Physics-Aware Neural Networks for Distribution System State Estimation

Abstract

The distribution system state estimation problem seeks to determine the network state from available measurements. Widely used Gauss-Newton approaches are very sensitive to the initialization and often not suitable for real-time estimation. Learning approaches are very promising for real-time estimation, as they shift the computational burden to an offline training stage. Prior machine learning approaches to power system state estimation have been electrical model-agnostic, in that they did not exploit the topology and physical laws governing the power grid to design the architecture of the learning model. In this paper, we propose a novel learning model that utilizes the structure of the power grid. The proposed neural network architecture reduces the number of coefficients needed to parameterize the mapping from the measurements to the network state by exploiting the separability of the estimation problem. This prevents overfitting and reduces the complexity of the training stage. We also propose a greedy algorithm for phasor measuring units placement that aims at minimizing the complexity of the neural network required for realizing the state estimation mapping. Simulation results show superior performance of the proposed method over the Gauss-Newton approach.

Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

“…As mentioned, our earlier papers [18,19] study the use of PMU measurements to design a physics-aware neural network model. The PMU is a three-phase synchronized measurement of the real-time measured value with very high accuracy.…”

“…Further, voltage regulators are excluded in this work. Generally, these modifications are common for this kind of study [16,18,24] without affecting the generality of the study. The DSSE algorithm is built in the MATLAB environment, and OpenDSS is used for the power flow calculation.…”

“…Overall, data-driven techniques present a very promising solution to improve grid distribution system monitoring and control. Motivated by these approaches, the authors of [18] proposed a physics-aware neural network model. The idea is to embed the physical connection of the distribution system into the neural network model; however, the method used fixed embedding of the network graph at all neural network layers, which adds significant unnecessary connections and weight to the neural network.…”

Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling

“…One of the most common ways to make machine learning models consistent with physical laws is by extending the loss function of the machine learning models to include physical constraints and other physical information [21]. Although the concept of integrating scientific knowledge and machine learning models has only become a popular topic of scientific research in the last few years, there is already extensive literature on this topic [22][23][24]. In the last decade, there has been an increase in utilizing operational flight data, namely Quick Access Recorder (QAR) or Flight Data Recorder (FDR) [25] for many applications such as performance monitoring, anomaly detection, or weather forecasting [26][27][28][29].…”

Voltage Estimation of Active Distribution Network Using PMU Technology

“…The authors of [11], proposed a methodology to estimate the target bus voltage with known bus information using a supervised learning algorithm. A neural network technique that offers robustness for any restructuring in the network topology discussed in [12]. A numerical method has been proposed in [13] to classify the distribution system's topology and estimate line parameters.…”