Author(s)

Ying Ding ¹

Affiliation(s)

¹ IT Solution Consulting, MOYI Inc, New York, NY, USA

Corresponding Author

Ying Ding

ABSTRACT

With the deep integration of the Internet of Things, big data, and artificial intelligence technology, smart grids are moving towards higher levels of automation and intelligent management. In today's rapidly changing smart grid technology, improving the intelligence and accuracy of distribution network management has become an urgent need for industry development. In response to this challenge, this study proposes a low-voltage distribution network engineering information recognition technology based on an improved random forest algorithm, aiming to solve the problem of discrepancies between the settlement engineering quantity and the actual engineering quantity in the settlement management of the power system distribution network. Extract data features through kernel principal component analysis algorithm, construct feature vectors of engineering information, and optimize random forest parameter selection through precise weighted decision tree and particle swarm optimization algorithm. The experimental results show that the algorithm can effectively identify abnormal engineering information, with higher accuracy and efficiency, providing new ideas and practical experience for the development of intelligent distribution network management technology.

KEYWORDS

Random Forest, Engineering Information, Kernel Principal Component Analysis, Precise Weighting, Classification Recognition, Low-Voltage Distribution Network

CITE THIS PAPER

Ying Ding, Research on Distribution Engineering Information Recognition Technology Based on Improved RF Algorithm. Journal of Electrotechnology, Electrical Engineering and Management (2024) Vol. 7: 153-158. DOI: http://dx.doi.org/10.23977/jeeem.2024.070120.

REFERENCES

[1] Liu C, Fu L, Li H, et al.Dynamic Prediction Algorithm for Low-Voltage Distribution Network Power Loss in a Smart City Based on Classification Decision Tree and Marketing Data[J].Journal of Testing and Evaluation: A Multidisciplinary Forum for Applied Sciences and Engineering, 2023.
[2] Tang Z , Xiao Y , Jiao Y ,et al.Research on Short-Term Low-Voltage Distribution Network Line Loss Prediction Based on Kmeans-LightGBM[J].Journal of circuits, systems and computers, 2022.DOI:10.1142/S0218126622502280.
[3] Samal L , Palo H K , Sahu B N .The recognition of 3-phase power quality events using optimal feature selection and random forest classifier[J].International journal of computational vision and robotics, 2023.
[4] Lai X , Cao M , Liu S ,et al.Low-voltage distribution network topology identification method based on characteristic current[C]//2021 6th Asia Conference on Power and Electrical Engineering (ACPEE).2021.DOI: 10.1109/ ACPEE51499.2021.9437092.

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