Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

The harsh operating environment aggravates the degradation of pumped storage units (PSUs). Degradation trend prediction (DTP) provides important support for the condition-based maintenance of PSUs. However, the complexity of the performance degradation index (PDI) sequence poses a severe challenge of the reliability of DTP. Additionally, the accuracy of healthy model is often ignored, resulting in an unconvincing PDI. To solve these problems, a combined DTP model that integrates the maximal information coefficient (MIC), light gradient boosting machine (LGBM), variational mode decomposition (VMD) and gated recurrent unit (GRU) is proposed. Firstly, MIC-LGBM is utilized to generate a high-precision healthy model. MIC is applied to select the working parameters with the most relevance, then the LGBM is utilized to construct the healthy model. Afterwards, a performance degradation index (PDI) is generated based on the LGBM healthy model and monitoring data. Finally, the VMD-GRU prediction model is designed to achieve precise DTP under the complex PDI sequence. The proposed model is verified by applying it to a PSU located in Zhejiang province, China. The results reveal that the proposed model achieves the highest precision healthy model and the best prediction performance compared with other comparative models. The absolute average (|AVG|) and standard deviation (STD) of fitting errors are reduced to 0.0275 and 0.9245, and the RMSE, MAE, and R2 are 0.00395, 0.0032, and 0.9226 respectively, on average for two operating conditions.

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Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Sensors ◽

10.3390/s20154277 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4277 ◽

Cited By ~ 1

Author(s):

Jianzhong Zhou ◽

Yahui Shan ◽

Jie Liu ◽

Yanhe Xu ◽

Yang Zheng

Keyword(s):

Short Term Memory ◽

Guide Vane ◽

Series Representation ◽

Engineering Application ◽

Percentage Error ◽

Degradation Index ◽

Entropy Weight ◽

Storage Unit ◽

Proposed Model ◽

Pumped Storage

Accurate degradation tendency prediction (DTP) is vital for the secure operation of a pumped storage unit (PSU). However, the existing techniques and methodologies for DTP still face challenges, such as a lack of appropriate degradation indicators, insufficient accuracy, and poor capability to track the data fluctuation. In this paper, a hybrid model is proposed for the degradation tendency prediction of a PSU, which combines the integrated degradation index (IDI) construction and convolutional neural network-long short-term memory (CNN-LSTM). Firstly, the health model of a PSU is constructed with Gaussian process regression (GPR) and the condition parameters of active power, working head, and guide vane opening. Subsequently, for comprehensively quantifying the degradation level of PSU, an IDI is developed using entropy weight (EW) theory. Finally, combining the local feature extraction of the CNN with the time series representation of LSTM, the CNN-LSTM model is constructed to realize DTP. To validate the effectiveness of the proposed model, the monitoring data collected from a PSU in China is taken as case studies. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) obtained by the proposed model are 1.1588, 0.8994, 0.0918, and 0.9713, which can meet the engineering application requirements. The experimental results show that the proposed model outperforms other comparison models.

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Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island

Remote Sensing ◽

10.3390/rs12142271 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2271 ◽

Cited By ~ 2

Author(s):

Jinwoong Park ◽

Jihoon Moon ◽

Seungmin Jung ◽

Eenjun Hwang

Keyword(s):

Solar Radiation ◽

Global Solar Radiation ◽

Jeju Island ◽

Gradient Boosting ◽

Probabilistic Forecasting ◽

Training Time ◽

Light Gradient ◽

Proposed Model ◽

Gradient Boosting Machine ◽

Time Problem

Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods.

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A combined multi-objective optimization model for degradation trend prediction of pumped storage unit

Measurement ◽

10.1016/j.measurement.2020.108373 ◽

2021 ◽

Vol 169 ◽

pp. 108373

Author(s):

Yahui Shan ◽

Jie Liu ◽

Yanhe Xu ◽

Jianzhong Zhou

Keyword(s):

Optimization Model ◽

Multi Objective Optimization ◽

Trend Prediction ◽

Storage Unit ◽

Multi Objective ◽

Pumped Storage

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Axial Force Analysis of the Support Bracket of Pumped Storage Unit

10.20944/preprints202112.0397.v1 ◽

2021 ◽

Author(s):

Buchao Xu ◽

Weiqiang Zhao ◽

Wenhua Lin ◽

Zhongyu Mao ◽

Ran Tao ◽

...

Keyword(s):

Structural Analysis ◽

Axial Force ◽

Cfd Simulation ◽

Operating Conditions ◽

Stable Operation ◽

Axial Loads ◽

Storage Unit ◽

Maximum Deformation ◽

Pumped Storage ◽

Variation Rule

During operation, the support bracket is the main part to withstand the axial loads of the pumped storage unit. Moreover, the effects of axial loads including the hydraulic thrust of runner flow and the weight of runner body may cause the support bracket deformation and fatigue damage. For the safe and stable operation, the simulation of the axial force and the structural analysis of the support bracket of a pumped storage unit was carried out in this paper. The CFD simulation result has revealed the variation rule of the axial force in different operating conditions. Using ANSYS Mechanical, the static stresses and deformation of support bracket with axial loads were calculated. The results release the location and variations of maximum stress and maximum deformation caused by the axial loads. By comparing the predicted maximum axial force with the admission force calculated by the structural analysis, it is found that the axial force of the researched machine is within the safe range. This study provides the reference for the safety and stable operation of the pumped storage unit.

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Short-Term Passenger Flow Forecast of Rail Transit Station Based on MIC Feature Selection and ST-LightGBM considering Transfer Passenger Flow

Scientific Programming ◽

10.1155/2020/3180628 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Zhe Zhang ◽

Cheng Wang ◽

Yueer Gao ◽

Jianwei Chen ◽

Yiwen Zhang

Keyword(s):

Feature Selection ◽

Gradient Boosting ◽

Short Term ◽

Bp Network ◽

Metro Station ◽

Passenger Flow ◽

Light Gradient ◽

Time Space ◽

Short Term Forecasting ◽

Maximal Information Coefficient

To solve the problems of current short-term forecasting methods for metro passenger flow, such as unclear influencing factors, low accuracy, and high time-space complexity, a method for metro passenger flow based on ST-LightGBM after considering transfer passenger flow is proposed. Firstly, using historical data as the training set to transform the problem into a data-driven multi-input single-output regression prediction problem, the problem of the short-term prediction of metro passenger flow is formalized and the difficulties of the problem are identified. Secondly, we extract the candidate temporal and spatial features that may affect passenger flow at a metro station from passenger travel data based on the spatial transfer and spatial similarity of passenger flow. Thirdly, we use a maximal information coefficient (MIC) feature selection algorithm to select the significant impact features as the input. Finally, a short-term forecasting model for metro passenger flow based on the light gradient boosting machine (LightGBM) model is established. Taking transfer passenger flow into account, this method has a low space-time cost and high accuracy. The experimental results on the dataset of Lianban metro station in Xiamen city show that the proposed method obtains higher prediction accuracy than SARIMA, SVR, and BP network.

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Fixed-speed and Variable-speed Pumped Storage Dispatch Model in Power Systems with High Renewable Penetration

E3S Web of Conferences ◽

10.1051/e3sconf/20183804014 ◽

2018 ◽

Vol 38 ◽

pp. 04014

Author(s):

Bo Yuan ◽

Jin Zong ◽

Zhicheng Xu

Keyword(s):

Power Systems ◽

Linear Optimization ◽

Operating Conditions ◽

Economic Life ◽

Mixed Integer ◽

Variable Speed ◽

Operating Characteristics ◽

Storage Unit ◽

New Energy ◽

Pumped Storage

According to different operating characteristics of pumped storage fixed speed unit and variable speed unit, a joint dispatching model of pumped storage unit and other types of units based on mixed integer linear optimization is constructed. The model takes into account the operating conditions, reservoir capacity, cycle type and other pumped storage unit constraints, but also consider the frequent start and stop and the stability of the operation of the unit caused by the loss. Using the Cplex solver to solve the model, the empirical example of the provincial power grid shows that the model can effectively arrange the pumping storage speed and the dispatching operation of the variable speed unit under the precondition of economic life of the unit, and give full play to the function of peak shaving and accommodating new energy. Because of its more flexible regulation characteristics of power generation and pumping conditions, the variable speed unit can better improve the operating conditions of other units in the system and promote the new energy dissipation.

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The Equilibrium Model of Electricity Market with Consideration of the Pumped Storage GenCo

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.614-615.1966 ◽

2012 ◽

Vol 614-615 ◽

pp. 1966-1972

Author(s):

Jian Lin Yang ◽

Hui Qing Lu ◽

Fang Di Shi

Keyword(s):

Electricity Market ◽

Test System ◽

Bidding Strategy ◽

Simulation Tool ◽

Storage Unit ◽

Bidding Strategies ◽

Efficient Simulation ◽

Proposed Model ◽

Pumped Storage ◽

Mixed Linear Complementarity Problem

Pumped storage is the largest-capacity form of grid energy storage available. A multi-period oligopolistic model for analyzing the bidding strategies of pumped storage GenCo (PSG) is proposed in this paper. In the pumping periods, the pumped storage unit (PSU) is simulated as a special load. While in generating periods, PSU is treated as a normal generator. In this model, all GenCos are assumed to exercise Cournot strategies to maximize their own profits. The resulting equilibrium formulation is established in terms of a mixed linear complementarity problem. The purpose of this paper is to provide an efficient simulation tool for the PSG to determine its bidding strategy in an oligopolistic environment. The proposed model can also be used to study various factors that may impact PSG’s profit. Results of a six-bus test system are analyzed to illustrate the characteristics of the proposed model.

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TEHD analysis of a bidirectional thrust bearing in a pumped storage unit

Industrial Lubrication and Tribology ◽

10.1108/ilt-07-2015-0092 ◽

2016 ◽

Vol 68 (3) ◽

pp. 315-324 ◽

Cited By ~ 3

Author(s):

Liming Zhai ◽

Zhengwei Wang ◽

Yongyao Luo ◽

Zhongjie Li

Keyword(s):

Thrust Bearing ◽

Three Dimensional ◽

Elastic Equilibrium ◽

Operating Conditions ◽

Storage Unit ◽

Content Type ◽

Thrust Bearings ◽

Tilting Pad ◽

Lubrication Characteristics ◽

Pumped Storage

Purpose The purpose of this paper is to analyze lubrication characteristics of a bidirectional thrust bearing in a pumped storage, considering the effect of the thermal elastic deformation of the pad and collar. Design/methodology/approach This study used the fluid–solid interaction (FSI) technique to investigate the lubrication characteristics of a bidirectional thrust bearing for several typical operating conditions. The influences of the operating conditions and the thrust load on the lubrication characteristics were analyzed. Then, various pivot eccentricities were investigated to analyze the effects of the pivot position. Findings It is found that the effect of the radial tilt angle of the collar runner on the oil film is compensated for by the radial tilt of the pad. The central pivot support system is the main factor limiting the loads of bidirectional thrust bearings. Originality/value This paper has preliminarily revealed the lubrication mechanism of bidirectional tilting-pad thrust bearings. A three-dimensional FSI method is suggested to evaluate the thermal–elastic–hydrodynamic deformations of thrust bearings instead of the conventional method, which iteratively solves the Reynolds equation, the energy equation, the heat conduction equation and the elastic equilibrium equation.

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Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression

Symmetry ◽

10.3390/sym14010160 ◽

2022 ◽

Vol 14 (1) ◽

pp. 160

Author(s):

Pyae-Pyae Phyo ◽

Yung-Cheol Byun ◽

Namje Park

Keyword(s):

Machine Learning ◽

Mean Squared Error ◽

Weather Data ◽

Gradient Boosting ◽

Percentage Error ◽

Short Term ◽

Energy Forecasting ◽

Light Gradient ◽

Proposed Model ◽

Term Energy

Meeting the required amount of energy between supply and demand is indispensable for energy manufacturers. Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system. This paper firstly compares multiple machine learning (ML) regressors during the training process. Five best ML algorithms, such as extra trees regressor (ETR), random forest regressor (RFR), light gradient boosting machine (LGBM), gradient boosting regressor (GBR), and K neighbors regressor (KNN) are trained to build our proposed voting regressor (VR) model. Final predictions are performed using the proposed ensemble VR and compared with five selected ML benchmark models. Statistical autoregressive moving average (ARIMA) is also compared with the proposed model to reveal results. For the experiments, usage energy and weather data are gathered from four regions of Jeju Island. Error measurements, including mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) are computed to evaluate the forecasting performance. Our proposed model outperforms six baseline models in terms of the result comparison, giving a minimum MAPE of 0.845% on the whole test set. This improved performance shows that our approach is promising for symmetrical forecasting using time series energy data in the power system sector.

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Utilizing Hybrid Feature Extraction, Tree-Based Learning and Data-Lightweight Model Re-Training for Accurate Short-Term and Day-Ahead Residential Load Forecasting

10.20944/preprints202108.0052.v2 ◽

2021 ◽

Author(s):

Napoleon Bezas ◽

Christos Timplalexis ◽

Athanasios I. Salamanis ◽

Vasileios Karapatsias ◽

Dimosthenis Ioannidis ◽

...

Keyword(s):

Relevant Literature ◽

Load Forecasting ◽

Gradient Boosting ◽

Short Term ◽

Training Time ◽

Light Gradient ◽

Electrical Grids ◽

Temporal Features ◽

Proposed Model ◽

Forecasting Performance

Residential load forecasting is one of the most important tasks of the overall supply management process in electrical grids, since it enables smart grid services such as demand response (DR). Hence, several approaches for accurate residential load forecasting have been proposed in the relevant literature. However, most of the existing methods focus on the forecasting performance and neglect other aspects of the problem like training time and model size (i.e. memory usage). In this paper, we introduce a new model for both short-term and day-ahead residential load forecasting. The model synthesizes an heterogeneous feature set, which is constituted by both automatically-selected lagged values from the load time series and manually-extracted temporal features. Then, the tree-based algorithm light gradient boosting machine (LGBM) is fed with the constructed feature set and used as a regression model. Finally, a data-lightweight strategy is used for retraining the proposed model, which leads to both high forecasting accuracy and low training times. The proposed model has been extensively evaluated on a large real-world residential load dataset. The experimental results indicate that the proposed model achieves both higher forecasting performance and lower training times and model sizes compared to state-of-the-art solutions.

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