Embedding Power Flow into Machine Learning for Parameter and State Estimation

by   Laurent Pagnier, et al.

Modern state and parameter estimations in power systems consist of two stages: the outer problem of minimizing the mismatch between network observation and prediction over the network parameters, and the inner problem of predicting the system state for given values of the parameters. The standard solution of the combined problem is iterative: (a) set the parameters, e.g. to priors on the power line characteristics, (b) map input observation to prediction of the output, (c) compute the mismatch between predicted and observed output, (d) make a gradient descent step in the space of parameters to minimize the mismatch, and loop back to (a). We show how modern Machine Learning (ML), and specifically training guided by automatic differentiation, allows to resolve the iterative loop more efficiently. Moreover, we extend the scheme to the case of incomplete observations, where Phasor Measurement Units (reporting real and reactive powers, voltage and phase) are available only at the generators (PV buses), while loads (PQ buses) report (via SCADA controls) only active and reactive powers. Considering it from the implementation perspective, our methodology of resolving the parameter and state estimation problem can be viewed as embedding of the Power Flow (PF) solver into the training loop of the Machine Learning framework (PyTorch, in this study). We argue that this embedding can help to resolve high-level optimization problems in power system operations and planning.


Initializing Successive Linear Programming Solver for ACOPF using Machine Learning

A Successive linear programming (SLP) approach is one of the favorable a...

Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities

Development of machine learning (ML) workflows is a tedious process of i...

Resonant Machine Learning Based on Complex Growth Transform Dynamical Systems

In this paper we propose an energy-efficient learning framework which ex...

Subsequent embedding in image steganalysis: Theoretical framework and practical applications

Steganalysis is a collection of techniques used to detect whether secret...

Inexact Methods for Sequential Fully Implicit (SFI) Reservoir Simulation

The sequential fully implicit (SFI) scheme was introduced (Jenny et al. ...

A Linear-Programming Approximation of AC Power Flows

Linear active-power-only DC power flow approximations are pervasive in t...

On the Performance of Machine Learning Methods for Breakthrough Curve Prediction

Reactive flows are important part of numerous technical and environmenta...

Please sign up or login with your details

Forgot password? Click here to reset