Artificial Intelligence-Based State Estimation in Power System and Phasor Measurement Units

References

• J. Zhao et al., “Power system dynamic state estimation: Motivations, definitions, methodologies, and future work,” IEEE Trans. Power Syst., vol. 34, no. 4, pp. 3188–3198, 2019. DOI: 10.1109/TPWRS.2019.2894769.
   Web of Science ®Google Scholar
• J. A. D. Massignan, J. B. A. London, C. D. Maciel, M. Bessani, and V. Miranda, “PMUs and SCADA measurements in power system state estimation through Bayesian inference,” presented at the 2019 IEEE Milan PowerTech, PowerTech, Italy, 2019. DOI: 10.1109/PTC.2019.8810750.
   Google Scholar
• A. G. Phadke and T. Bi, “Phasor measurement units, WAMS, and their applications in protection and control of power systems,” J. Mod. Power Syst. Clean Energy, vol. 6, no. 4, pp. 619–629, 2018. DOI: 10.1007/s40565-018-0423-3.
   Web of Science ®Google Scholar
• E. Farantatos, G. K. Stefopoulos, G. J. Cokkinides, and A. P. Meliopoulos, “PMU-based dynamic state estimation for electric power systems,” presented at the 2009 IEEE Power Energy Soc. Gen. Meet. PES, 2009. DOI: 10.1109/PES.2009.5275407.
   Google Scholar
• F. Aminifar, M. Shahidehpour, M. Fotuhi-Firuzabad, and S. Kamalinia, “Power system dynamic state estimation with synchronized phasor measurements,” IEEE Trans. Instrum. Meas., vol. 63, no. 2, pp. 352–363, 2014. DOI: 10.1109/TIM.2013.2278595.
   Web of Science ®Google Scholar
• J. Zhao et al., “Power system real-time monitoring by using PMU-based robust state estimation method,” IEEE Trans. Smart Grid, vol. 7, no. 1, pp. 300–309, 2016. DOI: 10.1109/TSG.2015.2431693.
   Web of Science ®Google Scholar
• N. M. Manousakis and G. N. Korres, “A hybrid power system state estimator using synchronized and unsynchronized sensors,” Int. Trans. Electr. Energ. Syst., vol. 28, no. 8, pp. e2580, 2018. DOI: 10.1002/etep.2580.
   Web of Science ®Google Scholar
• S. K. Kotha and B. Rajpathak, “Power System State Estimation using Non-Iterative reLeast Square method based on Wide Area Measurements with maximum redundancy,” Electr. Power Syst. Res., vol. 206, pp. 107794, 2022. DOI: 10.1016/j.epsr.2022.107794.
   Web of Science ®Google Scholar
• X. Li, A. Scaglione and T. H. Chang, “A framework for phasor measurement placement in hybrid state estimation via Gauss-Newton,” IEEE Trans. Power Syst., vol. 29, no. 2, pp. 824–832, 2014. DOI: 10.1109/TPWRS.2013.2283079.
   Web of Science ®Google Scholar
• D. Belega, D. Fontanelli, and D. Petri, “Dynamic phasor and frequency measurements by an improved taylor weighted least squares algorithm,” IEEE Trans. Instrum. Meas., vol. 64, no. 8, pp. 2165–2178, 2015. DOI: 10.1109/TIM.2014.2385171.
   Web of Science ®Google Scholar
• D. Macii, D. Fontanelli, and G. Barchi, “A distribution system state estimator based on an extended Kalman filter enhanced with a prior evaluation of power injections at unmonitored buses,” Energies, vol. 13, no. 22, pp. 6054, 2020. DOI: 10.3390/en13226054.
   Web of Science ®Google Scholar
• A. Louis, G. Ledwich, G. Walker, and Y. Mishra, “Measurement sensitivity and estimation error in distribution system state estimation using augmented complex Kalman filter,” J. Mod. Power Syst. Clean. Energy, vol. 8, no. 4, pp. 657–668, 2020. DOI: 10.35833/MPCE.2019.000160.
   Web of Science ®Google Scholar
• S. Afrasiabi, A. Saffarian, and E. Mashhour, “Dynamic state estimation of power systems using intelligent particle filtering based on ant colony optimisation for continuous domains,” IET Gener. Transm. Distrib., vol. 13, no. 13, pp. 2627–2636, 2019. DOI: 10.1049/iet-gtd.2018.7110.
   Web of Science ®Google Scholar
• P. Gopakumar, M. Jaya Bharata Reddy, and D. K. Mohanta, “A novel topological genetic algorithm-based phasor measurement unit placement and scheduling methodology for enhanced state estimation,” Electr. Power Compon. Syst., vol. 43, no. 16, pp. 1843–1858, 2015. DOI: 10.1080/15325008.2015.1057781.
   Web of Science ®Google Scholar
• Y. Peng, Z. Wu, W. Gu, S. Zhou, and P. Liu, “Optimal micro-PMU placement for improving state estimation accuracy via mixed-integer semidefinite programming,” J. Mod. Power Syst. Clean Energy, vol. 11, no. 2, pp. 468–478, 2023. DOI: 10.35833/MPCE.2021.000615.
   Google Scholar
• M. de Almeida and A. Garcia, “Regularized least squares power system state estimation,” IEEE Trans. Power Syst., vol. 27, pp. 290–297, 2011. DOI: 10.1109/pesgm.2012.6345057.
   Google Scholar
• S. Asefi, M. Mitrovic, D. Ćetenović, V. Levi, E. Gryazina and V. Terzija, “Power system anomaly detection and classification utilizing WLS-EKF state estimation and machine learning,” arXiv preprint arXiv: vol. 2209.12629, pp. 1–12, 2022. DOI: 10.2139/ssrn.4252481.
   Google Scholar
• R. d. Oliveira and M. H. J. Bollen, “Decision for power quality,” Electr. Power Syst. Res., vol. 214, pp. 108887, 2023. DOI: 10.1016/j.epsr.2022.108887.
   Web of Science ®Google Scholar
• D. Mukherjee, S. Chakraborty, and S. Ghosh, “Power system state forecasting using machine learning techniques,” Electr. Eng., vol. 104, no. 1, pp. 283–305, 2022. DOI: 10.1007/s00202-021-01328-z.
   Web of Science ®Google Scholar
• Tahir Cetin Akinci, “Applications of big data and AI in electric power systems engineering,” In AI and Big Data's Potential for Disruptive Innovation, IGI Global, pp. 240–260, 2020. DOI: 10.4018/978-1-5225-9687-5.ch009.
   Google Scholar
• M. K. Al-Sharman et al., “Deep-learning-based neural network training for state estimation enhancement: Application to attitude estimation,” IEEE Trans. Instrum. Meas., vol. 69, no. 1, pp. 24–34, 2020. DOI: 10.1109/TIM.2019.2895495.
   Web of Science ®Google Scholar
• H. S. Nogay, T. C. Akinci, M. I. Akbas, and A. Tokić, “Diagnosis of chaotic ferroresonance phenomena using deep learning,” IEEE Access, vol. 11, pp. 58937–58946, 2023. DOI: 10.1109/ACCESS.2023.3285816.
   Web of Science ®Google Scholar
• M. Khodayar, G. Liu, J. Wang, and M. E. Khodayar, “Deep learning in power systems research: A review,” CSEE J. Power Energy Syst., vol. 7, pp. 209–20, 2021. DOI: 10.17775/CSEEJPES.2020.02700.
   Web of Science ®Google Scholar
• Y. Gu, Z. Yu, R. Diao, and D. Shi, “Doubly-fed deep learning method for bad data identification in linear state estimation,” J. Mod. Power Syst. Clean Energy, vol. 8, no. 6, pp. 1140–1150, 2020. DOI: 10.35833/MPCE.2020.000533.
   Web of Science ®Google Scholar
• T. Ç. Akinci and A. A. Martinez-Morales, “Cognitive based electric power management system,” Balk J. Electr. Comput. Eng., vol. 10, no. 1, pp. 85–90, 2022. DOI: 10.17694/bajece.1060998.
   Google Scholar
• M. L. Di Silvestre et al., “Blockchain for power systems: Current trends and future applications,” Renew. Sustain. Energy Rev., vol. 119, pp. 109585, 2020. DOI: 10.1016/j.rser.2019.109585.
   Web of Science ®Google Scholar
• R. Jegatheesan, N. M. Nor, and M. F. Romlie, “Newton-Raphson power flow solution employing systematically constructed Jacobian matrix,” presented at the PECon 2008 - 2008 IEEE 2nd Int. Power Energy Conf., 2008, vol. 2, pp. 180–185. DOI: 10.1109/PECON.2008.4762458.
   Google Scholar
• B. N. Rao and R. Inguva, “Power system state estimation using weighted least squares (WLS) and regularized weighted least squares (RWLS) method,” Int. J. Eng. Res. Appl., vol. 6, pp. 1–6, 2016.
   Google Scholar
• K. Trzyniec and A. Kowalewski, “Use of an gatte to assess the degree of training of an operator of selected devices used in precision agriculture,” Energies, vol. 13, no. 23, pp. 6329, 2020. DOI: 10.3390/en13236329.
   Web of Science ®Google Scholar
• B. Charbuty and A. Abdulazeez, “Classification based on decision tree algorithm for machine learning,” JASTT, vol. 2, no. 01, pp. 20–28, 2021. DOI: 10.38094/jastt20165.
   Google Scholar
• R. A. Skoog et al., “Automatic identification of impairments using ensembe pattern classification on eye diagrams,” IEEE Photon. Technol. Lett., vol. 18, no. 22, pp. 2398–2400, 2006. DOI: 10.1109/LPT.2006.886146.
   Google Scholar
• A. S. Khwaja, A. Anpalagan, M. Naeem, and B. Venkatesh, “Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting,” Electr. Power Syst. Res., vol. 179, pp. 106080, 2020. DOI: 10.1016/j.epsr.2019.106080.
   Web of Science ®Google Scholar
• C. Xu, J. Shen, X. Du, and F. Zhang, “An intrusion detection system using a deep neural network with gated recurrent units,” IEEE Access, vol. 6, pp. 48697–48707, 2018. DOI: 10.1109/ACCESS.2018.2867564.
   Google Scholar
• J. Ma, Y. Ding, V. J. L. Gan, C. Lin, and Z. Wan, “Spatiotemporal prediction of PM2.5 concentrations at different time granularities using IDW-BLSTM,” IEEE Access, vol. 7, pp. 107897–107907, 2019. DOI: 10.1109/ACCESS.2019.2932445.
   Web of Science ®Google Scholar
• D. Wu and B. Wang, “Holomorphic embedding based continuation method for identifying multiple power flow solutions,” IEEE Access, vol. 7, pp. 86843–86853, 2019. DOI: 10.1109/ACCESS.2019.2925384.
   Web of Science ®Google Scholar