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Sabih M, Umer M, Farooq U, Gu J, Balas MM, Asad MU, Qureshi KK, Khan IA, Abbas G. Image processing based fault classification in power systems with classical and intelligent techniques. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-219293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This paper is devoted to develop interest of power system engineers in learning basic concepts of image processing and consequently using deep networks to solve problems of complex power system networks. To this end, we study fault classification in a power system through automation of equal area (EAC) criterion. By considering EAC graphs as images and using classical image processing techniques, we successfully distinguish between different transient conditions including sudden change of input power as well as short circuit at the sending end and middle points of a single and double circuit transmission lines. In addition to classification, some parameters are also determined from EAC images such as initial rotor angle, clearing angle, and maximum rotor angle. Further, the use of deep networks is introduced to perform the same task of fault classification and a comparison is drawn with multilayer perceptron neural networks. Developed algorithms are tested in MATLAB as well as Pytorch environments.
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Affiliation(s)
- Muhammad Sabih
- Intelligent Systems Laboratory & Automation Facility (ISLAF), University of the Punjab, Lahore, Pakistan
| | - Muhammad Umer
- Intelligent Systems Laboratory & Automation Facility (ISLAF), University of the Punjab, Lahore, Pakistan
| | - Umar Farooq
- Intelligent Systems Laboratory & Automation Facility (ISLAF), University of the Punjab, Lahore, Pakistan
- Department of Electrical & Computer Engineering, Dalhousie University, Halifax, N.S., Canada
| | - Jason Gu
- Department of Electrical & Computer Engineering, Dalhousie University, Halifax, N.S., Canada
| | - Marius M. Balas
- Department of Automatics & Applied Informatics, Aurel Vlaicu University, Arad, Romania
| | - Muhammad Usman Asad
- Department of Electrical & Computer Engineering, Dalhousie University, Halifax, N.S., Canada
| | - Khurram Karim Qureshi
- Department of Electrical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia
| | - Irfan A. Khan
- Department of Electrical Engineering, Texas A&M University, College Station, TX, USA
| | - Ghulam Abbas
- Department of Electrical Engineering, The University of Lahore, Lahore, Pakistan
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Fan CL. Data mining model for predicting the quality level and classification of construction projects. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-219182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Project managers supervise projects to ensure their smooth completion within a stipulated time frame and budget while guaranteeing construction quality. The relationships of various attributes with quality can be quantified and classified to facilitate such supervision. Therefore, this study used a data mining algorithm to analyze the relationships between defects, quality levels, contract sums, project categories, and progress in 1,015 inspection projects. In the first part, association rule mining (ARM), an unsupervised data mining approach, was used to obtain 11 rules relating two defect types (i.e., quality management system and construction quality) and determine the relationships between the four attributes (i.e., quality level, contract sum, project category, and progress). The resulting association rule may be beneficial for construction management because project managers can use it to determine the correlations between defects and attributes. In the second part, supervised data mining techniques, namely neural network (NN), support vector machine (SVM), and decision tree (C5.0 and QUEST) algorithms, were applied to develop a classification model for quality prediction. The target variable was quality, which was divided into four levels, and the decision variables comprised 499 defects, 3 contract sums, 7 project categories, and 2 progress variables. The results indicated that five defects were important. Finally, the four indicators of gain chart, break-even point (BEP), accuracy, and area under the curve (AUC) were calculated to evaluate the model. For the SVM model, the actual value predicted by the gain chart was 96.04%, the BEP was 0.95, and the AUC was 0.935. The SVM yielded optimal classification efficiency and effectively predicted the quality level. The data mining model developed in this study can serve as a reference for effective construction management.
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Affiliation(s)
- Ching-Lung Fan
- Department of Civil Engineering, the Republic of China Military Academy, Fengshan, Kaohsiung, Taiwan
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Chen J, Zhou D, Wang Y, Fu H, Wang M. Image feature extraction based on HOG and its application to fault diagnosis for rotating machinery. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-169521] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jiayu Chen
- School of Reliability and Systems Engineering, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
- Science & Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Beijing, China
| | - Dong Zhou
- School of Reliability and Systems Engineering, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
- Science & Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Beijing, China
| | - Yang Wang
- Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China
| | - Hongyong Fu
- Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China
| | - Mingfang Wang
- Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China
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