A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis
Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid...
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| Veröffentlicht in: | Energies (Basel) Jg. 16; H. 11; S. 4406 |
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| Sprache: | Englisch |
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30.05.2023
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| Abstract | Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid scenarios. Even though, to date, the traditional approaches play a vital role in providing a solution to the above issue, the limitations, such as the requirement of significant human effort and not being scalable for large-scale power systems, force us to think of alternative approaches. Looking at a better perspective, deep-learning (DL) has gained the main attraction for various researchers due to its inherent capability to classify the data by extracting dominating and prominent features. This manuscript attempts to provide a comprehensive review of PQ detection and classification based on DL approaches to explore its potential, efficiency, and consistency to produce results accurately. In addition, this state-of-the-art review offers an overview of the novel concepts and the step-by-step method for detecting and classifying PQ events. This review has been presented categorically with DL approaches, such as convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs), to analyze PQ data. This paper also highlights the challenges and limitations of using DL for PQ analysis, and identifies potential areas for future research. This review concludes that DL algorithms have shown promising PQ detection and classification results, and could replace traditional methods. |
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| AbstractList | Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid scenarios. Even though, to date, the traditional approaches play a vital role in providing a solution to the above issue, the limitations, such as the requirement of significant human effort and not being scalable for large-scale power systems, force us to think of alternative approaches. Looking at a better perspective, deep-learning (DL) has gained the main attraction for various researchers due to its inherent capability to classify the data by extracting dominating and prominent features. This manuscript attempts to provide a comprehensive review of PQ detection and classification based on DL approaches to explore its potential, efficiency, and consistency to produce results accurately. In addition, this state-of-the-art review offers an overview of the novel concepts and the step-by-step method for detecting and classifying PQ events. This review has been presented categorically with DL approaches, such as convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs), to analyze PQ data. This paper also highlights the challenges and limitations of using DL for PQ analysis, and identifies potential areas for future research. This review concludes that DL algorithms have shown promising PQ detection and classification results, and could replace traditional methods. |
| Audience | Academic |
| Author | Rout, Pravat Kumar Piecha, Marian Samanta, Indu Sekhar Prokop, Lukas Blazek, Vojtech Panda, Subhasis Bajaj, Mohit |
| Author_xml | – sequence: 1 givenname: Indu Sekhar surname: Samanta fullname: Samanta, Indu Sekhar – sequence: 2 givenname: Subhasis orcidid: 0000-0002-9753-7255 surname: Panda fullname: Panda, Subhasis – sequence: 3 givenname: Pravat Kumar surname: Rout fullname: Rout, Pravat Kumar – sequence: 4 givenname: Mohit orcidid: 0000-0002-1086-457X surname: Bajaj fullname: Bajaj, Mohit – sequence: 5 givenname: Marian surname: Piecha fullname: Piecha, Marian – sequence: 6 givenname: Vojtech orcidid: 0000-0003-0508-8518 surname: Blazek fullname: Blazek, Vojtech – sequence: 7 givenname: Lukas orcidid: 0000-0003-0495-5499 surname: Prokop fullname: Prokop, Lukas |
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| Cites_doi | 10.3390/en14196319 10.1016/j.measurement.2019.107393 10.1109/TAPENERGY.2017.8397249 10.1145/3422622 10.1109/DISCOVER47552.2019.9008009 10.1109/EnergyEconomics.2015.7235071 10.3390/su11040987 10.1109/TII.2019.2910416 10.1109/ACCESS.2020.3014732 10.1080/02564602.2016.1196620 10.3390/s22207958 10.1049/iet-gtd.2009.0498 10.3390/app11167637 10.1016/j.epsr.2022.108633 10.1109/ACCESS.2022.3233767 10.3233/JIFS-191274 10.1016/j.measurement.2020.108097 10.1109/TII.2019.2895054 10.1049/gtd2.12364 10.1109/TIM.2021.3054673 10.1109/TPEL.2020.3023770 10.1109/CISP.2009.5301526 10.1002/2050-7038.12010 10.1016/j.isatra.2019.07.001 10.1016/j.epsr.2022.108887 10.1007/s00202-020-01066-8 10.1007/s00202-022-01701-6 10.1109/TIM.2022.3214284 10.1007/s42835-020-00612-5 10.1109/TPWRS.2022.3153591 10.1016/j.epsr.2021.107682 10.1109/SmartGridComm.2018.8587510 10.1109/TSG.2017.2686012 10.1016/j.rser.2014.08.070 10.1002/er.7671 10.1109/JSEN.2021.3071935 10.1016/S0893-6080(02)00083-7 10.1186/s41601-023-00277-y 10.1016/j.neucom.2012.11.050 10.3390/en12010043 10.1049/gtd2.12122 10.1002/2050-7038.12008 10.1088/1674-1056/abd160 10.1016/j.measurement.2020.108691 10.1109/TIM.2022.3197801 10.1016/j.egyr.2022.02.300 10.1109/TII.2019.2920689 10.3390/app10196755 10.1016/j.apenergy.2021.118454 10.1016/j.aci.2018.01.004 10.3390/en14102839 10.1109/ACCESS.2019.2898211 10.1016/j.knosys.2014.08.013 10.1109/CoDIT.2019.8820557 10.1109/ACCESS.2019.2905015 10.1109/ICHQP.2018.8378893 10.1016/B978-0-12-815480-9.00015-3 10.5220/0010347103730380 10.3390/en12071280 10.1109/TSG.2021.3107908 10.1007/s10462-022-10213-5 10.1016/j.epsr.2019.105876 10.1080/15325008.2019.1666178 10.1016/j.jweia.2021.104529 10.1007/s42835-023-01423-0 10.1109/PRIA.2017.7983049 10.1016/j.knosys.2020.105596 10.3390/app12178914 10.1007/s42835-022-01177-1 10.1155/2022/7020979 10.1109/ACCESS.2019.2937193 10.3390/en16062685 10.1109/IJCNN.2010.5596468 10.1016/j.epsr.2021.107042 10.1109/SEST48500.2020.9203082 10.1145/3578938 10.1162/neco.1997.9.8.1735 10.3390/en15186650 10.1007/s11042-018-6463-x 10.1016/j.rser.2015.11.064 10.1504/IJSCC.2023.127482 10.3390/en16083431 10.1111/1541-4337.12773 10.1016/j.epsr.2006.04.007 10.1016/j.measurement.2021.110460 10.1109/MSP.2017.2765202 |
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| References | ref_94 ref_92 ref_90 ref_14 Rodriguez (ref_68) 2021; 16 ref_99 ref_95 Nandi (ref_54) 2021; 21 ref_18 ref_16 Efe (ref_67) 2021; 31 Sahani (ref_15) 2020; 36 Thung (ref_49) 2018; 77 Panda (ref_51) 2022; 8 Salles (ref_82) 2022; 71 Galvan (ref_84) 2021; 20 ref_25 ref_24 ref_21 ref_28 ref_27 Liu (ref_52) 2019; 47 ref_72 ref_70 ref_78 Yang (ref_30) 2022; 189 Chawda (ref_38) 2020; 8 ref_74 Abdelsalam (ref_65) 2021; 15 Wen (ref_19) 2019; 15 Wang (ref_61) 2020; 96 Panda (ref_98) 2022; 46 Ravindran (ref_81) 2021; 12 Rizk (ref_55) 2019; 15 Sekar (ref_58) 2022; 2022 Fiore (ref_96) 2013; 122 Xi (ref_100) 2022; 16 Skydt (ref_71) 2021; 170 Sahani (ref_76) 2021; 70 ref_87 ref_86 Goodfellow (ref_39) 2020; 63 Spassiani (ref_89) 2021; 210 Menghani (ref_9) 2023; 55 ref_50 Kow (ref_88) 2016; 56 ref_57 Ortiz (ref_91) 2014; 71 ref_53 Ramalingappa (ref_13) 2022; 12 Cai (ref_60) 2019; 7 Mahela (ref_69) 2015; 41 Qiu (ref_17) 2019; 16 Cong (ref_10) 2023; 56 Ge (ref_80) 2021; 194 Zhiyi (ref_75) 2020; 152 ref_66 ref_64 ref_63 Caicedo (ref_2) 2023; 8 Ge (ref_77) 2020; 70 Mohammadi (ref_46) 2022; 212 Baldi (ref_29) 2012; 27 Shi (ref_31) 2019; 7 Cheng (ref_42) 2022; 37 Ekici (ref_12) 2021; 103 ref_36 ref_34 Sengupta (ref_56) 2020; 194 ref_33 Jian (ref_44) 2021; 40 Smith (ref_83) 2002; 15 Liu (ref_35) 2019; 29 ref_37 Dash (ref_79) 2022; 309 Bollen (ref_7) 2023; 214 Topaloglu (ref_8) 2023; 18 Bentley (ref_85) 2010; 4 Ai (ref_22) 2007; 77 Deng (ref_59) 2019; 15 Chiam (ref_73) 2023; 14 Qiu (ref_32) 2019; 174 Hochreiter (ref_62) 1997; 9 Creswell (ref_40) 2018; 35 Shi (ref_20) 2017; 9 Liu (ref_11) 2023; 11 ref_43 Decelle (ref_93) 2021; 30 ref_1 ref_3 Nagata (ref_26) 2020; 164 ref_48 Mishra (ref_23) 2019; 29 Ma (ref_47) 2017; 34 Gao (ref_97) 2022; 204 ref_5 ref_4 Pan (ref_41) 2019; 7 ref_6 Cui (ref_45) 2022; 71 |
| References_xml | – ident: ref_4 doi: 10.3390/en14196319 – volume: 152 start-page: 107393 year: 2020 ident: ref_75 article-title: Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder publication-title: Measurement doi: 10.1016/j.measurement.2019.107393 – ident: ref_14 doi: 10.1109/TAPENERGY.2017.8397249 – volume: 63 start-page: 139 year: 2020 ident: ref_39 article-title: Generative adversarial networks publication-title: Commun. ACM doi: 10.1145/3422622 – ident: ref_70 doi: 10.1109/DISCOVER47552.2019.9008009 – ident: ref_53 doi: 10.1109/EnergyEconomics.2015.7235071 – ident: ref_63 doi: 10.3390/su11040987 – volume: 15 start-page: 5266 year: 2019 ident: ref_19 article-title: Real-time identification of power fluctuations based on LSTM recurrent neural network: A case study on Singapore power system publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2019.2910416 – ident: ref_94 – volume: 8 start-page: 146807 year: 2020 ident: ref_38 article-title: Comprehensive review on detection and classification of power quality disturbances in utility grid with renewable energy penetration publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3014732 – volume: 34 start-page: 408 year: 2017 ident: ref_47 article-title: Classification of power quality disturbances via deep learning publication-title: IETE Tech. Rev. doi: 10.1080/02564602.2016.1196620 – ident: ref_6 doi: 10.3390/s22207958 – volume: 4 start-page: 1188 year: 2010 ident: ref_85 article-title: Power quality disturbance source identification using self-organising maps publication-title: IET Gener. Transm. Distrib. doi: 10.1049/iet-gtd.2009.0498 – ident: ref_27 – ident: ref_34 doi: 10.3390/app11167637 – volume: 212 start-page: 108633 year: 2022 ident: ref_46 article-title: A protection scheme based on conditional generative adversarial network and convolutional classifier for high impedance fault detection in distribution networks publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2022.108633 – volume: 11 start-page: 890 year: 2023 ident: ref_11 article-title: Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3233767 – volume: 40 start-page: 3875 year: 2021 ident: ref_44 article-title: A novel semi-supervised method for classification of power quality disturbance using generative adversarial network publication-title: J. Intell. Fuzzy Syst. doi: 10.3233/JIFS-191274 – volume: 164 start-page: 108097 year: 2020 ident: ref_26 article-title: Real-time voltage sag detection and classification for power quality diagnostics publication-title: Measurement doi: 10.1016/j.measurement.2020.108097 – volume: 15 start-page: 4481 year: 2019 ident: ref_59 article-title: A sequence-to-sequence deep learning architecture based on bidirectional GRU for type recognition and time location of combined power quality disturbance publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2019.2895054 – volume: 16 start-page: 1552 year: 2022 ident: ref_100 article-title: Type identification and time location of multiple power quality disturbances based on KF-ML-aided DBN publication-title: IET Gener. Transm. Distrib. doi: 10.1049/gtd2.12364 – volume: 70 start-page: 9001912 year: 2021 ident: ref_76 article-title: Deep convolutional stack autoencoder of process adaptive VMD data with robust multikernel RVFLN for power quality events recognition publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2021.3054673 – volume: 36 start-page: 4006 year: 2020 ident: ref_15 article-title: FPGA-based deep convolutional neural network of process adaptive VMD data with online sequential RVFLN for power quality events recognition publication-title: IEEE Trans. Power Electron. doi: 10.1109/TPEL.2020.3023770 – ident: ref_87 doi: 10.1109/CISP.2009.5301526 – volume: 29 start-page: e12010 year: 2019 ident: ref_35 article-title: Classification of multiple power quality events via compressed deep learning publication-title: Int. Trans. Electr. Energy Syst. doi: 10.1002/2050-7038.12010 – volume: 96 start-page: 457 year: 2020 ident: ref_61 article-title: A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network publication-title: ISA Trans. doi: 10.1016/j.isatra.2019.07.001 – volume: 70 start-page: 2501110 year: 2020 ident: ref_77 article-title: Deep feature clustering for seeking patterns in daily harmonic variations publication-title: IEEE Trans. Instrum. Meas. – volume: 214 start-page: 108887 year: 2023 ident: ref_7 article-title: Deep learning for power quality publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2022.108887 – volume: 103 start-page: 67 year: 2021 ident: ref_12 article-title: Power quality event classification using optimized Bayesian convolutional neural networks publication-title: Electr. Eng. doi: 10.1007/s00202-020-01066-8 – ident: ref_25 doi: 10.1007/s00202-022-01701-6 – ident: ref_86 – volume: 71 start-page: 2519912 year: 2022 ident: ref_45 article-title: Detection and Classification of Multiple Power Quality Disturbances Using Stockwell Transform and Deep Learning publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2022.3214284 – volume: 16 start-page: 249 year: 2021 ident: ref_68 article-title: A classification method for power-quality disturbances using Hilbert–Huang transform and LSTM recurrent neural networks publication-title: J. Electr. Eng. Technol. doi: 10.1007/s42835-020-00612-5 – ident: ref_92 – volume: 37 start-page: 4807 year: 2022 ident: ref_42 article-title: Online power system event detection via bidirectional generative adversarial networks publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2022.3153591 – volume: 204 start-page: 107682 year: 2022 ident: ref_97 article-title: Power quality disturbance classification under noisy conditions using adaptive wavelet threshold and DBN-ELM hybrid model publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2021.107682 – ident: ref_21 doi: 10.1109/SmartGridComm.2018.8587510 – volume: 9 start-page: 5271 year: 2017 ident: ref_20 article-title: Deep learning for household load forecasting—A novel pooling deep RNN publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2017.2686012 – volume: 41 start-page: 495 year: 2015 ident: ref_69 article-title: A critical review of detection and classification of power quality events publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2014.08.070 – ident: ref_64 – volume: 46 start-page: 7021 year: 2022 ident: ref_98 article-title: A conceptual review on transformation of micro-grid to virtual power plant: Issues, modeling, solutions, and future prospects publication-title: Int. J. Energy Res. doi: 10.1002/er.7671 – volume: 21 start-page: 13695 year: 2021 ident: ref_54 article-title: Hyperbolic window S-transform aided deep neural network model-based power quality monitoring framework in electrical power system publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2021.3071935 – volume: 15 start-page: 1107 year: 2002 ident: ref_83 article-title: Applications of the self-organising map to reinforcement learning publication-title: Neural Netw. doi: 10.1016/S0893-6080(02)00083-7 – volume: 8 start-page: 3 year: 2023 ident: ref_2 article-title: A systematic review of real-time detection and classification of power quality disturbances publication-title: Prot. Control Mod. Power Syst. doi: 10.1186/s41601-023-00277-y – ident: ref_78 – volume: 27 start-page: 37 year: 2012 ident: ref_29 article-title: Autoencoders, unsupervised learning, and deep architectures publication-title: PMLR – volume: 122 start-page: 13 year: 2013 ident: ref_96 article-title: Network anomaly detection with the restricted Boltzmann machine publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.11.050 – ident: ref_57 doi: 10.3390/en12010043 – volume: 15 start-page: 1626 year: 2021 ident: ref_65 article-title: Categorisation of power quality problems using long short-term memory networks publication-title: IET Gener. Transm. Distrib. doi: 10.1049/gtd2.12122 – volume: 29 start-page: e12008 year: 2019 ident: ref_23 article-title: Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review publication-title: Int. Trans. Electr. Energy Syst. doi: 10.1002/2050-7038.12008 – ident: ref_90 – volume: 30 start-page: 040202 year: 2021 ident: ref_93 article-title: Restricted Boltzmann machine: Recent advances and mean-field theory publication-title: Chin. Phys. B doi: 10.1088/1674-1056/abd160 – volume: 170 start-page: 108691 year: 2021 ident: ref_71 article-title: A probabilistic sequence classification approach for early fault prediction in distribution grids using long short-term memory neural networks publication-title: Measurement doi: 10.1016/j.measurement.2020.108691 – volume: 71 start-page: 2516211 year: 2022 ident: ref_82 article-title: Analytics of waveform distortion variations in railway pantograph measurements by deep learning publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2022.3197801 – volume: 8 start-page: 3727 year: 2022 ident: ref_51 article-title: Residential Demand Side Management model, optimization and future perspective: A review publication-title: Energy Rep. doi: 10.1016/j.egyr.2022.02.300 – volume: 16 start-page: 3233 year: 2019 ident: ref_17 article-title: An automatic identification framework for complex power quality disturbances based on multifusion convolutional neural network publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2019.2920689 – ident: ref_24 doi: 10.3390/app10196755 – volume: 309 start-page: 118454 year: 2022 ident: ref_79 article-title: Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm publication-title: Appl. Energy doi: 10.1016/j.apenergy.2021.118454 – volume: 15 start-page: 81 year: 2019 ident: ref_55 article-title: Deep belief networks and cortical algorithms: A comparative study for supervised classification publication-title: Appl. Comput. Inform. doi: 10.1016/j.aci.2018.01.004 – ident: ref_36 doi: 10.3390/en14102839 – volume: 7 start-page: 20961 year: 2019 ident: ref_31 article-title: An independent component analysis classification for complex power quality disturbances with sparse auto encoder features publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2898211 – volume: 71 start-page: 322 year: 2014 ident: ref_91 article-title: Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2014.08.013 – ident: ref_28 doi: 10.1109/CoDIT.2019.8820557 – volume: 7 start-page: 36322 year: 2019 ident: ref_41 article-title: Recent progress on generative adversarial networks (GANs): A survey publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2905015 – ident: ref_72 doi: 10.1109/ICHQP.2018.8378893 – ident: ref_48 doi: 10.1016/B978-0-12-815480-9.00015-3 – ident: ref_66 – ident: ref_33 doi: 10.5220/0010347103730380 – ident: ref_16 doi: 10.3390/en12071280 – volume: 12 start-page: 22 year: 2022 ident: ref_13 article-title: Power quality event classification using complex wavelets phasor models and customized convolution neural network publication-title: Int. J. Electr. Comput. Eng. IJECE – volume: 12 start-page: 5444 year: 2021 ident: ref_81 article-title: Deep learning method with manual post-processing for identification of spectral patterns of waveform distortion in PV installations publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2021.3107908 – volume: 56 start-page: 1905 year: 2023 ident: ref_10 article-title: A review of convolutional neural network architectures and their optimizations publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-022-10213-5 – volume: 174 start-page: 105876 year: 2019 ident: ref_32 article-title: Power quality disturbances recognition using modified s transform and parallel stack sparse auto-encoder publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2019.105876 – volume: 47 start-page: 1332 year: 2019 ident: ref_52 article-title: Signal processing and deep learning techniques for power quality events monitoring and classification publication-title: Electr. Power Compon. Syst. doi: 10.1080/15325008.2019.1666178 – volume: 210 start-page: 104529 year: 2021 ident: ref_89 article-title: Application of Self-organizing Maps to classify the meteorological origin of wind gusts in Australia publication-title: J. Wind. Eng. Ind. Aerodyn. doi: 10.1016/j.jweia.2021.104529 – ident: ref_95 doi: 10.1007/s42835-023-01423-0 – ident: ref_50 doi: 10.1109/PRIA.2017.7983049 – volume: 194 start-page: 105596 year: 2020 ident: ref_56 article-title: A review of deep learning with special emphasis on architectures, applications and recent trends publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2020.105596 – ident: ref_43 doi: 10.3390/app12178914 – volume: 18 start-page: 77 year: 2023 ident: ref_8 article-title: Deep learning based a new approach for power quality disturbances classification in power transmission system publication-title: J. Electr. Eng. Technol. doi: 10.1007/s42835-022-01177-1 – ident: ref_18 – volume: 2022 start-page: 7020979 year: 2022 ident: ref_58 article-title: An improved power quality disturbance detection using deep learning approach publication-title: Math. Probl. Eng. doi: 10.1155/2022/7020979 – volume: 7 start-page: 119099 year: 2019 ident: ref_60 article-title: Classification of power quality disturbances using wigner-ville distribution and deep convolutional neural networks publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2937193 – ident: ref_1 doi: 10.3390/en16062685 – ident: ref_74 doi: 10.1109/IJCNN.2010.5596468 – volume: 194 start-page: 107042 year: 2021 ident: ref_80 article-title: Unsupervised deep learning and analysis of harmonic variation patterns using big data from multiple locations publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2021.107042 – ident: ref_37 doi: 10.1109/SEST48500.2020.9203082 – volume: 55 start-page: 1 year: 2023 ident: ref_9 article-title: Efficient deep learning: A survey on making deep learning models smaller, faster, and better publication-title: ACM Comput. Surv. doi: 10.1145/3578938 – volume: 9 start-page: 1735 year: 1997 ident: ref_62 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 31 start-page: e13204 year: 2021 ident: ref_67 article-title: CNN/Bi-LSTM-based deep learning algorithm for classification of power quality disturbances by using spectrogram images publication-title: Int. Trans. Electr. Energy Syst. – ident: ref_3 doi: 10.3390/en15186650 – volume: 77 start-page: 29705 year: 2018 ident: ref_49 article-title: A brief review on multi-task learning publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-018-6463-x – volume: 56 start-page: 334 year: 2016 ident: ref_88 article-title: A review on performance of artificial intelligence and conventional method in mitigating PV grid-tied related power quality events publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2015.11.064 – volume: 14 start-page: 22 year: 2023 ident: ref_73 article-title: Global attention-based LSTM for noisy power quality disturbance classification publication-title: Int. J. Syst. Control. Commun. doi: 10.1504/IJSCC.2023.127482 – ident: ref_5 doi: 10.3390/en16083431 – volume: 20 start-page: 3136 year: 2021 ident: ref_84 article-title: An overview of research of essential oils by self-organizing maps: A novel approach for meta-analysis study publication-title: Compr. Rev. Food Sci. Food Saf. doi: 10.1111/1541-4337.12773 – volume: 77 start-page: 462 year: 2007 ident: ref_22 article-title: Adaline and its application in power quality disturbances detection and frequency tracking publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2006.04.007 – volume: 189 start-page: 110460 year: 2022 ident: ref_30 article-title: Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review publication-title: Measurement doi: 10.1016/j.measurement.2021.110460 – volume: 35 start-page: 53 year: 2018 ident: ref_40 article-title: Generative adversarial networks: An overview publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2017.2765202 – ident: ref_99 |
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