Fault diagnosis method for oil-immersed transformers integrated digital twin model

www.nature.com/scientificreports OPEN Fault diagnosis method for oil‑immersed transformers integrated digital twin model Haiyan Yao 1, Xin Zhang 2, Qiang Guo 1, Yufeng Miao 1 & Shan Guan 3* To address the problems of low accuracy in fault diagnosis of oil-immersed transformers, poor state perception ability and real-time collaboration during diagnosis feedback, a fault diagnosis method for transformers based on the integration of digital twins is proposed. Firstly, fault sample balance is achieved through Iterative Nearest Neighbor Oversampling (INNOS), Secondly, nine-dimensional ratio features are extracted, and the correlation between dissolved gases in oil and fault types is established. Then, sparse principal component analysis (SPCA) is used for feature fusion and dimensionality reduction. Finally, the Aquila Optimizer (AO) is introduced to optimize the parameters of the Kernel Extreme Learning Machine (KELM), establishing the optimal AO-KELM diagnosis model. The final fault diagnosis accuracy reaches 98.1013%. Combining transformer digital twin models, real-time interaction mapping between physical entities and virtual space is achieved, enabling online diagnosis of transformer faults. Experimental results show that the method proposed in this paper has high diagnostic accuracy and strong stability, providing reference for the intelligent operation and maintenance of transformers. Keywords Transformer fault diagnosis, Digital twin, Imbalanced small sample, KELM, SPCA The transformer, as the hub of power systems, its health status directly impacts the stability and reliability of the electrical system’s operation. Therefore, the precise management of a transformer’s health status is paramount to ensuring the steadfast and secure operation of the power g rid1. Presently, the technology of Dissolved Gas Analysis (DGA) is extensively employed in the monitoring and identification of faults within oil-insulated transformers2,3, primarily encompassing: the IEC triad ratio method4, the Rogers quadruple ratio m ethod5, and the DUVAL triangle t echnique6. Despite their simplicity of operation, these approaches lack the depth of representation for fault characteristics and are limited by their capabilities, resulting in a blurred and indistinct encoding boundary, thereby leading to a low accuracy rate in fault recognition7. With the rapid advancement of artificial intelligence, eminent scholars have integrated machine learning with DGA technology, achieving notable results in the field of transformer fault detection. The literature8 optimizes the support vector machine parameters through the refinement of the scalar search algorithm, thereby augmenting both the convergence velocity and the diagnostic precision of the methodology. The l iterature9 proffers an SE-ELM diagnostic method, whose efficacy was validated through the verification across various datasets. The literature10 enhances the particle swarm optimization algorithm through the dynamic adjustment of inertial weights and acceleration factors, iteratively optimizing the parameters of XGBoost, thereby augmenting the model’s classification acumen. Additionally, methods such as Convolutional Neural N etworks11,12, Long ShortTerm Memory Networks13–15, LightGBM16, and the Capsule Network17 are extensively employed. With the advancement of big data and the Internet of Things (IoT) technologies, the Digital Twin (DT)18 technology has paved a new path for enhancing the efficiency of equipment health management. The core concept is to construct a holographic virtual twin model in the digital realm, utilizing advanced technologies such as intelligent sensing and data transmission, which accurately, comprehensively, and in real-time reflect the evolution of physical devices, achieving intelligent control over entities19–21. This technology has been extensively utilized in various sectors including aerospace, manufacturing, and healthcare. In the field of transformer fault diagnosis, scholars both domestically and internationally have carried out extensive research. R eferencing22, the study proposed a method for constructing a dual-driving twin model 1 Hangzhou Electric Power Equipment Manufacturing Co. Ltd Yuhang Qunli Complete Sets Electricity Manufacturing Branch Electric, Hangzhou 311000, China. 2Hangzhou Electric Power Equipment Manufacturing Co. Ltd., Hangzhou 311000, China. 3Northeast Electric Power University School of Mechanic Engineering, Jilin 132012, China. *email: Scientific Reports | (2024) 14:20355 | https://doi.org/10.1038/s41598-024-71107-w 1 Vol.:(0123456789) www.nature.com/scientificreports/ integrating data and models, focusing on 10 kv oil-immersed transformers. This approach enables the synchronization between the actual operating conditions of the transformer and the digital twin center. Referencing23, a digital twin fault diagnosis model was constructed based on the mechanism model and data model of transformers. Five characteristic gases extracted from DGA data were selected as input feature vectors for a CNN. Experimental results showed that the 1D-CNN model established in this study responded rapidly, had a short training time, and achieved high accuracy, thus validating the effectiveness of the model. R eferencing24, a fault diagnosis model based on digital twin was constructed for transformers, taking into account their structural characteristics and operational traits. By optimizing the smoothing factor δ in a probabilistic neural network through differential evolution algorithm, the diagnostic accuracy reached an impressive 96.7%, enabling precise monitoring of the transformer’s actual operating state. R eference25 conducts a statistical analysis of the operating data and state information quantity of power transformers, proposes a framework for a state evaluation system and fault detection system based on GCA-CNN, and verifies with 2000 real data cases that the model has higher accuracy and evaluation and detection effects. The literature26 establishes a high-fidelity simulation model of transformers to accurately simulate winding currents and the temperatures of different components, which can be used for the identification of early faults. However, the aforementioned research is only focused on a single dissolved gas in oil or vibration signal as the basis for fault diagnosis, but there are many factors affecting transformer faults. In the future, it may be possible to combine multi-source data for comprehensive judgment. In light of the above context, this paper proposes a fault diagnosis method for oil-immersed transformers that integrates a digital twin model. The main contributions of the paper are divided into several parts. Part 1 mainly elaborates on the research background of the paper and the future research direction. Part 2 establishes a transformer digital twin framework, based on geometric, physical, behavioral, and rule models, to achieve interaction mapping b (...truncated)