Power-Voltage Mapping Method Based on Comprehensive Probability Model and Deep Learning for Smart Grid

doi:10.12204/j.issn.1000-7229.2022.02.005

Abstract

Abstract:

With the increase in the penetration rate of new energy based distributed power generation, its characteristics such as strong intermittent and high volatility will have a greater impact on grid voltage fluctuations. How to calculate the steady-state voltage of the power grid with complex distributed power access more quickly is of great significance. In this paper, by sampling a great deal of real output data of photovoltaic and wind power, a comprehensive probability model is improved and generated on the basis of the traditional probability model, and the probability distribution deviation caused by the temporal and spatial characteristics is corrected through the Markov transition probability matrix. Then, taking the actual output data of adjacent photovoltaic and wind power stations in a certain area of southern China as a sample, the steady-state voltage at each node of the distribution network under different scenarios is calculated in the distribution network topology. Finally, the results of the calculation example show that the power grid model generated by the improved method simulation in this paper has high authenticity and applicability, and the calculated voltages have high accuracy rate. Moreover, compared with the traditional power system flow calculation, the calculation time is greatly reduced, so that it has better follow-up in the control effect, and is suitable for the calculation of the steady-state voltage of the complex power system with new energy.

Key words: new energy, comprehensive probability model, Markov chain, Monte Carlo simulation, artificial intelligence

CLC Number:

TM743

LI Jianyi, LI Peng, XU Xiaochun, SHI Ruyu, ZENG Pingliang, XIA Hui. Power-Voltage Mapping Method Based on Comprehensive Probability Model and Deep Learning for Smart Grid[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(2): 37-44.

Figures/Tables 10

Fig.1 Flowchart of power-voltage mapping

Fig.2 Flowchart of comprehensive probability density modeling of PV and wind

Fig.3 Power lag diagram of adjacent wind power generators

Fig.4 Power-voltage mapping method based on deep learning

Fig.5 Topology of distribution network

Table 1 Parameters of photovoltaic access nodes

Table 2 Parameters of wind power access nodes

Fig.6 Probability density model

Fig.7 Standard deviation of node voltages

Fig.8 Neural network training

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