scholarly journals Research on Early Warning Technology of Lightning Arrester Defects Based on Multi-stage Information and Bayesian Inference

2021 ◽  
Vol 2083 (2) ◽  
pp. 022098
Author(s):  
Ying Pei ◽  
Lin Niu ◽  
Haifeng Li ◽  
Yajin Li ◽  
Dayang Yu
Keyword(s):  
Bayesian Inference ◽  
Early Warning ◽  
Prior Probability ◽  
Small Sample ◽  
Power Company ◽  
Operation And Maintenance ◽  
Abnormal State ◽  
Multi Stage ◽  
Sample Characteristics ◽  
Online Monitoring System

Abstract The differences in the probability of occurrence of different equipment and defects lead to the small sample characteristics of the defect of the arrester, which makes it difficult to train an accurate prediction model. It is difficult to identify the abnormal state when the arrester monitoring data does not exceed the limit and increase steadily relying on the arrester monitoring index and threshold to judge the defect. Therefore, a lightning arrester defect early warning method based on multi-stage information and Bayesian inference is proposed. The Bayesian inference algorithm is used to calculate the probability of defect cause categories under different feature quantities. According to the new test evidence, the probability of the defect cause category under different feature quantities is updated to identify the defect cause. The algorithm automatically adjusts the prior probability indicators of equipment defects and causes in the model based on the new detection data and annotation conclusions to ensure the accuracy of defect cause classification. The lightning arrester operation and maintenance data and online monitoring system of a power company is used to analyze and verify the effectiveness and correctness of the method proposed in this paper, which provides effective supportfor the lightning arrester operation and maintenance.

Bayesian jackknife empirical likelihood

Biometrika ◽  
2019 ◽  
Vol 106 (4) ◽  
pp. 981-988
Author(s):  
Y Cheng ◽  
Y Zhao
Keyword(s):  
Bayesian Inference ◽  
Empirical Likelihood ◽  
Small Sample ◽  
New Approach ◽  
U Statistics ◽  
Posterior Inference ◽  
Jackknife Empirical Likelihood ◽  
General Tool ◽  
Control Study ◽  
Better Than

Summary Empirical likelihood is a very powerful nonparametric tool that does not require any distributional assumptions. Lazar (2003) showed that in Bayesian inference, if one replaces the usual likelihood with the empirical likelihood, then posterior inference is still valid when the functional of interest is a smooth function of the posterior mean. However, it is not clear whether similar conclusions can be obtained for parameters defined in terms of $U$-statistics. We propose the so-called Bayesian jackknife empirical likelihood, which replaces the likelihood component with the jackknife empirical likelihood. We show, both theoretically and empirically, the validity of the proposed method as a general tool for Bayesian inference. Empirical analysis shows that the small-sample performance of the proposed method is better than its frequentist counterpart. Analysis of a case-control study for pancreatic cancer is used to illustrate the new approach.

scholarly journals A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guo-Zheng Wang ◽  
Li Xiong ◽  
Hu-Chen Liu
Keyword(s):  
Monte Carlo ◽  
Bayesian Inference ◽  
Probability Distribution ◽  
Community Detection ◽  
Degree Distribution ◽  
Prior Probability ◽  
Bipartite Networks ◽  
Inference Method ◽  
Prior Probability Distribution ◽  
Bayesian Inference Method

Community detection is an important analysis task for complex networks, including bipartite networks, which consist of nodes of two types and edges connecting only nodes of different types. Many community detection methods take the number of communities in the networks as a fixed known quantity; however, it is impossible to give such information in advance in real-world networks. In our paper, we propose a projection-free Bayesian inference method to determine the number of pure-type communities in bipartite networks. This paper makes the following contributions: (1) we present the first principle derivation of a practical method, using the degree-corrected bipartite stochastic block model that is able to deal with networks with broad degree distributions, for estimating the number of pure-type communities of bipartite networks; (2) a prior probability distribution is proposed over the partition of a bipartite network; (3) we design a Monte Carlo algorithm incorporated with our proposed method and prior probability distribution. We give a demonstration of our algorithm on synthetic bipartite networks including an easy case with a homogeneous degree distribution and a difficult case with a heterogeneous degree distribution. The results show that the algorithm gives the correct number of communities of synthetic networks in most cases and outperforms the projection method especially in the networks with heterogeneous degree distributions.

Analysis of UHV Power Grid Operation and Maintenance System Construction and Optimization Strategy - Taking Shan-Xi UHV Grid as an Example

2014 ◽  
Vol 960-961 ◽  
pp. 1108-1111
Author(s):  
Ding Qiang Duan ◽  
Li Ting Jiao
Keyword(s):  
Power Grid ◽  
System Construction ◽  
Optimization Strategy ◽  
Power Company ◽  
Operation And Maintenance ◽  
Speed Up ◽  
Control Requirement ◽  
Grid Operation ◽  
Automation Technology

Compared with the traditional power grid, UHV power grid has the character of stronger system function, higher control requirement, and also set a higher request to the daily maintenance management. The operation and maintenance experience of Shan-xi province electric power company on the UHV power grid proved the importance of establishing the new mode of management, strengthening the application of automation technology, and guarenteeing the technology and resources . In order to improve the efficience and quality of UHV power grid operation and maintenance, we should speed up the formulation of operation guidelines for UHV live working and strengthen personnel training and reserve technical talents.

Bayesian Inference for the Demand of Engineering Products

2012 ◽  
Author(s):  
Ali E. Abbas ◽  
George A. Hazelrigg ◽  
Mahmood Alkindi
Keyword(s):  
Bayesian Inference ◽  
Probability Distribution ◽  
Expected Utility ◽  
Prior Probability ◽  
Consumer Preferences ◽  
Demand Estimation ◽  
Market Analysis ◽  
Design Parameters ◽  
The Given ◽  
Multiple Units

Within the context of a profit making firm, the job of a design engineer is to choose design parameters and product attributes that maximize the expected utility of profit. To do this effectively, the engineer needs to have an estimate of the demand for the product as a function of its price and its attributes. The firm may conduct a survey to elicit consumer preferences for the product at a given price and would like to update their belief about demand given the survey data. The purpose of this paper is to present a Bayesian methodology for demand estimation that meets this need. The estimation process begins with a prior probability distribution of demand at a given price. Using Bayesian analysis, we show how to update demand for the product given various pieces of information such as market analysis, polls and a variety of other methods. We also discuss situations where consumers can demand multiple units of the product at the given price.

Export injury early warning of the new energy industries in China

2017 ◽  
Vol 7 (2) ◽  
pp. 272-285 ◽  
Author(s):  
Jinjin Wang ◽  
Zhengxin Wang ◽  
Qin Li
Keyword(s):  
Smart Grid ◽  
Early Warning ◽  
Nuclear Power ◽  
Indicator System ◽  
Small Sample ◽  
Energy Industry ◽  
Actual Condition ◽  
Content Type ◽  
Forecast Result ◽  
New Energy

Purpose In recent years, continuous expansion of the scale of the new energy export industry in China caused a boycott of American and European countries. Export injury early warning research is an urgent task to develop the new energy industry in China. The purpose of this paper is to build an indicator system of exports injury early warning of the new energy industry in China and corresponding quantitative early warning models. Design/methodology/approach In consideration of the actual condition of the new energy industry in China, this paper establishes an indicator system according to four aspects: export price, export quantity, impact on domestic industry and impact on macro economy. Based on the actual data of new energy industry and its five sub-industries (solar, wind, nuclear power, smart grid and biomass) in China from 2003 to 2013, GM (1,1) model is used to predict early warning index values for 2014-2018. Then, the principal component analysis (PCA) is used to obtain the comprehensive early warning index values for 2003-2018. The 3-sigma principle is used to divide the early warning intervals according to the comprehensive early warning index values for 2003-2018 and their standard deviation. Finally, this paper determines alarm degrees for 2003-2018. Findings Overall export condition of the new energy industry in China is a process from cold to normal in 2003-2013, and the forecast result shows that it will be normal from 2014 to 2018. The export condition of the solar energy industry experienced a warming process, tended to be normal, and the forecast result shows that it will also be normal in 2014-2018. The biomass and other new energy industries and nuclear power industry show a similar development process. Export condition of the wind energy industry is relatively unstable, and it will be partially hot in 2014-2018, according to the forecast result. As for the smart grid industry, the overall export condition of it is normal, but it is also unstable, in few years it will be partially hot or partially cold. The forecast result shows that in 2014-2018, it will maintain the normal state. In general, there is a rapid progress in the export competitiveness of the new energy industry in China in the recent decade. Practical implications Export injury early warning research of the new energy industry can help new energy companies to take appropriate measures to reduce trade losses in advance. It can also help the relevant government departments to adjust industrial policies and optimize the new energy industry structure. Originality/value This paper constructs an index system that can measure the alarm degrees of the new energy industry. By combining the GM (1,1) model and the PCA method, the problem of warning condition detection under small sample data sets is solved.

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