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The pantograph–catenary system is crucial to the transmission of electrical power from catenary lines to electrified trains. The occurrence of arcs could damage or interrupt railway operations. We propose a CNN-based model to detect arcs and recognize their magnitudes. First, we decompose the pantograph videos recorded by a camera fixed on China High-Speed train into continuous frames, and grayscale-process and segment those images to obtain an arc image set. Then, we divide the image set into training samples and test samples. And the training samples are further divided into three classifications labeled as 0, 1, 2, which are used to train CNN model. The accuracy of CNN trained result reaches 0.95, and the loss function converges to 0.083. Second, we use the trained network to detect arcs in the images of the test samples, and convert arc detection results to a time series of arc scores. Thus the occurrence of arcs and their magnitudes can be determined. Finally, we conduct experiments to compare our approach with other models. The results demonstrate the approach's high accuracy, robustness, and high speed, when dealing with images taken from unstable surroundings. It could be applied to other EMU models or environments with adjusted parameters.

Neural network is one of the key technologies for deep learning. Experiments on some standard test datasets show that their recognition ability has reached the level of human beings. However, they are extremely vulnerable to adversarial examples, that is, adding some subtle perturbations to the input example can cause the model to give a wrong output with high confidence. In this paper, we propose a non-contact approach based on neural network and adversarial training to recognize the high-speed rail operating environment. We first built the environment dataset and trained neural network models to do the recognition. We found that our model had high prediction accuracy, but with poor security since it was easy to attack our model using Basic Iterative Methods (BIM). To improve its security, we performed adversarial training based on the adversarial training dataset we built. The evaluation experiments indicated that this approach could improve the security of our model at the same time ensuring the prediction accuracy on the original test dataset.

Electricity is the most commonly used form of energy for artificial lighting in modern society. Despite a rapid growth in the rate of electrification, 9% of electrified Indian households in our six sampled states continued to use kerosene as their primary lighting fuel in 2018. This appears as a puzzle considering the benefits of electric lights. Using a panel survey of rural households in six states in India, we examine why some grid-connected households primarily used kerosene lamps for illumination. We use a logistic regression model to test our hypothesis regarding the relationship between primary lighting choices and electricity quality. The results show that household primary lighting choices are correlated with nighttime duration of electricity service, daytime duration of electricity service, and the number of days without any electricity connection, at the 99% confidence level. Among these three factors, nighttime duration of electricity service has the greatest impact. To further promote the use of electric lights, intensive schemes to improve electricity quality are needed.

Solid fuel combustion is a major cause of household air pollution, a leading environmental health risk factor globally. In India, over 750 million people continue to rely on firewood and other solid fuels for daily cooking. We explore the drivers of adoption and use of liquefied petroleum gas (LPG), India’s dominant clean cooking fuel. We document strides in LPG ownership using a panel dataset of over 8,500 rural households from six Indian states surveyed in 2015 and 2018 (ACCESS), partially due to India’s flagship clean cooking policy Pradhan Mantri Ujjwala Yojana (PMUY). We further demonstrate that the drivers of initial LPG adoption also apply to use. While fuel stacking—using solid fuels and LPG jointly—is pervasive, improved rural incomes and education result in the increased use of clean cooking fuels. After adoption, general LPG customers are predicted to consume on average 93 kilograms of LPG yearly (95% confidence interval (CI): 91–95 kg/year). However, PMUY beneficiaries are predicted to consume 27 kilograms of LPG (95% CI: 24–30 kg/year) less on average than general customers each year, even after controlling for socio-economic differences and years of using LPG. Our findings suggest that additional strategies to accelerate the transition to exclusive LPG use among the 80 million households acquiring LPG through PMUY should aim to improve affordability and increase awareness to realize the full benefits of the Government of India’s investments in cleaner cooking.

Air emissions from power plants can have a significant effect on air quality. However, models used to analyze and project trends in the energy system are not easily coupled with air quality simulation models. Most downscaling approaches use simplistic averaging and “growth in place” methods that do not account for future changes in the pattern of generation due to renewable penetration, nor for important variations in time and space in emissions that have important implications for air quality. For instance, emissions of NOx are strongly correlated with temperature, which is also correlated with conditions favorable for ozone (smog) episodes. This paper presents a new methodology for downscaling electric sector results from the USDOE National Energy Modeling System (NEMS) for input into air quality modeling systems. The downscaling methodology will be used to analyze air quality and resulting health impacts of energy sector transitions, including transportation electrification and increased use of natural gas and renewable sources.