Prediction of the jump height of transmission lines after ice-shedding based on XGBoost and Bayesian optimization

Long, Xiaohong; Gu, Xiaodong; Lu, Chunde; Li, Zonglin; Ma, Yongtao; Zhou, Jing

doi:10.1016/j.coldregions.2023.103928

Cited by 16 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Icing disasters, a common and severe threat to transmission and distribution lines, endanger the safety and stability of these critical infrastructures [1][2][3]. Icing can result in catastrophic consequences, including pole and tower collapse, broken wires and strands due to uneven icing or de-icing, insulator flashover, and equipment damage from line movement (dancing) [4][5][6]. The transmission line icing disasters caused by micro-meteorology and micro-terrain on transmission lines are as follows: severe icing on transmission lines leads to disasters such as pole collapse and tower collapse; uneven icing or de icing of the line leads to wire and strand breakage disasters, insulator string icing flashover, and equipment damage caused by the swinging of transmission lines [7,8].…”

Section: Introductionmentioning

confidence: 99%

Ice Cover Prediction for Transmission Lines Based on Feature Extraction and an Improved Transformer Scheme

Ke,

Sun,

Zhao

et al. 2024

Electronics

View full text Add to dashboard Cite

Frequent and severe icing on transmission lines poses a serious threat to the stability and safe operation of the power system. Meteorological data, inherently stochastic and uncertain, requires effective preprocessing and feature extraction to ensure accurate and efficient prediction of transmission line icing thickness. We address this challenge by leveraging the meteorological features of icing phenomena and propose a novel feature preprocessing method that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and spectral clustering. This method effectively preprocesses raw time series data, extracts key features, and constructs a similarity matrix and feature vector. The resulting feature vector serves as a new data representation, facilitating cluster analysis to isolate meteorological and icing-related features specific to transmission lines. Subsequently, we introduce an enhanced Transformer model for predicting transmission line icing thickness. The proposed model leverages the extracted meteorological and icing features by independently embedding variable tokens for each input feature. This approach improves the model’s prediction accuracy under multiple feature inputs, leading to more effective learning. The experimental results demonstrate that the performance of the proposed prediction algorithm is better than the three baseline algorithms (hybrid CEEMDAN and LSTM (CEEMDAN-LSTM), hybrid CEEMDAN, spectral clustering, and LSTM (CEEMDAN-SP-LSTM), and hybrid CEEMDAN, spectral clustering, and Transformer (CEEMDAN-SP-Transformer)) under multiple feature inputs and different parameter settings.

show abstract

Section: Introductionmentioning

confidence: 99%

Ice Cover Prediction for Transmission Lines Based on Feature Extraction and an Improved Transformer Scheme

Ke,

Sun,

Zhao

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…In the field of classification and diagnostics, there have been developments such as the BO-SVM model for Parkinson's disease [9] and an automation system that utilizes CNN and KNN for the diagnosis of Alzheimer's [10]. In the field of prediction, Bayesian optimization has been used for diverse applications including predicting jump height after the release of ice on the transmission [11], cryptocurrency price prediction [12], forecasting the number of vehicles on the road [13], predicting the collapse of transmission foundation towers [14], real-time prediction of electric load on a smart grid [15], prediction of wind energy generation [16], and a decision support prediction system for the diagnosis of gestational diabetes [17]. Furthermore, Bayesian optimization has also been employed in predicting the state of health (SOH) of lithium-ion batteries [18].…”

Section: Introductionmentioning

confidence: 99%

Facebook Prophet Model with Bayesian Optimization for USD Index Prediction

Hamdani,

Swanjaya,

Helilintar

2023

JUITA

View full text Add to dashboard Cite

Accuracy is the primary focus in prediction research. Optimization is conducted to improve the performance of prediction models, thereby enhancing prediction accuracy. This study aims to optimize the Facebook Prophet model by performing hyperparameter tuning using Bayesian Optimization to improve the accuracy of USD Index Value prediction. Evaluation is conducted through multiple prediction experiments using different ranges of historical data. The results of the study demonstrate that performing hyperparameter tuning on the Facebook Prophet model yields better prediction results. Prior to parameter tuning, the MAPE indicator metric is 1.38% for the historical data range of 2014-2023, and it decreases to 1.33% after parameter tuning. Further evaluation shows improved prediction performance using different ranges of historical data. For the historical data range of 2015-2023, the MAPE value decreases from 1.39% to 1.20%. Similarly, for the data range of 2016-2023, the MAPE decreases from 1.12% to 0.80%. Furthermore, for the data range of 2017-2023, there is a decrease from 0.80% to 0.76%. This is followed by the data range of 2018-2023, with a decrease from 0.75% to 0.70%. Lastly, for the data range of 2019-2023, there is a decrease from 0.63% to 0.55%. These results demonstrate that performing Hyperparameter Optimization using Bayesian Optimization consistently improves prediction accuracy in the Facebook Prophet model.

show abstract

Fatigue reliability evaluation for impellers with consideration of multi‐source uncertainties using a WOA‐XGBoost surrogate model

Qian,

Li,

Wei

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

When using Monte Carlo simulation involving repeated finite element analysis (FEA) to perform fatigue reliability evaluation for an impeller, a variety of uncertainties should be considered to ensure the comprehensiveness of fatigue predictions. These uncertainties include the aleatory uncertainty from the geometric, material and load condition, and epistemic uncertainty from the parameters of the physics‐of‐failure (PoF) model to yield fatigue prediction. However, the latter uncertainty is often ignored in fatigue reliability analysis. And the reliability assessment will become computationally unaffordable and inefficient when there are many random variables involved, as an enormous amount of FEAs are demanded. To address this problem, a Whale Optimization Algorithm‐extreme gradient boosting (WOA‐XGBoost) surrogate model is developed, based on relatively few FEA results obtained using a Latin hypercube sampling (LHS). Its strengths lie in the interpretability of the design variables and effective determination of fine‐tuned hyperparameters. A case study on an impeller is conducted considering uncertainties from 11 input variables, where an efficient XGBoost model with an R2 greater than 0.93 on test set is established using 400 samples from practical FEAs. In addition, the importance analysis indicates that elasticity modulus and density play the greatest impact on the maximum strain, showing a combined importance of 82.3%. Furthermore, the reliability assessment results under fatigue parameter derived from the Median method tend to be more conservative compared to those obtained from the Seeger method.

show abstract

Prediction of the jump height of transmission lines after ice-shedding based on XGBoost and Bayesian optimization

Cited by 16 publications

References 39 publications

Ice Cover Prediction for Transmission Lines Based on Feature Extraction and an Improved Transformer Scheme

Ice Cover Prediction for Transmission Lines Based on Feature Extraction and an Improved Transformer Scheme

Facebook Prophet Model with Bayesian Optimization for USD Index Prediction

Fatigue reliability evaluation for impellers with consideration of multi‐source uncertainties using a WOA‐XGBoost surrogate model

Contact Info

Product

Resources

About