A power distribution network double-time-scale state estimation method and system

A technology of time scale and state estimation, applied in computing, data processing applications, instruments, etc., can solve problems such as incompatibility of time scale and time delay, inability to directly fuse data, etc., achieve low cost and improve computing efficiency

Pending Publication Date: 2019-05-31
STATE GRID SHANDONG ELECTRIC POWER +4
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, AMI data is not compatible with SCADA data in terms of sampling period, time scale, and time delay, that is, the two have two completely different time scales, and the two data cannot be directly fused. A State Estimation Method Based on Data Fusion

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  • A power distribution network double-time-scale state estimation method and system
  • A power distribution network double-time-scale state estimation method and system
  • A power distribution network double-time-scale state estimation method and system

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Embodiment 1

[0059] like figure 1 As shown, the present invention provides a dual-time-scale state estimation method for a distribution network, including:

[0060] Step S1, based on the advanced measurement system AMI and the data acquisition and monitoring control system SACDA, the measurement data of the state variables at different time scales are respectively obtained;

[0061] Step S2, bringing the measured data into the pre-built dual-time-scale state estimation model of the distribution network for solution to obtain estimated values ​​of the state variables;

[0062] Wherein, the dual-time-scale state estimation model of the distribution network includes: processing the measurement data of state variables at different time scales on the same time scale.

[0063] Step S2, bring the measured data into the pre-built dual-time-scale state estimation model of the distribution network for solution, and obtain the estimated value of the state variable, including:

[0064] Step A: provi...

Embodiment 2

[0113] In order to better understand the present invention and understand the advantages of the present invention over the prior art, this embodiment is further explained in conjunction with specific implementation.

[0114] like image 3 and 4 Shown is to illustrate the effectiveness of the LMBP neural network for voltage prediction, without loss of generality, the prediction data of node 824 and the prediction data of all nodes at 12:00 are selected for illustration:

[0115] from image 3 It can be seen that the LMBP neural network is more accurate in predicting the voltage of each load point no matter in time or space. However, due to the extremely short distance of some lines, usually less than 0.01 miles (about 16 meters), it is extremely easy to cause excessive power of the calculation branches, and it is considered to merge these nodes.

[0116] Perform the second step filtering operation on the above data, such as Figure 4 As shown, taking the voltage prediction ...

Embodiment 3

[0121] Based on the same inventive concept, the present invention also provides a dual-time-scale state estimation system for distribution networks, including:

[0122] The acquisition module is used to acquire the measurement data of state variables at different time scales based on the advanced measurement system AMI and the data acquisition and monitoring control system SACDA;

[0123] A solution module, configured to bring the measured data into a pre-built dual-time-scale state estimation model of the distribution network for solution, and obtain estimated values ​​of the state variables;

[0124] Wherein, the dual-time-scale state estimation model of the distribution network includes: processing the measurement data of state variables at different time scales on the same time scale.

[0125] In an embodiment, the system further includes: a construction module for constructing a dual-time-scale state estimation model of a distribution network;

[0126] The building block...

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Abstract

The invention provides a power distribution network double-time-scale state estimation method and system. The method comprises: acquiring measurement data of state variables under different time scales based on an advanced measurement system AMI and a data acquisition and monitoring control system SACDA; Substituting the measurement data into a pre-constructed power distribution network double-time-scale state estimation model for solving to obtain an estimated value of the state variable; Wherein the power distribution network double-time-scale state estimation model comprises the step of carrying out same-time-scale processing on measurement data of state variables under different time scales. According to the estimation method provided by the invention, the measurement data of the AMI are subjected to the same time scale processing, the direct fusion of the two kinds of data is realized, and the calculation efficiency and the state estimation precision are improved.

Description

technical field [0001] The invention relates to the field of power system scheduling automation, in particular to a dual-time-scale state estimation method and system for a distribution network. Background technique [0002] Power system state estimation refers to estimating the current operating state of the power system from various measurement information of the power system. It is the foundation and core of the energy management system. If the power system state estimation result is inaccurate, any subsequent analysis and calculation will be Impossible to get exact results. Now almost every large dispatch center has installed a state estimator, and state estimation has become the cornerstone of the safe operation of the power grid. However, compared with the transmission network, the network scale of the distribution network is larger, the number of measurements is relatively small, and the three phases are usually unbalanced, which makes the state estimation of the tra...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/06
Inventor 盛万兴王振河王金丽蒋涛杨红磊于洋孙学锋文艳孟海磊刘明林房牧李建修刘文安吕东飞方恒福刘海波董啸赵辰宇左新斌沈玉兰陈艳波
Owner STATE GRID SHANDONG ELECTRIC POWER
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