A New Processing Method Combined with BP Neural Network for Francis Turbine Synthetic Characteristic Curve Research

Li, Junyi; Han, Chunyan; Yu, Fei

doi:10.1155/2017/1870541

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…The most common method for the pump turbine nonlinear modeling is through the complete characteristic curves [14]. The main idea of the nonlinear modeling based on complete curves is to first extract certain discrete data points from the practical curves, and then the extracted data points are fitted or extended to obtain the modeling curves [14]. The mathematical model of the flow and moment characteristics to express the pump turbine characteristics is as follows [2].…”

Section: Nonlinear Modeling Of the Pump Turbinementioning

confidence: 99%

“…The complete curves of pump turbine include the flow characteristic curve and the moment characteristic curve. These curves contain the inherent nonlinear characteristics of the pump turbine under various normal operational conditions or transient working conditions [14,15]. However, due to the existence of the "S" characteristic and hump area, the estimation of complete characteristic curves is highly complex [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…An in-depth and extensive research study has been conducted to extend the complete characteristic curves in recent years. The common methods to estimate the complete curves of pumped turbine mainly include the assistant mesh processing [18], the Suter transformation, its improved versions [19,20], and the 3D surface fitting [14].…”

Section: Introductionmentioning

confidence: 99%

“…The pre-processed curves are then fitted using the least squares or neural network models. Li et al [14] applied a backpropagation (BP) neural network to process the synthetic characteristic curve for the Francis turbine. Actual engineering applications validate that the proposed method can provide not only higher precision data for the transition process but also better investigation on the real hydraulic unit.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics

et al. 2019

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Complete characteristic curves of a pump turbine are fundamental for improving the modeling accuracy of the pump turbine in a pump turbine governing system. In view of the difficulty in modeling the "S" characteristic region of the complete characteristic curves in the pump turbine, a novel Autoencoder and partial least squares regression based extreme learning machine model (AE-PLS-ELM) was proposed to describe the pump turbine characteristics. First, a mathematical model was formulated to describe the flow and moment characteristic curves. The improved Suter transformation was employed to transfer the original curves into WH and WM curves. Second, the ELM-Autoencoder technique and the partial least squares regression (PLSR) method were introduced to the architecture of the original ELM network. The ELM-Autoencoder technique was employed to obtain the initial weights of the Autoencoder based extreme learning machine (AE-ELM) model. The PLS method was exploited to avoid the multicollinearity problem of the Moore-Penrose generalized inverse. Lastly, the effectiveness of the proposed AE-PLS-ELM model has been verified using real data from a pumped storage unit in China. The results demonstrated that the AE-PLS-ELM model can obtain better modeling accuracy and generalization performance than the traditional models and, thus, can be exploited as an effective and sufficient approach for the modeling of pump turbine characteristics.

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Section: Nonlinear Modeling Of the Pump Turbinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics

et al. 2019

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Damping characteristics analysis of hydropower units under full operating conditions and control parameters: Accurate quantitative evaluation based on refined models

Liu

Tan

et al. 2021

Applied Energy

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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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A New Processing Method Combined with BP Neural Network for Francis Turbine Synthetic Characteristic Curve Research

Cited by 3 publications

References 13 publications

An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics

An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics

Damping characteristics analysis of hydropower units under full operating conditions and control parameters: Accurate quantitative evaluation based on refined models

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

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