Fuzzy system applied to a hydraulic turbine efficiency curve fitting

Abritta, Ramon; Panoeiro, Frederico F.; Aguiar, Eduardo Pestana de; Honório, Leonardo de Mello; Marcato, André L. M.; Silva, Ivo Chaves da

doi:10.1007/s00202-020-00951-6

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…The downstream quota is also given by a polynomial of the fourth degree, however, depending on the outflow of the plant (turbined flow plus spilled flow). In this context, study [22] evaluates techniques of fitting a hydraulic turbine efficiency curve. In addition, studies in [23,24] evaluate techniques for predicting and prevent undesired spillage.…”

Section: Hydraulic Production Functionmentioning

confidence: 99%

“…For each stage after the first, the initial volume of each reservoir is equal to the final volume calculated in the previous stage. At the end of each stage, the variable Z sup (upper limit) is increased by the value corresponding to the immediate cost, Equation (22). Moreover, at the end of the forward process Z sup will be equal to the sum of the immediate costs, Equation (23).…”

Section: Applied Dual Deterministic Dynamic Programmingmentioning

confidence: 99%

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Evaluating the Impact of Streamflow Rating Curve Precision on Firm Energy of Hydropower Plants

Westin¹,

Conceição

Bortoni

et al. 2021

Water

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In Brazil, the correct measurement of the individual firm energy of a plant is important, since it influences directly the determination of its assured energy which is used to establish contracts between power plants and distribution companies, free consumers, and traders. With increasing technological development and greater reliability in the use of automated techniques for monitoring, the use of the Acoustic Doppler Current Profiler (ADCP), has become a reality in Brazil. The ADCP has many advantages over the traditional techniques used for monitoring flows in gage stations of the national hydrometeorological network. In this context, the purpose of this work is to evaluate the impact of the streamflow rating curve measurement on the evaluation of the firm energy of a hydropower plant. A linear optimization model based on dynamic programming was used to calculate the firm energy and it was considered possible measurement errors in the plant’s inflow values and in the parameters of its polynomials that defines the upward and downward elevation. The results pointed that the two considerations had an impact on the calculated firm energy: the inflow measurements and the streamflow rating curve. Therefore, it is shown the importance of an accurate measurement of inflows for the evaluation of the plant’s firm energy.

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Section: Hydraulic Production Functionmentioning

confidence: 99%

Section: Applied Dual Deterministic Dynamic Programmingmentioning

confidence: 99%

Evaluating the Impact of Streamflow Rating Curve Precision on Firm Energy of Hydropower Plants

Westin¹,

Conceição

Bortoni

et al. 2021

Water

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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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Intelligent Low-Consumption Optimization Strategies: Economic Operation of Hydropower Stations Based on Improved LSTM and Random Forest Machine Learning Algorithm

Pan,

Yang,

et al. 2024

Mathematics

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The economic operation of hydropower stations has the potential to increase water use efficiency. However, there are some challenges, such as the fixed and unchangeable flow characteristic curve of the hydraulic turbines, and the large number of variables in optimal load distribution, which limit the progress of research. In this paper, we propose a new optimal method of the economic operation of hydropower stations based on improved Long Short-Term Memory neural network (I-LSTM) and Random Forest (RF) algorithm. Firstly, in order to accurately estimate the water consumption, the LSTM model’s hyperparameters are optimized using improved particle swarm optimization, and the I-LSTM method is proposed to fit the flow characteristic curve of the hydraulic turbines. Secondly, the Random Forest machine learning algorithm is introduced to establish a load-distribution model with its powerful feature extraction and learning ability. To improve the accuracy of the load-distribution model, we use the K-means algorithm to cluster the historical data and optimize the parameters of the Random Forest model. A Hydropower Station in China is selected for a case study. It is shown that (1) the I-LSTM method fits the operating characteristics under various working conditions and actual operating characteristics of hydraulic turbines, ensuring that they are closest to the actual operating state; (2) the I-LSTM method is compared with Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Long Short-Term Memory neural network (LSTM). The prediction results of SVM have a large error, but compared with ELM and LSTM, MSE is reduced by about 46% and 38% respectively. MAE is reduced by about 25% and 21%, respectively. RMSE is reduced by about 27% and 24%, respectively; (3) the RF algorithm performs better than the traditional dynamic programming algorithm in load distribution. With the passage of time and the increase in training samples, the prediction accuracy of the Random Forest model has steadily improved, which helps to achieve optimal operation of the units, reducing their average total water consumption by 1.24%. This study provides strong support for the application of intelligent low-consumption optimization strategies in hydropower fields, which can bring higher economic benefits and resource savings to renewable energy production.

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Fuzzy system applied to a hydraulic turbine efficiency curve fitting

Cited by 3 publications

References 38 publications

Evaluating the Impact of Streamflow Rating Curve Precision on Firm Energy of Hydropower Plants

Evaluating the Impact of Streamflow Rating Curve Precision on Firm Energy of Hydropower Plants

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Intelligent Low-Consumption Optimization Strategies: Economic Operation of Hydropower Stations Based on Improved LSTM and Random Forest Machine Learning Algorithm

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