Abstract
Due to the long prediction time and the large range of data filtering, the traditional algorithm has low system operation efficiency. For this reason, distributed learning based on machine learning is widely used to predict the power grid output. First, establish a grid output prediction model to limit the system’s line loss and transformer losses. Secondly, based on the distributed photovoltaic power generation output prediction model, the vector moment method and the information method are used to narrow the search space. Based on the data concentration and fitness function values, the calculation formula of voltage output prediction of distribution network nodes with distributed photovoltaics is derived to realize the power grid output prediction algorithm. Finally, it is proved by experiments that distributed PV large-scale access to power grid output prediction algorithm can effectively improve system operation efficiency.
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References
Liu, S., Luo, F., Wang, C., et al.: Calculation method of hosting capacity for distributed grid-connected photovoltaic based on improved big bang-big crunch algorithm. Proceedings of the CSU-EPSA 11, 5–10 (2017)
Cheng, M., Dai, X., Wang, S., et al.: Study on voltage characteristics of distributed photovoltaic access to active distribution network. Northeast. Power Technol. 38(5), 2118–2221 (2017)
Wang, H., Ge, L., Li, H., et al.: Summary of characteristics analysis and prediction methods of distributed photovoltaic power generation. Electr. Power Constr. 38(7), 2321–2329 (2017)
Yang, F., Tian, C., Li, Z., et al.: Distributed photovoltaic spatial-temporal distribution prediction considering space capacity saturation. Power Grid Technol. 23(12), 3917–3925 (2017)
Li, L., Wang, J., He, Y., et al.: Multi-point PV-DG day-ahead allocation plan based on K-means clustering particle swarm optimization. High Volt. Technol. 44(4), 1263–1270 (2017)
Huang, W., Gao, Y., Zhang, Y., et al.: Limit capacity assessment of distributed photovoltaic access distribution network considering uncertainty. Power Syst. Prot. Control. 11(14), 1453–1654 (2018)
Jin, Z., Xiang, T., Chen, H., et al.: Distributed photovoltaic access planning method considering power quality problems [J]. Power system protection and control 45(9), 1231–1238 (2017)
Yang, C., Pan, Yu., Zeyang, W., et al.: Output optimization and capacity allocation method of distributed photovoltaic-energy storage system considering power loss. Renew. Energy 35(2), 245–251 (2017)
He, X., Zhang, M., Zhu, Y., et al.: Application analysis of centralized and string inverter in photovoltaic power plant. J. Xi’an Polytech. Univ. 04, 443–448 (2018)
Zhang, W., Qi, W., Xu, Q., et al.: Study on location and capacity of distributed photovoltaic access distribution network considering time series characteristics. J. Nanjing Norm. Univ. (Eng. Ed.) 17(3), 1122–1128 (2017)
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Lei, Z., Yang, Yb., Xu, Xh. (2019). Distributed Learning Algorithm for Distributed PV Large-Scale Access to Power Grid Based on Machine Learning. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-36402-1_47
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DOI: https://doi.org/10.1007/978-3-030-36402-1_47
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