Evaluating Suppliers of Chinese Power Grid Companies Based on BP Neural Network

2013 ◽  
Vol 8 (6) ◽  
pp. 81-90
Author(s):  
Yanfu Zhang ◽  
Qian Wang ◽  
Nan Shen ◽  
Hongqing Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Power Grid

Fault Identification of Power Grid Based on BP Neural Network

2021 ◽  
pp. 361-367
Author(s):  
Mingjiu Pan ◽  
Zhou Lan ◽  
Kai Yang ◽  
Zhifang Yu ◽  
Huaiyue Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Power Grid ◽  
Fault Identification ◽  
Grid Based

scholarly journals Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network

2021 ◽  
Vol 13 (24) ◽  
pp. 13746
Author(s):  
Xiaomin Xu ◽  
Luyao Peng ◽  
Zhengsen Ji ◽  
Shipeng Zheng ◽  
Zhuxiao Tian ◽  
...  
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Search Algorithm ◽  
Power Grid ◽  
Engineering System ◽  
Project Cost ◽  
Total Investment ◽  
Engineering Cost

The prediction of power grid engineering cost is the basis of fine management of power grid engineering, and accurate prediction of substation engineering cost can effectively ensure the fine operation of engineering funds. With the continuous expansion of the engineering system, the influencing factors and data dimensions of substation project investment are gradually diversified and complex, which further increases the uncertainty and complexity of substation project cost. Based on the concept of substation engineering data space, this paper investigates the influencing factors and constructs the static total investment intelligent prediction model of substation engineering. The emerging swarm intelligence algorithm, sparrow search algorithm (SSA), is used to optimize the parameters of the BP neural network to improve the prediction accuracy and convergence speed of neural network. In order to test the validity of the model, an example analysis is carried out based on the data of a provincial substation project. It was found that the SSA-BP can effectively improve the prediction accuracy and provide new methods and approaches for practical application and research.

Research on the Prediction Model of Urban Power Grid Short-Term Load Based on BP Neural Network Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 954-957
Author(s):  
Pei Long Xu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Power Grid ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Short Term ◽  
Network Algorithm ◽  
Neural Network Algorithm

Objective: The paper aims to establish the prediction model of urban power grid short-term load based on BP neural network algorithm. Method: Five factors influencing the urban power grid short-term load are used to establish the neural network model: date type, weather, daily maximum temperature, daily minimum temperature and daily average temperature. Matlab toolbox is used to develop the testing platform through VC++ programming. Result: The variable learning rates are 0.35 and 0.64. With 23410 iterations, the model is converged, and the global error is 0.00032. Conclusion: Through the data comparison and analysis, the relative error is within 5%, thus indicating the model is accurate and effective, and it can be used to predict the change of urban power grid short-term load.

scholarly journals Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 154827-154835 ◽  
Author(s):  
Qin Jiang ◽  
Ruanming Huang ◽  
Yichao Huang ◽  
Shujuan Chen ◽  
Yuqing He ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Power Grid ◽  
Investment Risk ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization

scholarly journals A BP Neural Network-Based Hierarchical Investment Risk Evaluation Method Considering the Uncertainty and Coupling for the Power Grid

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 110279-110289
Author(s):  
Shujuan Chen ◽  
Qin Jiang ◽  
Yuqing He ◽  
Ruanming Huang ◽  
Jiayong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Evaluation Method ◽  
Power Grid ◽  
Investment Risk

Improved GSA-GA Algorithm Based Bad Data Detection, Identification and Correction for Power Grid

2013 ◽  
Vol 860-863 ◽  
pp. 2470-2473
Author(s):  
Hai Feng Liang ◽  
Ding Hui Shen ◽  
Xiao Lei Yu ◽  
Jing Zhang ◽  
Cheng Shan Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Clustering Algorithm ◽  
Power Grid ◽  
Bp Algorithm ◽  
Data Detection ◽  
Grid Data ◽  
Bad Data Detection ◽  
Ga Algorithm ◽  
Bad Data

This paper presents an improved GSA-GA algorithm to achieve bad-data detection, identification and correction in power grid. The algorithm combines BP neural network, K-means clustering algorithm, gap statistical algorithm (GSA) and genetic algorithm together. BP neural network preprocesses the data, K-means algorithm clusters the preprocessed data and GSA algorithm determines the optimal clustering number and identifies the presence of bad data. After identifying the bad data, GA-BP algorithm is used to correct the identified data. This paper takes simulation tests to verify the proposed algorithms correctness and effectiveness based on actual grid data considering multiple types of existed bad data.

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