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  • IOP Publishing  (5)
  • He, Shengguo  (5)
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  • IOP Publishing  (5)
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  • 1
    Online Resource
    Online Resource
    Research on Attack Identification Method and Device Method Based on Random Forest Algorithm
    IOP Publishing ; 2020
    In:  Journal of Physics: Conference Series Vol. 1646, No. 1 ( 2020-09-01), p. 012012-
    Details
    In: Journal of Physics: Conference Series, IOP Publishing, Vol. 1646, No. 1 ( 2020-09-01), p. 012012-
    Abstract: Because the energy storage system has the function of stabilizing the voltage and frequency of the grid, when the energy storage system is connected to the grid, it is necessary to obtain its operating status information in a timely and accurate manner. The operating status data of the energy storage system is uploaded to the higher-level dispatch center via the energy storage coordination control device. However, with the intelligence and informationization of the power grid, various cyber attack methods have emerged endlessly. There are various security risks in the way of information interaction based on the network. Attackers can analyze the communication protocols within them, or implement Eavesdropping attacks, Dos attacks, and tampering with sensitive data, etc., causing the energy storage coordination control device to obtain the wrong data sent by the dispatch center. The wrong data causes the energy storage system to output the wrong power, which may have the opposite effect when adjusting the voltage and frequency of the power grid, causing an imbalance in the voltage and frequency of the power grid, resulting in great economic losses, and even causing casualties due to equipment failure. Attackers can also gradually invade the upper-level dispatch center by using the energy storage coordination control device as a springboard. In view of the above problems, this paper uses a random forest-based network attack detection module is used to make real-time judgments on all data flows of the energy storage coordination control device. That is, when the energy storage coordination control device is subjected to a network attack, all data flows of the energy storage coordination control device pass a trained random forest attack detection model, thereby implementing attack detection on real-time data flows.
    Type of Medium: Online Resource
    ISSN: 1742-6588 , 1742-6596
    URL: Article
    DOI: 10.1088/1742-6596/1646/1/012012
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2166409-2
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Research on Attack Detection Method of Microgrid Central Controller Based on Convolutional Neural Network
    IOP Publishing ; 2020
    In:  Journal of Physics: Conference Series Vol. 1646, No. 1 ( 2020-09-01), p. 012076-
    Details
    In: Journal of Physics: Conference Series, IOP Publishing, Vol. 1646, No. 1 ( 2020-09-01), p. 012076-
    Abstract: The microgrid central controller (MGCC) integrates the functions of control, monitoring, and communication in microgrid system, and has powerful capabilities of information collection and data processing. However, with the development of microgrid system worldwide, the information security management capabilities of the MGCC are poor, If information / network attacks cannot be actively detected and identified, it will easily reduce the reliability of the microgird system operation. Attackers can use abnormal information or use the MGCC as a springboard to further attack the upper-layer system. Aiming at the above problems, this paper presents an attack detection method based on convolutional neural network, and a detailed design process of attack detection model of the MGCC is proposed. In the attack detection method, the important data streams in the MGCC are used as the input of the convolutional neural network model, then the convolutional neural network model detects or classifies these data streams, finally, intercept the data flow with attack behavior and give a warning prompt, and forward data without attack behaviors normally.
    Type of Medium: Online Resource
    ISSN: 1742-6588 , 1742-6596
    URL: Article
    DOI: 10.1088/1742-6596/1646/1/012076
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2166409-2
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Research on CCHP System Grid Connection Interface Device Based on Elman Neural Network
    IOP Publishing ; 2020
    In:  Journal of Physics: Conference Series Vol. 1646, No. 1 ( 2020-09-01), p. 012094-
    Details
    In: Journal of Physics: Conference Series, IOP Publishing, Vol. 1646, No. 1 ( 2020-09-01), p. 012094-
    Abstract: With the promotion of the concepts of “energy Internet” and “multi energy complementation”, the combined cold heat and power (CCHP) system technology has been paid more and more attention by the energy power circles at home and abroad. CCHP system generates electricity through internal combustion engine, meanwhile, the waste heat discharged after power generation is supplied with heat and cooling to users through waste heat recovery equipment, so as to effectively improve the energy utilization rate. However, with the development of information and intelligence in the power grid, the threat of various network malicious attacks on the power industrial control terminal is growing. The traditional CCHP system grid connection interface device operating environment is open and vulnerable to network malicious attacks. In this paper, an interface device of CCHP based on Elman neural network algorithm is proposed. The technical problem to be solved is to be able to realize active defense against unknown attacks and improve the security and operation reliability of CCHP system.
    Type of Medium: Online Resource
    ISSN: 1742-6588 , 1742-6596
    URL: Article
    DOI: 10.1088/1742-6596/1646/1/012094
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2166409-2
    Location Call Number Limitation Availability
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Research on Attack Detection Method of Photovoltaic Grid-connected Interface Device Based on Gradient Boosting Decision Tree
    IOP Publishing ; 2020
    In:  Journal of Physics: Conference Series Vol. 1646, No. 1 ( 2020-09-01), p. 012029-
    Details
    In: Journal of Physics: Conference Series, IOP Publishing, Vol. 1646, No. 1 ( 2020-09-01), p. 012029-
    Abstract: Photovoltaic grid-connected interface devices are an important class of smart devices in microgrids. The authenticity and reliability of the data they acquire, as well as the safety and stability of operation, are related to the safe and reliable operation of the entire microgrid system. However, in the context of microgrid intelligence and informatization, information / network attacks will become the norm, making network-dependent information interaction methods subject to various security risks. The photovoltaic grid-connected interface device involves an open operating environment and is extremely vulnerable to network attacks. The attack information will occupy the space or resources of the photovoltaic grid-connected interface device, making the photovoltaic grid-connected interface device unable to respond to other important requests or instructions in a timely manner, and in severe cases, will cause the device to be paralyzed and affect the normal system operation. Aiming at the above problems, this paper presents an attack detection method based on the gradient-upgraded decision tree model, and gives a detailed design process of attack detection model of the photovoltaic grid-connected interface device. That is, the important data flow in the photovoltaic grid-connected interface device is used as the input of the gradient-upgraded decision tree model, and then the gradient-upgraded decision tree model detect or classify flows, finally, intercept the data flow with attack behavior and give a warning prompt, and forward data without attack behaviors normally.
    Type of Medium: Online Resource
    ISSN: 1742-6588 , 1742-6596
    URL: Article
    DOI: 10.1088/1742-6596/1646/1/012029
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2166409-2
    Location Call Number Limitation Availability
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    Online Resource
  • 5
    Online Resource
    Online Resource
    Research on Deep Belief Network of Wind Power Control Management Unit Based on Attack Identification
    IOP Publishing ; 2020
    In:  Journal of Physics: Conference Series Vol. 1646, No. 1 ( 2020-09-01), p. 012120-
    Details
    In: Journal of Physics: Conference Series, IOP Publishing, Vol. 1646, No. 1 ( 2020-09-01), p. 012120-
    Abstract: With the continuous improvement of the level of economic development and the increasingly serious environmental problems, countries around the world are focusing more on renewable energy. Wind energy is an important category of renewable energy because of its advantages. However, wind power is very dependent on the climate environment. It operates in an open operating environment, and its communication depends on the network interaction method. With the proposal of the Internet of Everything, the power grid is developing in the direction of information and intelligence. There are more and more attacks, and the security and stability of wind power interface devices have been threatened. As the power grid involves many areas and households, once the power outage occurs, the economic losses will be huge and even cause major security accidents. This paper proposes a deep belief network research of wind power control management unit based on attack recognition to improve the safety and operational reliability of wind power generation.
    Type of Medium: Online Resource
    ISSN: 1742-6588 , 1742-6596
    URL: Article
    DOI: 10.1088/1742-6596/1646/1/012120
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2166409-2
    Location Call Number Limitation Availability
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    Online Resource
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