Thermal evaluation optimization analysis for non‐rated load oil‐natural air‐natural transformer with auxiliary cooling equipment

Zhang, Hao; Liu, Gang; Lin, Biren; Deng, Honglei; Li, Yingguang; Wang, Pengyu

doi:10.1049/gtd2.12502

Cited by 4 publications

(2 citation statements)

References 49 publications

(88 reference statements)

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“…A physical model for malfunction detection in cooling fans of ONAF power transformers was presented in [18] by monitoring top-oil temperature and using an oil exponent as the critical criterion. In [19] an improved online presented method was proposed for estimating the hot-spot temperature of ONAN transformers when auxiliary cooling fans are employed. A novel online algorithm for fan malfunction detection in ONAF transformers integrated with renewable energy resources was proposed in [20].…”

Section: ) Overviewmentioning

confidence: 99%

Acoustic-Based Online Monitoring of Cooling Fan Malfunction in Air-Forced Transformers Using Learning Techniques

Nematirad,

Behrang,

Pahwa

2024

IEEE Access

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Cooling fans are one of the critical components of air-forced dry-type transformers for regulating internal temperatures. Therefore, effective malfunction detection is crucial to maintain the transformer temperature within an acceptable range and prevent overheating. Current malfunction detection of cooling fans in certain types of transformers relies on complementary indicators, such as top-oil temperature, oil convection, dissolved gas, and oil quality. However, these conventional indicators are not directly applicable to air-forced transformers, which primarily use cooling fans as their cooling system. To overcome this challenge, this study utilizes cooling fan audio records as indicators. The audio signals are classified into normal and malfunctioning classes using advanced learning algorithms, including convolutional neural networks and random forests. Learning algorithms require transforming recorded audio data into proper formats. Accordingly, convolutional neural networks are trained based on spectrogram images derived from audio signals. For random forests, various time-frequency feature extraction methods are used to derive meaningful representations from audio signals. Besides, multiple data augmentation techniques are employed to enhance the dataset size and diversity. Algorithmic performance is optimized through hyperparameter tuning and classifier threshold adjustment. To further validate the model, a test is conducted on another dataset to evaluate the fitted learning model applicability in real-world applications. Simulations reveal that convolutional neural networks outperform random forests, whereas the latter provides superior interpretability of acoustic features compared to the former.

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Section: ) Overviewmentioning

confidence: 99%

Acoustic-Based Online Monitoring of Cooling Fan Malfunction in Air-Forced Transformers Using Learning Techniques

Nematirad,

Behrang,

Pahwa

2024

IEEE Access

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“…However, these approaches require considerable time for modeling and mesh division, heavily depend on hardware capabilities, take long to solve, and consume substantial operational memory. To address this issue, some researchers have adopted combined 1D-3D modeling [20] techniques or have simplified the modeling analysis by focusing only on certain structures [21]. Additionally, neural networks [22], IoT sensor data [23], and thermal lattice network modeling [24] are alternatives for analyzing hotspot temperatures.…”

Section: Introductionmentioning

confidence: 99%

Full-Scale Modeling and FBGs Experimental Measurements for Thermal Analysis of Converter Transformer

Yang,

Gao,

Wang

et al. 2024

Sensors

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As the imbalance between power demand and load capacity in electrical systems becomes increasingly severe, investigating the temperature variations in transformers under different load stresses is crucial for ensuring their safe operation. The thermal analysis of converter transformers poses challenges due to the complexity of model construction. This paper develops a full-scale model of a converter transformer using a multi-core high-performance computer and explores its thermal state at 80%, 100%, and 120% loading ratios using the COUPLED iteration method. Additionally, to validate the simulation model, 24 FBGs are installed in the experimental transformer to record the temperature data. The results indicate a general upward trend in winding the temperature from bottom to top. However, an internal temperature rise followed by a decrease is observed within certain sections. Moreover, as the loading ratio increases, both the peak temperature and temperature differential of the transformer windings rise, reaching a peak temperature of 107.9 °C at a 120% loading ratio. The maximum discrepancy between the simulation and experimental results does not exceed 3.5%, providing effective guidance for the transformer design and operational maintenance.

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Fast prediction of temperature distributions in oil natural air natural transformers using proper orthogonal decomposition reduced‐order data‐driven modelling

Lan,

Tang,

Gong

et al. 2024

High Voltage

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In response to the time‐consuming computational fluid dynamics simulations faced in naturally convective oil‐immersed transformers, which result from complex models and a high degree of freedom, an innovative reduced‐order digital twin prediction model for transformer temperature fields is proposed. This model facilitates fast predictions of transient temperature distributions. Initially, a comprehensive full‐order finite element model of transformer temperature distributions is established. Subsequently, a hybrid approach, combining proper orthogonal decomposition (POD)‐Galerkin and data‐driven techniques, is proposed to create a reduced‐order model (ROM). In this model, a Fourier number is utilised as a criterion to select POD training snapshot sets. Subsequently, the dynamic predictive capability of the proposed model under changing operational conditions is validated. Finally, the ROM is employed for fast predictions of temperature field, and its computational errors and time efficiency are compared across diverse operating conditions with full‐order models. The research findings confirm the precision, timeliness, and dynamic nature of the reduced‐order prediction model, offering a substantial improvement in prediction efficiency and capabilities, all while preserving the accuracy of the digital twin model.

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Thermal evaluation optimization analysis for non‐rated load oil‐natural air‐natural transformer with auxiliary cooling equipment

Cited by 4 publications

References 49 publications

Acoustic-Based Online Monitoring of Cooling Fan Malfunction in Air-Forced Transformers Using Learning Techniques

Acoustic-Based Online Monitoring of Cooling Fan Malfunction in Air-Forced Transformers Using Learning Techniques

Full-Scale Modeling and FBGs Experimental Measurements for Thermal Analysis of Converter Transformer

Fast prediction of temperature distributions in oil natural air natural transformers using proper orthogonal decomposition reduced‐order data‐driven modelling

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