# The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature

arXiv: https://arxiv.org/abs/2304.11880 · HTML: https://mlsanity.com/paper/2304.11880/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 108 references that are in the map.
- First posted on arXiv 24 Apr 2023.

## Builds on (most cited first, 28 of 28)

- [Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach](https://mlsanity.com/paper/2209.00778/) (2022)
- [Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach](https://mlsanity.com/paper/2107.07738/) (2021)
- [Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting](https://mlsanity.com/paper/2108.06764/) (2021)
- [Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2211.02674/) (2022)
- [Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-level Approach](https://mlsanity.com/paper/2107.07772/) (2021)
- [A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems](https://mlsanity.com/paper/2112.03265/) (2021)
- [Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management](https://mlsanity.com/paper/2301.00641/) (2022)
- [Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties](https://mlsanity.com/paper/1808.05691/) (2018)
- [A Two-Stage Approach for Combined Heat and Power Economic Emission Dispatch: Combining Multi-Objective Optimization with Integrated Decision Making](https://mlsanity.com/paper/1808.05704/) (2018)
- [Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments](https://mlsanity.com/paper/2108.08055/) (2021)
- [Data-Driven Distributionally Robust Scheduling of Community Integrated Energy Systems with Uncertain Renewable Generations Considering Integrated Demand Response](https://mlsanity.com/paper/2301.08861/) (2023)
- [Application of EOS-ELM with binary Jaya-based feature selection to real-time transient stability assessment using PMU data](https://mlsanity.com/paper/1809.02859/) (2018)
- [Non-intrusive load decomposition based on CNN-LSTM hybrid deep learning model](https://mlsanity.com/paper/2109.01236/) (2021)
- [Joint Planning of Distributed Generations and Energy Storage in Active Distribution Networks: A Bi-Level Programming Approach](https://mlsanity.com/paper/2201.05932/) (2022)
- [Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition](https://mlsanity.com/paper/2005.00777/) (2020)
- [Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2109.14789/) (2021)
- [Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach](https://mlsanity.com/paper/2111.12322/) (2021)
- [Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach](https://mlsanity.com/paper/2204.07905/) (2022)
- [Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach](https://mlsanity.com/paper/2212.13472/) (2022)
- [Method for Extracting Patterns of Coordinated Network Attacks on Electric Power CPS based on Temporal-Topological Correlation](https://mlsanity.com/paper/2003.10440/) (2020)
- [Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process](https://mlsanity.com/paper/1808.05708/) (2018)
- [Feature selection for transient stability assessment based on kernelized fuzzy rough sets and memetic algorithm](https://mlsanity.com/paper/1808.08790/) (2018)
- [Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors](https://mlsanity.com/paper/1810.08652/) (2018)
- [Dynamic State Estimation of Generators Under Cyber Attacks](https://mlsanity.com/paper/1909.01153/) (2019)
- [A multifeature fusion approach for power system transient stability assessment using PMU data](https://mlsanity.com/paper/1809.03875/) (2018)
- [Rule extraction based on extreme learning machine and an improved ant-miner algorithm for transient stability assessment](https://mlsanity.com/paper/1810.00658/) (2018)
- [Robust Kalman filter-based dynamic state estimation of natural gas pipeline networks](https://mlsanity.com/paper/2102.13251/) (2021)
- [Resilience assessment and improvement for electric power transmission systems against typhoon disasters: A data-model hybrid driven approach](https://mlsanity.com/paper/2208.09115/) (2022)

## Abstract (arXiv)

Transformer fault diagnosis (TFD) is a critical aspect of power system maintenance and management. This review paper provides a comprehensive overview of the current state of the art in TFD using artificial intelligence (AI) and dissolved gas analysis (DGA). The paper presents an analysis of recent advancements in this field, including the use of deep learning algorithms and advanced data analytics techniques, and their potential impact on TFD and the power industry as a whole. The review also highlights the benefits and limitations of different approaches to transformer fault diagnosis, including rule-based systems, expert systems, neural networks, and machine learning algorithms. Overall, this review aims to provide valuable insights into the importance of TFD and the role of AI in ensuring the reliable operation of power systems.