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Reactive Power Optimization of Power Project Management System Based on K-Means Algorithm

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Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 98 )


With the continuous improvement of our country’s economic and technological level, the rapid expansion of the scale of the power system, and the continuous application of advanced power technology in the power system, which makes the complexity and urgency of reactive PO more prominent. Its connotation is also changing day by day, which increases the randomness, uncertainty, unpredictability and other factors in the reactive PO of the power system. It is precisely because of the emergence of these new challenges that it has always been one of the hot issues in the field of power system optimization. The reactive PO calculation method has been developed so far, and it is mainly divided into the bionic intelligent optimization algorithm and the traditional conventional mathematical algorithm. Conventional mathematical algorithms face the shortcomings of non-linear, discrete and continuous variable mixed, multi-constrained reactive PO inverse problems, it is easy to fall into the local optimal solution or even fail to find the optimal solution. When the bionic intelligent optimization method deals with the above problems, it is widely used in the field of power system reactive PO due to its high flexibility, high robustness, strong adaptability and other characteristics. This article explores the reactive PO of the power project management system based on the K-means algorithm. First, consult the relevant information to get a general understanding of the reactive PO of the power project management system, and then summarize the reactive PO methods of the power project management system based on the data. According to the method, the reactive PO steps of the power project management system based on the K-means algorithm are proposed, and then the experimental verification is carried out for the optimized power project management system. The experimental results show that the active power loss after optimization using the standard particle swarm algorithm is reduced from the initial 0.159 to 0.133, which is a 16.352% reduction; when the IPSO algorithm is used for optimization, the active power loss value is reduced to 0.131, which is a decrease of 17.610%; after adopting the K-means algorithm, the active power loss value drops to 0.125, and the reduction rate is 21.384%.


  • K-means algorithm
  • Reactive PO
  • Power system
  • Particle swarm optimization

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  1. Wang, Q., Chao, L., Yong, L., et al.: A dynamic reactive power optimization method of receiving power system containing infeasible buses with violated voltage constraint. Diangong Jishu Xuebao/Trans. China Electrotech. Soc. 33(5), 1096–1105 (2018)

    Google Scholar 

  2. Zheng, F., Wei, W., Pu, T., et al.: Power system reactive power optimization based on fuzzy formulation and interior point filter algorithm. Energy Power Eng. 05(4), 693–697 (2016)

    Google Scholar 

  3. Jangir, P., Parmar, S., et al.: A novel hybrid particle swarm optimizer with multi verse optimizer for global numerical optimization and optimal reactive power dispatch problem. Eng. Sci. Technol. Int. J. 20(2), 570–586 (2017)

    Google Scholar 

  4. Abdelhady, S., Osama, A., Shaban, A., et al.: A real-time optimization of reactive power for an intelligent system using genetic algorithm. IEEE Access 8(1), 11991–12000 (2020)

    CrossRef  Google Scholar 

  5. Hao, W., Liu, B., Yao, S., et al.: Reactive power optimization of distribution network with distributed generation based on genetic and immune algorithm. J. Eng. 2019(16), 1280–1284 (2018)

    CrossRef  Google Scholar 

  6. Kassem, A.M., Abdelaziz, A.Y.: Firefly optimization algorithm for the reactive power control of an isolated wind-diesel system. Electr. Power Componen. Syst. 45(13), 1413–1425 (2017)

    CrossRef  Google Scholar 

  7. Tabrizi, N., Babaei, E., Mehdinejad, M.: An interactive fuzzy satisfying method based on particle swarm optimization for multi-objective function in reactive power market. Iran. J. Electr. Electron. Eng. 12(1), 65–72 (2016)

    Google Scholar 

  8. Zhu, Y., Zhuo, F., Wang, F., et al.: A virtual impedance optimization method for reactive power sharing in networked microgrid. IEEE Trans. Power Electron. 31(4), 2890–2904 (2016)

    CrossRef  Google Scholar 

  9. Sheng, W., Liu, K.-y., Liu, Y., Ye, X., He, K.: A reactive power coordinated optimization method with renewable distributed generation based on improved harmony search. IET Gener. Transm. Distrib. 10(13), 3152–3162 (2016)

    Google Scholar 

  10. Zhang, X., Yu, T., Yang, B., Cheng, L., et al.: Accelerating bio-inspired optimizer with transfer reinforcement learning for reactive power optimization. Knowl.-Based Syst. 116, 26–38 (2017)

    CrossRef  Google Scholar 

  11. Lizhen, W.U., Jiang, L., Hao, X.: Reactive power optimization of active distribution network based on optimal scenario generation algorithm. Power Syst. Prot. Control 45(15), 152–159 (2017)

    Google Scholar 

  12. Niu, T., Guo, Q., Sun, H., et al.: Dynamic reactive power reserve optimization in wind power integration areas. IET Gener. Transm. Distrib. 12(2), 507–517 (2017)

    CrossRef  Google Scholar 

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Chai, C., Liu, T., Wang, C. (2022). Reactive Power Optimization of Power Project Management System Based on K-Means Algorithm. In: Macintyre, J., Zhao, J., Ma, X. (eds) The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIoT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 98 . Springer, Cham.

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