High impedance fault (HIF) has been a challenging task to detect in distribution networks. On one hand, although several types of HIF models are available for HIF study, they are still not exhibiting satisfactory fault waveforms. On the other hand, utilizing historical data has been a trend recently for using machine learning methods to improve HIF detection. Nonetheless, most proposed methodologies address the HIF issue starting with investigating a limited group of features and can hardly provide a practical and implementable solution. This paper, however, proposes a systematic design of feature extraction, based on an HIF detection and classification method. For example, features are extracted according to when, how long, and what magnitude the fault events create. Complementary power expert information is also integrated into the feature pools. Subsequently, we propose a ranking procedure in the feature pool for balancing the information gain and the complexity to avoid over-fitting. For implementing the framework, we create an HIF detection logic from a practical perspective. Numerical methods show the proposed HIF detector has very high dependability and security performance under multiple fault scenarios comparing with other traditional methods. Index TermsHigh impedance fault, distribution network, data mining, feature selection. arXiv:1808.04454v1 [eess.SP] 13 Aug 2018 S KF = {s 5 , s 6 , s 7 , s 8 , s 9 , s 10 } = {KF V a cos H3, KF V b cos H3, KF V c cos H3, KF V a sin H3, KF V b sin H3, KF V c sin H3}
For accommodating more electric vehicles (EVs) to battle against fossil fuel emission, the problem of charging station placement is inevitable and could be costly if done improperly. Some researches consider a general setup, using conditions such as driving ranges for planning. However, most of the EV growths in the next decades will happen in the urban area, where driving ranges is not the biggest concern. For such a need, we consider several practical aspects of urban systems, such as voltage regulation cost and protection device upgrade resulting from the large integration of EVs. Notably, our diversified objective can reveal the trade-off between different factors in different cities worldwide. To understand the global optimum of large-scale analysis, we add constraint one-by-one to see how to preserve the problem convexity. Our sensitivity analysis before and after convexification shows that our approach is not only universally applicable but also has a small approximation error for prioritizing the most urgent constraint in a specific setup. Finally, numerical results demonstrate the trade-off, the relationship between different factors and the global objective, and the small approximation error. A unique observation in this study shows the importance of incorporating the protection device upgrade in urban system planning on charging stations. Index TermsElectric vehicle charging station, distribution grid, convexification, protective devices upgrade. I. INTRODUCTIONU NDER the Paris agreement signed in 2016, the model of a sustainable urban city -Singapore, pledged to cut emissions intensity by 36% below 2005 levels by 2030. To meet the commitment, emissions reduction worldwide in the transport sector is crucial, and large-scale electric vehicle (EV) adoption in the future is, therefore, utmost essential to Singapore and many other cities/countries. For example, Singapore took several important steps in this direction such as 1) an announcement of a new Vehicular Emissions Scheme and 2) the launch of the electric vehicle car-sharing program, etc. However, one of the major barriers to successful adoption of EVs at a large scale is the limited number of available charging stations. Thus, it is important to properly deploy EV charging infrastructure to enhance the adoption of EVs efficiently.EV charging station placement has therefore been an active research area for intercity and urban infrastructure planning. In freeway charging infrastructure planning, [1] tackles the EV charging station placement problem in a simple round freeway, whereas [2] proposes a capacitated-flow refueling location model to capture PEV charging demands in a more complicated meshed transport network. However, both papers share the similarity of considering the driving range in the freeway. In contrast, the driving range constraints are not prominent in the urban area charging infrastructure planning since the charging stations are easily accessible, therefore, researchers have considered various aspects dedicated for urban area charg...
In this paper, the optimal demand response strategy of a commercial building-based virtual power plant with real-world implementation in heavily urbanised area is studied. Instead of modelling the decision-making process as an optimisation problem, a reinforcement learning method is used to seek the optimal strategy, which could update its performance with minimal manpower manipulation. Specifically, the data collection from several commercial buildings, including hotel, shopping mall and office, in Huangpu district, Shanghai city is analysed to deploy the demand response program. Compared with the conventional demand response strategy based on optimisation, the learnt strategy does not rely on the forecasting information as input and could adapt to the changing demand response incentive automatically. It may not produce the best result every time, but can guarantee the benefit in a non-deterministic way in long-term operation. The real-world deployment of the Huangpu virtual power plant involving hardware and software platform is also introduced, as well as its future development projection.
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