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BP neural network-based state estimation bad data identification method

A BP neural network and state estimation technology, applied in neural learning methods, biological neural network models, electrical digital data processing, etc., can solve problems that affect the effect of identification

Active Publication Date: 2018-09-04
NARI TECH CO LTD +2
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  • Application Information

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Problems solved by technology

The disadvantage of the neural network method is that it is highly dependent on the training process of the network. The selection and representativeness of the training samples will directly affect the final identification effect.

Method used

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  • BP neural network-based state estimation bad data identification method
  • BP neural network-based state estimation bad data identification method
  • BP neural network-based state estimation bad data identification method

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Embodiment Construction

[0079] Further description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0080] Such as figure 1 As shown, a BP neural network-based state estimation bad data identification method includes the following steps:

[0081] Step 1. Obtain raw data, including state estimation preservation history section and real-time measurement data section, wherein state estimation preservation history section includes power grid measurement information and corresponding state estimation value;

[0082] Step 2. Establish a bad data identification model based on BP neural network, refer to figure 2 , establish a three-layer neural network, select the internal transfer function of the network, and determine the appropriate number of neurons in the hidden layer according to the number of neurons in the input and output layers of the neural network. The specific steps include:

[0083] 1) Determine the input of the neural network as the state estima...

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Abstract

The invention discloses a BP neural network-based state estimation bad data identification method. The method comprises the following steps of: aiming at relatively high requirement, for training samples, of BP neural network-based bad data identification method, establishing a BP neural network model for carrying out bad data identification on the basis of state estimation results; carrying out training by taking an online state estimation calculation result section as a sample; taking a measured value as input data and taking a state estimation value as an expected output; correcting a connection weight value and a threshold value on the basis of repeated iteration of the sample through counter-propagation of errors between the input and the output; training a measurement-based neural network; detecting a new measured section through the trained neural network; and when deviation between the measured value and a predicted value is relatively large, judging the data as bad data. According to the method, state estimation calculation results are directly utilized as the samples to carry out training, and samples with relatively high correctness are provided, so that the bad data identification precision of neural network methods is improved.

Description

technical field [0001] The invention belongs to the field of power system operation and automation, and in particular relates to a method for identifying bad data of state estimation based on a BP neural network. Background technique [0002] The operation data of the power grid mainly includes the topology structure of the power grid, model parameters and measurement and collection data. Accurate and reliable power grid operation data is the basis for intelligent dispatching. The measurement data system of the power system inevitably has errors and errors due to the collection and forwarding of multiple links. The measurement errors are subject to the conditions of the measurement data acquisition system and cannot be eliminated. Usually, after digital filtering, Improving data redundancy and other technologies can be used to deal with it, while erroneous measurement data mainly refers to data that deviates far from the actual measurement data change trajectory, also known ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06N3/08
CPCG06N3/084
Inventor 王毅宁剑闪鑫王茂海张勇彭龙陆娟娟张哲罗玉春江长明邹德虎查国强杨科
Owner NARI TECH CO LTD
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