Data-driven fault diagnosis in SOFC-based power plants under off-design operating conditions

2019 ◽  
Vol 44 (54) ◽  
pp. 29002-29006 ◽  
Author(s):  
Paola Costamagna ◽  
Andrea De Giorgi ◽  
Gabriele Moser ◽  
Lissy Pellaco ◽  
Andrea Trucco
Keyword(s):  
Fault Diagnosis ◽  
Power Plants ◽  
Operating Conditions ◽  
Data Driven

scholarly journals Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

2019 ◽  
Vol 9 (4) ◽  
pp. 783 ◽  
Author(s):  
Silvio Simani ◽  
Paolo Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Offshore Wind ◽  
Data Driven ◽  
Offshore Wind Turbine ◽  
Wind Turbine System ◽  
High Degree ◽  
Offshore Plants

Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.

Robustness Analysis and Improvement of Fault Diagnosis Model for Nuclear Power Plants Based on Random Forest

2021 ◽  
Author(s):  
Jiangkuan Li ◽  
Meng Lin
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Robustness Analysis ◽  
Noisy Data ◽  
Data Driven ◽  
Model Based ◽  
Diagnosis Model

Abstract With the development of artificial intelligence technology, data-driven methods have become the core of fault diagnosis models in nuclear power plants. Despite the advantages of high flexibility and practicability, data-driven methods may be sensitive to the noise in measurement data, which is inevitable in the process of data measurement in nuclear power plants, especially under fault conditions. In this paper, a fault diagnosis model based on Random Forest (RF) is established. Firstly, its diagnostic performance on noiseless data and noisy data set containing 13 operating conditions (one steady state condition and 12 fault conditions) is analyzed, which shows that the model based on RF has poor robustness under noisy data. In order to improve the robustness of the model under noisy data, a method named ‘Train with Noisy Data’ (TWND) is proposed, the results show that TWND method can effectively improve the robustness of the model based on RF under noisy data, and the degree of improvement is related to the noise levels of added noisy data. This paper can provide reference for robustness analysis and robustness improvement of nuclear power plants fault diagnosis models based on other data-driven methods.

scholarly journals Information Value-Based Fault Diagnosis of Train Door System under Multiple Operating Conditions

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3952
Author(s):  
Seokgoo Kim ◽  
Nam Ho Kim ◽  
Joo-Ho Choi
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Fault Isolation ◽  
Information Value ◽  
Operating Conditions ◽  
Data Driven ◽  
Conditional Probability Table ◽  
Probability Table ◽  
Operating Regimes

While there are many data-driven diagnosis algorithms for fault isolation of complex systems, a new challenge arises in the case of multiple operating regimes. In this case, the diagnosis is usually carried out for each regime for better accuracy. However, the problem is that different results can be derived from each regime and they can conflict with each other, which may invalidate the performance of fault diagnosis. To address this challenge, a methodology for selecting the most reliable one among the different diagnostic results is proposed, which combines the Bayesian network (BN) and the information value (IV). The BN is trained for each regime and a conditional probability table is obtained for probabilistic fault diagnosis. The IV is then employed to evaluate the value of several diagnostic results. The proposed approach is applied to the fault diagnosis of a train door system and its effectiveness is proven.

Using Multiple-Model Adaptive Estimation and System Identification for Fault Detection in Nuclear Power Plants

2018 ◽  
Author(s):  
Jacob A. Farber ◽  
Daniel G. Cole
Keyword(s):  
System Identification ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Adaptive Estimation ◽  
Operating Conditions ◽  
Data Driven ◽  
Multiple Model ◽  
System Models ◽  
Multiple Model Adaptive Estimation

One challenge in nuclear power plant operation is the detection and identification of system faults and plant transients. Timely and accurate identification will reduce operational costs and increase plant safety. This paper describes a combined model-based and data-driven approach to identifying faults in nuclear power plants. Faults are detected for a GSES Generic Pressurized Water Reactor simulator using the multiple-model adaptive estimation (MMAE) technique. In this technique, multiple input-output system models are used that represent different operating conditions. The models predict sensor measurements for both normal and faulted operating conditions simultaneously. The predicted measurements are then compared to the sensor measurements to determine the most likely operating condition. The system models are obtained using system identification techniques for a specific set of faulted conditions. This technique uses sensor measurements from the simulation to identify appropriate parameters for the system models. The MMAE technique is then used to detect similar faults using the identified model. This combination of model-based and data-driven techniques can ultimately be used to create robust fault models that take advantage of both the models created during the design and validation process and real plant data.

scholarly journals Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

2021 ◽  
Vol 9 ◽  
Author(s):  
Guang Hu ◽  
Taotao Zhou ◽  
Qianfeng Liu
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
State Of The Art ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Detection And Diagnosis

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded.

Could biomass-fueled boilers be operated at higher steam temperatures? Part 3: Initial analysis of costs and benefits

TAPPI Journal ◽  
2014 ◽  
Vol 13 (8) ◽  
pp. 65-78 ◽  
Author(s):  
W.B.A. (SANDY) SHARP ◽  
W.J. JIM FREDERICK ◽  
JAMES R. KEISER ◽  
DOUGLAS L. SINGBEIL
Keyword(s):  
Flue Gas ◽  
Power Plants ◽  
Superheated Steam ◽  
Black Liquor ◽  
Simulation Software ◽  
Operating Conditions ◽  
Costs And Benefits ◽  
Steam Generation ◽  
Fireside Corrosion ◽  
Corrosion Resistant

The efficiencies of biomass-fueled power plants are much lower than those of coal-fueled plants because they restrict their exit steam temperatures to inhibit fireside corrosion of superheater tubes. However, restricting the temperature of a given mass of steam produced by a biomass boiler decreases the amount of power that can be generated from this steam in the turbine generator. This paper examines the relationship between the temperature of superheated steam produced by a boiler and the quantity of power that it can generate. The thermodynamic basis for this relationship is presented, and the value of the additional power that could be generated by operating with higher superheated steam temperatures is estimated. Calculations are presented for five plants that produce both steam and power. Two are powered by black liquor recovery boilers and three by wood-fired boilers. Steam generation parameters for these plants were supplied by industrial partners. Calculations using thermodynamics-based plant simulation software show that the value of the increased power that could be generated in these units by increasing superheated steam temperatures 100°C above current operating conditions ranges between US$2,410,000 and US$11,180,000 per year. The costs and benefits of achieving higher superheated steam conditions in an individual boiler depend on local plant conditions and the price of power. However, the magnitude of the increased power that can be generated by increasing superheated steam temperatures is so great that it appears to justify the cost of corrosion-mitigation methods such as installing corrosion-resistant materials costing far more than current superheater alloys; redesigning biomassfueled boilers to remove the superheater from the flue gas path; or adding chemicals to remove corrosive constituents from the flue gas. The most economic pathways to higher steam temperatures will very likely involve combinations of these methods. Particularly attractive approaches include installing more corrosion-resistant alloys in the hottest superheater locations, and relocating the superheater from the flue gas path to an externally-fired location or to the loop seal of a circulating fluidized bed boiler.

"Recent Patents on the Triboelectrostatic Separation of Fly Ash"

2019 ◽  
Vol 13 ◽  
Author(s):  
Haisheng Li ◽  
Wenping Wang ◽  
Yinghua Chen ◽  
Xinxi Zhang ◽  
Chaoyong Li
Keyword(s):  
Fly Ash ◽  
Power Plants ◽  
High Efficiency ◽  
Separation Efficiency ◽  
High Reliability ◽  
Operating Conditions ◽  
Security Requirements ◽  
Unburned Carbon ◽  
Efficient Operation ◽  
Triboelectrostatic Separation

Background: The fly ash produced by coal-fired power plants is an industrial waste. The environmental pollution problems caused by fly ash have been widely of public environmental concern. As a waste of recoverable resources, it can be used in the field of building materials, agricultural fertilizers, environmental materials, new materials, etc. Unburned carbon content in fly ash has an influence on the performance of resource reuse products. Therefore, it is the key to remove unburned carbon from fly ash. As a physical method, triboelectrostatic separation technology has been widely used because of obvious advantages, such as high-efficiency, simple process, high reliability, without water resources consumption and secondary pollution. Objective: The related patents of fly ash triboelectrostatic separation had been reviewed. The structural characteristics and working principle of these patents are analyzed in detail. The results can provide some meaningful references for the improvement of separation efficiency and optimal design. Methods: Based on the comparative analysis for the latest patents related to fly ash triboelectrostatic separation, the future development is presented. Results: The patents focused on the charging efficiency and separation efficiency. Studies show that remarkable improvements have been achieved for the fly ash triboelectrostatic separation. Some patents have been used in industrial production. Conclusion: According to the current technology status, the researches related to process optimization and anti-interference ability will be beneficial to overcome the influence of operating conditions and complex environment, and meet system security requirements. The intelligent control can not only ensure the process continuity and stability, but also realize the efficient operation and management automatically. Meanwhile, the researchers should pay more attention to the resource utilization of fly ash processed by triboelectrostatic separation.

scholarly journals Selection of Heat Pump Capacity Used at Thermal Power Plants under Electricity Market Operating Conditions

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 226
Author(s):  
Milana Treshcheva ◽  
Irina Anikina ◽  
Vitaly Sergeev ◽  
Sergey Skulkin ◽  
Dmitry Treshchev
Keyword(s):  
Electric Power ◽  
Heat Pump ◽  
Heat Load ◽  
Power Plants ◽  
Thermal Power ◽  
Heat Pumps ◽  
Operating Conditions ◽  
Energy Resources ◽  
Thermal Power Plants ◽  
Maximum Heat

The percentage of heat pumps used in thermal power plants (TPPs) in the fuel and energy balance is extremely low in in most countries. One of the reasons for this is the lack of a systematic approach to selecting and justifying the circuit solutions and equipment capacity. This article aims to develop a new method of calculating the maximum capacity of heat pumps. The method proposed in the article has elements of marginal analysis. It takes into account the limitation of heat pump capacity by break-even operation at electric power market (compensation of fuel expenses, connected with electric power production). In this case, the heat pump’s maximum allowable capacity depends on the electric capacity of TPP, electricity consumption for own needs, specific consumption of conditional fuel for electricity production, a ratio of prices for energy resources, and a conversion factor of heat pump. For TPP based on combined cycle gas turbine (CCGT) CCGT-450 with prices at the Russian energy resources markets at the level of 2019, when operating with the maximum heat load, the allowable heat pump capacity will be about 50 MW, and when operating with the minimum heat load—about 200 MW.

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