A Review on Distribution System State Estimation Algorithms
Source abstract
Abstract
The modern energy requirements and the orientation towards Renewable Energy Sources (RES) integration promote the transition of distribution grids from passive, unidirectional, fossil fuel-based into active, bidirectional, environmental-friendly architectures. For this purpose, advanced control algorithms and optimization processes are implemented, the performance of which relies on the Distribution System State Estimation (DSSE). DSSE algorithms provide the Distribution System Operator (DSO) with detailed information regarding the network’s state in order to derive the optimal decisions. However, this task is quite complex as the distribution system has inherent unbalance issues, often faces lack of adequate measurements, etc. The purpose of this paper is to review the DSSE algorithms that a system can incorporate with emphasis on their particular requirements, the mathematical formulation of the problem, the analysis of the existing model-based and data-driven approaches and the recommended solutions regarding observability issues, bad data detection, and meter placement strategies. Furthermore, special attention is paid to DSSE applications, including the use cases where they can be deployed, the approaches that are usually followed, the integrated distributed power supply units, as well as their future trends and challenges, thus highlighting their business-related aspects.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Grid Energy Management · Optimal Power Flow Distribution · Smart Grid Security and Resilience
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Centre for Research and Technology Hellas, Chemical Process and Energy Resources Institute, 15125 Athens, Greece
Department of Computer Engineering and Informatics Graduate, University of Patras, 26504 Patras, Greece
Funding
European Commission’s H2020 programme, INTERPRETER
References (100+)
Show source-provided references
1. The role of the state in sustainable energy transitions: A case study of large smart grid demonstration projects in Japan10.1016/j.enpol.2013.07.106 ↗
2. Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation10.1016/j.rser.2016.06.030 ↗
3. Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality10.1016/j.rser.2018.03.068 ↗
4. A review on the features and technologies for energy efficiency of smart grid10.1002/er.3852 ↗
5. Gust, G., Biener, W., Brandt, T., Dallmer-Zerbe, K., Neumann, D., and Wille-Haussmann, B. (2016, January 4–8). Decision Support for Distribution Grid Planning. Proceedings of the 2016 IEEE International Energy Conference (ENERGYCON), Leuven, Belgium.10.1109/ENERGYCON.2016.7514135 ↗
6. Advanced metering infrastructure smart metering based on cloud architecture for low voltage distribution networks in application of smart grid monitoring
7. Distribution system state estimation: An overview of recent developments10.1631/FITEE.1800590 ↗
8. Watitwa, J., and Awodele, K. (2019, January 28–30). A Review on Active Distribution System State Estimation. Proceedings of the 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA), Bloemfontein, South Africa.10.1109/RoboMech.2019.8704833 ↗
9. A Review on Distribution System State Estimation10.1109/TPWRS.2016.2632156 ↗
10. Comparison of State Estimation Process on Transmission and Distribution Systems10.1007/978-3-030-24318-0_50 ↗
11. Distribution system state estimation-A step towards smart grid10.1016/j.rser.2017.06.071 ↗
12. Radhoush, S., Bahramipanah, M., Nehrir, H., and Shahooei, Z. (2022). A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges. Sustainability, 14.10.3390/su14052520 ↗
13. A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems10.1109/TSG.2018.2870600 ↗
14. A combined survey on distribution system state estimation and false data injection in cyber-physical power distribution networks10.1049/cps2.12000 ↗
15. Táczi, I., Sinkovics, B., Vokony, I., and Hartmann, B. (2021). The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review. Energies, 14.10.3390/en14175363 ↗
16. Gómez-Expósito, A., Conejo, A.J., and Cañizares, C.A. (2016). Electric Energy Systems: Analysis and Operation, CRC Press.10.1201/9781420007275 ↗
17. Majumdar, A., and Pal, B.C. (2016, January 8–10). A three-phase state estimation in unbalanced distribution networks with switch modelling. Proceedings of the 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI), Kolkata, India.10.1109/CMI.2016.7413793 ↗
18. Power system state estimation using three-phase models10.1109/59.387922 ↗
19. Optimized Measurement Allocation for Power Distribution Systems Using Mixed Integer SDP10.1109/TIM.2017.2731019 ↗
20. Clements, K.A. (2011, January 24–28). The Impact of Pseudo-Measurements on State Estimator Accuracy. Proceedings of the 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, USA.10.1109/PES.2011.6039370 ↗
21. Baran, M.E., Zhu, J., and Kelley, A.W. (1995, January 7–12). Meter Placement for Real-Time Monitoring of Distribution Feeders. Proceedings of the Power Industry Computer Applications Conference, Salt Lake City, UT, USA.
22. Power System State Estimation using Non-Iterative Weighted Least Square method based on Wide Area Measurements with maximum redundancy10.1016/j.epsr.2022.107794 ↗
23. Gholami, M., Tehrani-Fard, A., Lehtonen, M., Moeini-Aghtaie, M., and Fotuhi-Firuzabad, M. (2021). A Novel Multi-Area Distribution State Estimation Approach for Active Networks. Energies, 14.10.3390/en14061772 ↗
24. State Estimation for Distribution Grids with a Single-Point Grounded Neutral Conductor
25. Robust Measurement Placement for Distribution System State Estimation10.1109/TSTE.2017.2775862 ↗
26. Distribution system state estimation10.1109/59.373946 ↗
27. Harmonic state estimation for distribution systems based on optimization models considering daily load profiles10.1016/j.epsr.2019.01.033 ↗
28. A Revised Branch Current-Based Distribution System State Estimation Algorithm and Meter Placement Impact10.1109/TPWRS.2003.821426 ↗
29. Efficient Branch-Current-Based Distribution System State Estimation Including Synchronized Measurements10.1109/TIM.2013.2272397 ↗
30. An Efficient and Accurate Solution for Distribution System State Estimation with Multiarea Architecture10.1109/TIM.2016.2642598 ↗
31. Meriem, M., Bouchra, C., Abdelaziz, B., Jamal, S.O.B., Faissal, E.M., and Nazha, C. (2016, January 26–28). Study of State Estimation Using Weighted-Least-Squares Method (WLS). Proceedings of the 2016 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), Marrakech, Morocco.10.1109/CISTEM.2016.8066820 ↗
32. Pau, M., Pegoraro, P.A., and Sulis, S. (2015, January 23–25). Performance of Three-Phase WLS Distribution System State Estimation Approaches. Proceedings of the 2015 IEEE International Workshop on Applied Measurements for Power Systems (AMPS), Aachen, Germany.10.1109/AMPS.2015.7312752 ↗
33. Single Iteration Conditional Based DSSE Considering Spatial and Temporal Correlation10.1016/j.ijepes.2018.12.025 ↗
34. Real-Time Monitoring of Distribution System Based on State Estimation10.1109/TIM.2016.2583239 ↗
35. Robust distribution system state estimation with hybrid measurements10.1049/iet-gtd.2020.0260 ↗
36. Jin, T., and Shen, X. (2018). A Mixed WLS Power System State Estimation Method Integrating a Wide-Area Measurement System and SCADA Technology. Energies, 11.10.3390/en11020408 ↗
37. LAV Based Robust State Estimation for Systems Measured by PMUs10.1109/TSG.2014.2302213 ↗
38. Linear LAV-based state estimation integrating hybrid SCADA/PMU measurements10.1049/iet-gtd.2019.1850 ↗
39. A robust WLAV state estimator using transformations10.1109/59.141693 ↗
40. Weng, Y., Negi, R., Liu, Q., and Ilić, M.D. (2011, January 17–19). Robust state-estimation procedure using a Least Trimmed Squares pre-processor. Proceedings of the ISGT 2011, Anaheim, CA, USA.
41. Least median of squares estimation in power systems10.1109/59.76693 ↗
42. Zhao, J., Huang, C., Mili, L., Zhang, Y., and Min, L. (2020, January 17–20). Robust Medium-Voltage Distribution System State Estimation Using Multi-Source Data. Proceedings of the 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA.10.1109/ISGT45199.2020.9087787 ↗
43. Enhanced Robustness of State Estimator to Bad Data Processing Through Multi-innovation Analysis10.1109/TII.2016.2626782 ↗
44. State Estimation in Electric Power Grids: Meeting New Challenges Presented by the Requirements of the Future Grid10.1109/MSP.2012.2187037 ↗
45. Comparisons on Kalman-Filter-Based Dynamic State Estimation Algorithms of Power Systems10.1109/ACCESS.2020.2979735 ↗
46. Shafiei, M., Ledwich, G., Nourbakhsh, G., Arefi, A., and Pezeshki, H. (2018). Layered Based Augmented Complex Kalman Filter for Fast Forecasting-Aided State Estimation of Distribution Networks. arXiv.
47. Robust dynamic state estimation of power systems based on M-Estimation and realistic modeling of system dynamics10.1109/59.736273 ↗
48. State forecasting in electric power systems10.1049/ip-c.1983.0046 ↗
49. Zhang, Z., Qiu, J., and Ma, W. (2019). Adaptive Extended Kalman Filter with Correntropy Loss for Robust Power System State Estimation. Entropy, 21.10.3390/e21030293 ↗
50. Antončič, M., Papič, I., and Blažič, B. (2019). Robust and Fast State Estimation for Poorly-Observable Low Voltage Distribution Networks Based on the Kalman Filter Algorithm. Energies, 12.10.3390/en12234457 ↗
51. A photovoltaics-aided interlaced extended Kalman filter for distribution systems state estimation10.1016/j.segan.2021.100438 ↗
52. Optimal parameterization of Kalman filter based three-phase dynamic state estimator for active distribution networks10.1016/j.ijepes.2018.04.008 ↗
53. Wan, E.A., and Van Der Merwe, R. (2000, January 4). The Unscented Kalman Filter for Nonlinear Estimation. Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373), Lake Louise, AB, Canada.
54. Simpson, R.H. (2000, January 19–23). Power System Data Base Management. Proceedings of the Conference Record of 2000 Annual Pulp and Paper Industry Technical Conference (Cat. No.00CH37111), Atlanta, GA, USA.
55. Integrating Model-Driven and Data-Driven Methods for Power System Frequency Stability Assessment and Control10.1109/TPWRS.2019.2919522 ↗
56. A systematic review of machine learning applications in the operation of smart distribution systems10.1016/j.egyr.2022.09.068 ↗
57. Sah, S. (2020). Machine Learning: A Review of Learning Types. Math. Comput. Sci.10.20944/preprints202007.0230.v1 ↗
58. Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System10.1021/acs.energyfuels.2c01006 ↗
59. A novel intelligent transport system charging scheduling for electric vehicles using Grey Wolf Optimizer and Sail Fish Optimization algorithms
60. Dridi, S. (2022, September 26). Supervised Learning–A Systematic Literature Review. Available online: https://www.researchgate.net/publication/354996999_Supervised_Learning_-_A_Systematic_Literature_Review.
61. Neural networks and pseudo-measurements for real-time monitoring of distribution systems10.1109/19.492803 ↗
62. A Scalable Data-Driven Monitoring Approach for Distribution Systems10.1109/TIM.2015.2398991 ↗
63. Zhang, L., Wang, G., and Giannakis, G.B. (2019, January 2–5). Distribution System State Estimation Via Data-Driven and Physics-Aware Deep Neural Networks. Proceedings of the 2019 IEEE Data Science Workshop (DSW), Minneapolis, MN, USA.10.1109/DSW.2019.8755581 ↗
64. Physics-Aware Neural Networks for Distribution System State Estimation10.1109/TPWRS.2020.2988352 ↗
65. Tran, M.-Q., Zamzam, A.S., and Nguyen, P.H. (July, January 28). Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks. Proceedings of the 2021 IEEE Madrid PowerTech, Madrid, Spain.10.1109/PowerTech46648.2021.9494950 ↗
66. Tran, M.-Q., Zamzam, A., Nguyen, P., and Pemen, G. (2021). Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks. Energies, 14.10.3390/en14113025 ↗
67. Hybrid State Estimation for Distribution Systems With AMI and SCADA Measurements10.1109/ACCESS.2019.2937096 ↗
68. Data-Driven Learning-Based Optimization for Distribution System State Estimation10.1109/TPWRS.2019.2909150 ↗
69. Fantin, C.A., Castillo, M.R.C., de Carvalho, B.E.B., and London, J.B.A. (2014, January 10–13). Using Pseudo and Virtual Measurements in Distribution System State Estimation. Proceedings of the 2014 IEEE PES Transmission & Distribution Conference and Exposition–Latin America (PES T&D-LA), Medellin, Colombia.10.1109/TDC-LA.2014.6955287 ↗
70. Distribution system state estimation through Gaussian mixture model of the load as pseudo-measurement10.1049/iet-gtd.2009.0167 ↗
71. Statistical Representation of Distribution System Loads Using Gaussian Mixture Model10.1109/TPWRS.2009.2030271 ↗
72. Manitsas, E., Singh, R., Pal, B., and Strbac, G. (2008, January 23–24). Modelling of Pseudo-Measurements for Distribution System State Estimation. Proceedings of the CIRED Seminar 2008: SmartGrids for Distribution, Frankfurt, Germany.10.1049/ic:20080447 ↗
73. Maximum Likelihood from Incomplete Data via the EM Algorithm10.1111/j.2517-6161.1977.tb01600.x ↗
74. Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling10.1109/TPWRS.2012.2187804 ↗
75. A robust state estimator for medium voltage distribution networks10.1109/TPWRS.2012.2215927 ↗
76. A Closed-Loop State Estimation Tool for MV Network Monitoring and Operation10.1109/TSG.2014.2378035 ↗
77. Reliable Bad Data Processing for Real-Time State Estimation10.1109/TPAS.1983.318053 ↗
78. Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter10.1109/TCNS.2014.2357531 ↗
79. False data injection attacks against state estimation in electric power grids10.1145/1952982.1952995 ↗
80. Rahman, M.A., and Mohsenian-Rad, H. (2013, January 21–25). False Data Injection Attacks against Nonlinear State Estimation in Smart Power Grids. Proceedings of the 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, Canada.10.1109/PESMG.2013.6672638 ↗
81. A Review of False Data Injection Attacks Against Modern Power Systems10.1109/TSG.2015.2495133 ↗
82. Ayad, A., Farag, H.E.Z., Youssef, A., and El-Saadany, E.F. (2018, January 19–22). Detection of False Data Injection Attacks in Smart Grids Using Recurrent Neural Networks. Proceedings of the 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA.10.1109/ISGT.2018.8403355 ↗
83. A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids10.1109/TSG.2019.2949998 ↗
84. Kallitsis, M.G., Bhattacharya, S., Stoev, S., and Michailidis, G. (2016, January 7–9). Adaptive Statistical Detection of False Data Injection Attacks in Smart Grids. Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, USA.10.1109/GlobalSIP.2016.7905958 ↗
85. Immunity Toward Data-Injection Attacks Using Multisensor Track Fusion-Based Model Prediction
86. Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid10.1109/ACCESS.2017.2728681 ↗
87. Sreenath, J.G., Meghwani, A., Chakrabarti, S., Rajawat, K., and Srivastava, S.C. (2017, January 16–20). A recursive state estimation approach to mitigate false data injection attacks in power systems. Proceedings of the 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA.10.1109/PESGM.2017.8274070 ↗
88. Resilient Distributed DC Optimal Power Flow Against Data Integrity Attack10.1109/TSG.2016.2633943 ↗
89. A Prediction Algorithm to Enhance Grid Resilience Toward Cyber Attacks in WAMCS Applications10.1109/JSYST.2017.2741483 ↗
90. Chung, H.-M., Li, W.-T., Yuen, C., Chung, W.-H., and Wen, C.-K. (2017, January 23–27). Local Cyber-Physical Attack with Leveraging Detection in Smart Grid. Proceedings of the 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), Dresden, Germany.10.1109/SmartGridComm.2017.8340712 ↗
91. Khalaf, M., Youssef, A., and El-Saadany, E. (2017, January 22–25). Detection of False Data Injection in Automatic Generation Control Systems Using Kalman Filter. Proceedings of the 2017 IEEE Electrical Power and Energy Conference (EPEC), Saskatoon, SK, Canada.10.1109/EPEC.2017.8286194 ↗
92. Karimipour, H., and Dinavahi, V. (2017, January 14–17). On False Data Injection Attack against Dynamic State Estimation on Smart Power Grids. Proceedings of the 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada.10.1109/SEGE.2017.8052831 ↗
93. Robust Massively Parallel Dynamic State Estimation of Power Systems Against Cyber-Attack10.1109/ACCESS.2017.2786584 ↗
94. Shi, W., Wang, Y., Jin, Q., and Ma, J. (2018, January 23–27). PDL: An Efficient Prediction-Based False Data Injection Attack Detection and Location in Smart Grid. Proceedings of the 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan.10.1109/COMPSAC.2018.10317 ↗
95. Abnormal traffic-indexed state estimation: A cyber–physical fusion approach for Smart Grid attack detection10.1016/j.future.2014.10.002 ↗
96. Detection of false data injection attacks in smart-grid systems10.1109/MCOM.2015.7045410 ↗
97. Jiang, Y., and Hui, Q. (2017, January 14–17). Kalman Filter with Diffusion Strategies for Detecting Power Grid False Data Injection Attacks. Proceedings of the 2017 IEEE International Conference on Electro Information Technology (EIT), Lincoln, NE, USA.10.1109/EIT.2017.8053365 ↗
98. Short-Term State Forecasting-Aided Method for Detection of Smart Grid General False Data Injection Attacks10.1109/TSG.2015.2492827 ↗
99. Detection of energy theft and defective smart meters in smart grids using linear regression10.1016/j.ijepes.2017.04.005 ↗
100. Ferragut, E.M., Laska, J., Olama, M.M., and Ozmen, O. (2017, January 14–16). Real-Time Cyber-Physical False Data Attack Detection in Smart Grids Using Neural Networks. Proceedings of the 2017 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.10.1109/CSCI.2017.1 ↗