Deep ensemble learning-based approach to real-time power system state estimation

• Published in: International journal of electrical power & energy systems 2021-07, Vol.129, p.106806, Article 106806
• Main Authors: Bhusal, Narayan, Shukla, Raj Mani, Gautam, Mukesh, Benidris, Mohammed, Sengupta, Shamik
• Format: Article
• Language: English
• Subjects: Deep ensemble learning; Multivariate linear regression; Power system state estimation; Residual neural net
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2021.106806

•Generate historical measurements and states using WLS and Gauss-Newton algorithm.•Investigate various machine learning models for the proposed ensemble learning based PSSE.•Develop dense residual neural network (ResNetD) suitable for ensemble learning setup for the PSSE.•Develop a meta-learner multivariate linear regression to architect the ensemble learning setup.•Develop state forecasting framework using MLR for real-time power system operation. Power system state estimation (PSSE) is commonly formulated as weighted least-square (WLS) algorithm and solved using iterative methods such as Gauss-Newton methods. However, iterative methods have become more sensitive to system operating conditions than ever before due to the deployment of intermittent renewable energy sources, zero emission technologies (e.g., electric vehicles), and demand response programs. Appropriate PSSE approaches are required to avoid pitfalls of the WLS-based PSSE computations for accurate prediction of operating conditions. This paper proposes a data-driven real-time PSSE using a deep ensemble learning algorithm. In the proposed approach, the ensemble learning setup is formulated with dense residual neural networks as base-learners and multivariate-linear regressor as meta-learner. Historical measurements and states are utilised to train and test the model. The trained model can be used in real-time to estimate power system states (voltage magnitudes and phase angles) using real-time measurements. Most of current data-driven PSSE methods assume the availability of a complete set of measurements, which may not be the case in real power system data-acquisition. This paper adopts multivariate linear regression to forecast system states for instants of missing measurements to assist the proposed PSSE technique. Case studies are performed on various IEEE standard benchmark systems to validate the proposed approach. The results show that the proposed approach outperforms existing data-driven PSSE techniques. The developed source code of the proposed solution is publicly available athttps://github.com/nbhusal/Power-System-State-Estimation.