Plasma Confinement State Classification in Fusion Power Plants: Profile Reflectometer and Ensemble Diagnostics

TL;DR

This paper develops machine learning classifiers for plasma confinement states in fusion reactors using limited diagnostics, achieving high accuracy with a profile reflectometer and ensemble methods to aid control in fusion power plants.

Contribution

It introduces a novel ensemble diagnostic approach combining Profile Reflectometer and Electron Cyclotron Emission data for plasma state classification in fusion reactors.

Findings

Profile Reflectometer classifier achieves 97% accuracy.

Ensemble model combining diagnostics reaches 99% accuracy.

Supports development of limited-diagnostic tools for fusion power plant control.

Abstract

As Fusion Pilot Plants (FPPs) are increasingly viewed as within reach, many engineering challenges remain. Not many diagnostics are expected to be available in a reactor environment. Survivability, maintainability, and limited port space substantially restrict the number of FPP-relevant diagnostics. One remaining challenge is developing tools and devices to extract plasma state information necessary for controlling an FPP from a limited subset of diagnostics. This work is part of an overarching project to address this challenge. The specific diagnostic subset to be used in FPPs is still under debate. We take the approach of developing machine-learning-based tools for different significant plasma state parameters, using already known FPP-viable diagnostics. Previously we developed a plasma confinement mode classifier utilizing the Electron Cyclotron Emission (ECE) diagnostic. Here, we expand on this by developing a Profile Reflectometer (PR) based classifier with 97\% test accuracy, and an ensemble model that combines the ECE and PR models into a single model, achieving 99\% test accuracy.