Fault Detection and Classification Based on Co-training of Semisupervised Machine Learning

Abdelgayed, Tamer S.; Morsi, Walid G.; Sidhu, T.S.

doi:10.1109/tie.2017.2726961

Cited by 178 publications

(63 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…); f (t) can be expressed as Equation (25). (25) where A j f (t) is the approximate signal on the j-level decomposition scale within the frequency band (0, 2 −j ). D j f (t) is the detail signal on the j-level decomposition scale within the frequency band (2 −j , 2 −j+1 ).…”

Section: Multi-resolution Signal Decomposition (Msd)mentioning

confidence: 99%

“…Firstly, the phase-module transform is applied to change the phase current to α module and 0 module via the transformation matrix shown in (19). Then, the α module and 0 module are processed with discrete wavelet transform, which is performed in multi-resolution signal decomposition (MSD) to obtain approximate signal A j and detailed signal D j under different decomposing scales, shown in Equation (25). After that, modules maxima are picked up in the detailed signal with Equations (26) and (27).…”

Section: Theory Applicationmentioning

confidence: 99%

“…Among the existing fault detection methodologies in voltage source converter-based high-voltage direct current (VSC-HVDC) transmission systems and conventional AC transmission lines, wavelet analysis is universally applied, especially in combination with entropy theory [14,[16][17][18][19][20], and a boundary condition is applied to distinguish internal from external faults [17,[21][22][23]]. An artificial neural network [24] and machine learning [25,26] are utilized to train the proposed fault detection model or algorithm for improved accuracy. As mentioned in Reference [27], previous protection schemes in AC systems cannot be directly applied to an AC/DC hybrid system because of the different pattern in fault current characteristics between AC systems and AC/DC hybrid systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rapid Fault Diagnosis of a Back-to-Back MMC-HVDC Transmission System under AC Line Fault

et al. 2018

View full text Add to dashboard Cite

Abstract:The integration of a modular multilevel converter-based high-voltage direct current (MMC-HVDC) transmission system in power networks has led to a high requirement for the rapidity of fault recognition. This study focused on the rapid fault diagnosis of an alternating current (AC) line fault in a back-to-back (BTB) MMC-HVDC system via fault detection and classification. Discrete wavelet transform and modulus maxima were applied to extract the fault features. Phase-mode transformation and normalization were adopted to widen the application range. Simulation and calculation results indicated that the proposed method can detect all fault types in an AC transmission line on the basis of single-side fault information within 1 ms under different values of transition resistance, fault inception angle, and fault distance. The BTB MMC-HVDC model was built using real-time laboratory (RT-LAB) based on the matrix laboratory (MATLAB) software platform, and the fault diagnosis algorithm was performed in MATLAB.

show abstract

Section: Multi-resolution Signal Decomposition (Msd)mentioning

confidence: 99%

Section: Theory Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rapid Fault Diagnosis of a Back-to-Back MMC-HVDC Transmission System under AC Line Fault

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Abdelgayed proposed a semi-supervised machine learning method based on co-training to solve fault classification in transmission and distribution systems of microgrids [17]. Yu proposed a multi-classifier integrated progressive semi-supervised learning method, using different classifiers obtained by random subspace technology and expanded the training set through a progressive training set generation process [18]. Liu used a random forest algorithm for semi-supervised learning to select pseudo-labeled samples from unlabeled samples and considered the label confidence of the unlabeled samples and the positional relationship with the edge of the classification surface to improve the classification performance [19].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Ensemble Classification Method Based on Near Neighbor and Its Application

Xie

Chen

2020

Processes

View full text Add to dashboard Cite

Semi-supervised learning can be used to solve the problem of insufficient labeled samples in the process industry. However, in an actual scenario, traditional semi-supervised learning methods usually do not achieve satisfactory performance when the small number of labeled samples is subjective and inaccurate and some do not consider how to develop a strategy to expand the training set. In this paper, a new algorithm is proposed to alleviate the above two problems, and consequently, the information contained in unlabeled samples can be fully mined. First, the multivariate adaptive regression splines (MARS) and adaptive boosting (Adaboost) algorithms are adopted for co-training to make the most of the deep connection between samples and features. In addition, the strategies, pseudo-labeled dataset selection algorithm based on near neighbor degree (DSSA) and pseudo-labeled sample detection algorithm based on near neighbor degree selection (SPDA) are adopted to enlarge the dataset of labeled samples. When we select the samples from the pseudo-labeled data to join the training set, the confidence degree and the spatial relationship with labeled samples are considered, which are able to improve classifier accuracy. The results of tests on multiple University of California Irvine (UCI) datasets and an actual dataset in the aluminum electrolysis industry demonstrate the effectiveness of the proposed algorithm.

show abstract

“…NILM is an intelligent energy monitoring technique which utilizes a single energy monitor to retrieve information of appliances from aggregated loads, such as power consumption and appliance type, in a non-intrusive way. Apart from energy savings, NILM is a helpful tool for predictive maintenance [1] or the determination of motor speed [31]. 08 Figure 1: The first plot shows an actual OFF, followed by an ON event of a monitor.…”

Section: Introductionmentioning

confidence: 99%

Appliance Event Detection - A Multivariate, Supervised Classification Approach

Kahl

Kriechbaumer

Jorde

et al. 2019

Proceedings of the Tenth ACM International Conference on Future Energy Systems

View full text Add to dashboard Cite

Non-intrusive load monitoring (NILM) is a modern and still expanding technique, helping to understand fundamental energy consumption patterns and appliance characteristics. Appliance event detection is an elementary step in the NILM pipeline. Unfortunately, several types of appliances (e.g., switching mode power supply (SMPS) or multi-state) are known to challenge state-of-theart event detection systems due to their noisy consumption profiles. Classical rule-based event detection system become infeasible and complex for these appliances. By stepping away from distinct event definitions, we can learn from a consumer-configured event model to differentiate between relevant and irrelevant event transients.We introduce a boosting oriented adaptive training, that uses false positives from the initial training area to reduce the number of false positives on the test area substantially. The results show a false positive decrease by more than a factor of eight on a dataset that has a strong focus on SMPS-driven appliances. To obtain a stable event detection system, we applied several experiments on different parameters to measure its performance. These experiments include the evaluation of six event features from the spectral and time domain, different types of feature space normalization to eliminate undesired feature weighting, the conventional and adaptive training, and two common classifiers with its optimal parameter settings. The evaluations are performed on two publicly available energy datasets with high sampling rates: BLUED and BLOND-50.

show abstract

Fault Detection and Classification Based on Co-training of Semisupervised Machine Learning

Cited by 178 publications

References 31 publications

Rapid Fault Diagnosis of a Back-to-Back MMC-HVDC Transmission System under AC Line Fault

Rapid Fault Diagnosis of a Back-to-Back MMC-HVDC Transmission System under AC Line Fault

Semi-Supervised Ensemble Classification Method Based on Near Neighbor and Its Application

Appliance Event Detection - A Multivariate, Supervised Classification Approach

Contact Info

Product

Resources

About