Cited by 180Power System Optimization and StabilitySmart Grid Security and ResiliencePower Systems Fault Detection

The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.

Classification of Disturbances and Cyber-Attacks in Power Systems Using Heterogeneous Time-Synchronized Data

Pan, Shengyi, Morris, Thomas, Adhikari, Uttam·2015·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2015.2420951

Open this paper in CoChat — free account

Abstract

Visualization and situational awareness are of vital importance for power systems, as the earlier a power-system event such as a transmission line fault or cyber-attack is identified, the quicker operators can react to avoid unnecessary loss. Accurate time-synchronized data, such as system measurements and device status, provide benefits for system state monitoring. However, the time-domain analysis of such heterogeneous data to extract patterns is difficult due to the existence of transient phenomena in the analyzed measurement waveforms. This paper proposes a sequential pattern mining approach to accurately extract patterns of power-system disturbances and cyber-attacks from heterogeneous time-synchronized data, including synchrophasor measurements, relay logs, and network event monitor logs. The term common path is introduced. A common path is a sequence of critical system states in temporal order that represent individual types of disturbances and cyber-attacks. Common paths are unique signatures for each observed event type. They can be compared to observed system states for classification. In this paper, the process of automatically discovering common paths from labeled data logs is introduced. An included case study uses the common path-mining algorithm to learn common paths from a fusion of heterogeneous synchrophasor data and system logs for three types of disturbances (in terms of faults) and three types of cyber-attacks, which are similar to or mimic faults. The case study demonstrates the algorithm's effectiveness at identifying unique paths for each type of event and the accompanying classifier's ability to accurately discern each type of event.

Integrity signals

Registry

Crossref · 10.1109/tii.2015.2420951

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
Mining sequential patterns
Agrawal, R., & Srikant, R. (2002) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.1995.380415 · Cited by 5,150
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Han, J., Pei, J., Yin, Y., & Mao, R. (2003) · Data Mining and Knowledge Discovery · doi:10.1023/b:dami.0000005258.31418.83 · Cited by 2,656
Wide-Area Monitoring, Protection, and Control of Future Electric Power Networks
Terzija, V. V., Valverde, G., Cai, D., Regulski, P., Madani, V., Fitch, J., Skok, S., Begovic, M. M., & PHADKE, A. G. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2060450 · Cited by 769
Smart Transmission Grid Applications and Their Supporting Infrastructure
Bose, A. (2010) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2010.2044899 · Cited by 474
Analysis of faulted power systems,
Magnusson, P. C. (1974) · Journal of the Franklin Institute · doi:10.1016/0016-0032(74)90099-4 · Cited by 413
Fault Detection and Classification in Transmission Lines Based on Wavelet Transform and ANN
Silva, K. M., Souza, B. A., & Brito, N. S. D. (2006) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2006.876659 · Cited by 413
Machine learning for power system disturbance and cyber-attack discrimination
Hink, R. B., Beaver, J. M., Buckner, M. A., Morris, T., Adhikari, U., & Pan, S. (2014) · International Symposium on Resilient Control Systems (ISRCS) · doi:10.1109/isrcs.2014.6900095 · Cited by 336
Self-citation
Fault Detection and Classification in Medium Voltage DC Shipboard Power Systems With Wavelets and Artificial Neural Networks
Li, W., Monti, A., & Ponci, F. (2014) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2014.2313035 · Cited by 293
A Multidimensional Critical State Analysis for Detecting Intrusions in SCADA Systems
Carcano, A., Coletta, A., Guglielmi, M., Masera, M., Nai Fovino, I., & Trombetta, A. (2011) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2010.2099234 · Cited by 259

Flagged first, then by citations

Show all 29 references

Research works printed but not linked: 3

saadat, 2010, Power System Analysis
ferrer, 2010, Modern Solutions for Protection Control and Monitoring of Electric Power Systems, 57
polzin, 2012, Arizona Southern California Outage on September 8 2011

Sorted by citations

Items 101–177 of 177

AES Cryptography Enabled Responsible Federated Foundation Model Using Transformer LLM and LSTM for Smart Grid IIoT Networks
Hasan, M. K., Kabir, S. R., Islam, S., Abdullah, S., Abbas, H. S., Pandey, B., & Gadekallu, T. R. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3608807 · Cited by 7
Intrusion Detection Datasets for IIoT and ICS: A Taxonomic Review with a Decision-Aid Scoring Rubric
Termanini, A., Bourdoucen, H., Al‐Abri, D., & Maashri, A. A. (2026) · Sensors · doi:10.3390/s26134099 · Cited by 7
RADIANT: Reactive Autoencoder Defense for Industrial Adversarial Network Threats
Khan, I., Wali, S. W., & Farrukh, Y. A. (2023) · Computers & Security · doi:10.1016/j.cose.2025.104403 · Cited by 7
Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems
Intriago, G., & Zhang, Y. (2021) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm46819.2021.9637891 · Cited by 7
Dynamic Incremental Learning for real-time disturbance event classification
Veerakumar, N., Cremer, J. L., & Popov, M. (2023) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2023.108988 · Cited by 7
Detection and Localization of Faults in a Regional Power Grid
Kokoszka, P. S., Rimkus, M., Hosur, S., Duan, D., & Wang, H. (2023) · Austrian Journal of Statistics · doi:10.17713/ajs.v52i4.1511 · Cited by 6
Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attacks
Yao, L., Shi, Q., Yang, Z., Shao, S., & Hariri, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-94895-4_24 · Cited by 6
Pattern Classification Applying Neighbourhood Component Analysis and Swarm Evolutionary Algorithms: A Coupled Methodology
Leite, G. M. C., Marcelino, C. G., Wanner, E. F., Pedreira, C. E., Jiménez-Fernández, S., & Salcedo‐Sanz, S. (2021) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec45853.2021.9504702 · Cited by 6
Smart City
Lai, C. S., Lai, L. L., & Lai, Q. H. (2020) · Springer eBooks · doi:10.1007/978-3-030-52155-4_1 · Cited by 6
Anomaly behavior analysis for smart grid automation system
Orozco, A., Pacheco, J., & Hariri, S. (2017) · IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) · doi:10.1109/ropec.2017.8261614 · Cited by 5
Intelligent Approach for Analysis and Diagnosis of Attack, Fault and Load Variation in SCADA Systems: A Power System Application
Mohan, V. S., & Sankaran, S. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-6081-8_1 · Cited by 5
Resilient Machine Learning (rML) Against Adversarial Attacks on Industrial Control Systems
Yao, L., Shao, S., & Hariri, S. (2023) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa59173.2023.10479279 · Cited by 5
A Novel Dynamic Index to Determine Stressful Transmission Lines
Panahi, H., Abedini, M., & Sanaye‐Pasand, M. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3163142 · Cited by 5
Machine Learning in Industrial Control System Security: A Survey
Jiang, D., & Zhao, J. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-32-9698-5_35 · Cited by 5
SCADA Networks Anomaly-based Intrusion Detection System
Almehmadi, A. (2018) · International Conference on Security of Information and Networks (SIN) · doi:10.1145/3264437.3264471 · Cited by 5
Adversarial Attack against Intrusion Detectors in Cyber-Physical Systems With Minimal Perturbations
Bai, M., Liu, P., Lv, F., Fang, D., Lv, S., Zhang, W., & Sun, L. (2024) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa63168.2024.00109 · Cited by 5
Statistical Approach with Machine Learning- Based Intrusion Detection System for CyberAttack Discrimination in the Smart Grid
Nakkeeran, M., Narayanan, V. A., Sivakumar, P. B., & Balamurugan, S. (2024) · Power Research · doi:10.33686/pwj.v20i1.1162 · Cited by 4
Cyber-Attack Detection: Modeling and Roof-PV Generation System Protection
Qiu, W., Sun, K., Li, K., Li, Y., Duan, J., & Zhu, K. (2022) · IEEE/IAS Industrial and Commercial Power Systems Technical Conference · doi:10.1109/icps54075.2022.9773850 · Cited by 4
Anomaly Detection and Classification in Streaming PMU Data in Smart Grids
Amutha, A. L., Uthra, R. A., Roselyn, J. P., & Brunet, R. G. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.029904 · Cited by 4
Sensor Based Cyber Attack Detections in Critical Infrastructures Using Deep Learning Algorithms
Çatak, F. Ö., Yılmaz, M. K., & Gül, E. (2019) · Computer Science · doi:10.7494/csci.2019.20.2.3191 · Cited by 4
Cyber–physical power systems resilience: Definition, threats, and assessment
Binqadhi, H. M., & Almuhaini, M. M. (2026) · Energy Reports · doi:10.1016/j.egyr.2026.109209 · Cited by 3
The Role of Machine Learning Algorithms in Smart Grid Cybersecurity
Diaba, S. Y. (2024) · International Workshop on Artificial Intelligence and Machine Learning for Energy Transformation (AIE) · doi:10.1109/aie61866.2024.10561386 · Cited by 3
Optimal defense resource allocation against cyber-attacks in distributed generation systems
Mo, H., Xiao, X., Sansavini, G., & Dong, D. (2023) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x231196259 · Cited by 3
Diagnosing Faults in Smart Grids using Liquid Time-Constant Network
Samadi, M., Kharrati, H., Badamchizadeh, M. A., Hassani, H., & Nikan, S. (2023) · International Conference on Engineering and Emerging Technologies (ICEET) · doi:10.1109/iceet60227.2023.10525812 · Cited by 3
Discovering Multi-type Correlated Events with Time Series for Exception Detection of Complex Systems
Xun, P., Zhu, P., Li, C.-L., & Zhu, H. (2016) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2016.0012 · Cited by 3
A Data Preparation Method for Machine-Learning-Based Power System Cyber-Attack Detection
Chen, H., Wang, J., & Shi, D. (2018) · International Conference on Power System Technology (POWERCON) · doi:10.1109/powercon.2018.8602194 · Cited by 3
An adaptive ensemble classification framework for real-time data streams by distributed control systems
Sufang, W. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04759-0 · Cited by 3
A Detailed Analysis of Public Industrial Control System Datasets
Mubarak, S., Islam, M. R., Balla, A., & Habaebi, M. H. (2023) · International Journal of Security and Networks · doi:10.1504/ijsn.2023.10058681 · Cited by 3
Load shedding control strategy for power system voltage regulation in distribution networks
Hasan, A.-Y., Tang, Y. P., Zhao, P., Li, W., & Li, C. (2017) · IEEE Conference on Energy Internet and Energy System Integration (EI2) · doi:10.1109/ei2.2017.8245688 · Cited by 3
STEP-GAN: A Step-by-Step Training for Multi Generator GANs with application to Cyber Security in Power SystemsPreprint
Adiban, M., Safari, A., & Salvi, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05184 · Cited by 3
Data Availability and Integrity Attacks for Power Systems via Nonlinear Spatial-Temporal Modeling
Sun, H., Duan, D., Zhang, H., Choi, S. L., Zhang, J., Wang, X., Wang, H., Luo, J., & Yang, L. (2025) · IEEE Open Access Journal of Power and Energy · doi:10.1109/oajpe.2025.3567209 · Cited by 2
Decentralized cybersecurity in smart grids: Leveraging location-fedavg for rapid threat detection and adaptive resilience
Nakkeeran, M., Narayanan, V. A., Sivakumar, P. B., & Balamurugan, S. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.108518 · Cited by 2
Advancing resilience with SDN: A comprehensive review of cyber-physical strategies for smart grid
Zhong, J. L., Xu, Z., Chen, C., Li, Z., Kim, Y., Sakaguchi, K., & Yu, T. (2025) · Smart power & energy security. · doi:10.1016/j.spes.2025.12.003 · Cited by 2
Generative adversarial imbalanced learning for DC microgrid anomaly detection
Rong, J., Yang, Y., & Jiang, F. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131811 · Cited by 2
Guaranteed Data Recovery and Oscillatory Mode Estimation in Power System Using Novel RPCA
Kumar, V., Rai, S., & Kumar, R. R. (2023) · International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) · doi:10.1109/smartgencon60755.2023.10442682 · Cited by 2
Enhanced Data-Driven Framework for Anomaly Detection in Smart Grid IEDs
Son, N. K., Sangaiah, A. K., Medhane, D. V., Alenazi, M. J. F., AlQahtani, S. A., & Aborokbah, M. M. (2024) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126625501634 · Cited by 2
Research on Deep Learning Algorithm Optimization for Data Classification and Grading in the Power Industry
Zhang, X., Dong, Y., Yin, Y., Wei, Z., Huang, Y., Ai, X., & Wei, J. (2024) · International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC) · doi:10.1109/peeec63877.2024.00140 · Cited by 2
MO-Transformer-IDS: Multi-objective Optimization-based Transformer for Multi-Classification Intrusion Detection of Cyber-Physical Power Systems
Wang, R., Zeng, G., & Lu, K. (2024) · IEEE International Conference on Energy Internet (ICEI) · doi:10.1109/icei63732.2024.10917183 · Cited by 2
Attention‐Enhanced Bidirectional LSTM for Accurate False Data Injection Attack Detection in Smart Grid
Ishfaq, H., Amin, W., Ashfaq, S., Mushtaq, N., & Shi, X. (2025) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70238 · Cited by 2
Association rules analysis between power system operating states and transient stability margin
Shi, F., Hu, X., Yu, Z., Huang, Y., & Lu, G. (2017) · IEEE Innovative Smart Grid Technologies-Asia · doi:10.1109/isgt-asia.2017.8378450 · Cited by 2
Classification of Faults in Power Transmission Systems Using Modern Techniques
Prasad, A., Edward, J. B., & Ravi, K. (2017) · Advances in knowledge acquisition, transfer, and management book series/Advances in knowledge acquisition, transfer and management book series · doi:10.4018/978-1-5225-2375-8.ch007 · Cited by 2
USING MACHINE LEARNING METHODS FOR INDUSTRIAL CONTROL SYSTEMS INTRUSION DETECTION
Shikhaliyev, R. (2023) · Problems of Information Technology · doi:10.25045/jpit.v14.i2.05 · Cited by 2
RIBS: Risky Blind-Spots for Attack Classification Models
Joaristi, M., Putnam, A., Cuzzocrea, A., & Serra, E. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006356 · Cited by 2
False Data Injection Attacks in Power Systems
Amir, A., & Sarjan, H. (2023) · Wiley Encyclopedia of Electrical and Electronics Engineering · doi:10.1002/047134608x.w8446 · Cited by 2
Randomization based shallow and federated-deep learning for smart grid security using label-encoded vulnerabilities and distributed LSTM computation
Hasan, M. K., Kabir, S. R., Islam, S., Abdullah, S., Gadekallu, T. R., & Abbas, H. S. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3354 · Cited by 1
Learning from time-synchronized power system measurements with rare anomalous event records to detect PMU data manipulation attacks
Wang, J., & Shi, D. (2017) · IEEE Conference on Energy Internet and Energy System Integration (EI2) · doi:10.1109/ei2.2017.8245273 · Cited by 1
Towards Comprehensive Modeling of CPSs to Discover and Study Interdependencies
Akbarzadeh, A., & Katsikas, S. K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25460-4_1 · Cited by 1
Commissioning Random Matrix Theory and Synthetic Minority Oversampling Technique for Power System Faults Detection and Classification
Sinha, A., Dwivedi, S., Shukla, S. K., & Vyas, O. P. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-1648-1_43 · Cited by 1
Anomaly detection for mobile computing based smart vertical approaches
Gao, Y., & Sun, X. (2023) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-023-02092-y · Cited by 1
A Novel Protocol-Free Bandage-Cover Cryptographer
Sun, E. Y.-N., Wu, H., Huang, S. C.-H., & Kuan, Y. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3117235 · Cited by 1
On Analysis of Effectiveness of Ensemble, Distance and Tree based Methods for Secure Power Systems
Tripathi, A., & Bhoj, N. (2021) · International Conference on Data Analytics for Business and Industry (ICDABI) · doi:10.1109/icdabi53623.2021.9655814 · Cited by 1
Introduction to Cyber-Physical Security and Resilience
Abbaszadeh, M., & Zemouche, A. (2022) · Springer eBooks · doi:10.1007/978-3-030-97166-3_2 · Cited by 1
Online Hybrid Kernel Learning Machine with Dynamic Forgetting Mechanism
Wang, Y., Li, D., Xu, Y., & Wang, H. (2023) · Communications in computer and information science · doi:10.1007/978-981-19-9697-9_22 · Cited by 1
Longitudinal Analysis and Modeling of Video Game Play and Addiction Behaviours
Wong, U. (2016) · Libraries and Cultural Resources (University of Calgary) · doi:10.11575/prism/26782
Wavelet Technique-Based Fault Classification in Transmission Lines
Prasad, A., & Edward, J. B. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-4394-9_40
Semi-Supervised Outlier Detection and Deep Feature Extraction for Detecting Cyber-Attacks in Smart Grids Using PMU Data
Qi, R., Rasband, C., Zheng, J. J., & Longoria, R. G. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-43020-7_67
2015 Index IEEE Transactions on Industrial Informatics Vol. 11
(2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2514183
Flare: An Online Low Complexity Event and Intrusion Detection for Power Transmission NetworksPreprint
Wahab, A. (2022) · doi:10.36227/techrxiv.19617216.v1
Um método baseado em consciência situacional para a percepção de eventos em sistemas de distribuição
de Souza, E. C. C. (2023) · doi:10.11606/d.18.2023.tde-22032023-102112
Exploratory Data Analysis and Recurrent Expansion for Power Systems Cybersecurity Forensics
Berghout, T., Lim, W. H., Amirat, Y., Tayeb, F. B.-S., & Benbouzid, M. (2024) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon55916.2024.10905101
Resilient Machine Learning Ensemble (rMLe): Design, Analysis and Evaluation
Al-Nashif, Y. B., Hariri, S., Benjamin, Z., Truong, S., Khan, S., & Blasch, E. (2025) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon65708.2025.11235417
Information flow-based UAV flight control adaptive approximate modeling for perturbation generalization
Wang, R., Li, Y., Liu, M., Zheng, M., Xu, X., & Zhang, T. (2025) · ISA Transactions · doi:10.1016/j.isatra.2025.12.045
Enhancing Cyber-physical Security and Reliability for Energy Management Systems in CAEVs
Chandra, K. S., Srinivasan, K., & Anand, P. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-9720-5_34
Towards Secure Smart Grid Infrastructure in Saudi Arabia: A Review and Perspective of AI-Powered False Data Injection Attack Detection Methods.
Alsaedi, A., Alofi, G., Abdulla, J., & Tari, Z. (2024) · Saudi Arabia Smart Grid Conference (SASG) · doi:10.1109/sasg64132.2024.11535873
LSTM time-series max-value selection based cryptographic federated learning with load forecasting anomalies observations for autonomous smart grid system
Kabir, S. R., Hasan, M. K., Bajaj, S. K., Islam, S., Abdullah, S., & Gadekallu, T. R. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115847
Resilient Machine Learning (rML) Against Adversarial Machine Learning Attacks on Industrial Control Systems
Yao, L., Shao, S., & Hariri, S. (2026) · Springer eBooks · doi:10.1007/978-3-031-88574-7_31
Hyperbolic adaptive spatial-aware multivariate time series anomaly detection
Han, J., Huo, X., Xiao, Y., Ye, Z., Zheng, Y., Zhao, H., Sun, M., & Liu, Z. (2026) · Nature Communications · doi:10.1038/s41467-026-75881-1
SCADA Digital Twin and Cryptographic AMI Cyber-Twin for Smart Grid Security
Kabir, S. R., Hasan, M. K., Pavleska, T., & Abdullah, S. (2026) · Applied Sciences · doi:10.3390/app16157655
Quantum Diffusion with Attentive Twin-Stream Sparse Spatiotemporal for Cyberattack Detection in Smart GridsPreprint
Adhikari, I., Saffari, M., & Asrari, ‎. (2026) · Research Square · doi:10.21203/rs.3.rs-10334218/v1
Machine Learning for Enhanced Cybersecurity: A Comparative Analysis of False Data Injection Attack Detection in Power Systems
Paul, R., Pattanayak, S., & Hote, Y. V. (2026) · International Conference on Power, Control and Computing Technologies (ICPC2T) · doi:10.1109/icpc2t68221.2026.11646280
Learning fuzzy normal-operation regimes for interpretable power system anomaly detection
Azadi, N., Efatinasab, E., & Rampazzo, M. (2026) · Electric Power Systems Research · doi:10.1016/j.epsr.2026.114221
Physics-Informed Contrastive Learning (PICL) for Detection of Stealthy Cyber–Physical Attacks in Smart Power Grids
Lu, Y. H., Lin, C.-M., & Chou, C. (2026) · IEEE Access · doi:10.1109/access.2026.3735797
Artificial-Intelligence-Enabled Wind-Grid Intelligence Systems: A Systematic Review of AI Evolution, Functional Intelligence Layers, and Deployment Readiness
Asad, M. U., & Sánchez, J. A. (2026) · Sustainability · doi:10.3390/su18199921
False data injection attack detection and rejection for line current differential relays
Soltani, S., Mottaghizadeh, M., Hussain, A., & Kamwa, I. (2026) · Electric Power Systems Research · doi:10.1016/j.epsr.2026.114282
Smart contract-enabled blockchain framework for cyber-resilient microgrid protection
Kumar, K., Kar, S., & Samantaray, S. R. (2026) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2026.102540
Edge-ready anomaly detection using cyber–physical consistency assessment in power systems: A co-simulation-based approach
Behdadnia, T., Savarro, D., Terruggia, R., & Deconinck, G. (2026) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2026.100911
EvoACC-BD-MIDS: Evolutionary Activation Clustering Combination-Based Backdoor Defense for Multi-Class Intrusion Detection Systems in Smart Grids
Wang, R., Lu, K., Zeng, G., Geng, G., & Weng, J. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3744644

Authors: Pan, S., Morris, T., & Adhikari, U.
Affiliations: Mississippi State University (US)
ORCID: Pan, Shengyi
Morris, Thomas
Adhikari, Uttam
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2015-06
Volume: 11
Issue: 3
Pages: 650-662
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2015.2420951
OpenAlex: W2020524857
Cited by: 180
References: 29
Citations by year: 2026: 19 · 2025: 16 · 2024: 17 · 2023: 30 · 2022: 17 · 2021: 16 · 2020: 17 · 2019: 19 · 2018: 7 · 2017: 16 · 2016: 4 · 2015: 2
FWCI: 4.66
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Industrial Informatics (publishedVersion)
Funders: Division of Undergraduate Education (1315726)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
Physical Sciences › Engineering › Control and Systems Engineering › Power Systems Fault Detection
Keywords: cyberattack detection, sequential pattern mining, synchrophasor measurements, fault classification, situational awareness

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Classification of Disturbances and Cyber-Attacks in Power Systems Using Heterogeneous Time-Synchronized Data

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks