Transient security evaluation method based on boundary region importance sampling and kernel vector machine

A technology of importance sampling and boundary areas, applied in nuclear methods, computer components, instruments, etc., can solve time-consuming problems

Pending Publication Date: 2020-07-10
CHINA THREE GORGES UNIV
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Problems solved by technology

Therefore, using round-by-round optimization to determine the input feature set of SVM will be quite time-consuming.

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  • Transient security evaluation method based on boundary region importance sampling and kernel vector machine
  • Transient security evaluation method based on boundary region importance sampling and kernel vector machine
  • Transient security evaluation method based on boundary region importance sampling and kernel vector machine

Examples

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Embodiment

[0120] This inventive example uses the IEEE39 node system. Such as image 3 As shown, the test system involves 39 nodes, 10 generators, and 46 transmission lines. The reference power is 100MVA, and the reference voltage is 345kV. It is assumed that synchro-vector measurement units are installed on all buses in order to collect large data sets. To generate a reasonable dataset, the operating conditions of the test system were varied randomly. Consider 10 different load levels (80%, 85%, 90%, 95%, 100%, 105%, 110%, 115%, 120%, 125%) and change the generator output accordingly. On this basis, the method of variable load and variable generation is used to solve the power flow problem of the power system. The contingencies considered are mainly three phase-to-earth faults on each busbar, and three locations on each transmission line (25%, 50% and 75% of the line length). The simulation assumes that a specific fault occurs at 0.1 seconds and is cut off at 0.3 seconds (or 0.35 s...

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Abstract

A transient security assessment method based on boundary region importance sampling and a kernel vector machine comprises the following steps: 1, obtaining a system operation sample, constructing a transient security index, and establishing a corresponding sample database; 2, sampling the sample database to efficiently generate an offline training sample set, and normalizing the offline training sample set to reduce the calculation burden of a machine; 3, constructing a transient security evaluation model of apower system, and carrying out offline training on the model by utilizing the training sample set; 4, based on the real-time operation data of the power system, completing evaluation of the real-time transient security state of the power system by using the continuously updated transient safety evaluation model to obtain a final transient stability evaluation result. The invention aims to provide the method which is beneficial to improving the speed of the training process and theprecision of the evaluation process, has higher applicability in the field of transient security evaluation of the power system, and can avoid blackout accidents.

Description

technical field [0001] The invention relates to the field of transient security assessment of electric power systems, in particular to a transient security assessment scheme of boundary area importance sampling and kernel vector machines. Background technique [0002] On the one hand, due to the interconnected environment of the power system, a large number of devices, such as smart meters and new energy sources, have been connected to the grid. Due to the continuous expansion of the scale of the power grid, the complexity of the power grid is also increasing, which brings great challenges to the safe operation of the power grid. At the same time, with the continuous development of the national economy and society, the requirements for the safe and stable operation of the power system and the reliability of power supply are getting higher and higher. The reform of my country's power system continues to deepen, and the power system is developing in the direction of long-dist...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N20/10
CPCG06N20/10G06F18/2411
Inventor 刘颂凯陈浩刘峻良晏光辉张涛李文武李欣郭攀锋刁良涛江进波曹成王丰李丹
Owner CHINA THREE GORGES UNIV
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