Second-order Synchrosqueezing Modified S Transform for wind turbine fault diagnosis

Power equipment fault diagnostics is a critical aspect of ensuring the stability of the power grid system. However, it presents substantial challenges in obtaining labeled data that is spatial-temporal, multi-scale, and multi-domain, low noise for effective fault analysis and diagnosis. To address this issue, we propose a novel approach called hierarchical dynamic aggregation graph (HDAG) modeling for self-supervised fault diagnosis of power transformers using vibration data. Firstly, HDAG focuses on modeling the spatial and temporal correlations within the fault vectors, before converting them into time–frequency images for visualization. Secondly, our proposed fault diagnosis approach, (comprising the ST-sparse swin-transformer and multi-domain transformer fusion module), is integrated into the methodology. The ST-sparse swin transformer incorporates soft threshold modules, enabling the retention of relevant information while discarding irrelevant information. The multi-domain transformer fusion is designed to utilize the intra-domain and inter-domain signal characteristics to achieve a comprehensive feature representation. Finally, we present case studies based on experimental data that demonstrate the feasibility and effectiveness of our approach. Comparative evaluations against eight state-of-the-art techniques validate the improved information representation and diagnostic capabilities of our recommended strategy.

Section: Ablation Studymentioning

confidence: 99%

“…Various diagnostic criteria, such as frequency sweep response [4,5], short-circuit impedance, sound [6], and vibration [7], play a significant role in identifying power transformer faults. Notably, vibration-based techniques for diagnosis are gaining popularity [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical dynamic aggregation graph modeling of vibration for semi-supervised fault diagnosis of power transformer

Liu,

2023

“…The objectives in power transformer fault diagnosis typically encompass short-circuit impedance, frequency sweep response [4,5], sound [6], vibration [7], and so on. Therefore, the vibration-based diagnostic approaches are gaining popularity [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Power transformer fault diagnosis using dynamic multiscale graph modeling and M2SGCN network based on statistical fusion

Liu,

2024

Abstract—Power equipment fault diagnostics hold significant importance for the stability of power grid systems. In pursuit of this objective, this paper proposes a fault diagnosis method that utilizes dynamic multiscale graph modeling and the M2SGCN network, incorporating statistical fusion. Specifically, we propose a novel dynamic multiscale graph (DMG) modeling method to derive visibility graph (VG) data and horizontal visibility graph (HVG) data from vibration signals across multiple scales. Next, we establish M2SGCN, a comprehensive neural network architecture that learns global and local features simultaneously, providing a more precise representation. Finally, we utilize a Dempster Shafer evidence theory statistical fusion technique combined with an adaptive threshold model (DSTFusion) to integrate primary decision results for enhanced fault diagnosis accuracy. Furthermore, we analyze two datasets obtained from single-phase and three-phase power transformers to demonstrate the evolution process. When compared to state-of-the-art indicators such as accuracy, precision, recall, and F1-scores, the proposed method excels in multiple aspects, successfully detecting fault states prior to their occurrence and achieving outstanding performance.

“…Power transformer fault diagnosis methods usually include short-circuit impedance, frequency sweep response [4], frequency response analysis [5], sound [6] and vibration methods [7]. Therefore, vibration-based online methods are a relatively promising field [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A multi-stream multi-scale lightweight SwinMLP network with an adaptive channel-spatial soft threshold for online fault diagnosis of power transformers

Liu

2023

The fault diagnosis of power equipment is extremely crucial to the stability of the power grid system. However, complex operating environments, high costs and limitations of single-modal signals are the biggest bottlenecks. To this end, a Multi-Stream Multi-Scale Lightweight SwinMLP Network (MLSNet) with adaptive channel-spatial soft threshold is proposed in this paper. First, a Res2net-based feature-enhanced method is used to learn the correlated features of vibration and voltage multi-modal signals. Second, a novel MLSNet isdesigned to combine the benefits of SwinMLP with lightweight CNN, and employs a staged model to extractvarious scale features. Third,module is intended an adaptive deep fusion approachto integrate employing a channel-spatial soft thresholdand recalibrate staged information at different scales. The overall accuracy ofthe proposed method can reach 99.20% by various experiments, aiming to make it an effective method for online fault diagnosis of power transformers.