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  • Institute of Physics (IOP)  (4)
  • 1
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    Research on Promoting the International Competitiveness of China’s Manufacturing Industry under the Background of TPP (2018)
    Institute of Physics (IOP)
    Details
    Publication Date: 2018-08-21
    Description: As the United States, Japan, Australia and other 9 countries joined trans-pacific partnership (TPP), it has become the highest standard and the largest free trade agreement in the history. But China is excluded, and with the development of the negotiation, contracting states gradually formed a sort of circle around China, which will no doubt have a significant impact on China’s foreign trade development, especially on China’s manufacturing industry. No matter whether China will join TPP or not, it will never take the easy way out. So, exploring how to improve China’s international competitiveness of manufacturing has been an important topic.
    Print ISSN: 1757-8981
    Electronic ISSN: 1757-899X
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Institute of Physics (IOP)
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  • 2
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    Overload Analysis of Distribution Transformers Based on Data Mining (2018)
    Institute of Physics (IOP)
    Details
    Publication Date: 2018-11-06
    Description: Overload problems of distribution transformers frequently occur in distribution networks. To avoid the in-advertent effect on the networks and take corresponding measures, the association rules are used to analyze the heavy overload phenomenon of distribution transformers. For the operation of the distribution network, it is very important to study the strong association rules between the heavy overload phenomenon of the transformer in different areas and the seasons, weather and holidays. In this paper, the data preprocessing of heavy overload data and other data of transformer network is first processed, and then a data mining model is established. Finally, the strong association rules of heavy overload are found by using Apriori algorithm. The strong association rules can be used to guide the operation of regional distribution network and avoid the influence of heavy load overload on power supply reliability.
    Print ISSN: 1757-8981
    Electronic ISSN: 1757-899X
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Institute of Physics (IOP)
    Location Call Number Expected Availability
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  • 3
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    Research on EMS Alarm Analysis and Secondary Equipment Status Evaluation (2018)
    Institute of Physics (IOP)
    Details
    Publication Date: 2018-11-06
    Description: The intellectualization level and information level of the secondary equipment in the intelligent substation have been greatly improved, which is of great significance for the normal operation of the power system. However, the secondary equipment will generate a lot of redundant alarms information in the EMS system. These alarms are not conducive to finding equipment faults, and there is a lack of an active maintenance method for secondary equipment. In this paper, an intelligent analysis method of EMS alarm information is proposed. This method can find fault alarm information accurately, and we used the actual data of intelligent substation to verify it. A status evaluation method for the secondary equipment is also proposed. The method can be used for early warning of equipment failure and efficient operation and maintenance.
    Print ISSN: 1757-8981
    Electronic ISSN: 1757-899X
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Institute of Physics (IOP)
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  • 4
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    Short-term Load Forecasting Considering Meteorological Factors and Electric Vehicles (2018)
    Institute of Physics (IOP)
    Details
    Publication Date: 2018-11-06
    Description: A short-term load forecasting method considering meteorological factors and electric vehicles is essential to the successful operation of the power system. This paper proposes a unique short-term load forecasting method based on neural network. First, through the analysis of typical daily load data, it is demonstrated that the short-term load data changes with the daily, weekly, weather type and the charging of electric vehicles. Then, the load forecasting model based on the neural network is set up with historical data, meteorological data and electric vehicle charging data as input. Finally, the prediction model is simulated to improve the accuracy of load forecasting.
    Print ISSN: 1757-8981
    Electronic ISSN: 1757-899X
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Institute of Physics (IOP)
    Location Call Number Expected Availability
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    PAPER CURRENT
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