PR-KELM: Icing level prediction for transmission lines in smart grid

Chen, Yunliang; Fan, J; Deng, Ze; Du, Bo; Huang, Xiaohui; Gui, Qirui

doi:10.1016/j.future.2019.08.002

Cited by 18 publications

(8 citation statements)

References 25 publications

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“…Furthermore, the RMSE indicated the SD of material pattern inside the disparities between expected (i.e., qi ) and observed (i.e., q i ) values. As one of the regularly adopted variance indices in statistics, the RMSE is described as [19]: Moreover, the RMSE, mean absolute error (MAE), and mean absolute percentage error (MAPE) are the major variance assessment indicators which are frequently utilized in commerce, and thence were adopted to identify the prediction models [21]. As such, the MAE was the Lastly, the MAPE is a statistical assessment index for the accuracy of forecasting of a prediction process.…”

Section: Variance Estimate For the Crude Oil Spot Price Prediction Pe...mentioning

confidence: 99%

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A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

Chen

2022

Int J Comput Intell Syst

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This research attempts to reinforce the cultivating expression of radial basis function neural network (RBFnet) through computational intelligence (CI) and swarm intelligence (SI) learning methods. Consequently, the artificial immune system (AIS) and ant colony optimization (ACO) approaches are utilized to cultivate RBFnet for function approximation issue. The proposed hybridization of AIS and ACO approaches optimization (HIAO) algorithm combines the complementarity of exploitation and exploration to realize problem solving. It allows the solution domain having the advantages of intensification and diversification, which further avoids the situation of immature convergence. In addition, the empirical achievements have confirmed that the HIAO algorithm not only obtained the best accurate function approximation for theoretically standard nonlinear problems, it can be further applied on the instance solving for practical crude oil spot price prediction.

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Section: Variance Estimate For the Crude Oil Spot Price Prediction Pe...mentioning

confidence: 99%

“…As such, the MAE was the Lastly, the MAPE is a statistical assessment index for the accuracy of forecasting of a prediction process. It generally indicates the percentage ratio of the emerging variance and is described as below [19]:…”

Section: Variance Estimate For the Crude Oil Spot Price Prediction Pe...mentioning

confidence: 99%

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

Chen

2022

Int J Comput Intell Syst

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“…e thickness of icing will affect the health status of transmission lines. Different thickness grades can be calculated according to the model [32], which will reflect the health status of transmission lines to a certain extent.…”

Section: Data Sourcementioning

confidence: 99%

LPR‐MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks

Chen

Zhang

et al. 2021

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The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict the health status of the power grid sensor network. The LPR-MLP model consists of two parts: (1) local binary pattern (LBP), principal component analysis (PCA), and ReliefF are used to process image data and meteorological and mechanical data and (2) the multilayer perceptron (MLP) method is then applied to build the prediction model. The results obtained from extensive experiments on the real-world data collected from the online system of China Southern Power Grid demonstrate that this new LPR-MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods.

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“…With the development of computer technology, artificial intelligence prediction algorithm has been widely used in recent years [15][16][17][18]. Reference [19] proposed a short-term prediction model of transmission line icing thickness based on a grey support vector machine and analyzed the methods of eliminating bad data and data preprocessing.…”

Section: Introductionmentioning

confidence: 99%

Transmission Line Ice Disaster Early Warning Method Based on Ice Thickness Prediction Using GM(1, 6) Model

et al. 2024

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The operation of the electricity system faces the difficulty of minimizing ice damage to transmission lines. Due to the uncertainty of transmission line icing thickness, accurate prediction of icing thickness is very important to guide transmission line planning and power grid anti-icing design effectively. Scientific and reasonable early warning assessment of conductor icing is also helpful to make timely and accurate response measures to the possible freezing disaster risk, so as to effectively ensure the safe and stable operation of power grid and reduce the occurrence of the freezing disaster. Based on this, this paper proposes a multi-factor prediction model based on GM (1, 6) grey theory, which considers the transmission line conductor icing model under the influence of multiple meteorological factors. Based on the conductor icing model, the conductor icing degree can be predicted in real time according to meteorological parameters, so as to realize the purpose of transmission line conductor icing disaster risk early warning. It is worth noting that the paper improves the traditional grey model by adding random data functions, which makes the model solve the problem of inaccurate prediction of small samples. Finally, a case study is carried out, and the icing disaster risk is divided into five levels. It is found that the maximal prediction error of icing thickness based on GM (1, 6) grey theory multi-factor prediction model is 10.06%(The average value is only 4.22%), and the accuracy of transmission line conductor icing disaster risk early warning is 88.9%. In addition, a certain safety margin value is added to the predicted value near the critical value of icing thickness, reducing the probability of judging the high-risk level as low-risk. Applying risk early warning method in ice areas can guide the antiice work of transmission line.

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PR-KELM: Icing level prediction for transmission lines in smart grid

Cited by 18 publications

References 25 publications

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

LPR‐MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks

Transmission Line Ice Disaster Early Warning Method Based on Ice Thickness Prediction Using GM(1, 6) Model

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