Moisture Prediction of Transformer Oil-Immersed Polymer Insulation by Applying a Support Vector Machine Combined with a Genetic Algorithm

Zhang, Yiyi; Li, Jiaxi; Fan, Xianhao; Liu, Jiefeng; Zhang, Heng

doi:10.3390/polym12071579

Cited by 15 publications

(9 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Support vectors are the main points that are needed to set up an SVM model. Figure 4 depicts the margin condition that can be built by support vectors, where the large margin indicates the ability of the SVM model to provide good classification accuracy with new observations [29,30].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…A hyperplane is both a line that can separate two input features and a plane that can be used to separate among three features; when the input features increase to more than three features, the separation process becomes difficult Support vectors refer to the points close to the hyperplane and have a great effect on the position and orientation of the hyperplane; they can be used to maximize the margin of a classifier. Support vectors are the main points that are needed to set up an SVM model Figure 4 depicts the margin condition that can be built by support vectors, where the large margin indicates the ability of the SVM model to provide good classification accuracy with new observations [29,30].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

See 1 more Smart Citation

Determination of Transformers’ Insulating Paper State Based on Classification Techniques

Ghoneim

2021

Processes

View full text Add to dashboard Cite

The continuity of transformer operation is very necessary for utilities to maintain a continuity of power flow in networks and achieve a desired revenue. Most failures in a transformer are due to the degradation of the insulating system, which consists of insulating oil and paper. The degree of polymerization (DP) is a key detector of insulating paper state. Most research in the literature has computed the DP as a function of furan compounds, especially 2-furfuraldehyde (2-FAL). In this research, a prediction model was constructed based on some of most periodical tests that were conducted on transformer insulating oil, which were used as predictors of the insulating paper state. The tests evaluated carbon monoxide (CO), carbon dioxide (CO2), breakdown voltage (VBD), interfacial tension (IF), acidity (ACY), moisture (M), oil color (OC), and 2-furfuraldehyde (2-FAL). The DP, which was used as the key indicator for the paper state, was categorized into five classes labeled 1, 2, 3, 4, and 5 to express the insulating paper normal aging rate, accelerating aging rate, excessive aging danger zone, high risk of failure, and the end of expected life, respectively. The classification techniques were applied to the collected data samples to construct a prediction model for the insulating paper state, and the results revealed that the fine tree was the best classifier of the data samples, with a 96.2% prediction accuracy.

show abstract

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Determination of Transformers’ Insulating Paper State Based on Classification Techniques

Ghoneim

2021

Processes

View full text Add to dashboard Cite

show abstract

“…SVM algorithms are typically used for chemical concentration detection [17][18][19][20]. C. Robert [21] used both linear and non-linear SVM models to identify complete beef and lamb meats.…”

Section: Introductionmentioning

confidence: 99%

Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm

Lu,

Zou,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Spectral collinearity and limited spectral datasets are the problems influencing Chemical Oxygen Demand (COD) modeling. To address the first problem and obtain optimal modeling range, the spectra are preprocessed using six methods including Standard Normal Variate, Savitzky-Golay Smoothing Filtering (SG) etc. Subsequently, the 190–350 nm spectral range is divided into 10 subintervals, and Interval Partial Least Squares (IPLS) is used to perform PLS modeling on each interval. The results indicate that it is best modeled in the 7th range (238~253 nm). The values of Mean Square Error (MSE), Mean Absolute Error (MAE) and R2score of the model without pretreatment are 1.6489, 1.0661, and 0.9942. After pretreatment, the SG is better than others, with MSE and MAE decreasing to 1.4727, 1.0318 and R2score improving to 0.9944. Using the optimal model, the predicted COD for three samples are 10.87 mg/L, 14.88 mg/L, and 19.29 mg/L. To address the problem of the small dataset, using Generative Adversarial Networks for data augmentation, three datasets are obtained for Support Vector Machine (SVM) modeling. The results indicate that, compared to the original dataset, the SVM’s MSE and MAE have decreased, while its accuracy has improved by 2.88%, 11.53%, and 11.53%, and the R2score has improved by 18.07%, 17.40%, and 18.74%.

show abstract

“…Danial developed both ANN and GA-based ANN techniques for the prediction of AOP [ 28 ]. Zhang et al combined a support vector machine (SVM) with GA to predict the moisture in oil-immersed insulations and obtained highly accurate results [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

An HGA-LSTM-Based Intelligent Model for Ore Pulp Density in the Hydrometallurgical Process

Zou

Zhou

et al. 2022

Materials

View full text Add to dashboard Cite

This study focused on the intelligent model for ore pulp density in the hydrometallurgical process. However, owing to the limitations of existing instruments and devices, the feed ore pulp density of thickener, a key hydrometallurgical equipment, cannot be accurately measured online. Therefore, aiming at the problem of accurately measuring the feed ore pulp density, we proposed a new intelligent model based on the long short-term memory (LSTM) and hybrid genetic algorithm (HGA). Specifically, the HGA refers to a novel optimization search algorithm model that can optimize the hyperparameters and improve the modeling performance of the LSTM. Finally, the proposed intelligent model was successfully applied to an actual thickener case in China. The intelligent model prediction results demonstrated that the hybrid model outperformed other models and satisfied the measurement accuracy requirements in the factory well.

show abstract

Moisture Prediction of Transformer Oil-Immersed Polymer Insulation by Applying a Support Vector Machine Combined with a Genetic Algorithm

Cited by 15 publications

References 31 publications

Determination of Transformers’ Insulating Paper State Based on Classification Techniques

Determination of Transformers’ Insulating Paper State Based on Classification Techniques

Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm

An HGA-LSTM-Based Intelligent Model for Ore Pulp Density in the Hydrometallurgical Process

Contact Info

Product

Resources

About