Effective Electrical Properties and Fault Diagnosis of Insulating Oil Using the 2D Cell Method and NSGA-II Genetic Algorithm

Monzón-Verona, José Miguel; González-Domínguez, Pablo; García-Alonso, Santiago

doi:10.3390/s23031685

Cited by 2 publications

(3 citation statements)

References 28 publications

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“…In this work, the distributed parameter model was followed [ 47 ], which is more accurate than the other two, because unlike them, it considers the 3D geometry of the measurement cell and the electrodes.…”

Section: Pd In Gaseous Bubbles Dissolved In Oilmentioning

confidence: 99%

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Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks

Monzón-Verona,

González-Domínguez,

García-Alonso

2024

Sensors

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In this work, an exhaustive analysis of the partial discharges that originate in the bubbles present in dielectric mineral oils is carried out. To achieve this, a low-cost, high-resolution CMOS image sensor is used. Partial discharge measurements using that image sensor are validated by a standard electrical detection system that uses a discharge capacitor. In order to accurately identify the images corresponding to partial discharges, a convolutional neural network is trained using a large set of images captured by the image sensor. An image classification model is also developed using deep learning with a convolutional network based on a TensorFlow and Keras model. The classification results of the experiments show that the accuracy achieved by our model is around 95% on the validation set and 82% on the test set. As a result of this work, a non-destructive diagnosis method has been developed that is based on the use of an image sensor and the design of a convolutional neural network. This approach allows us to obtain information about the state of mineral oils before breakdown occurs, providing a valuable tool for the evaluation and maintenance of these dielectric oils.

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Section: Pd In Gaseous Bubbles Dissolved In Oilmentioning

confidence: 99%

“…In [ 47 ], a distributed parameter model is developed, applying the finite formulation [ 48 ] and using effective property parameters. This is the model followed in this work.…”

Section: Pd In Gaseous Bubbles Dissolved In Oilmentioning

confidence: 99%

Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks

Monzón-Verona,

González-Domínguez,

García-Alonso

2024

Sensors

Self Cite

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“…The NSGA-II algorithm is a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm. The concept of a fast non-dominated sorting algorithm and crowding degree is introduced, and the improvement of left and right crowding degree calculation and dynamic crowding degree sorting strategy is proposed [31,32]. In order to ensure the diversity of the population, the Pareto frontier is obtained by sorting the optimized solutions.…”

Section: Model Solutionmentioning

confidence: 99%

Optimal Determination and Dynamic Control Analysis of the Graded and Staged Drought Limit Water Level of Typical Plateau Lakes

Wang

et al. 2023

Water

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The technical research on determining the drought limit water level can be used as an important basis for starting the emergency response of drought resistance in the basin and guiding the drought resistance scheduling of water conservancy projects. When the concept of drought limit water level was first proposed, the main research object was reservoirs, and the method for determining the lake drought limit water level was not established. Referring to the calculation method of reservoir drought limit water level, the drought limit water level is used as a single warning indicator throughout the year, which lacks graded and staged standards, and also lacks rationality and effectiveness in practical application. Therefore, this article has improved the concept of lake drought limit water level (flow). Under different degrees of drought and water use patterns during the drought period, combined with the characteristics of lake water inflow, considering the factors such as ecology, water supply, and demand, lake inflow, evapotranspiration loss, a graded and staged standard of lake drought limit water level has been developed. For different types of lakes, a general method for determining the lake’s graded and staged drought limit water level has been established. The SCSSA-Elman neural network is used to construct the medium and long-term water inflow prediction model for lakes, and the calculation results of this model are used for the warning and dynamic control analysis of the lake drought limit water level. The application of this method has the characteristics of strong applicability and high reliability. Finally, the determination method and dynamic control method of the lake’s graded and staged drought limit water level have been successfully applied at Dianchi Lake in Yunnan.

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Effective Electrical Properties and Fault Diagnosis of Insulating Oil Using the 2D Cell Method and NSGA-II Genetic Algorithm

Cited by 2 publications

References 28 publications

Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks

Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks

Optimal Determination and Dynamic Control Analysis of the Graded and Staged Drought Limit Water Level of Typical Plateau Lakes

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