Early Fault Detection of Gas Turbine Hot Components Based on Exhaust Gas Temperature Profile Continuous Distribution Estimation

Liu, Jinfu; Bai, Mingliang; Long, Zhenhua; Liu, Jiao; Ma, Yujia; Yu, Daren

doi:10.3390/en13225950

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Currently, the PSO algorithm is very popular; it is applied more frequently than the classical GA method. It is reported to solve multiple problems, e.g., parameter identification [26], optimal siting and sizing methodology to design an energy storage system [27], gas turbine modelling for fault detection [28], optimisation of control policy in MPC [22], optimisation of hybrid energy systems [24], optimisation of power dispatch problems [29].…”

Section: Particle Swarm Optimisation Algorithmmentioning

confidence: 99%

A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation

Ocłoń

Ławryńczuk

Czamara³

2021

Energies

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The objectives of this work are: (a) to present a new system for building heating which is based on underground energy storage, (b) to develop a mathematical model of the system, and (c) to optimise the energy performance of the system. The system includes Photovoltaic Thermal Hybrid Solar Panels (PVT) panels with cooling, an evacuated solar collector and a water-to-water heat pump. Additionally, storage tanks, placed underground, are used to store the waste heat from PVT panels cooling. The thermal energy produced by the solar collectors is used for both domestic hot water preparation and thermal energy storage. Both PVT panels and solar collectors are assembled with a sun-tracking system to achieve the highest possible solar energy gain. Optimisation of the proposed system is considered to achieve the highest Renewable Energy Sources (RES) share during the heating period. Because the resulting optimisation problem is nonlinear, the classical gradient-based optimisation algorithm gives solutions that are not satisfying. As alternatives, three heuristic global optimisation methods are considered: the Genetic Algorithm (GA), the Particle Swarm Optimisation (PSO) algorithm, and the Jaya algorithm. It is shown that the Jaya algorithm outperforms the GA and PSO methods. The most significant result is that 93% of thermal energy is covered by using the underground energy storage unit consisting of two tanks.

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Section: Particle Swarm Optimisation Algorithmmentioning

confidence: 99%

A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation

Ocłoń

Ławryńczuk

Czamara³

2021

Energies

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“…An early fault detection strategy can identify the current status based on historical operating information of process or equipment and give a warning signal on the abnormal operation, thereby providing enough time to take risk mitigation measures. Early fault detection models have been widely applied in different industrial fields [3][4][5][6][7]. Some models are developed according to the physical mechanism; however, it is usually difficult to obtain adequate knowledge for a non-linear complex industrial process, thus affecting the prediction accuracy in condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

Guo

Zhang

et al. 2021

Energies

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The induced draft (ID) fan is an important piece of auxiliary equipment in coal-fired power plants. Early fault detection of the ID fan can provide predictive maintenance and reduce unscheduled shutdowns, thus improving the reliability of the power generation. In this study, an adaptive model was developed to achieve the early fault detection of ID fans. First, a non-parametric monitoring model was constructed to describe the normal operating characteristics with the multivariate state estimation technique (MSET). A similarity index representing operation status was defined according to the prediction deviations to produce warnings of early faults. To deal with the model accuracy degradation because of variant condition operation of the ID fan, an adaptive strategy was proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the MSET model, thereby improving the fault detection results. The proposed method was applied to a 300 MW coal-fired power plant to achieve the early fault detection of an ID fan. In addition, fault detection by using the model without an update was also compared. Results show that the update strategy can greatly improve the MSET model accuracy when predicting normal operations of the ID fan; accordingly, the fault can be detected more than 4 h earlier by using the strategy with the adaptive update when compared to the model without an update.

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Fault Diagnosis Method of Gas Turbine Combustion Chamber Based on CNN-GRU Model Analysis

Wang,

Ying,

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Early Fault Detection of Gas Turbine Hot Components Based on Exhaust Gas Temperature Profile Continuous Distribution Estimation

Cited by 4 publications

References 10 publications

A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation

A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation

An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

Fault Diagnosis Method of Gas Turbine Combustion Chamber Based on CNN-GRU Model Analysis

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