Mapping the Research Landscape of PMU-Based Fault Detection, Classification, and Localization in Power Systems
Keywords:
Phasor Measurement Unit; Fault Detection; Fault Classification; Fault Localization; Power SystemsAbstract
The rising complexity of the modern power systems and the transition towards smart grids have made the development of rapid and reliable fault detection techniques a need. The Phasor Measurement Unit (PMU) has emerged as a crucial enabler for real-time monitoring and security of power networks with its capabilities to provide time-synchronized high-resolution measurements. We propose a systematic and bibliometric review of PMU-based fault detection, classification and localization algorithms in power systems for the period 2014–2023, based on a comprehensive dataset of 162 research articles. The surveyed literature is divided into five primary categories, including signal processing-based methods, model-based approaches, machine learning techniques, deep learning frameworks and hybrid intelligent methods. The extensive examination of these categories shows the transition from conventional signal processing methods to data-driven and deep learning based approaches, driven by the rising availability of PMU data. From the bibliometric data, it is clear that the number of publications has increased substantially after 2018 indicating a strong trend towards artificial intelligence based solutions. On comparing the existing approaches, it is found that the traditional methods are simple and respond quickly but are not flexible enough to be used in dynamically changing operational situations. On the other hand, machine learning and deep learning methods show more accuracy and robustness but require large data sets and computer resources. Despite considerable progress, there are still a number of hurdles, including communication delays, data quality, cybersecurity, and limited real-time deployment in practical systems. This review emphasizes significant research gaps and future initiatives such as integration of edge computing, development of cyber resilient frameworks, and use of advanced deep learning models for real-time fault analysis. The results of this study give a systematic insight into the available methodologies and represent an essential reference for researchers and practitioners in the future of PMU-based power system protection.
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References
Abasi, M., et al. (2021) ‘Fault classification and fault area detection in GUPFC-compensated double-circuit transmission lines based on the analysis of active and reactive powers measured by PMUs’, Measurement: Journal of the International Measurement Confederation, Vol. 169, No., https://doi.org/10.1016/j.measurement.2020.108499
Abd el-Ghany, H.A., Soliman, I.A. and Elgebaly, A.E. (2022) ‘An advanced wide-area fault detection and location technique for transmission lines considering optimal phasor measurement units allocation’, Alexandria Engineering Journal, Vol. 61, No. 5, pp.3971-3984, https://doi.org/10.1016/j.aej.2021.09.022
Afrasiabi, S., et al. (2020) ‘Fault localisation and diagnosis in transmission networks based on robust deep Gabor convolutional neural network and PMU measurements’, IET Generation, Transmission and Distribution, Vol. 14, No. 26, pp.6484-6492, https://doi.org/10.1049/iet-gtd.2020.0856
Agustoni, M., et al. (2023) ‘Time Synchronization Sensitivity in SV-based PMU Consistency Assessment’, Metrology, Vol. 3, No. 1, pp.99-112, https://doi.org/10.3390/metrology3010006
Ahmed, A., et al. (2019) ‘Cyber Physical Security Analytics for Anomalies in Transmission Protection Systems’, IEEE Transactions on Industry Applications, Vol. 55, No. 6, pp.6313-6323, https://doi.org/10.1109/TIA.2019.2928500
Ahmed, A., et al. (2021) ‘Anomaly Detection, Localization and Classification Using Drifting Synchrophasor Data Streams’, IEEE Transactions on Smart Grid, Vol. 12, No. 4, pp.3570-3580, https://doi.org/10.1109/TSG.2021.3054375
Almutairi, A., et al. (2022) ‘A blockchain-enabled secured fault allocation in smart grids based on μPMUs and UT’, IET Renewable Power Generation, Vol. 16, No. 16, pp.3496-3506, https://doi.org/10.1049/rpg2.12332
Alqudah, M., Kezunovic, M. and Obradovic, Z. (2023) ‘Automated Power System Fault Prediction and Precursor Discovery Using Multi-Modal Data’, IEEE Access, Vol. 11, No., pp.7283-7296, https://doi.org/10.1109/ACCESS.2022.3233219
Alqudah, M., et al. (2022) ‘Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units’, IEEE Transactions on Power Systems, Vol. 37, No. 5, pp.3434-3442, https://doi.org/10.1109/TPWRS.2021.3135336
Al-Shamaain, Z.S., Al-Majali, H.D. and Al-Majali, B.H. (2023) ‘Out-of-Step Detection based on Phasor Measurement Unit’, WSEAS Transactions on Power Systems, Vol. 18, No., pp.354-363, https://doi.org/10.37394/232016.2023.18.36
Amutha, A.L., et al. (2023) ‘Anomaly Detection and Classification in Streaming PMU Data in Smart Grids’, Computer Systems Science and Engineering, Vol. 46, No. 3, pp.3387-3401, https://doi.org/10.32604/csse.2023.029904
Anguswamy, M.P., et al. (2022) ‘Optimal Micro-PMU Placement in Distribution Networks Considering Usable Zero-Injection Phase Strings’, IEEE Transactions on Smart Grid, Vol. 13, No. 5, pp.3662-3675, https://doi.org/10.1109/TSG.2022.3174917
Appasani, B. and Mohanta, D.K. (2018) ‘A review on synchrophasor communication system: communication technologies, standards and applications’, Protection and Control of Modern Power Systems, Vol. 3, No. 1, https://doi.org/10.1186/s41601-018-0110-4
Arefin, A.A., et al. (2022) ‘Review of the Techniques of the Data Analytics and Islanding Detection of Distribution Systems Using Phasor Measurement Unit Data’, Electronics (Switzerland), Vol. 11, No. 18, https://doi.org/10.3390/electronics11182967
Arefin, A.A., et al. (2021) ‘A novel island detection threshold setting using phasor measurement unit voltage angle in a distribution network’, Energies, Vol. 14, No. 16, https://doi.org/10.3390/en14164877
Asgharigoavr, S. and Seyedi, H. (2017) ‘Development of PMU-based backup wide area protection for power systems considering HIF detection’, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 25, No. 4, pp.2846-2859, https://doi.org/10.3906/elk-1605-200
Babu, N.V.P., et al. (2023) ‘A Synchrophasor-Based Line Protection for Single Phase-Ground Faults’, Journal of Electrical Engineering and Technology, Vol. 18, No. 3, pp.1693-1704, https://doi.org/10.1007/s42835-022-01312-y
Bakdi, A., et al. (2021) ‘Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence’, International Journal of Electrical Power and Energy Systems, Vol. 125, No., https://doi.org/10.1016/j.ijepes.2020.106457
Banna, H.U., et al. (2023) ‘Proactive anomaly source identification using novel ensemble learning with adaptive mitigation measures for microgrids’, Electric Power Systems Research, Vol. 218, No., https://doi.org/10.1016/j.epsr.2023.109157
Bansal, Y. and Sodhi, R. (2022) ‘A novel μPMUs assisted loss-of-mains detection technique for active distribution systems’, Electric Power Systems Research, Vol. 202, No., https://doi.org/10.1016/j.epsr.2021.107578
Barreto, N.E.M., et al. (2021) ‘Artificial Neural Network Approach for Fault Detection and Identification in Power Systems with Wide Area Measurement Systems’, Journal of Control, Automation and Electrical Systems, Vol. 32, No. 6, pp.1617-1626, https://doi.org/10.1007/s40313-021-00785-y
Basumallik, S., Ma, R. and Eftekharnejad, S. (2019) ‘Packet-data anomaly detection in PMU-based state estimator using convolutional neural network’, International Journal of Electrical Power and Energy Systems, Vol. 107, No., pp.690-702, https://doi.org/10.1016/j.ijepes.2018.11.013
Belagoune, S., et al. (2021) ‘Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems’, Measurement: Journal of the International Measurement Confederation, Vol. 177, No., https://doi.org/10.1016/j.measurement.2021.109330
Biswal, C., et al. (2023) ‘Real-Time Grid Monitoring and Protection: A Comprehensive Survey on the Advantages of Phasor Measurement Units’, Energies, Vol. 16, No. 10, https://doi.org/10.3390/en16104054
Biswal, M., Brahma, S.M. and Cao, H. (2016) ‘Supervisory Protection and Automated Event Diagnosis Using PMU Data’, IEEE Transactions on Power Delivery, Vol. 31, No. 4, pp.1855-1863, https://doi.org/10.1109/TPWRD.2016.2520958
Chatterjee, S., et al. (2023) ‘Adaptive Divided Difference Filter for Power Systems Dynamic State Estimation With Outliers and Unknown Noise Covariance’, IEEE Transactions on Industry Applications, Vol. 59, No. 6, pp.7529-7544, https://doi.org/10.1109/TIA.2023.3296576
Chawla, A., et al. (2023) ‘Deep-learning-based data-manipulation attack resilient supervisory backup protection of transmission lines’, Neural Computing and Applications, Vol. 35, No. 7, pp.4835-4854, https://doi.org/10.1007/s00521-021-06106-3
Chawla, A., et al. (2022) ‘Denial-of-Service Attacks Pre-Emptive and Detection Framework for Synchrophasor Based Wide Area Protection Applications’, IEEE Systems Journal, Vol. 16, No. 1, pp.1570-1581, https://doi.org/10.1109/JSYST.2021.3093494
Chen, L., et al. (2020) ‘Harmonic Phasor Estimator for P-Class Phasor Measurement Units’, IEEE Transactions on Instrumentation and Measurement, Vol. 69, No. 4, pp.1556-1565, https://doi.org/10.1109/TIM.2019.2916961
Chen, L., et al. (2021) ‘Harmonic Phasor Estimation Based on Frequency-Domain Sampling Theorem’, IEEE Transactions on Instrumentation and Measurement, Vol. 70, No., https://doi.org/10.1109/TIM.2020.3039618
Chintakindi, R. and Mitra, A. (2022) ‘WAMS challenges and limitations in load modeling, voltage stability improvement, and controlled island protection—A review’, Energy Reports, Vol. 8, No., pp.699-709, https://doi.org/10.1016/j.egyr.2021.11.217
Chougule, M. and Soman, S.A. (2020) ‘Real-Time data-Assisted replay attack detection in wide-Area protection system’, IET Generation, Transmission and Distribution, Vol. 14, No. 19, pp.4021-4032, https://doi.org/10.1049/iet-gtd.2020.0215
Ciancetta, F., et al. (2023) ‘Micro Phasor Measurement Units: a Review from the Prosumer Point of View’, Renewable Energy and Power Quality Journal, Vol. 21, No., pp.303-308, https://doi.org/10.24084/repqj21.307
Čišija-Kobilica, N., et al. (2019) ‘A new approach for the fault identification, localization, and classification in the power system’, Journal of Engineering Research (Kuwait), Vol. 7, No. 2, pp.259-280,
Das, S., Singh, S.P. and Panigrahi, B.K. (2017) ‘Transmission line fault detection and location using Wide Area Measurements’, Electric Power Systems Research, Vol. 151, No., pp.96-105, https://doi.org/10.1016/j.epsr.2017.05.025
Desai, J.P. and Makwana, V.H. (2021) ‘Phasor Measurement Unit Incorporated Adaptive Out-of-step Protection of Synchronous Generator’, Journal of Modern Power Systems and Clean Energy, Vol. 9, No. 5, pp.1032-1042, https://doi.org/10.35833/MPCE.2020.000277
Dua, G.S., Tyagi, B. and Kumar, V. (2023) ‘Fault Detection Technique for Distribution Networks and Microgrids Using Synchrophasor Data’, IEEE Transactions on Industry Applications, Vol. 59, No. 6, pp.7368-7381, https://doi.org/10.1109/TIA.2023.3305362
Dua, G.S., Tyagi, B. and Kumar, V. (2023) ‘Microgrid Differential Protection Based on Superimposed Current Angle Employing Synchrophasors’, IEEE Transactions on Industrial Informatics, Vol. 19, No. 8, pp.8775-8783, https://doi.org/10.1109/TII.2022.3222319
Dubey, R., et al. (2016) ‘Data-mining model based adaptive protection scheme to enhance distance relay performance during power swing’, International Journal of Electrical Power and Energy Systems, Vol. 81, No., pp.361-370, https://doi.org/10.1016/j.ijepes.2016.02.014
Dubey, R., et al. (2018) ‘Koopman analysis based wide-area back-up protection and faulted line identification for series-compensated power network’, IEEE Systems Journal, Vol. 12, No. 3, pp.2634-2644, https://doi.org/10.1109/JSYST.2016.2615898
Ebrahim, M.A., Wadie, F. and Abd-Allah, M.A. (2019) ‘Integrated fault detection algorithm for transmission, distribution, and microgrid networks’, IET Energy Systems Integration, Vol. 1, No. 2, pp.104-113, https://doi.org/10.1049/iet-esi.2019.0002
Ebrahim, M.A., Wadie, F. and Abd-Allah, M.A. (2020) ‘An Algorithm for Detection of Fault, Islanding, and Power Swings in DG-Equipped Radial Distribution Networks’, IEEE Systems Journal, Vol. 14, No. 3, pp.3893-3903, https://doi.org/10.1109/JSYST.2019.2944870
El Mrabet, Z., et al. (2022) ‘Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems’, Sensors, Vol. 22, No. 2, https://doi.org/10.3390/s22020458
Elghazaly, H., Emam, A. and Saber, A. (2017) ‘A backup wide-area protection technique for power transmission network’, IEEJ Transactions on Electrical and Electronic Engineering, Vol. 12, No. 5, pp.702-709, https://doi.org/10.1002/tee.22456
Elhabashy, M.M., Sharaf, H.M. and Ibrahim, D.K. (2023) ‘Reliable protection for static synchronous series compensated double-circuit transmission lines based on positive sequence active power calculations using PMUs’, Electric Power Systems Research, Vol. 223, No., https://doi.org/10.1016/j.epsr.2023.109695
Esmaeilian, A. and Kezunovic, M. (2016) ‘Fault Location Using Sparse Synchrophasor Measurement of Electromechanical-Wave Oscillations’, IEEE Transactions on Power Delivery, Vol. 31, No. 4, pp.1787-1796, https://doi.org/10.1109/TPWRD.2015.2510585
Gharavi, H. and Hu, B. (2018) ‘Space-Time Approach for Disturbance Detection and Classification’, IEEE Transactions on Smart Grid, Vol. 9, No. 5, pp.5132-5140, https://doi.org/10.1109/TSG.2017.2680742
Gholami, A., Srivastava, A.K. and Pandey, S. (2019) ‘Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies’, Journal of Modern Power Systems and Clean Energy, Vol. 7, No. 4, pp.767-778, https://doi.org/10.1007/s40565-019-0541-6
Gholami, M., et al. (2020) ‘Detecting the Location of Short-Circuit Faults in Active Distribution Network Using PMU-Based State Estimation’, IEEE Transactions on Smart Grid, Vol. 11, No. 2, pp.1396-1406, https://doi.org/10.1109/TSG.2019.2937944
Gilanifar, M., et al. (2020) ‘Multi-Task Logistic Low-Ranked Dirty Model for Fault Detection in Power Distribution System’, IEEE Transactions on Smart Grid, Vol. 11, No. 1, pp.786-796, https://doi.org/10.1109/TSG.2019.2938989
Gopakumar, P., et al. (2018) ‘Remote monitoring system for real time detection and classification of transmission line faults in a power grid using PMU measurements’, Protection and Control of Modern Power Systems, Vol. 3, No. 1, https://doi.org/10.1186/s41601-018-0089-x
Gopakumar, P., Reddy, M.J.B. and Mohanta, D.K. (2015) ‘Fault Detection and Localization Methodology for Self-healing in Smart Power Grids Incorporating Phasor Measurement Units’, Electric Power Components and Systems, Vol. 43, No. 6, pp.695-710, https://doi.org/10.1080/15325008.2014.995839
Gopakumar, P., Reddy, M.J.B. and Mohanta, D.K. (2015) ‘Transmission line fault detection and localisation methodology using PMU measurements’, IET Generation, Transmission and Distribution, Vol. 9, No. 11, pp.1033-1042, https://doi.org/10.1049/iet-gtd.2014.0788
Gopakumar, P., Reddy, M.J.B. and Mohanta, D.K. (2015) ‘Adaptive fault identification and classification methodology for smart power grids using synchronous phasor angle measurements’, IET Generation, Transmission and Distribution, Vol. 9, No. 2, pp.133-145, https://doi.org/10.1049/iet-gtd.2014.0024
Granados-Lieberman, D., et al. (2021) ‘Harmonic PMU and Fuzzy Logic for Online Detection of Short-Circuited Turns in Transformers’, Electric Power Systems Research, Vol. 190, No., https://doi.org/10.1016/j.epsr.2020.106862
Guo, L., et al. (2022) ‘Data-Driven Cyber-Attack Detection for PV Farms via Time-Frequency Domain Features’, IEEE Transactions on Smart Grid, Vol. 13, No. 2, pp.1582-1597, https://doi.org/10.1109/TSG.2021.3136559
Guo, Y., et al. (2015) ‘Synchrophasor-Based Islanding Detection for Distributed Generation Systems Using Systematic Principal Component Analysis Approaches’, IEEE Transactions on Power Delivery, Vol. 30, No. 6, pp.2544-2552, https://doi.org/10.1109/TPWRD.2015.2435158
Hai, A.A., et al. (2021) ‘Transfer Learning for Event Detection from PMU Measurements with Scarce Labels’, IEEE Access, Vol. 9, No., pp.127420-127432, https://doi.org/10.1109/ACCESS.2021.3111727
Hajnorouzi, A.A. and Shayanfar, H.A. (2019) ‘A response-based approach to online prediction of generating unit angular stability’, Scientia Iranica, Vol. 26, No. 6, pp.3592-3605, https://doi.org/10.24200/sci.2019.53966.3517
Harish, A., Asok, P. and Jayan, M.V. (2023) ‘A comparative evaluation of Stacked Auto-Encoder neural network and Multi-Layer Extreme Learning Machine for detection and classification of faults in transmission lines using WAMS data’, Energy and AI, Vol. 14, No., https://doi.org/10.1016/j.egyai.2023.100301
Harish, A., Prince, A. and Jayan, M.V. (2022) ‘Fault Detection and Classification for Wide Area Backup Protection of Power Transmission Lines Using Weighted Extreme Learning Machine’, IEEE Access, Vol. 10, No., pp.82407-82417, https://doi.org/10.1109/ACCESS.2022.3196769
Hatata, A.Y., Essa, M.A. and Sedhom, B.E. (2022) ‘Implementation and Design of FREEDM System Differential Protection Method Based on Internet of Things’, Energies, Vol. 15, No. 15, https://doi.org/10.3390/en15155754
Hu, C., Yan, J. and Liu, X. (2023) ‘Reinforcement Learning-Based Adaptive Feature Boosting for Smart Grid Intrusion Detection’, IEEE Transactions on Smart Grid, Vol. 14, No. 4, pp.3150-3163, https://doi.org/10.1109/TSG.2022.3230730
Iqbal, A. and Jain, T. (2022) ‘Real-Time Event Detection Based on Weibull Distribution Using Synchrophasor Measurements for Enhanced Situational Awareness’, IEEE Transactions on Power Systems, Vol. 37, No. 2, pp.1425-1436, https://doi.org/10.1109/TPWRS.2021.3108481
Jafarzadeh, S. and Genc, V.M.I. (2021) ‘Real-time transient stability prediction of power systems based on the energy of signals obtained from PMUs’, Electric Power Systems Research, Vol. 192, No., https://doi.org/10.1016/j.epsr.2020.107005
Jena, M.K. and Panigrahi, B.K. (2019) ‘Transient Potential Power Based Supervisory Zone-1 Operation during Unstable Power Swing’, IEEE Systems Journal, Vol. 13, No. 2, pp.1823-1830, https://doi.org/10.1109/JSYST.2018.2820013
Jena, M.K., Samantaray, S.R. and Panigrahi, B.K. (2018) ‘A New Decentralized Approach to Wide-Area Back-Up Protection of Transmission Lines’, IEEE Systems Journal, Vol. 12, No. 4, pp.3161-3168, https://doi.org/10.1109/JSYST.2017.2694453
Jiang, H., et al. (2016) ‘Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis’, IEEE Transactions on Smart Grid, Vol. 7, No. 5, pp.2525-2536, https://doi.org/10.1109/TSG.2016.2552229
Jiang, Y. and Srivastava, A.K. (2020) ‘Data-Driven Event Diagnosis in Transmission Systems with Incomplete and Conflicting Alarms Given Sensor Malfunctions’, IEEE Transactions on Power Delivery, Vol. 35, No. 1, pp.214-225, https://doi.org/10.1109/TPWRD.2019.2947671
Jin, Z., et al. (2021) ‘An improved algorithm for cubature Kalman filter based forecasting-aided state estimation and anomaly detection’, International Transactions on Electrical Energy Systems, Vol. 31, No. 5, https://doi.org/10.1002/2050-7038.12714
Kabra, P., Lalitha, S.V.N.L. and Donepudi, S.R. (2023) ‘Bad data analysis and detection using PMU with UPQC integration to grid during fault conditions’, International Journal of Power Electronics and Drive Systems, Vol. 14, No. 1, pp.256-265, https://doi.org/10.11591/ijpeds.v14.i1.pp256-265
Khan, I. and Centeno, V. (2023) ‘Realtime Detection of PMU Bad Data and Sequential Bad Data Classifications in Cyber-Physical Testbed’, IEEE Access, Vol. 11, No., pp.71235-71249, https://doi.org/10.1109/ACCESS.2023.3292059
Killi, V., P, R. and Mp, S. (2023) ‘Detection and isolation of faulty line in an active distribution network using intelligent numerical relay’, Electric Power Systems Research, Vol. 214, No., https://doi.org/10.1016/j.epsr.2022.108921
Kiruthika, M. and Bindu, S. (2023) ‘A security enabled real time fault detection and classification of power system conditions’, International Journal of Advanced Technology and Engineering Exploration, Vol. 10, No. 107, pp.1260-1278, https://doi.org/10.19101/IJATEE.2022.10100425
Krishnan, K. and Iyengar, S. (2022) ‘Fault detection in an interconnected power system using optimal number of phasor measurement unit’, International Journal of Power Electronics and Drive Systems, Vol. 13, No. 4, pp.2109-2119, https://doi.org/10.11591/ijpeds.v13.i4.pp2109-2119
Kumar, B., Yadav, A. and Pazoki, M. (2019) ‘Impedance differential plane for fault detection and faulty phase identification of FACTS compensated transmission line’, International Transactions on Electrical Energy Systems, Vol. 29, No. 4, https://doi.org/10.1002/etep.2804
Kumar, G.P. and Jena, P. (2021) ‘Pearson's Correlation Coefficient for Islanding Detection Using Micro-PMU Measurements’, IEEE Systems Journal, Vol. 15, No. 4, pp.5078-5089, https://doi.org/10.1109/JSYST.2020.3021922
Kumar, M. and Kumar, J. (2023) ‘A Solution to Islanding Event Detection Using Superimposed Negative Sequence Components-Based Scheme’, Arabian Journal for Science and Engineering, Vol. 48, No. 11, pp.14639-14653, https://doi.org/10.1007/s13369-023-07787-9
Kundu, P. and Pradhan, A.K. (2014) ‘Synchrophasor-assisted zone 3 operation’, IEEE Transactions on Power Delivery, Vol. 29, No. 2, pp.660-667, https://doi.org/10.1109/TPWRD.2013.2276071
Kundu, P. and Pradhan, A.K. (2019) ‘Supervisory protection of islanded network using synchrophasor data’, IEEE Transactions on Smart Grid, Vol. 10, No. 2, pp.1772-1780, https://doi.org/10.1109/TSG.2017.2777873
Lal, M.D. and Varadarajan, R. (2023) ‘A Review of Machine Learning Approaches in Synchrophasor Technology’, IEEE Access, Vol. 11, No., pp.33520-33541, https://doi.org/10.1109/ACCESS.2023.3263547
Lam, H.A. and Dong, Z.Y. (2021) ‘Transfer learning based dynamic security assessment’, IET Generation, Transmission and Distribution, Vol. 15, No. 16, pp.2333-2343, https://doi.org/10.1049/gtd2.12181
Laverty, D.M., Best, R.J. and Morrow, D.J. (2015) ‘Loss-of-mains protection system by application of phasor measurement unit technology with experimentally assessed threshold settings’, IET Generation, Transmission and Distribution, Vol. 9, No. 2, pp.146-153, https://doi.org/10.1049/iet-gtd.2014.0106
Le Roux, P.F. and Bansal, R.C. (2017) ‘Detection of Network Instability using Area-Based Centre of Inertia-Referred Frame’, Technology and Economics of Smart Grids and Sustainable Energy, Vol. 2, No. 1, https://doi.org/10.1007/s40866-017-0037-2
Lee, J.W., et al. (2016) ‘Fault area estimation using traveling wave for wide area protection’, Journal of Modern Power Systems and Clean Energy, Vol. 4, No. 3, pp.478-486, https://doi.org/10.1007/s40565-016-0222-7
Li, B., et al. (2023) ‘Coordinated Cloud-Edge Anomaly Identification for Active Distribution Networks’, IEEE Transactions on Cloud Computing, Vol. 11, No. 2, pp.1204-1216, https://doi.org/10.1109/TCC.2022.3155441
Li, W.T., et al. (2016) ‘Location identification of power line outages using PMU measurements with bad data’, IEEE Transactions on Power Systems, Vol. 31, No. 5, pp.3624-3635, https://doi.org/10.1109/TPWRS.2015.2495214
Liang, X., Wallace, S.A. and Nguyen, D. (2017) ‘Rule-Based Data-Driven Analytics for Wide-Area Fault Detection Using Synchrophasor Data’, IEEE Transactions on Industry Applications, Vol. 53, No. 3, pp.1789-1798, https://doi.org/10.1109/TIA.2016.2644621
Liu, G., et al. (2017) ‘Low-Complexity Nonlinear Analysis of Synchrophasor Measurements for Events Detection and Localization’, IEEE Access, Vol. 6, No., pp.4982-4993, https://doi.org/10.1109/ACCESS.2017.2772287
Liu, G., et al. (2022) ‘Hessian Locally Linear Embedding of PMU Data for Efficient Fault Detection in Power Systems’, IEEE Transactions on Instrumentation and Measurement, Vol. 71, No., https://doi.org/10.1109/TIM.2022.3146905
Livanos, N.A.I., et al. (2023) ‘OpenEdgePMU: An Open PMU Architecture with Edge Processing for Future Resilient Smart Grids’, Energies, Vol. 16, No. 6, https://doi.org/10.3390/en16062756
Ma, H., et al. (2023) ‘Deep-Learning Based Power System Events Detection Technology Using Spatio-Temporal and Frequency Information’, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Vol. 13, No. 2, pp.545-556, https://doi.org/10.1109/JETCAS.2023.3252667
Mahdi, M. and Genc, V.M.I. (2018) ‘Post-fault prediction of transient instabilities using stacked sparse autoencoder’, Electric Power Systems Research, Vol. 164, No., pp.243-252, https://doi.org/10.1016/j.epsr.2018.08.009
Manoharan, H., et al. (2023) ‘Application of solar cells and wireless system for detecting faults in phasor measurement units using non-linear optimization’, Energy Exploration and Exploitation, Vol. 41, No. 1, pp.210-223, https://doi.org/10.1177/01445987221113122
Mazhari, S.M., et al. (2018) ‘A Hybrid Fault Cluster and Thévenin Equivalent Based Framework for Rotor Angle Stability Prediction’, IEEE Transactions on Power Systems, Vol. 33, No. 5, pp.5594-5603, https://doi.org/10.1109/TPWRS.2018.2823690
Mingotti, A., Peretto, L. and Tinarelli, R. (2021) ‘Low‐impact current‐based distributed monitoring system for medium voltage networks’, Energies, Vol. 14, No. 17, https://doi.org/10.3390/en14175308
Mirshekali, H., et al. (2022) ‘Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU’, Sensors, Vol. 22, No. 3, https://doi.org/10.3390/s22030945
Mnyanghwalo, D., et al. (2020) ‘Deep learning approaches for fault detection and classifications in the electrical secondary distribution network: Methods comparison and recurrent neural network accuracy comparison’, Cogent Engineering, Vol. 7, No. 1, https://doi.org/10.1080/23311916.2020.1857500
Mohsenian-Rad, H. and Xu, W. (2023) ‘Synchro-Waveforms: A Window to the Future of Power Systems Data Analytics’, IEEE Power and Energy Magazine, Vol. 21, No. 5, pp.68-77, https://doi.org/10.1109/MPE.2023.3288583
Na, S., et al. (2019) ‘Detecting instant of multiple faults on the transmission line and its types using time-frequency analysis’, IET Generation, Transmission and Distribution, Vol. 13, No. 22, pp.5248-5256, https://doi.org/10.1049/iet-gtd.2018.5572
Nagananda, K.G., Kishore, S. and Blum, R.S. (2015) ‘A PMU Scheduling Scheme for Transmission of Synchrophasor Data in Electric Power Systems’, IEEE Transactions on Smart Grid, Vol. 6, No. 5, pp.2519-2528, https://doi.org/10.1109/TSG.2014.2388238
Najafzadeh, M., et al. (2023) ‘A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data’, International Transactions on Electrical Energy Systems, Vol. 2023, No., https://doi.org/10.1155/2023/1754305
Nale, R., Verma, H. and Biswal, M. (2021) ‘An Enhanced Fuzzy Rule Based Protection Scheme for TCSC Compensated Double Circuit Transmission System’, International Journal of Modelling and Simulation, Vol. 41, No. 2, pp.120-130, https://doi.org/10.1080/02286203.2019.1692299
Nguyen, B.L.H., et al. (2023) ‘Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems’, IEEE Access, Vol. 11, No., pp.46039-46050, https://doi.org/10.1109/ACCESS.2023.3273292
Oubrahim, Z., et al. (2023) ‘Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review’, Energies, Vol. 16, No. 6, https://doi.org/10.3390/en16062685
Palepu, S.B. and Reddy, M.D. (2022) ‘Binary spider monkey algorithm approach for optimal siting of the phasor measurement for power system state estimation’, IAES International Journal of Artificial Intelligence, Vol. 11, No. 3, pp.1033-1040, https://doi.org/10.11591/ijai.v11.i3.pp1033-1040
Pignati, M., et al. (2017) ‘Fault Detection and Faulted Line Identification in Active Distribution Networks Using Synchrophasors-Based Real-Time State Estimation’, IEEE Transactions on Power Delivery, Vol. 32, No. 1, pp.381-392, https://doi.org/10.1109/TPWRD.2016.2545923
Pourramezan, R., Karimi, H. and Mahseredjian, J. (2023) ‘Synchrophasor network-based detection and classification of power system events: A singular value decomposition approach’, Electric Power Systems Research, Vol. 223, No., https://doi.org/10.1016/j.epsr.2023.109645
Prada Hurtado, A.A., et al. (2022) ‘Application of IIA Method and Virtual Bus Theory for Backup Protection of a Zone Using PMU Data in a WAMPAC System’, Energies, Vol. 15, No. 9, https://doi.org/10.3390/en15093470
Qasim Khan, M., Mohamud Ahmed, M. and Haidar, A.M.A. (2022) ‘An accurate algorithm of PMU-based wide area measurements for fault detection using positive-sequence voltage and unwrapped dynamic angles’, Measurement: Journal of the International Measurement Confederation, Vol. 192, No., https://doi.org/10.1016/j.measurement.2022.110906
Radhakrishnan, R.M., Sankar, A. and Rajan, S. (2020) ‘Synchrophasor based islanding detection for microgrids using moving window principal component analysis and extended mathematical morphology’, IET Renewable Power Generation, Vol. 14, No. 12, pp.2089-2099, https://doi.org/10.1049/iet-rpg.2019.1240
Radhakrishnan, R.M., Sankar, A. and Rajan, S. (2020) ‘A combined islanding detection algorithm for grid connected multiple microgrids for enhanced microgrid utilisation’, International Transactions on Electrical Energy Systems, Vol. 30, No. 2, https://doi.org/10.1002/2050-7038.12232
Rajaraman, P., et al. (2018) ‘Robust fault analysis in transmission lines using Synchrophasor measurements’, Protection and Control of Modern Power Systems, Vol. 3, No. 1, https://doi.org/10.1186/s41601-018-0082-4
Rajeev, A. and Binu Ben Jose, D.R. (2017) ‘Identifying the fault location in distribution feeders with optimally placed PMU’S’, Journal of Electrical Engineering, Vol. 17, No. 2, pp.1-8,
Rajpoot, S.C., et al. (2021) ‘A Dynamic-SUGPDS Model for Faults Detection and Isolation of Underground Power Cable Based on Detection and Isolation Algorithm and Smart Sensors’, Journal of Electrical Engineering and Technology, Vol. 16, No. 4, pp.1799-1819, https://doi.org/10.1007/s42835-021-00715-7
Rao, A.V.K., et al. (2022) ‘A Faulty Line Detection Technique for Series Compensated Line using Synchrophasor Data’, Journal of The Institution of Engineers (India): Series B, Vol. 103, No. 5, pp.1407-1413, https://doi.org/10.1007/s40031-022-00736-4
Rao, J.T., et al. (2022) ‘Synchrophasor Assisted Power Swing Detection Scheme for Wind Integrated Transmission Network’, IEEE Transactions on Power Delivery, Vol. 37, No. 3, pp.1952-1962, https://doi.org/10.1109/TPWRD.2021.3101846
Rezaeian Koochi, M.H., Dehghanian, P. and Esmaeili, S. (2020) ‘PMU Placement with Channel Limitation for Faulty Line Detection in Transmission Systems’, IEEE Transactions on Power Delivery, Vol. 35, No. 2, pp.819-827, https://doi.org/10.1109/TPWRD.2019.2929097
Rezaeieh, M.R.H., Bolandi, T.G. and Jalalat, S.M. (2023) ‘A novel approach for resilient protection of AC microgrid based on differential phase angle of superimposed complex power’, Sustainable Energy, Grids and Networks, Vol. 34, No., https://doi.org/10.1016/j.segan.2023.101024
Rinaldi, G., et al. (2021) ‘Adaptive dual-layer super-twisting sliding mode observers to reconstruct and mitigate disturbances and communication attacks in power networks’, Automatica, Vol. 129, No., https://doi.org/10.1016/j.automatica.2021.109656
Rizvi, S.M.H., Sadanandan, S.K. and Srivastava, A.K. (2021) ‘Data-Driven Short-Term Voltage Stability Assessment Using Convolutional Neural Networks Considering Data Anomalies and Localization’, IEEE Access, Vol. 9, No., pp.128345-128358, https://doi.org/10.1109/ACCESS.2021.3107248
Rovatsos, G., et al. (2017) ‘Statistical Power System Line Outage Detection Under Transient Dynamics’, IEEE Transactions on Signal Processing, Vol. 65, No. 11, pp.2787-2797, https://doi.org/10.1109/TSP.2017.2673802
Saha Roy, B.K., et al. (2017) ‘Faulty Line Identification Algorithm for Secured Backup Protection Using PMUs’, Electric Power Components and Systems, Vol. 45, No. 5, pp.491-504, https://doi.org/10.1080/15325008.2016.1266417
Saifuddin, M.R.B.M., et al. (2019) ‘Apprehending Fault Crises for an Autogenous Nanogrid System: Sustainable Buildings’, IEEE Systems Journal, Vol. 13, No. 3, pp.3254-3265, https://doi.org/10.1109/JSYST.2018.2853078
Samantaray, S.R. and Kamwa, I. (2022) ‘A Missing Data Tolerant Wide-Area Back-Up Protection Scheme for Transmission Network’, IEEE Access, Vol. 10, No., pp.88001-88011, https://doi.org/10.1109/ACCESS.2022.3200549
Seyedi, Y. and Karimi, H. (2018) ‘Coordinated protection and control based on synchrophasor data processing in smart distribution networks’, IEEE Transactions on Power Systems, Vol. 33, No. 1, pp.634-645, https://doi.org/10.1109/TPWRS.2017.2708662
Seyedi, Y., Karimi, H. and Grijalva, S. (2017) ‘Distributed generation monitoring for hierarchical control applications in smart microgrids’, IEEE Transactions on Power Systems, Vol. 32, No. 3, pp.2305-2314, https://doi.org/10.1109/TPWRS.2016.2610322
Seyedi, Y., et al. (2022) ‘A Supervised Learning Approach for Centralized Fault Localization in Smart Microgrids’, IEEE Systems Journal, Vol. 16, No. 3, pp.4060-4070, https://doi.org/10.1109/JSYST.2021.3112710
Seyedi, Y., et al. (2020) ‘A New Approach to Reliability Assessment and Improvement of Synchrophasor Communications in Smart Grids’, IEEE Transactions on Smart Grid, Vol. 11, No. 5, pp.4415-4426, https://doi.org/10.1109/TSG.2020.2993944
Shabani, H.R., Kalantar, M. and Hajizadeh, A. (2022) ‘Real-Time Transient Instability Detection in the Power System With High DFIG-Wind Turbine Penetration via Transient Energy’, IEEE Systems Journal, Vol. 16, No. 2, pp.3013-3024, https://doi.org/10.1109/JSYST.2021.3079253
Shadi, M.R., Ameli, M.T. and Azad, S. (2022) ‘A real-time hierarchical framework for fault detection, classification, and location in power systems using PMUs data and deep learning’, International Journal of Electrical Power and Energy Systems, Vol. 134, No., https://doi.org/10.1016/j.ijepes.2021.107399
Shafiei Chafi, S.C., Afrakhte, H. and Borghetti, A. (2023) ‘μPMU-based islanding detection method in power distribution systems’, International Journal of Electrical Power and Energy Systems, Vol. 151, No., https://doi.org/10.1016/j.ijepes.2023.109102
Shanmugapriya, J. and Baskaran, K. (2023) ‘Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System’, Intelligent Automation and Soft Computing, Vol. 35, No. 2, pp.1581-1594, https://doi.org/10.32604/iasc.2023.024514
Sharma, N.K. and Samantaray, S.R. (2019) ‘Assessment of PMU-based wide-area angle criterion for fault detection in microgrid’, IET Generation, Transmission and Distribution, Vol. 13, No. 19, pp.4301-4310, https://doi.org/10.1049/iet-gtd.2019.0027
Sharma, N.K. and Samantaray, S.R. (2020) ‘PMU Assisted Integrated Impedance Angle-Based Microgrid Protection Scheme’, IEEE Transactions on Power Delivery, Vol. 35, No. 1, pp.183-193, https://doi.org/10.1109/TPWRD.2019.2925887
Sharma, P. and Saxena, A. (2017) ‘Critical investigations on performance of ANN and wavelet fault classifiers’, Cogent Engineering, Vol. 4, No. 1, https://doi.org/10.1080/23311916.2017.1286730
Shazdeh, S., Golpîra, H. and Bevrani, H. (2023) ‘A PMU-based back-up protection scheme for fault detection considering uncertainties’, International Journal of Electrical Power and Energy Systems, Vol. 145, No., https://doi.org/10.1016/j.ijepes.2022.108592
Sheta, A.N., Abdulsalam, G.M. and Eladl, A.A. (2021) ‘Online tracking of fault location in distribution systems based on PMUs data and iterative support detection’, International Journal of Electrical Power and Energy Systems, Vol. 128, No., https://doi.org/10.1016/j.ijepes.2021.106793
Sodin, D., et al. (2021) ‘Advanced edge-cloud computing framework for automated PMU-based fault localization in distribution networks’, Applied Sciences (Switzerland), Vol. 11, No. 7, https://doi.org/10.3390/app11073100
Soni, B.P., et al. (2018) ‘Identification of generator criticality and transient instability by supervising real-time rotor angle trajectories employing RBFNN’, ISA Transactions, Vol. 83, No., pp.66-88, https://doi.org/10.1016/j.isatra.2018.08.008
Sreelekha, V. and Prince, A. (2023) ‘ANFIS-Based Fault Distance Locator With Active Power Differential-Based Faulty Line Identification Algorithm for Shunt and Series Compensated Transmission Line Using WAMS’, IEEE Access, Vol. 11, No., pp.91500-91510, https://doi.org/10.1109/ACCESS.2023.3307466
Srivastava, A. and Parida, S.K. (2022) ‘Data driven approach for fault detection and Gaussian process regression based location prognosis in smart AC microgrid’, Electric Power Systems Research, Vol. 208, No., https://doi.org/10.1016/j.epsr.2022.107889
Srivastava, A., et al. (2023) ‘Transmission Line Protection Using Dynamic State Estimation and Advanced Sensors: Experimental Validation’, IEEE Transactions on Power Delivery, Vol. 38, No. 1, pp.162-176, https://doi.org/10.1109/TPWRD.2022.3184479
Su, H.Y. and Liu, T.Y. (2018) ‘Enhanced-online-random-forest model for static voltage stability assessment using wide area measurements’, IEEE Transactions on Power Systems, Vol. 33, No. 6, pp.6696-6704, https://doi.org/10.1109/TPWRS.2018.2849717
Sun, D., et al. (2023) ‘Development of Synchronized Waveform Measurement and Its Application on Fault Detection’, IEEE Transactions on Instrumentation and Measurement, Vol. 72, No., https://doi.org/10.1109/TIM.2023.3315390
Swain, K., et al. (2015) ‘IoT Based Transmission Line Fault Classification Using Regularized RBF-ELM and Virtual PMU in a Smart Grid’, CMES - Computer Modeling in Engineering and Sciences, Vol. 145, No. 2, pp.1993-2015,
Swain, K.B., Mahato, S.S. and Cherukuri, M. (2019) ‘Expeditious situational awareness-based transmission line fault classification and prediction using synchronized phasor measurements’, IEEE Access, Vol. 7, No., pp.168187-168200, https://doi.org/10.1109/ACCESS.2019.2954337
Usman, M.U. and Faruque, M.O. (2019) ‘Applications of synchrophasor technologies in power systems’, Journal of Modern Power Systems and Clean Energy, Vol. 7, No. 2, pp.211-226, https://doi.org/10.1007/s40565-018-0455-8
Veerakumar, N., et al. (2023) ‘PMU-based Real-time Distribution System State Estimation Considering Anomaly Detection, Discrimination and Identification’, International Journal of Electrical Power and Energy Systems, Vol. 148, No., https://doi.org/10.1016/j.ijepes.2022.108916
Velpula, R., et al. (2023) ‘A simple approach for the protection of EHV transmission lines’, Electric Power Systems Research, Vol. 224, No., https://doi.org/10.1016/j.epsr.2023.109744
Vosughi, A., Sadanandan, S.K. and Srivastava, A.K. (2022) ‘Synchrophasor-Based Event Detection, Classification, and Localization Using Koopman, Transient Energy Matrix, Best Worth Method, and Dynamic Graph’, IEEE Transactions on Power Delivery, Vol. 37, No. 3, pp.1986-1996, https://doi.org/10.1109/TPWRD.2021.3102148
Wang, C., et al. (2021) ‘MILP-Based Fault Diagnosis Model in Active Power Distribution Networks’, IEEE Transactions on Smart Grid, Vol. 12, No. 5, pp.3847-3857, https://doi.org/10.1109/TSG.2021.3071871
Wang, C. and Zhang, Y. (2015) ‘Fault correspondence analysis in complex electric power systems’, Advances in Electrical and Computer Engineering, Vol. 15, No. 1, pp.11-16, https://doi.org/10.4316/AECE.2015.01002
Wu, X., et al. (2019) ‘A Genetic-Algorithm Support Vector Machine and D-S Evidence Theory Based Fault Diagnostic Model for Transmission Line’, IEEE Transactions on Power Systems, Vol. 34, No. 6, pp.4186-4194, https://doi.org/10.1109/TPWRS.2019.2922734
Xing, J. and Mu, L. (2023) ‘A Novel Islanding Detection Method for Distributed PV System Based on μpMUs’, IEEE Transactions on Smart Grid, Vol. 14, No. 5, pp.3696-3706, https://doi.org/10.1109/TSG.2023.3236790
Yang, B., et al. (2018) ‘Cost-Efficient Low Latency Communication Infrastructure for Synchrophasor Applications in Smart Grids’, IEEE Systems Journal, Vol. 12, No. 1, pp.948-958, https://doi.org/10.1109/JSYST.2016.2556420
Yang, D., et al. (2019) ‘Fault Diagnosis for Energy Internet Using Correlation Processing-Based Convolutional Neural Networks’, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 49, No. 8, pp.1739-1748, https://doi.org/10.1109/TSMC.2019.2919940
Yang, F., et al. (2019) ‘Detection and analysis of multiple events based on high-dimensional factor models in power grid’, Energies, Vol. 12, No. 7, https://doi.org/10.3390/en12071360
Yu, F., et al. (2019) ‘Wide-area backup protection and protection performance analysis scheme using PMU data’, International Journal of Electrical Power and Energy Systems, Vol. 110, No., pp.630-641, https://doi.org/10.1016/j.ijepes.2019.03.060
Yu, S., et al. (2016) ‘State Estimation of Doubly Fed Induction Generator Wind Turbine in Complex Power Systems’, IEEE Transactions on Power Systems, Vol. 31, No. 6, pp.4935-4944, https://doi.org/10.1109/TPWRS.2015.2507620
Zare, J., Aminifar, F. and Sanaye-Pasand, M. (2015) ‘Synchrophasor-based wide-area backup protection scheme with data requirement analysis’, IEEE Transactions on Power Delivery, Vol. 30, No. 3, pp.1410-1419, https://doi.org/10.1109/TPWRD.2014.2377202
Zhang, T., et al. (2023) ‘Fault diagnosis and protection strategy based on spatio-temporal multi-agent reinforcement learning for active distribution system using phasor measurement units’, Measurement: Journal of the International Measurement Confederation, Vol. 220, No., https://doi.org/10.1016/j.measurement.2023.113291
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