scholarly journals A Novel Approach to Arcing Faults Characterization Using Multivariable Analysis and Support Vector Machine

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2126 ◽  
Author(s):  
John Morales ◽  
Eduardo Muñoz ◽  
Eduardo Orduña ◽  
Gina Idarraga-Ospina
Keyword(s):  
Support Vector Machine ◽  
Power Systems ◽  
Transmission Line ◽  
Transmission Lines ◽  
Multivariable Analysis ◽  
Support Vector ◽  
Committee Report ◽  
Fault Type ◽  
Novel Approach ◽  
Convolution Process

Based on the Institute of Electrical and Electronics Engineers (IEEE) Standard C37.104-2012 Power Systems Relaying Committee report, topics related to auto-reclosing in transmission lines have been considered as an imperative benefit for electric power systems. An important issue in reclosing, when performed correctly, is identifying the fault type, i.e., permanent or temporary, which keeps the faulted transmission line in service as long as possible. In this paper, a multivariable analysis was used to classify signals as permanent and temporary faults. Thus, by using a simple convolution process among the mother functions called eigenvectors and the fault signals from a single end, a dimensionality reduction was determined. In this manner, the feature classifier based on the support vector machine was used for acceptably classifying fault types. The algorithm was tested in different fault scenarios that considered several distances along the transmission line and representation of first and second arcs simulated in the alternative transients program ATP software. Therefore, the main contribution of the analysis performed in this paper is to propose a novel algorithm to discriminate permanent and temporary faults based on the behavior of the faulted phase voltage after single-phase opening of the circuit breakers. Several simulations let the authors conclude that the proposed algorithm is effective and reliable.

Tension Prediction Model of Ice-Coated Transmission Line Based on YULE-WALK Auto-Regressive Model and Support Vector Machine

2013 ◽  
Vol 437 ◽  
pp. 331-334
Author(s):  
Lei Yang ◽  
Da Da Wang ◽  
Xin Wu ◽  
Lin Li ◽  
Xiao Ming Rui ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Power Systems ◽  
Prediction Model ◽  
Transmission Line ◽  
Break Line ◽  
Support Vector ◽  
Absolute Maximum ◽  
Regressive Model ◽  
Auto Regressive ◽  
Auto Regressive Model

Large tension of ice-coated transmission line will cause line overload and conductor galloping, accidents such as break line and tower collapse will be caused, it bring great threat to safety and stability of power systems. Therefore, there is an important physical meaning for preventing above accidents to in-depth study tension prediction model of ice-coated transmission line.In this paper,we establishes a tension prediction model of ice-coated transmission line based on the Yule-Wake auto-regressive model and support vector machine, the model contains the micrometeorological and tension historical data, etc. Through studying the tension prediction of Gan-Zhen 155# transmission line in Zhaotong area of Yunnan province,it shows the prediction obtained by this model in the next eight hours is in accord with the actual monitoring data pretty well, the absolute maximum error is less than 5.86%, and the maximum absolute mean error is less than 2.74%.So, the feasibility and accuracy of this model is verified.

Membership Calculation Based on Dimension Hierarchical Division

2013 ◽  
Vol 475-476 ◽  
pp. 312-317
Author(s):  
Ping Zhou ◽  
Jin Lei Wang ◽  
Xian Kai Chen ◽  
Guan Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Bottom Up ◽  
Generalization Ability ◽  
Clustering Technique ◽  
Novel Approach ◽  
Training Samples ◽  
Noise Sample

Since dataset usually contain noises, it is very helpful to find out and remove the noise in a preprocessing step. Fuzzy membership can measure a samples weight. The weight should be smaller for noise sample but bigger for important sample. Therefore, appropriate sample memberships are vital. The article proposed a novel approach, Membership Calculate based on Hierarchical Division (MCHD), to calculate the membership of training samples. MCHD uses the conception of dimension similarity, which develop a bottom-up clustering technique to calculate the sample membership iteratively. The experiment indicates that MCHD can effectively detect noise and removes them from the dataset. Fuzzy support vector machine based on MCHD outperforms most of approaches published recently and hold the better generalization ability to handle the noise.

scholarly journals Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Kun Zhang ◽  
Minrui Fei ◽  
Xin Li ◽  
Huiyu Zhou
Keyword(s):  
Support Vector Machine ◽  
Mean Shift ◽  
Least Square ◽  
Support Vector ◽  
Svm Classifier ◽  
Least Square Estimation ◽  
Unstructured Environments ◽  
Novel Approach ◽  
Traditional Approaches ◽  
Improved Support Vector Machine

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

scholarly journals Intelligent Prediction of Transmission Line Project Cost Based on Least Squares Support Vector Machine Optimized by Particle Swarm Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Yi ◽  
Hao Zheng ◽  
Yu Tian ◽  
Jin-peng Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Transmission Line ◽  
Least Squares ◽  
Principal Component ◽  
Support Vector ◽  
Swarm Optimization ◽  
Empirical Parameter ◽  
Parameter Prediction

In order to meet the demand of power supply, the construction of transmission line projects is constantly advancing, and the level of cost control is constantly improving, which puts forward higher requirements for the accuracy of cost prediction. This paper proposes an intelligent cost prediction model based on least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO). Originally extracting natural, technological, and economic indexes from the perspective of cost composition, principal component analysis (PCA) is used to reduce the dimension of indexes. And PSO is innovatively introduced to optimize the parameters of LSSVM model to obtain the optimal parameters. The obtained principal component data are imported into empirical parameter LSSVM prediction model and the optimized parameter PSO-LSSVM prediction model, respectively, for modeling and prediction, and then comparing the prediction results to analyze the effect of model optimization. The results show that the absolute deviation of the optimized parameter prediction model is less than 9%. And the prediction accuracy of the optimized parameter prediction model is better than that of the empirical parameter model, which can provide a reliable basis for investment decision-making of transmission line projects.

scholarly journals Improving Accuracy of Quasars' Photometric Redshift Estimation by Integration of KNN and SVM

2015 ◽  
Vol 11 (A29A) ◽  
pp. 209-209
Author(s):  
Bo Han ◽  
Hongpeng Ding ◽  
Yanxia Zhang ◽  
Yongheng Zhao
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Unsolved Problem ◽  
Support Vector ◽  
Catastrophic Failure ◽  
K Nearest Neighbor ◽  
Photometric Redshift ◽  
Novel Approach ◽  
Improving Accuracy ◽  
Fusion Approach

AbstractCatastrophic failure is an unsolved problem existing in the most photometric redshift estimation approaches for a long history. In this study, we propose a novel approach by integration of k-nearest-neighbor (KNN) and support vector machine (SVM) methods together. Experiments based on the quasar sample from SDSS show that the fusion approach can significantly mitigate catastrophic failure and improve the accuracy of photometric redshift estimation.

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