Similar-Density-Array-Based Equipment Outage Prediction Method for Distribution Network Considering Weather Factors

2021 ◽  
Author(s):  
Wenxiong Mo ◽  
Zhong Xu ◽  
Kai Zhou ◽  
Le Luan ◽  
Weinan Fan ◽  
...  
Keyword(s):  
Distribution Network ◽  
Prediction Method ◽  
Weather Factors ◽  
Similar Density

Multi-Stage Distribution Network Space Load Forecasting Method Considering Demand Side Resources

2020 ◽  
Vol 15 (12) ◽  
pp. 1474-1481
Author(s):  
Zhidong Yang ◽  
Guangjiu Chen ◽  
Jianwu Ding ◽  
Xiaojing Kang ◽  
Meng Sheng
Keyword(s):  
Distribution Network ◽  
Load Forecasting ◽  
Processing System ◽  
Prediction Method ◽  
Operation Time ◽  
Specific Information ◽  
Demand Side ◽  
Multi Stage ◽  
Prediction Function ◽  
Forecasting Method

Under the background of the further development of electric power, this paper forecasts the spatial load of distribution network, and proposes a multi-stage spatial load forecasting method considering the demand side resources. Firstly, the load of distribution network is pretreated to improve the prediction function of the processing system, and the working efficiency of the whole system is enhanced to solve the maximum load value. Then, the different conditions of demand side resources are considered step by step to realize the fine analysis, confirm the saturation density value of load, understand the specific information of spatial load, master the predicted data status, and finally carry out the comprehensive prediction method research of spatial load to realize the prediction research of spatial load of distribution network. The experimental results show that the multi-stage spatial load forecasting method considering demand side resources has high accuracy and reliability, and its forecasting effect can improve the system forecasting performance to a certain extent, reduce unnecessary operation time, reduce energy and resource consumption, and promote the development of load forecasting research.

scholarly journals A Novel Machine Learning-Based Short-Circuit Current Prediction Method for Active Distribution Networks

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3793 ◽  
Author(s):  
Zheng ◽  
Wang ◽  
Jiang ◽  
He
Keyword(s):  
Distribution Systems ◽  
Large Scale ◽  
Short Circuit ◽  
Distribution Network ◽  
Prediction Method ◽  
Short Circuit Current ◽  
Distribution Networks ◽  
Test System ◽  
High Penetration ◽  
Active Distribution Networks

The traditional mechanism models used in short-circuit current calculations have shortcomings in terms of accuracy and speed for distribution systems with inverter-interfaced distributed generators (IIDGs). Faced with this issue, this paper proposes a novel data-driven short-circuit current prediction method for active distribution systems. This method can be used to accurately predict the short-circuit current flowing through a specified measurement point when a fault occurs at any position in the distribution network. By analyzing the features related to the short-circuit current in active distribution networks, feature combination is introduced to reflect the short-circuit current. Specifically, the short-circuit current where IIDGs are not connected into the system is treated as the key feature. The accuracy and efficiency of the proposed method are verified using the IEEE 34-node test system. The requirement of the sample sizes for distribution systems of different scale is further analyzed by using the additional IEEE 13-node and 69-node test systems. The applicability of the proposed method in large-scale distribution network with high penetration of IIDGs is verified as well.

scholarly journals Spatial Load Prediction Considering Spatiotemporal Distribution of Electric Vehicle Charging Load

2021 ◽  
Vol 256 ◽  
pp. 01001
Author(s):  
Xiang Gao ◽  
Lingyan Wei ◽  
Bing Wang ◽  
Guiru Chen ◽  
Xiaoyue Wu
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Large Scale ◽  
Distribution Network ◽  
Prediction Method ◽  
Temporal Distribution ◽  
Load Prediction ◽  
Electric Vehicle Charging ◽  
Urban Distribution ◽  
Vehicle Charging

In view of the influence of large-scale electric vehicle access to the distribution network on spatial load prediction, this paper proposes a spatial load prediction method for urban distribution network considering the spatial and temporal distribution of electric vehicle charging load. Firstly, electric vehicles are classified according to charging mode and travel characteristics of various types of vehicles. Secondly, the probability distribution function is fitted to the travel rules of electric vehicles according to the travel survey and statistical data of residents. Then, the model of electric vehicle travel chain is constructed, and the charging load in different regions and different times is calculated by Monte Carlo method. Finally, based on the actual data of a certain area, the predicted spatial load values of different functional communities in one day are obtained, which can provide reference for future urban distribution network planning.

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