Probabilistic Prognostics and Health Management of Power Transformers Using Dissolved Gas Analysis Sensor Data and Duval’s Polygons

TL;DR

A new probabilistic framework uses dissolved gas analysis data to improve fault detection and maintenance decisions for power transformers.

Contribution

A novel probabilistic PHM framework integrating self-adaptive ARIMA and Duval’s polygons for transformer fault diagnosis and prognosis.

Findings

The framework enables uncertainty-aware fault classification and failure risk estimation from DGA sensor data.

Case studies show improved diagnostic reliability and early fault detection in power transformers.

The method supports real-time fault tracking aligned with industry standards like IEC and IEEE.

Abstract

What are the main findings?
A probabilistic framework is proposed for transformer fault detection, diagnosis, and prognosis using Dissolved Gas Analysis (DGA) sensor data.The method integrates self-adaptive ARIMA forecasting with probabilistic extensions of Duval’s polygons, enabling uncertainty-aware fault classification and failure risk estimation.

A probabilistic framework is proposed for transformer fault detection, diagnosis, and prognosis using Dissolved Gas Analysis (DGA) sensor data.

The method integrates self-adaptive ARIMA forecasting with probabilistic extensions of Duval’s polygons, enabling uncertainty-aware fault classification and failure risk estimation.

What is the implication of the main findings?
The framework improves reliability of transformer condition monitoring by providing early warnings and robust fault evolution tracking.It supports risk-based maintenance decisions in smart grid environments, enhancing operational safety and asset lifetime.

The framework improves reliability of transformer condition monitoring by providing early warnings and robust fault evolution tracking.

It supports risk-based maintenance decisions in smart grid environments, enhancing operational safety and asset lifetime.

Power transformers are critical assets in modern power grids, where failures can lead to significant operational disruptions and financial losses. Dissolved Gas Analysis (DGA) is a key sensor-based technique widely used for condition monitoring, but traditional diagnostic approaches rely on deterministic thresholds that overlook uncertainty in degradation dynamics. This paper proposes a probabilistic framework for Prognostics and Health Management (PHM) of power transformers, integrating self-adaptive Auto Regressive Integrated Moving Average modeling with a probabilistic reformulation of Duval’s graphical methods. The framework enables automated estimation of fault types and failure likelihood directly from DGA sensor data, without requiring labeled datasets or expert-defined rules. Dissolved gas dynamics are forecasted using time-series models with residual-based uncertainty quantification, allowing probabilistic fault inference from predicted gas trends without assuming deterministic persistence of a specific fault type. A sequential pipeline is developed for real-time fault tracking and reliability assessment, aligned with IEC, IEEE, and CIGRE standards. Two case studies validate the method: one involving gas loss in an experimental setup and another examining thermal degradation in a 345 kV transformer. Results show that the framework improves diagnostic reliability, supports early fault detection, and enhances predictive maintenance strategies. By combining probabilistic modeling, time-series forecasting, and sensor-based diagnostic inference, this work contributes a practical and interpretable PHM solution for sensor-enabled monitoring environments in modern power grids.

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