Operation of Steam Turbines under Blade Failures during the Summer Peak Load Periods

Lee, Chien Hsing; Huang, Shih‐Yu; Chang, Chia An; Chen, Bin Kwie

doi:10.3390/en7117415

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…To date, several efficiency strategies have been developed to achieve this optimal operation [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Ref.…”

Section: Of 15mentioning

confidence: 99%

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The Optimal Energy Dispatch of Cogeneration Systems in a Liberty Market

Lin

Yang

et al. 2019

Energies

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This paper proposes a novel approach toward solving the optimal energy dispatch of cogeneration systems under a liberty market in consideration of power transfer, cost of exhausted carbon, and the operation condition restrictions required to attain maximal profit. This paper investigates the cogeneration systems of industrial users and collects fuel consumption data and data concerning the steam output of boilers. On the basis of the relation between the fuel enthalpy and steam output, the Least Squares Support Vector Machine (LSSVM) is used to derive boiler and turbine Input/Output (I/O) operation models to provide fuel cost functions. The CO2 emission of pollutants generated by various types of units is also calculated. The objective function is formulated as a maximal profit model that includes profit from steam sold, profit from electricity sold, fuel costs, costs of exhausting carbon, wheeling costs, and water costs. By considering Time-of-Use (TOU) and carbon trading prices, the profits of a cogeneration system in different scenarios are evaluated. By integrating the Ant Colony Optimization (ACO) and Genetic Algorithm (GA), an Enhanced ACO (EACO) is proposed to come up with the most efficient model. The EACO uses a crossover and mutation mechanism to alleviate the local optimal solution problem, and to seek a system that offers an overall global solution using competition and selection procedures. Results show that these mechanisms provide a good direction for the energy trading operations of a cogeneration system. This approach also provides a better guide for operation dispatch to use in determining the benefits accounting for both cost and the environment in a liberty market.

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“…To date, several efficiency strategies have been developed to achieve this optimal operation [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Ref.…”

Section: Of 15mentioning

confidence: 99%

“…Ref. [10] presented the operation of steam turbines experiencing blades failures during peak load times of the summer months at a cogeneration plant. The author of [11] applied some possible technologies to integrate pulp and paper production within the context of a high-efficiency cogeneration system.…”

Section: Of 15mentioning

confidence: 99%

The Optimal Energy Dispatch of Cogeneration Systems in a Liberty Market

Lin

Yang

et al. 2019

Energies

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“…The problem of peak load regulation caused by power load fluctuation has been widely studied in the world. Lee 1 studied turbine blade faults of peak shaving units in summer for power plants in Taiwan Province of China. Feili 2 analyzed the risk and failure of power plants under load fluctuation based on the data of renewable energy and traditional fossil energy in the United States.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method of peak-load-regulation steam turbine based on improved PCA-HKNN artificial neural network

De-ren

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Clean energy is now developing rapidly, especially in the United States, China, the Britain and the European Union. To ensure the stability of power production and consumption, and to give higher priority to clean energy, it is essential for large power plants to implement peak shaving operation, which means that even the 1000 MW steam turbines in large plants will undertake peak shaving tasks for a long period of time. However, with the peak load regulation, the steam turbines operating in low capacity may be much more likely to cause faults. In this paper, aiming at peak load shaving, a fault diagnosis method of steam turbine vibration has been presented. The major models, namely hierarchy-KNN model on the basis of improved principal component analysis (Improved PCA-HKNN) has been discussed in detail. Additionally, a new fault diagnosis method has been proposed. By applying the PCA improved by information entropy, the vibration and thermal original data are decomposed and classified into a finite number of characteristic parameters and factor matrices. For the peak shaving power plants, the peak load shaving state involving their methods of operation and results of vibration would be elaborated further. Combined with the data and the operation state, the HKNN model is established to carry out the fault diagnosis. Finally, the efficiency and reliability of the improved PCA-HKNN model is discussed. It’s indicated that compared with the traditional method, especially handling the large data, this model enhances the convergence speed and the anti-interference ability of the neural network, reduces the training time and diagnosis time by more than 50%, improving the reliability of the diagnosis from 76% to 97%.

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