Fault Identification of Power Grid Based on BP Neural Network

With the help of wide area information, a new fault identification algorithm of power grid based on PNN is proposed. This algorithm gives a definition of the line associated domain, the elements’ action information of the line associated domain gathered by line IEDs can form the feature vector into PNN classifier, and then the fault elements of power grid would be identified on PNN classifier. Through a large number of simulation experiments, it shows that the new fault identification algorithm of power grid based on PNN and wide area information has high accuracy and good fault tolerance.

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Safety Assessment of Transmission Grid Based on Fuzzy Comprehensive Evaluation and BP Neural Network

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.439-440.528 ◽

2010 ◽

Vol 439-440 ◽

pp. 528-533

Author(s):

Yuan Sheng Huang ◽

Wei Fang ◽

Cheng Fang Tian

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Safety Assessment ◽

Evaluation Method ◽

Comprehensive Evaluation ◽

Fuzzy Comprehensive Evaluation ◽

Transmission Grid ◽

Comprehensive Evaluation Method ◽

System Method ◽

Grid Based

In the practice of safety assessment on transmission grid, there is the variation degree of many indexes which can not be accurately described, and fuzzy comprehensive evaluation method can reflect the safety degree of every element. In addition, the combination use of BP neural network and expert system method can determine impact extent of assessment factors on safety of transmission grid and the weight of each factor relative to safety of transmission grid. Therefore, the paper proposes the safety assessment of transmission grid based on BP neural network and fuzzy comprehensive evaluation. Finally, an example is used to prove the method is high precision and practical.

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

Sustainability ◽

10.3390/su132413746 ◽

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.

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