Optimization of transformer oil blended with natural ester oils using Taguchi-based grey relational analysis

Fang

IET Generation Trans & Dist

et al. 2021

The information of dissolved gas in transformer oil can reflect the potential fault in oil immersed power transformer. In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis model based on IFA-LPboost-CART is proposed here. First, a LPboost-CART model is established. The classification and regression tree (CART) are used as the weak classifiers, and the linear programming boosting (LPboost) ensemble learning method is used to adjust the weight of each weak classifier to construct a strong classifier. Then the improved firefly algorithm (IFA) is adopted to optimize the number of CART and the maximum number of splits of CART in LPboost-CART to obtain the IFA-LPboost-CART model. The experimental results show that, compared with the existing methods, such as CART and support vector machine (SVM), the proposed IFA-LPboost-CART model has higher fault diagnosis accuracy, which can provide technical support for transformer fault diagnosis.

“…The process is shown in Figure 1. The number of CART N [1,200] the maximum number of splits of CART D [2,20] The specific process is as follows:…”

Section: Linear Programming Boosting (Lpboost)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on transformer fault diagnosis: Based on improved firefly algorithm optimized LPboost–classification and regression tree

Fang

IET Generation Trans & Dist

et al. 2021

International Journal of Photoenergy

“…Satellite technology allows us to fly around the world. In addition to being easily installed on the roof of the building, PV modules will act as stand-alone solar power generators [5][6][7]. The installation of photovoltaic panels has increased every year in recent years.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms

Kabilan¹,

Venkatesan

Yogapriya³

et al. 2021

Self Cite

One of the biggest challenges is towards ensuring large-scale integration of photovoltaic systems into buildings. This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. The proposed prediction methodology comprises a data quality stage, machine learning algorithm, weather clustering assessment, and an accuracy assessment. The results showed that the application of linear regression coefficients to the forecast outputs of the developed photovoltaic power generation neural network improved the PV power generation’s forecast output. The final model resulted from accurate forecasts, exhibiting a root mean square error of 4.42% in NN, 16.86% in QSVM, and 8.76% in TREE. The results are presented with the building facade and roof application such as flat roof, south façade, east façade, and west façade.

Advances in Polymer Technology

“…To overcome these effects, halogen-free elements such as silicon and boron and some nanocarbon materials, hydroxides, and clays are used. In some research, hybrid fillers with different sizes and shapes are used to enhance the thermal properties of epoxy-based polymers by simplifying the development of compacted and effective heat transfer network [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Application of r-GO-MMT Hybrid Nanofillers for Improving Strength and Flame Retardancy of Epoxy/Glass Fibre Composites

Kavimani

Stalin²,

Gopal

et al. 2021

Self Cite

The application of nanomaterials as a strengthening agent in the fabrication of polymer nanocomposites has gained significant attention due to distinctive properties which can be utilised in structural applications. In this study, reduced graphene oxide (r-GO) and montmorillonite (MMT) nanoclay were used as filler materials to fabricate hybrid epoxy-based nanocomposites. The synergistic effect of nanomaterials on flammability and mechanical behaviour of nanocomposites were studied. Results revealed that the addition of nanofiller showcases 97% and 44.5% improvement in tensile and flexural strength. However, an increment in the percentage of filler material over 0.3% exhibits a decremental mechanical property trend. Likewise, the addition of nanofiller increases the nonignition timing of the glass-fibre-reinforced epoxy composites. Fracture surface morphology displays the occurrence of the ductile fracture mechanism owing to the presence of hybrid fillers.