Probability prediction method of transmission line icing fault based on adaptive relevance vector machine

Zhao, Jie; Zhang, Huaixun; Zou, Hongliang; Pan, Jianguo; Zeng, Chengshi; Xiao, Siyi; Wang, Jun

doi:10.1016/j.egyr.2022.02.018

Cited by 10 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Enhancing and optimizing meta-heuristic algorithms is imperative to augment the predictive accuracy of conventional machine learning models. Additionally, it is essential to refine and optimize the standard machine-learning model to bolster its predictive precision [14]. Consequently, to further elevate the predictive performance, both the algorithms and models necessitate enhancements and advancements.…”

Section: Machine Learning Modelmentioning

confidence: 99%

Online Prediction Method of Transmission Line Icing Based on Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion and Improved Beluga Whale Optimization Algorithm–Least Squares Support Vector Regression

Li,

Liao,

Cui

et al. 2024

ASI

View full text Add to dashboard Cite

Due to the prevalent challenges of inadequate accuracy, unstandardized parameters, and suboptimal efficiency with regard to icing prediction, this study introduces an innovative online method for icing prediction based on Robust STL–BTSF and IBWO–LSSVR. Firstly, this study adopts the Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion (Robust STL–BTSF) approach, which is demonstrably effective for short-term and limited sample data preprocessing. Subsequently, injecting a multi-faceted enhancement approach to the Beluga Whale Optimization algorithm (BWO), which integrates a nonlinear balancing factor, a population optimization strategy, a whale fall mechanism, and an ascendant elite learning scheme. Then, using the Improved BWO (IBWO) above to optimize the key hyperparameters of Least Squares Support Vector Regression (LSSVR), a superior offline predictive part is constructed based on this approach. In addition, an Incremental Online Learning algorithm (IOL) is imported. Integrating the two parts, the advanced online icing prediction model for transmission lines is built. Finally, simulations based on actual icing data unequivocally demonstrate that the proposed method markedly enhances both the accuracy and speed of predictions, thereby presenting a sophisticated solution for the icing prediction on the transmission lines.

show abstract

Section: Machine Learning Modelmentioning

confidence: 99%

Online Prediction Method of Transmission Line Icing Based on Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion and Improved Beluga Whale Optimization Algorithm–Least Squares Support Vector Regression

Li,

Liao,

Cui

et al. 2024

ASI

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…At the same time, a priori probability is introduced to solve the problems of the difficulty in determining the regularization coefficient of SVM and the restriction of the Mercer condition of the kernel function. In [18], a prediction method for the transmission line icing fault probability is proposed based on an adaptive correlation vector machine. This method is based on the RVM model and combines the quantum particle swarm optimization algorithm with K-fold cross-validation to optimize model parameters.…”

Section: Introductionmentioning

confidence: 99%

Research on Adaptive TSSA-HKRVM Model for Regression Prediction of Crane Load Spectrum

Qian¹,

Qin²,

Huang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

For the randomness of crane working load leading to the decrease of load spectrum prediction accuracy with time, an adaptive TSSA-HKRVM model for crane load spectrum regression prediction is proposed. The heterogeneous kernel relevance vector machine model (HKRVM) with comprehensive expression ability is established using the complementary advantages of various kernel functions. The combination strategy consisting of refraction reverse learning, golden sine, and Cauchy mutation + logistic chaotic perturbation is introduced to form a multi-strategy improved sparrow algorithm (TSSA), thus optimizing the relevant parameters of HKRVM. The adaptive updating mechanism of the heterogeneous kernel RVM model under the multi-strategy improved sparrow algorithm (TSSA-HKMRVM) is defined by the sliding window design theory. Based on the sample data of the measured load spectrum, the trained adaptive TSSA-HKRVM model is employed to complete the prediction of the crane equivalent load spectrum. Applying this method to QD20/10 t × 43 m × 12 m general bridge crane, the results show that: compared with other prediction models, although the complexity of the adaptive TSSA-HKRVM model is relatively high, the prediction accuracy of the load spectrum under long periods has been effectively improved, and the completeness of the load information during the whole life cycle is relatively higher, with better applicability.

show abstract

Probability prediction method of transmission line icing fault based on adaptive relevance vector machine

Cited by 10 publications

References 9 publications

Online Prediction Method of Transmission Line Icing Based on Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion and Improved Beluga Whale Optimization Algorithm–Least Squares Support Vector Regression

Online Prediction Method of Transmission Line Icing Based on Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion and Improved Beluga Whale Optimization Algorithm–Least Squares Support Vector Regression

Transmission line ice disaster early warning method based on ice thickness prediction using GM(1, 6) model

Research on Adaptive TSSA-HKRVM Model for Regression Prediction of Crane Load Spectrum

Contact Info

Product

Resources

About