The Local-World Evolving Network Model for Power Grid Considering Different Voltage Levels

Reasonable and strong structure is an important foundation for the smart transmission grid. For vigorously promoting construction of the smart grid, it is of great significance to have a thorough understanding of the complex structural characteristics of the power grid. The structural characteristics of several actual large-scale power grids of China are studied in this paper based on the complex network theory. Firstly, the topology-based network model of power grid is recalled for analyzing the statistical characteristic parameters. The result demonstrated that although some statistical characteristic parameters could reflect the topological characteristics of power grid from different ways, they have certain limitation in representing the electrical characteristics of power grid. Subsequently, the network model based on the electrical distance is established considering the limitation of topology-based model, which reflects that current and voltage distribution in the power grid are subject to Ohm's Law and Kirchhoff's Law. Comparing with the topology-based model, the electrical distance-based model performs better in reflecting the natural electrical characteristic structure of power grid, especially intuitive and effective in analyzing clustering characteristics and agglomeration characteristics of power grid. These two models could complement each other.

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Short-Term Load Forecasting Method Based on Deep Reinforcement Learning for Smart Grid

Mobile Information Systems ◽

10.1155/2021/8453896 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Wei Guo ◽

Kai Zhang ◽

Xinjie Wei ◽

Mei Liu

Keyword(s):

Reinforcement Learning ◽

Smart Grid ◽

Prediction Model ◽

Network Model ◽

Power Grid ◽

Load Forecasting ◽

Model Parameters ◽

Short Term ◽

Forecasting Method ◽

Short Term Load Forecasting

Short-term load forecasting is an important part to support the planning and operation of power grid, but the current load forecasting methods have the problem of poor adaptive ability of model parameters, which are difficult to ensure the demand for efficient and accurate power grid load forecasting. To solve this problem, a short-term load forecasting method for smart grid is proposed based on multilayer network model. This method uses the integrated empirical mode decomposition (IEMD) method to realize the orderly and reliable load state data and provides high-quality data support for the prediction network model. The enhanced network inception module is used to adaptively adjust the parameters of the deep neural network (DNN) prediction model to improve the fitting and tracking ability of the prediction network. At the same time, the introduction of hybrid particle swarm optimization algorithm further enhances the dynamic optimization ability of deep reinforcement learning model parameters and can realize the accurate prediction of short-term load of smart grid. The simulation results show that the mean absolute percentage error e MAPE and root-mean-square error e RMSE of the performance indexes of the prediction model are 10.01% and 2.156 MW, respectively, showing excellent curve fitting ability and load forecasting ability.

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Security analysis of large power grid based on complex network theory and Bayesian network model

2014 International Conference on Power System Technology ◽

10.1109/powercon.2014.6993604 ◽

2014 ◽

Author(s):

Li-Min Liu ◽

Junyong Liu ◽

Zhen-Bo Wei ◽

Chang-Ning Shang ◽

Guo-Jun Zhu ◽

...

Keyword(s):

Bayesian Network ◽

Complex Network ◽

Network Model ◽

Network Theory ◽

Power Grid ◽

Security Analysis ◽

Bayesian Network Model ◽

Large Power ◽

Complex Network Theory ◽

Grid Based

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Study on Adaptive Distance Relay to Prevent Cascading Trips in Complex Power Grid

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.472.353 ◽

2014 ◽

Vol 472 ◽

pp. 353-358

Author(s):

Song Lin Gu ◽

Zeng Ping Wang

Keyword(s):

New England ◽

Network Model ◽

Power Grid ◽

Impedance Change ◽

Distance Relay ◽

Change Rate ◽

Complex Power ◽

Bus System

Cascading trips caused by flow transferring are the crucial reason for a lot of well-known blackouts. Aiming at the distance relay which is most applied in power grid, this paper puts forward an adaptive setting scheme to prevent cascading trips in complex power grid. In this paper, using two-port network model to fast calculating apparent impedance, and than calculating the apparent impedance change rate which is caused by flow transferring. According to the apparent impedance change rate modify the setting value which would contract the range of the impedance circle. After accomplishing the contraction, the relay action check will start. Through the process of modification and checking, the adaptive setting scheme which is not affected by the flow transferring will be formed. Taking the New England 10-unit 39-bus system which also used this method as the example, in this case, this method is confirmed by the BPA simulation.

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