Data-mining experiments on a hydroelectric power plant

Ohana, Ivaldo; Bezerra, Ubiratan Holanda; Vieira, Joao Paulo Abreu

doi:10.1049/iet-gtd.2011.0594

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…Classification techniques have applications concentrated in deriving better operation rules for hydropower release or/and decision-making for the operation of reservoirs/hydropower plants [19,21,47], that is, hydropower/reservoirs operation optimization. In addition, decision tree modeling is often found in classification algorithms in these papers, such as in [47,49,65].…”

Section: Results Analysismentioning

confidence: 99%

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Bernardes

Santos

Abreu

et al. 2022

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The optimal dispatch of hydropower plants consists of the challenge of taking advantage of both available head and river flows. Despite the objective of delivering the maximum power to the grid, some variables are uncertain, dynamic, non-linear, and non-parametric. Nevertheless, some models may help hydropower generating players with computer science evolution, thus maximizing the hydropower plants’ power production. Over the years, several studies have explored Machine Learning (ML) techniques to optimize hydropower plants’ dispatch, being applied in the pre-operation, real-time and post-operation phases. Hence, this work consists of a systematic review to analyze how ML models are being used to optimize energy production from hydropower plants. The analysis focused on criteria that interfere with energy generation forecasts, operating policies, and performance evaluation. Our discussions aimed at ML techniques, schedule forecasts, river systems, and ML applications for hydropower optimization. The results showed that ML techniques have been more applied for river flow forecast and reservoir operation optimization. The long-term scheduling horizon is the most common application in the analyzed studies. Therefore, supervised learning was more applied as ML technique segment. Despite being a widely explored theme, new areas present opportunities for disruptive research, such as real-time schedule forecast, run-of-river system optimization and low-head hydropower plant operation.

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Section: Results Analysismentioning

confidence: 99%

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Bernardes

Santos

Abreu

et al. 2022

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“…The result show most defects occur in the electric system and are caused due to aging and electrical, thermal and mechanical load. [21] performed a statistical analysis on the discrete events in the hydroelectric generator. The result show the simple statistical analysis in discrete variables may reveal important operational characteristics and may raise questions with respect to the system overall operation performance that may be indicating conditions not acceptable in the future.…”

Section: ) Statistical Methodsmentioning

confidence: 99%

“…Due to the simplicity and the classifying speed, the decision tree is widely used in fault diagnosis and prognosis. [21] applied the decision tree to analyze the performance of the HTG. The turbine water discharge and the upstream water level are taken as the condition attributes, and the generator power is taken as target attribute.…”

Section: ) Other Data Mining Techniquesmentioning

confidence: 99%

“…Such analysis is useful in many decision-making processes, such as fault diagnosis, operation performance etc. [21] applied the association analysis to historical records for the hydroelectric generators alarm. The result show there are strong relation between certain some generators according to their alarm occurrence time, which need further analysis and maybe helpful to improve the operation.…”

Section: ) Other Data Mining Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Data-driven Predictive Maintenance Approach for Hydro Turbines/Generators

Wang¹,

Wang²,

Li³

2016

Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

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Abstract-Hydroelectricity as a renewable energy to respond the increasing population and environment crisis is widely used in the world. With the Hydro Turbines/Generators (HTG) being more and more complicated, the maintenance play a more and more important role in the production management in the hydro power plant. Many researches had concentrated on the predictive maintenance for the HTG in recent years. From the perspective of data-driven, this paper reviews and summarizes the key techniques regarding data acquisition, data processing, data analysis and data mining for the predictive maintenance of HTG. Especially, it place emphasis on the data-driven models for the diagnostics and prognostics. Finally, the paper concludes the current practices and presents a future research work.

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“…In addition, the setting of the limits is excessively dependent on the experience of operators, which reduces the reliability of power plant operation. In fact, most power plants have accumulated a large amount of data about the daily operation of equipment, but these data have not been effectively used (Ohana et al, 2012). It is necessary to introduce data mining technology into the fault early warning system of equipment to improve the stability of plant operation (Du et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Research on early fault warning system of coal mills based on the combination of thermodynamics and data mining

Zhu

Qian

Chai

2019

Transactions of the Institute of Measurement and Control

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This paper presents a fault early warning approach of coal mills based on the Thermodynamic Law and data mining. The Thermodynamic Law is used to describe the working characteristics of coal mills and to determine the multi-parameter vector that characterize the operating state of the coal mill. Data mining technology is applied to analysis the interrelationships among elements of the multi-parameter vector. Then the abnormal boundaries of parameters are calculated based on the distribution of parameters under different working conditions according to the Pauta criterion. Finally, the fault early warning model is implemented combining the abnormal boundaries and the confidence algorithm that can detect the working status of coal mills. Two actual numerical examples are used to illustrate the proposed method is capable of estimating the abnormality of coal mills before the fault happens.

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Data-mining experiments on a hydroelectric power plant

Cited by 11 publications

References 8 publications

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

A Review on Data-driven Predictive Maintenance Approach for Hydro Turbines/Generators

Research on early fault warning system of coal mills based on the combination of thermodynamics and data mining

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