scholarly journals Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1449 ◽  
Author(s):  
Ferhat Ucar ◽  
Jose Cordova ◽  
Omer F. Alcin ◽  
Besir Dandil ◽  
Fikret Ata ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Extreme Learning Machine ◽  
Quality Analysis ◽  
Quality Data ◽  
Real Field ◽  
Support Vector ◽  
Transmission Networks ◽  
Power Quality Analysis ◽  
Learning Machine

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

Design of Power Quality Management Software Based on Virtual Instrument and Oracle

2013 ◽  
Vol 427-429 ◽  
pp. 2441-2444
Author(s):  
Wei Chen ◽  
Long Chen ◽  
Ming Li
Keyword(s):  
Power Quality ◽  
Software Design ◽  
Virtual Instrument ◽  
Quality Analysis ◽  
Quality Data ◽  
Data Handling ◽  
Data Display ◽  
Report Generation ◽  
Management Capacity ◽  
Power Quality Analysis

This paper presents a software design useful for power quality analysis and data management. The software was programmed in LabVIEW and Oracle, running on Windows in a regular PC. LabVIEW acquires data continuously from the lower machine via TCP/IP. Using its database connection toolkit, LabVIEW accesses to Oracle to stores and retrieve the power quality data according to different indicators. A friendly GUI was built for data display and user operation, taking advantage of the powerful data-handling capacity of LabVIEW and its rich controls. Moreover, Excel reports can be exported using report generation toolkit in LabVIEW. The software greatly improves the data analysis and management capacity.

scholarly journals Intelligent Plant Disease Identification System Using Machine Learning

2020 ◽  
Vol 2 (1) ◽  
pp. 49
Author(s):  
Paramasivam Alagumariappan ◽  
Najumnissa Jamal Dewan ◽  
Gughan Narasimhan Muthukrishnan ◽  
Bhaskar K. Bojji Raju ◽  
Ramzan Ali Arshad Bilal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Plant Disease ◽  
Agricultural Sector ◽  
Plant Diseases ◽  
Support Vector ◽  
Identification System ◽  
Learning Machine

Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for identification of plant disease. Furthermore, the performance of three machine learning algorithms, such as Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with linear and polynomial kernels was analyzed. Results demonstrate that the performance of the extreme learning machine is better when compared to the adopted support vector machine classifier. It is also observed that the sensitivity of the support vector machine with a polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance, because the developed real-time hardware is capable of detecting different plant diseases.

scholarly journals Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2615 ◽  
Author(s):  
Yang Du ◽  
Ke Yan ◽  
Zixiao Ren ◽  
Weidong Xiao
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Weather Conditions ◽  
Learning Technologies ◽  
Energy Harvest ◽  
Support Vector ◽  
Pv Systems ◽  
Weighted Extreme Learning Machine ◽  
Learning Machine ◽  
Power Point

A maximum power point tracker (MPPT) should be designed to deal with various weather conditions, which are different from region to region. Customization is an important step for achieving the highest solar energy harvest. The latest development of modern machine learning provides the possibility to classify the weather types automatically and, consequently, assist localized MPPT design. In this study, a localized MPPT algorithm is developed, which is supported by a supervised weather-type classification system. Two classical machine learning technologies are employed and compared, namely, the support vector machine (SVM) and extreme learning machine (ELM). The simulation results show the outperformance of the proposed method in comparison with the traditional MPPT design.

scholarly journals Predicting China's Economic Running State Using Machine Learning

2018 ◽  
Vol 232 ◽  
pp. 03036
Author(s):  
Jianxiang Luo ◽  
Yonggang Fu
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Extreme Learning Machine ◽  
Early Warning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Daunting Task ◽  
Coincidence Index ◽  
Learning Machine

China's business index of macro-economic includes early warning index, coincidence index, leading index and lagging index, among which early warning index reflects the economic running state. However, obtaining these indexes is a complex and daunting task. To simplify the task, this article mainly explores how to use machine learning algorithms including multiple linear regression (MLR), support vector machine regression (SVM), random forest (RF), artificial neural network (ANN) and extreme learning machine (ELM) to accurately predict early warning index. Finally, it can be found that the warning index can be well predicted by above machine learning algorithms with coincidence index, leading index and lagging index to be variables, furthermore, extreme learning machine and random forest are superior to other methods.

scholarly journals Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nico Surantha ◽  
Tri Fennia Lesmana ◽  
Sani Muhamad Isa
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Sleep Stages ◽  
Support Vector ◽  
Swarm Optimization ◽  
Learning Machine ◽  
Portable Sensor

AbstractRecent developments of portable sensor devices, cloud computing, and machine learning algorithms have led to the emergence of big data analytics in healthcare. The condition of the human body, e.g. the ECG signal, can be monitored regularly by means of a portable sensor device. The use of the machine learning algorithm would then provide an overview of a patient’s current health on a regular basis compared to a medical doctor’s diagnosis that can only be made during a hospital visit. This work aimed to develop an accurate model for classifying sleep stages by features of Heart Rate Variability (HRV) extracted from Electrocardiogram (ECG). The sleep stages classification can be utilized to predict the sleep stages proportion. Where sleep stages proportion information can provide an insight of human sleep quality. The integration of Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) was utilized for selecting features and determining the number of hidden nodes. The results were compared to Support Vector Machine (SVM) and ELM methods which are lower than the integration of ELM with PSO. The results of accuracy tests for the combined ELM and PSO were 62.66%, 71.52%, 76.77%, and 82.1% respectively for 6, 4, 3, and 2 classes. To sum up, the classification accuracy can be improved by deploying PSO algorithm for feature selection.

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