arXiv subjects

Markos Asprou

Publications and source records attributed to Markos Asprou.

4 recordsLinked to original sources

arXiv2026

Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks

Power System State Estimation (PSSE) converts geographically distributed measurements into the voltage magnitudes and phase angles needed for grid monitoring and control. Learned estimators can perform this mapping rapidly, but model-aware False Data Injection Attacks (FDIAs) may corrupt their inputs while retaining AC plausibility and residual-based stealth. Physics-Informed Neural Networks (PINNs) limit candidate states through power-flow consistency; however, their robustness depends on balancing supervised and physics losses whose scales and gradient contributions evolve during training. This paper proposes a PINN that jointly learns homoscedastic uncertainty parameters and uses them to adapt the two objectives. The formulation assigns trainable log-uncertainties to the active-power, reactive-power, voltage, and angle losses while safeguarding against an underweighted physics objective. The estimator is trained only on clean steady-state data and is evaluated, without adversarial retraining, under baseline state-distortion and stricter residual-profile-matching regimes on the IEEE~118-bus system. Accuracy is measured against the uncompromised system state. Relative to a fixed-weight PINN, dynamic weighting reduces overall Mean Absolute Error (MAE) by $55\%$ while also improving voltage- and angle-estimation accuracy. The results show that learning the physics/data balance from clean data improves robustness to unseen FDIAs.

cs.LG↗

arXiv2025

Robust Power System State Estimation using Physics-Informed Neural Networks

Modern power systems face significant challenges in state estimation and real-time monitoring, particularly regarding response speed and accuracy under faulty conditions or cyber-attacks. This paper proposes a hybrid approach using physics-informed neural networks (PINNs) to enhance the accuracy and robustness, of power system state estimation. By embedding physical laws into the neural network architecture, PINNs improve estimation accuracy for transmission grid applications under both normal and faulty conditions, while also showing potential in addressing security concerns such as data manipulation attacks. Experimental results show that the proposed approach outperforms traditional machine learning models, achieving up to 83% higher accuracy on unseen subsets of the training dataset and 65% better performance on entirely new, unrelated datasets. Experiments also show that during a data manipulation attack against a critical bus in a system, the PINN can be up to 93% more accurate than an equivalent neural network.

cs.LG↗

arXiv2023

Physics-Informed Neural Networks for Accelerating Power System State Estimation

State estimation is the cornerstone of the power system control center since it provides the operating condition of the system in consecutive time intervals. This work investigates the application of physics-informed neural networks (PINNs) for accelerating power systems state estimation in monitoring the operation of power systems. Traditional state estimation techniques often rely on iterative algorithms that can be computationally intensive, particularly for large-scale power systems. In this paper, a novel approach that leverages the inherent physical knowledge of power systems through the integration of PINNs is proposed. By incorporating physical laws as prior knowledge, the proposed method significantly reduces the computational complexity associated with state estimation while maintaining high accuracy. The proposed method achieves up to 11% increase in accuracy, 75% reduction in standard deviation of results, and 30% faster convergence, as demonstrated by comprehensive experiments on the IEEE 14-bus system.

cs.LG↗

arXiv2018

Monitoring of Low Voltage Distribution Grid Considering the Neutral Conductor

The most widely used method for monitoring Low Voltage Distribution Grids (LVDGs) is the three phase Weighted Least Squares (WLS) State Estimation (SE), which was initially developed for Medium Voltage Distribution Grids (MVDGs). This methodology is implicitly applied in LVDGs with an assumption that the neutral conductor represents a zero potential across the whole system. However, this assumption is often not valid for LVDGs as the consumers loads are highly asymmetrical and the neutral conductor is usually grounded only at the MV-LV transformer substation. Therefore, if this method is applied for the monitoring of LVDGs it may deteriorate the performance of the WLS SE leading to inaccurate results. In this paper, an investigation is initially conducted in order to evaluate the performance of the conventional WLS SE methodology when applied for the monitoring of LVDGs. The results of this study indicate that the performance of this methodology is not consistent and is highly affected by the operating conditions of the LVDG. In view of these results, a new methodology is proposed and developed that takes into consideration the effect of the neutral conductor and as a result an outstanding performance is achieved compared to the conventional methodology.

eess.SP↗