scholarly journals Fault Calculation Method of Distribution Network Based on Deep Learning

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1086
Author(s):  
Cong Zhang ◽  
Ke Peng ◽  
Huan Li ◽  
Bingyin Xu ◽  
Yu Chen
Keyword(s):  
Deep Learning ◽  
Calculation Method ◽  
Low Voltage ◽  
Voltage Drop ◽  
Learning Algorithm ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Calculation Model ◽  
Fault Calculation ◽  
Deep Learning Algorithm

Under the low voltage ride through (LVRT) control strategy, the inverter interfaced distributed generation (IIDG) needs to change the output mode of the inverter according to the voltage of the connected nodes. The short-circuit current is related to the system rated capacity, network short-circuit impedance, and distributed power output. So, based on the deep learning algorithm, a predicting method of the voltage drop is proposed. By predicting the voltage of connected nodes, the output mode of IIDG can be determined based on the LVRT control. Thus, the fault calculation model of IIDG is accurately established. Compared with the three-phase asymmetric Gaussian fault calculation method, the proposed method can achieve fault calculation accurately. Finally, a case study is built to verify the effectiveness of the proposed method. The results indicate that the proposed method can make accurate voltage prediction and improve the computation speed of the fault calculation.

scholarly journals Integrated Power Flow and Short Circuit Calculation Method for Distribution Network with Inverter Based Distributed Generation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shan Yang ◽  
Xiangqian Tong
Keyword(s):  
Distributed Generation ◽  
Calculation Method ◽  
Power Flow ◽  
Low Voltage ◽  
Short Circuit ◽  
Distribution Network ◽  
Short Circuit Current ◽  
Theoretical Research ◽  
Equivalent Model ◽  
Short Circuit Calculation

Power flow calculation and short circuit calculation are the basis of theoretical research for distribution network with inverter based distributed generation. The similarity of equivalent model for inverter based distributed generation during normal and fault conditions of distribution network and the differences between power flow and short circuit calculation are analyzed in this paper. Then an integrated power flow and short circuit calculation method for distribution network with inverter based distributed generation is proposed. The proposed method let the inverter based distributed generation be equivalent toIθbus, which makes it suitable to calculate the power flow of distribution network with a current limited inverter based distributed generation. And the low voltage ride through capability of inverter based distributed generation can be considered as well in this paper. Finally, some tests of power flow and short circuit current calculation are performed on a 33-bus distribution network. The calculated results from the proposed method in this paper are contrasted with those by the traditional method and the simulation method, whose results have verified the effectiveness of the integrated method suggested in this paper.

scholarly journals Research on Theoretical Calculation Methods of Photovoltaic Power Short-Circuit Current and Influencing Factors of Its Fault Characteristics

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 316 ◽  
Author(s):  
Huiyuan Liu ◽  
Kehan Xu ◽  
Zhe Zhang ◽  
Wei Liu ◽  
Jianyong Ao
Keyword(s):  
Theoretical Calculation ◽  
Low Voltage ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Calculation Model ◽  
Load Level ◽  
Low Voltage Ride Through ◽  
Complex Fault ◽  
Power Load ◽  
Bus Voltage

With the substantial increase in the capacity of grid-connected photovoltaic (PV) power, the adverse effects of its complex fault characteristics on grid relay protection are increasingly highlighted. Based on the introduction of the topology and the control strategy on low-voltage ride through of PV power, a theoretical solution method for solving the fault current of PV power is proposed by taking account of the DC bus voltage fluctuation, and a theoretical calculation model of its transient and steady state is established. The correctness of the theoretical results is verified by the numerical simulation results and low-voltage ride through (LVRT) experiment results. Furthermore, to gain a better understanding of the factors influencing PV power fault characteristics, the effects of several factors including proportional integral (PI) controller parameters, fault voltage sag depth, and PV power load level on PV power fault characteristics are analyzed by using simple variable method. The obtained results can provide a theoretical reference for the control parameter design and protection research of PV power.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

Sign in / Sign up

Export Citation Format

Share Document