A Novel Hybrid Machine Learning Classifier Based Digital Differential Protection Scheme for Intertie Zone of Large-Scale Centralized DFIG based Wind Farms

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Nima Rezaei ◽  
Mohammad Uddin ◽  
Khairul Amin Ifte ◽  
Mohammad Lutfi Othman ◽  
Marayati Marsadek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Wind Farms ◽  
Differential Protection ◽  
Protection Scheme ◽  
Learning Classifier ◽  
Hybrid Machine

scholarly journals An Efficient Hybrid Machine Learning Classifier for Rainfall Prediction

2021 ◽  
Vol 1770 (1) ◽  
pp. 012012
Author(s):  
P. Asha ◽  
A. Jesudoss ◽  
S. Prince Mary ◽  
K. V. Sai Sandeep ◽  
K. Harsha Vardhan
Keyword(s):  
Machine Learning ◽  
Rainfall Prediction ◽  
Learning Classifier ◽  
Hybrid Machine

scholarly journals Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs

2018 ◽  
Vol 19 (3) ◽  
Author(s):  
Khe Foon Hew ◽  
Chen Qiao ◽  
Ying Tang
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Online Courses ◽  
Subject Area ◽  
Online Course ◽  
Valuable Insight ◽  
Course Quality ◽  
Learning Classifier ◽  
Quality Rating ◽  
Open Courses

Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study offers a new original contribution by using a machine learning classifier to analyze 24,612 reflective sentences posted by 5,884 students, who participated in one or more of 18 highly rated MOOCs. Highly rated MOOCs were sampled because they exemplify good practices or teaching strategies. We selected highly rated MOOCs from Coursetalk, an open user-driven aggregator and discovery website that allows students to search and review various MOOCs. We defined a highly rated MOOC as a free online course that received an overall five-star course quality rating, and received at least 50 reviews from different learners within a specific subject area. We described six specific themes found across the entire data corpus: (a) structure and pace, (b) video, (c) instructor, (d) content and resources, (e) interaction and support, and (f) assignment and assessment. The findings of this study provide valuable insight into factors that students find engaging in large-scale open online courses.

scholarly journals Trainable High Resolution Melt Curve Machine Learning Classifier for Large-Scale Reliable Genotyping of Sequence Variants

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e109094 ◽  
Author(s):  
Pornpat Athamanolap ◽  
Vishwa Parekh ◽  
Stephanie I. Fraley ◽  
Vatsal Agarwal ◽  
Dong J. Shin ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Large Scale ◽  
Sequence Variants ◽  
High Resolution Melt ◽  
Learning Classifier

scholarly journals Improved Collecting Line Current Protection Scheme against Large-scale Wind Generators Tripping off

2018 ◽  
Author(s):  
Wei Jin ◽  
Yuping Lu ◽  
Tao Huang
Keyword(s):  
Power System ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Risk Indicators ◽  
Melting Time ◽  
Security Risk ◽  
Line Current ◽  
Protection Scheme ◽  
Wind Generators

There have been several cases of large-scale wind generators (WGs) tripping off caused by untimely fault removing in recent years. Currently, the discoordination between the box-type transformer fuse protection (BTFP) and two-section collecting line current protection (CLCP) brings a security risk to wind farm. In order to ensure the selectivity, the first section (Sec-I) CLCP should be set a enough interval that is longer than the fuse melting time, and another interval is set for the Sec-II CLCP, which weakens the speed of the CLCP. When a fault occurs on the collecting line, there is no doubt that WGs cannot work too long in abnormal operation, which may cause WGs to be placed off the grid. For a power system with high penetration of wind power, large-scale WGs tripping off will cause a great power shortage, and affect the stability of the power system. The selectivity and sensitivity of the CLCP is analyzed in detail to make the CLCP speed better. Considering the fault ride-through ability of WGs, the fault clear time is an important factor to lead to large-scale WGs tripping off. Two main works are done in this paper. The first is to accelerate the speed of the Sec-I CLCP though reducing the protection zone. Another one is introduce the risk assessment module into the CLCP, which not only improve the speed of the CLCP but also ensure the safety of the wind farm during faults. According to the deference in trip-off causes of WGs, the matching functions are created to assess the trip-off risk of WGs on the spot. In the case of fault, the trip-off risk indicators of WGs are timely updated to data sharing center and open to the CLCPs. The set of risk indicators is divided into several subsets according to the risk range. The dynamic changes of the subsets during fault help to improved CLCP scheme. This scheme can accelerate protection speed based on the increasing risk of large-scale WGs tripping off in wind farms. Compared with traditional CLCP, this approach can make the CLCP combines selectivity and speed better based on the analysis of the ride- through ability of WGs.

scholarly journals Differential Protection for an Outgoing Transformer of Large-Scale Doubly Fed Induction Generator-Based Wind Farms

Energies ◽  
2014 ◽  
Vol 7 (9) ◽  
pp. 5566-5585 ◽  
Author(s):  
Bingtuan Gao ◽  
Wei Wei ◽  
Luoma Zhang ◽  
Ning Chen ◽  
Yingjun Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
Wind Farms ◽  
Induction Generator ◽  
Differential Protection ◽  
Doubly Fed Induction Generator ◽  
Doubly Fed
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