Probabilistic outage analysis of a combined-cycle power plant

Klug, Christian; Teichmann, J.; Balling, L.

doi:10.1049/pe:19950307

Cited by 6 publications

(5 citation statements)

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“…Options of boilers, gas turbines and HRSGs, and their reliability data from the literature [34,38,42,43] are listed in Table 4. Steam turbines are quite reliable assuming no failure during the operation.…”

Section: Case Studymentioning

confidence: 99%

Reliable and flexible steam and power system design

Sun

Liu

2015

Applied Thermal Engineering

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Section: Case Studymentioning

confidence: 99%

Reliable and flexible steam and power system design

Sun

Liu

2015

Applied Thermal Engineering

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“…Along with the deployment of advanced sensing technologies (e.g., smart sensors, internet of things (IoT)), data-driven maintenance has been recognized as an essential scheme for not only improving asset availability but also reducing the costs for repair or replacement [10]. Particularly for the complex system like power plant, big data analytics and artificial intelligence (AI) techniques for maintenance activities have facilitated a paradigm shift from time-based maintenance that can not completely avoid unnecessary shutdowns to predictive maintenance or condition-based maintenance [15]. By integrating historical data into the diagnosis of system state, it becomes feasible to improve the performance of a power plant by sustaining it healthy status [8].…”

Section: Introductionmentioning

confidence: 99%

Depth-Based Condition Monitoring and Contributing Factor Analysis for Anomalies in Combined Cycle Power Plant

Lim,

Kim,

Jin

et al. 2024

IEEE Access

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Unexpected fault or failure in the power plant have caused high maintenanc costs, the loss of energy production, and even safety issues. Developments in sensor technologies and data analytics have aided proper preventive maintenance actions for the system to improve asset availability and reduce repair costs. Nevertheless, effective condition monitoring of a power plant experiences a considerable nuisance from challenging issues such as inherent data characteristics such as high correlations between process variables, irrelevant information from environmental noises, and system complexity. To resolve these problems, this paper proposes an integrated monitoring scheme for performing efficient corrective actions by identifying the variables related to anomalies in combined cycle power plants. The scheme includes a clustering-based linear discriminant analysis to extract key variables for reducing dimensionality to efficiently handle the data, followed by employing the Mahalanobis depth statistics for anomaly detection and causal analysis via contribution scores. The proposed monitoring scheme is applied to condition monitoring data of a combined cycle power plant in South Korea, which include two types of anomalous operations. The reliability and robustness of the proposed condition monitoring scheme are validated by comparing other state-of-the-art methods. The proposed method shows a potential in efficiently detecting anomalies during operation and even early detecting the precursors of anomalies.

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“…37 CCPP systems have been studied under random phenomena. [38][39][40][41] Several optimization techniques have been utilized for performance…”

Section: Introductionmentioning

confidence: 99%

“…Diesel and gas propulsion turbine system's reliability has been investigated through simulation studies 37 . CCPP systems have been studied under random phenomena 38–41 . Several optimization techniques have been utilized for performance evaluation of power plants and power distributed systems, maintenance planning 42–46 .…”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling and availability optimization of condenser used in steam turbine power plants using GA and PSO

Saini

Gupta

Shankar

et al. 2022

Quality & Reliability Eng

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The prominent objective of the present study is to optimize the availability of condenser units in steam turbine power plants. For this purpose, a novel stochastic model is proposed by considering a condenser as a system consisting of seven subsystems. All the time‐dependent random variables associated with failure rates followed exponential distribution while repair rates are arbitrarily distributed. Furthermore, to attain the optimum value of system availability nature‐inspired algorithm has been applied. Predicting the future phenomena is very difficult and most of the existing algorithms have some limitations like stuck at local minima, and slow convergence rate, and so forth. So, in this study proposed model has been optimized using a genetic algorithm (GA) and particle swarm optimization (PSO) The expressions for steady‐state availability of the system have been assessed using Chapman‐Kolmogorov differential‐difference equations developed by applying the Markov birth‐death process. The supplementary variable technique has been adopted to solve differential‐difference equations For a comparative analysis, the steady‐state values, GA, and PSO results are compared. It is revealed that the GA gives the maximum value of availability as 0.9886 corresponding to crossover probability 0.65, population size 50, evaluation 300, and mutation probability 0.5. While using PSO optimum availability value is 0.998152 at 27 iterations, population 55, inertia weight 1, damping ratio 0.85, personal best 1.5, and global best 2. So, PSO results outperform the steady‐state and GA results. It is revealed that mixed bed filter and ejector are the most sensitive components of the condenser those influences most the system effectiveness.

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Probabilistic outage analysis of a combined-cycle power plant

Cited by 6 publications

References 0 publications

Reliable and flexible steam and power system design

Reliable and flexible steam and power system design

Depth-Based Condition Monitoring and Contributing Factor Analysis for Anomalies in Combined Cycle Power Plant

Stochastic modeling and availability optimization of condenser used in steam turbine power plants using GA and PSO

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