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Analysis method of big data distribution network fault line selection based on improved particle swarm optimization algorithm

A technology for power distribution network failure and particle swarm improvement, applied in the field of big data communication, can solve problems such as reduced line selection accuracy

Active Publication Date: 2022-03-04
BAOJI POWER SUPPLY COMPANY OF STATE GRID SHAANXI ELECTRIC POWER +2
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Problems solved by technology

However, an important part of SVM is the kernel function. If the parameters of the kernel function are not properly selected, the line selection accuracy will be reduced, and the traditional particle swarm optimization algorithm is easy to fall into the local optimal solution and cannot achieve the optimal line selection result.

Method used

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  • Analysis method of big data distribution network fault line selection based on improved particle swarm optimization algorithm
  • Analysis method of big data distribution network fault line selection based on improved particle swarm optimization algorithm
  • Analysis method of big data distribution network fault line selection based on improved particle swarm optimization algorithm

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Embodiment Construction

[0057] Below in conjunction with accompanying drawing and concrete implementation computing example, the present invention will be further described:

[0058] Taking the parameters of #2 busbar and its 4 feedback lines on the 10kV side of a 110kV substation in an urban area of ​​Zhejiang Province as an example, the line structure is as follows: figure 2 As shown, where G is the 110kV ideal power supply, T is the main transformer, R f is the fault point transition resistance, K, L m , R are respectively the control switch, inductance coil and median resistance in the arc suppressing coil complete set. Line L 1 is a pure overhead line with a length of l 1 =9.3km; route L 3 It is a pure cable line with a length of l 3 = 6.3km; route L 2 , L 4 It is an overhead line-cable hybrid line, and the lengths are l 2 = 5.9 km, l 4 = 7.1 km. The feeder parameters of overhead lines and cable lines are shown in Table 1. In the table, R 1 , L 1 、C 1 Respectively, the positive seq...

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Abstract

The invention discloses a large data distribution network fault line selection analysis method based on the improved particle swarm algorithm. The method improves the traditional particle swarm algorithm, not only has a faster optimization speed for SVM model parameters, but also is not easy to fall into a local optimum. Combined with the fifth harmonic method and the wavelet packet change method, the efficient line selection of the ground fault in the resonant system is realized. The invention not only achieves high speed and good accuracy, but also is not affected by grounding resistance, fault distance and other factors.

Description

technical field [0001] The invention belongs to the field of big data communication, and in particular relates to a fault line selection analysis method of a big data distribution network based on an improved particle swarm algorithm. Background technique [0002] With the development of power grid technology and the improvement of users' requirements for power supply quality, ensuring the reliability of power supply has become the top priority, and the rapid and accurate line selection for power distribution system line faults has become a research hotspot. When a single-phase ground fault occurs in a distribution network system with neutral point resonant grounding, the success rate of the neutral point resonant grounding system is often not high enough due to reasons such as weak zero-sequence current and uncertain direction, and complex and changeable ground fault forms. Ordinary line selection methods are often not suitable for neutral point resonant ground fault system...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/00G06Q50/06
CPCG06N3/006G06Q50/06G06F18/2411G06F18/214
Inventor 门强蔺庚立李睿郭海涛徐迪严海波白晓斌张俊马林张翀郑乾
Owner BAOJI POWER SUPPLY COMPANY OF STATE GRID SHAANXI ELECTRIC POWER
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