power plant safety and reliability.

AI-assistance for predictive maintenance of renewable energy systems

AI-assistance significantly improves technical inspectors' specificity and time efficiency in identifying bearing faults in wind farms, with greater improvements for generalists.

Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector

AI-based optimization of industrial-scale steam turbines can improve efficiency by 1.43, 5.09, and 3.40%, contributing to higher-energy efficiency and contributing to net-zero targets in the energy sector.

Application of Artificial Neural Networks in Investigations of Steam Turbine Cascades

Artificial neural networks (ANNs) can effectively estimate flow parameters in steam turbine cascades and significantly reduce design optimization time.

Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design

Artificial neural networks and electric circuit design can enhance steam turbine performance in thermal power plants with a maximum deviation between experimental data and predicted results of less than 1%.

Data mining based on improved neural network and its application in fault diagnosis of steam turbine

The improved neural network-based data mining algorithm accurately diagnoses steam turbine faults with 84% diagnosis precision, making it a feasible and valuable method for developing fault diagnosis systems in the electric power industry.

Thermoelastic steam turbine rotor control based on neural network

NARX neural networks effectively control steam turbine stress in industrial power plants by locating critical stress and temperature points during transient states.

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