Comprehensive prediction method for failure rate of transmission line based on multi‐dimensional cloud model

Lei, Jiazhi; Gong, Qingjie

doi:10.1049/iet-gtd.2018.6302

Cited by 10 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The power industry stands as a crucial pillar in the modern economic landscape, marked by the extensive construction of transmission lines that imposes higher demands on the monitoring and maintenance of these complex environments [1]. Transmission lines often exhibit specificity and significant dispersion, influenced by various factors such as terrain and topography.…”

Section: Introductionmentioning

confidence: 99%

STAE‐YOLO: Intelligent detection algorithm for risk management of construction machinery intrusion on transmission lines based on visual perception

Ji,

Zhang,

Huang

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

The distribution of transmission lines is complex, influenced by terrain and topography, making their maintenance and management challenging. With the advancement of global mechanization, various types of construction machinery and large‐scale mechanical equipment are continually increasing. Accidental contact of construction machinery near transmission lines poses a serious threat to the stability of the power system. Traditional drones and inspection robots face difficulties in achieving real‐time monitoring of construction machinery and equipment around transmission lines, quickly identifying potential risks. Conventional image processing techniques and convolutional neural networks struggle with effective handling of small targets and densely packed detection tasks involving multiple targets. To address these challenges, this paper proposes an intelligent detection algorithm Swin Transformer attention efficient algorithm‐you only look once (YOLO) (STAE‐YOLO). It is based on the Swin Transformer global self‐attention mechanism, cross‐channel fusion attention mechanism, enhanced small object detection framework, and a focused and efficient regression loss function. The experimental results show that the STAE‐YOLO algorithm model improved by 6.3% in mean average precision, 3.7% in precision, and 3.1% in recall compared to the baseline model. Meanwhile, deploying the window multi‐head self‐attention global self‐attention mechanism in the model can strengthen the global multi‐scale semantic information in detection.

show abstract

Section: Introductionmentioning

confidence: 99%

STAE‐YOLO: Intelligent detection algorithm for risk management of construction machinery intrusion on transmission lines based on visual perception

Ji,

Zhang,

Huang

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…e combination prediction method is the main development direction of current research. For this reason, some scholars propose combined failure rate prediction methods, such as genetic neural network [27], Hidden Semi-Markov Model (HSMM) [10], life and data-driven models [28], Holt-Winters seasonal model [29], EMD and RVM-GM model [30], EMD-GMDH model [31], Weighted Hierarchical Bayes (WHB) method [32], Well distribution and time series [33], BP and dual-parameter Weibull distribution [34], cloud model and clolesky sub-model [35], radar chart and SVM model [36], and neural network and fuzzy recognition [37]. Researchers have carried out research on the use of combined models to predict equipment failure rates.…”

Section: Introductionmentioning

confidence: 99%

Method for Predicting Failure Rate of Airborne Equipment Based on Optimal Combination Model

Hou

Sun

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Accurate prediction of airborne equipment failure rate can provide correct repair and maintenance decisions and effectively establish a health management mechanism. This plays an important role in ensuring the safe use of the aircraft and flight safety. This paper proposes an optimal combination forecasting model, which mixes five single models (Multiple Linear Regression model (MLR), Gray model GM (1, N), Partial Least Squares model (PLS), Artificial Neural Network model (BP), and Support Vector Machine model (SVM)). The combined model and its single model are compared with the other three algorithms. Seven classic comparison functions are used for predictive performance evaluation indicators. The research results show that the combined model is superior to other models in terms of prediction accuracy. This paper provides a practical and effective method for predicting the airborne equipment failure rate.

show abstract

“…As a tool for human beings to understand and describe the world and various phenomena, the mathematics and physics models based on mathematical tools provide the theoretical models which can best measure the health status of transmission lines. A lot of work has been carried out and achieved several positive results [7][8][9]. A modified Firework Algorithm (FA) based on time series [10] is established for transmission lines to guide the prediction of the current health status of transmission lines.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Zhang

et al. 2021

Complexity

View full text Add to dashboard Cite

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.

show abstract

Comprehensive prediction method for failure rate of transmission line based on multi‐dimensional cloud model

Cited by 10 publications

References 24 publications

STAE‐YOLO: Intelligent detection algorithm for risk management of construction machinery intrusion on transmission lines based on visual perception

STAE‐YOLO: Intelligent detection algorithm for risk management of construction machinery intrusion on transmission lines based on visual perception

Method for Predicting Failure Rate of Airborne Equipment Based on Optimal Combination Model

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

Contact Info

Product

Resources

About