Condition monitoring and fault diagnostics for hydropower plants

Selak, Luka; Butala, Peter; Sluga, Alojzij

doi:10.1016/j.compind.2014.02.006

Cited by 66 publications

(39 citation statements)

References 24 publications

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“…The recent developments in Information and Communication Technologies (ICT) enable remote process and condition monitoring [4,5,6] and automated control enabling the transformation of SHP from pure products to industrial product service systems [7,8]. In turn, more efficient operations and maintenance (O&M) can be achieved through newly developed products and services [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Assessing Feasibility of Operations and Maintenance Automation – A Case of Small Hydropower Plants

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Assessing Feasibility of Operations and Maintenance Automation – A Case of Small Hydropower Plants

et al. 2015

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“…In recent years, some specialized monitoring systems (SMSs) have been applied to HGSs, which have improved the intelligent level of hydropower stations to some extent, such as SMS for vibration [4], turbine cavitation [5], partial discharge of generator [6] and partial discharge of transformer [7]. To further improve the intelligent level of hydropower stations, the integrated monitoring systems are established to strengthen the link among SMSs and eliminate information island of a hydropower plant.…”

Section: Introductionmentioning

confidence: 99%

“…The overview state data are the average, maximum and minimum values of the supervisory control state data in every 15 min, used to observe the variation states over a long time, such as 24 h, a week, a month or even one year[12] (4). The hourly data are the average, maximum, minimum values of overview state data in every hour or the values of supervisory control state data on the hour, mainly used for intelligent report forms (5).…”

mentioning

confidence: 99%

Vibration Monitoring Approach in Integrated System of Hydroelectric Generating Sets for Smart Power Stations

Wu¹,

Li²,

Li³

et al. 2016

International Journal of Power and Energy Systems

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Vibration monitoring is the most important module of an integrated monitoring system of hydroelectric generating sets (HGSs) due to the fact that most faults of HGSs are reflected in the form of vibration.To improve the vibration monitoring ability of the integrated monitoring system of HGSs, a novel integrated monitoring approach based on team intelligence is proposed in this paper. The monitoring functions are assigned to some team members with complementary capabilities and expertise, based on which the integrated monitoring framework of HGSs is established. The vibration state data are divided into five layers, for which selective storage strategies are proposed to improve the pertinence of the data and save storage space. In addition, real-time and time-lapse cooperative monitoring are realized with the driving of condition synchronization, event synchronization and time-lapse inspections. Meanwhile, the selective storage and associative storage of the state data are completed during the process. Besides, the temporary data continuously stored can be extracted and permanently stored by experts to prevent the omission of selective storage. Currently, the proposed approach has been successfully applied in Gezhouba Hydropower Station, which significantly improves the intelligent level of the power station.

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“…SVM has been chosen since it is a mature empirical method for developing a classifier and its satisfactory classification performance in fault diagnostic applications has been verified [31][32][33]. 100…”

Section: Introductionmentioning

confidence: 99%

A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

Baraldi

Maio

et al. 2017

Mechanical Systems and Signal Processing

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Fault diagnostic methods are challenged by their applications to industrial components operating in evolving environments of their working conditions. To overcome this problem, we propose a Systematic Semi-Supervised 10 Self-adaptable Fault Diagnostics approach (4SFD), which allows dynamically selecting the features to be used for performing the diagnosis, detecting the necessity of updating the diagnostic model and automatically updating it.Within the proposed approach, the main novelty is the semi-supervised feature selection method developed to dynamically select the set of features in response to the evolving environment. An artificial Gaussian and a real world bearing datasets are considered for the verification of the proposed approach. 15

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Condition monitoring and fault diagnostics for hydropower plants

Cited by 66 publications

References 24 publications

Assessing Feasibility of Operations and Maintenance Automation – A Case of Small Hydropower Plants

Assessing Feasibility of Operations and Maintenance Automation – A Case of Small Hydropower Plants

Vibration Monitoring Approach in Integrated System of Hydroelectric Generating Sets for Smart Power Stations

A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

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