Power system probabilistic-optimal power flow calculation method based on stacked denoising autoencoder

A noise-reducing automatic coding and optimal power flow technology, which is applied to AC networks, circuit devices, and AC network circuits with the same frequency from different sources, can solve problems such as long calculation time and inability to handle probabilistic optimal power flow

Active Publication Date: 2019-04-09
CHONGQING UNIV +2
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Problems solved by technology

The former is only suitable for certain types of probability distributions, and cannot handle the general case of probabilistic optimal power flow in practical applications
The latter has accurate calculation results and flexible application, but involves a large number of...

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  • Power system probabilistic-optimal power flow calculation method based on stacked denoising autoencoder
  • Power system probabilistic-optimal power flow calculation method based on stacked denoising autoencoder
  • Power system probabilistic-optimal power flow calculation method based on stacked denoising autoencoder

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Embodiment 1

[0090] see figure 1 and figure 2 , a probabilistic optimal power flow calculation method for power systems based on stacked denoising autoencoders, mainly includes the following steps:

[0091] 1) Establish the SDAE optimal power flow model. Utilizing the characteristics of SDAE's deep stack structure and encoding and decoding process, which can effectively mine the high-order characteristics of the nonlinear optimal power flow model, an optimal power flow model based on SDAE is established.

[0092] Considering that the optimal power flow model contains nonlinear equality and inequality constraints, which lead to complex nonlinear characteristics between input and output, a combination of maximum and minimum normalized data preprocessing methods and a small-batch gradient descent learning algorithm based on momentum learning rates is proposed. Deep neural network training method to improve training accuracy and speed. The trained SDAE optimal power flow model can non-iter...

Embodiment 2

[0175] see image 3 a to image 3 d, A simulation experiment of a power system probabilistic optimal power flow calculation method based on a stacked denoising autoencoder, which mainly includes the following steps:

[0176] 1) Optimal power flow sample acquisition.

[0177] In this embodiment, the IEEE118 standard system is used for simulation. In this embodiment, wind farms are introduced on busbars 59, 80 and 90, and the maximum outputs of the wind farms are 220, 200, and 260MW respectively, and photovoltaic power stations are introduced on busbars 13, 14, 16 and 23, and the maximum outputs of the photovoltaic power stations are respectively 100MW. , 150, 100, 150MW.

[0178] Among them, it is assumed that the wind speed obeys the two-parameter Weibull distribution, its scale parameter is 2.016, and its shape parameter is 5.089. The light intensity obeys the Beta distribution. The shape parameters of the photovoltaic power station and the cut-in wind speed, rated wind s...

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Abstract

The invention discloses a power system probabilistic-optimal power flow calculation method based on a stacked denoising autoencoder. The calculation method comprises the following main steps that: 1)establishing a SDAE (stacked denoising autoencoder) optimal power flow model; 2) obtaining the input sample X of a SDAE optimal power flow model input layer; 3) initializing the SDAE optimal power flow model; 4) training the SDAE optimal power flow model so as to obtain a trained SDAE optimal power flow model; 5) adopting a MCS (Modulating Control System) method to carry out sampling on the randomvariable of a power system to be subjected to probabilistic power flow calculation so as to obtain a calculation sample; 6) inputting training sample data obtained in S5 into the SDAE optimal power flow model which finishes being trained in S4) in one time so as to calculate an optimal power flow online probability; and 7) analyzing the optimal power flow online probability, i.e., drawing the probability density curve of the output variable of the SDAE optimal power flow model. The method can be widely applied to the probabilistic-optimal power flow solving of the power system, and is especially suitable for an online analysis situation that system uncertainty is enhanced due to high new energy permeability.

Description

technical field [0001] The invention relates to the field of electric power system and its automation, in particular to a calculation method for power system probabilistic optimal power flow based on a stacked noise-reduction autoencoder. Background technique [0002] Uncertainty in power systems has skyrocketed as renewable energy has grown in popularity. Probabilistic Optimal Power Flow (POPF) can take into account various uncertain factors in power system operation, and has become an important tool to ensure the safe and economical operation of power systems. Existing probabilistic optimal power flow solving techniques can generally be divided into analytical methods and simulation methods. The former is only applicable to certain types of probability distributions, and cannot handle the general case of probabilistic optimal power flow in practical applications. The latter is accurate in calculation results and flexible in application, but involves a large number of sam...

Claims

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Application Information

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IPC IPC(8): H02J3/06
CPCH02J3/06H02J2203/20Y02E40/70Y04S10/50
Inventor 余娟杨燕杨知方向明旭代伟雷星雨杨高峰金黎明古济铭韩思维
Owner CHONGQING UNIV
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