• Energy Research

AbstractThe adoption of electric vehicles (EVs) on a large scale is crucial for meeting the desired climate commitments, where affordability plays a vital role. However, the expected surge in prices of lithium, cobalt, nickel, and manganese, four critical materials in EV batteries, could hinder EV uptake. To explore these impacts in the context of China, the world’s largest EV market, we expand and enrich an integrated assessment model. We find that under a high material cost surge scenario, EVs would account for 35% (2030) and 51% (2060) of the total number of vehicles in China, significantly lower than 49% (2030) and 67% (2060) share in the base-line, leading to a 28% increase in cumulative carbon emissions (2020-2060) from road transportation. While material recycling and technical battery innovation are effective long-term countermeasures, securing the supply chains of critical materials through international cooperation is highly recommended, given geopolitical and environmental fragilities.

AbstractThe adoption of electric vehicles (EVs) on a large scale is crucial for meeting the desired climate commitments, where affordability plays a vital role. However, the expected surge in prices of lithium, cobalt, nickel, and manganese, four critical materials in EV batteries, could hinder EV uptake. To explore these impacts in the context of China, the world’s largest EV market, we expand and enrich an integrated assessment model. We find that under a high material cost surge scenario, EVs would account for 35% (2030) and 51% (2060) of the total number of vehicles in China, significantly lower than 49% (2030) and 67% (2060) share in the base-line, leading to a 28% increase in cumulative carbon emissions (2020-2060) from road transportation. While material recycling and technical battery innovation are effective long-term countermeasures, securing the supply chains of critical materials through international cooperation is highly recommended, given geopolitical and environmental fragilities.

Support vector machine (SVM), which serves as one kind of artificial intelligence technique, has been widely employed in transformer fault diagnosis when involving dissolved gas analysis (DGA). However, when using SVM, it is easy to misclassify samples which are located near the decision boundary, resulting in a decrease in the accuracy of fault diagnosis. Given this issue, this paper proposed a genetic algorithm (GA) optimized probabilistic SVM (GAPSVM) integrated with the fuzzy three-ratio (FTR) method, in which the GAPSVM can judge whether a sample is near the decision boundary according to its output probabilities and diagnose the samples which are not near the decision boundary. Then, FTR is used to diagnose the samples which are near the decision boundary. Combining GAPSVM and FTR, the integrated model can accurately diagnose samples near the decision boundary of SVM. In addition, to avoid redundant and erroneous features, this paper also used GA to select the optimal DGA features. The diagnostic accuracy of the proposed GAPSVM integrated with the FTR fault diagnosis method reached 86.80% after 10 repeated calculations using 118 groups of IEC technical committee (TC) 10 samples. Moreover, the robustness is also proven through 30 groups of DGA samples from the State Grid Co. of China and 15 practical cases with missing values.

Support vector machine (SVM), which serves as one kind of artificial intelligence technique, has been widely employed in transformer fault diagnosis when involving dissolved gas analysis (DGA). However, when using SVM, it is easy to misclassify samples which are located near the decision boundary, resulting in a decrease in the accuracy of fault diagnosis. Given this issue, this paper proposed a genetic algorithm (GA) optimized probabilistic SVM (GAPSVM) integrated with the fuzzy three-ratio (FTR) method, in which the GAPSVM can judge whether a sample is near the decision boundary according to its output probabilities and diagnose the samples which are not near the decision boundary. Then, FTR is used to diagnose the samples which are near the decision boundary. Combining GAPSVM and FTR, the integrated model can accurately diagnose samples near the decision boundary of SVM. In addition, to avoid redundant and erroneous features, this paper also used GA to select the optimal DGA features. The diagnostic accuracy of the proposed GAPSVM integrated with the FTR fault diagnosis method reached 86.80% after 10 repeated calculations using 118 groups of IEC technical committee (TC) 10 samples. Moreover, the robustness is also proven through 30 groups of DGA samples from the State Grid Co. of China and 15 practical cases with missing values.

AbstractEngineering experience shows that for transformers with long service life and serious moisture exposure, the inter‐turn paper insulation inside the winding will precipitate bubbles under the condition of electric field and conductor heating, which will endanger the equipment insulation. In order to explore the influence factors of bubbles formation in the moist insulating paper of transformers, based on the existing research on the influence of moisture content and gas dissolution in transformer oil, this study further considers the influence of air pressure (AP) and electric field and establishes an experimental platform to change the air pressure and electric field, thereby studying the formation characteristics of bubbles in oil‐paper insulation. The results show that air pressure and electric field can affect the initial temperature, volume, and shape of bubble formation. From 0 to −0.05 MPa for air pressure, the initial temperature of bubble generation decreased by 41°C, its maximum two‐dimensional projected area increased by 418.4% with more severe distortion, and the breakdown voltage decreased. When the AC voltage applied on the needle plate electrode increased from 0 to 10 kV, the initial temperature of bubble formation increased by 29°C, and the maximum two‐dimensional projection area increased by 336.6%.

AbstractEngineering experience shows that for transformers with long service life and serious moisture exposure, the inter‐turn paper insulation inside the winding will precipitate bubbles under the condition of electric field and conductor heating, which will endanger the equipment insulation. In order to explore the influence factors of bubbles formation in the moist insulating paper of transformers, based on the existing research on the influence of moisture content and gas dissolution in transformer oil, this study further considers the influence of air pressure (AP) and electric field and establishes an experimental platform to change the air pressure and electric field, thereby studying the formation characteristics of bubbles in oil‐paper insulation. The results show that air pressure and electric field can affect the initial temperature, volume, and shape of bubble formation. From 0 to −0.05 MPa for air pressure, the initial temperature of bubble generation decreased by 41°C, its maximum two‐dimensional projected area increased by 418.4% with more severe distortion, and the breakdown voltage decreased. When the AC voltage applied on the needle plate electrode increased from 0 to 10 kV, the initial temperature of bubble formation increased by 29°C, and the maximum two‐dimensional projection area increased by 336.6%.