An improved distribution network reliability analysis algorithm considering complex load transfer and scheduled outage

Author(s):  
Shengmin Qiu ◽  
Lin Guan ◽  
Haohao Wang
2021 ◽  
Vol 242 ◽  
pp. 03004
Author(s):  
Kaiyu Zhang ◽  
Shanshan Shi ◽  
Shu Liu ◽  
Junjie Wan ◽  
Lijia Ren

In order to accurately and efficiently analyze the reliability of distribution network, this paper proposes a method of analyzing the reliability of distribution network based on a deep belief network. The Deep Belief Network (DBN) is composed of limiting Boltzmann machine layer-by-layer stacking. It has a strong advantage of automatic feature extraction, which overcomes the shortcomings of traditional neural networks in extracting data features. The entire training process of DBN can be roughly divided into two stages: pre-training and fine-tuning.First of all, the pre-training of the DBN model is realized by training the Restricted Boltzmann Machine (RBM) layer by layer, then the BP algorithm is used for reverse fine-tuning to complete the training process of the entire network. finally, the reliability analysis of distribution network is performed by the trained DBN. Compared with the BP neural network method and the traditional Monte Carlo simulation method, it is verified that the proposed model of distribution network reliability analysis has high accuracy.


Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3242
Author(s):  
Hamid Mirshekali ◽  
Rahman Dashti ◽  
Karsten Handrup ◽  
Hamid Reza Shaker

Distribution networks transmit electrical energy from an upstream network to customers. Undesirable circumstances such as faults in the distribution networks can cause hazardous conditions, equipment failure, and power outages. Therefore, to avoid financial loss, to maintain customer satisfaction, and network reliability, it is vital to restore the network as fast as possible. In this paper, a new fault location (FL) algorithm that uses the recorded data of smart meters (SMs) and smart feeder meters (SFMs) to locate the actual point of fault, is introduced. The method does not require high-resolution measurements, which is among the main advantages of the method. An impedance-based technique is utilized to detect all possible FL candidates in the distribution network. After the fault occurrence, the protection relay sends a signal to all SFMs, to collect the recorded active power of all connected lines after the fault. The higher value of active power represents the real faulty section due to the high-fault current. The effectiveness of the proposed method was investigated on an IEEE 11-node test feeder in MATLAB SIMULINK 2020b, under several situations, such as different fault resistances, distances, inception angles, and types. In some cases, the algorithm found two or three candidates for FL. In these cases, the section estimation helped to identify the real fault among all candidates. Section estimation method performs well for all simulated cases. The results showed that the proposed method was accurate and was able to precisely detect the real faulty section. To experimentally evaluate the proposed method’s powerfulness, a laboratory test and its simulation were carried out. The algorithm was precisely able to distinguish the real faulty section among all candidates in the experiment. The results revealed the robustness and effectiveness of the proposed method.


Author(s):  
Guowei Li ◽  
Qinghui Zeng ◽  
Hao Liu ◽  
Junbo Wang ◽  
Rongbo Luo ◽  
...  

2014 ◽  
Vol 672-674 ◽  
pp. 1441-1446 ◽  
Author(s):  
Yu Qiang Ou ◽  
Le Feng Cheng ◽  
Jian Zhong Wen ◽  
Xuan Yu Qiu ◽  
Tao Yu

Research on reliability of distribution network has very important meaning and function to ensure the quality of power supply. This paper introduces some basic concepts of reliability in distribution network, including distribution network reliability definition, task and index. The classical reliability evaluation method was reviewed, and focused on specific distribution network, an example analysis was given, and specific reliability evaluation indexes were calculated. Finally, the future development of distribution network reliability evaluation was made a simple prospect.


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