Suchergebnisse - "PQ detection algorithm"

Maximal overlap discrete wavelet transform and deep learning for robust denoising and detection of power quality disturbance.

Autoren: Xiao, Fei Lu, Tianguang Wu, Mingli et al.

Quelle: IET Generation, Transmission & Distribution (Wiley-Blackwell). Jan2020, Vol. 14 Issue 2, p140-147. 8p.

Dynamic Power Quality Disturbance Classification in Grid-Integrated PV Systems: Leveraging Clark Transformed Modal Voltage and Subspace Weighted KNN

Autoren: Sairam Mishra Ranjan Kumar Mallick Pravati Nayak et al.

Quelle: IEEE Access, Vol 12, Pp 116572-116586 (2024)

Schlagwörter: power quality classification, microgrid, kth nearest neighbour, Clark transform, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, maximum overlap discrete wavelet transform, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, 7. Clean energy, TK1-9971

Dateibeschreibung: application/pdf

Zugangs-URL: https://doaj.org/article/98ad1ac537db4b16b314e3344f8890d9

PQ event identification in PV-wind based distribution network with variational mode decomposition and novel feature enabled random forest classifier.

Autoren: Mishra, Sairam Nayak, Pravati Mallick, Ranjan Kumar et al.

Quelle: International Journal of Emerging Electric Power Systems; Jun2024, Vol. 25 Issue 3, p393-404, 12p

Schlagwörter: RANDOM forest algorithms, FEATURE extraction, DECISION trees

Dynamic Power Quality Disturbance Classification in Grid-Integrated PV Systems: Leveraging Clark Transformed Modal Voltage and Subspace Weighted KNN

Autoren: Mishra S Mallick RK Nayak P et al.

Quelle: IEEE Access, 2024

Dateibeschreibung: application/pdf

Relation: https://eprints.ncl.ac.uk/300605; https://eprints.ncl.ac.uk/fulltext.aspx?url=300605/08A4FCFA-11E6-4F78-958D-C1861CA52818.pdf&pub_id=300605

Verfügbarkeit: https://eprints.ncl.ac.uk/300605

Data Analytics Based Power Quality Investigations in Emerging Electric Power System Using Sparse Decomposition.

Autoren: Sharma, Monika Rajpurohit, Bharat Singh Agnihotri, Samar et al.

Quelle: IEEE Transactions on Power Delivery; Dec2022, Vol. 37 Issue 6, p4838-4847, 10p

Schlagwörter: ELECTRIC power systems, ELECTRIC power consumption, ELECTRIC power distribution grids, ELECTRICAL harmonics, POWER supply quality, MATHEMATICAL analysis, SIGNAL processing

Statistical Models of EAF Harmonics Developed for Harmonic Estimation Directly From Waveform Samples Using Deep Learning Framework.

Autoren: Severoglu, Nagihan Salor, Ozgul

Quelle: IEEE Transactions on Industry Applications; Nov/Dec2021, Vol. 57 Issue 6, p6730-6740, 11p

Schlagwörter: DEEP learning, STATISTICAL models, ARC furnaces, ELECTRIC power filters, ELECTRIC furnaces, ELECTRIC arc, LOAD forecasting (Electric power systems)

Hyperbolic Window S-Transform Aided Deep Neural Network Model-Based Power Quality Monitoring Framework in Electrical Power System.

Autoren: Nandi, Kiron Das, Arup Kumar Ghosh, Riddhi et al.

Quelle: IEEE Sensors Journal; 6/15/2021, Vol. 21 Issue 12, p13695-13703, 9p

Power‐quality disturbance recognition based on time‐frequency analysis and decision tree.

Autoren: Zhong, Tie Zhang, Shuo Cai, Guowei et al.

Quelle: IET Generation, Transmission & Distribution (Wiley-Blackwell); Oct2018, Vol. 12 Issue 19, p4153-4162, 10p

Power Quality Detection in Distribution System with Wind Energy Penetration Using Discrete Wavelet Transform.

Autoren: Mahela, Om Prakash Shaik, Abdul Gafoor

Quelle: 2014 International Conference on Information Visualization Theory & Applications (IVAPP); 2014, p328-333, 6p