RESEARCH ON INTELLIGENT ENERGY PREDICTION AND CONTROL FOR PHOTOVOLTAIC MONITORING DEVICES OF TRANSMISSION LINES

Wang Chuanyu; Liu Ziyan; Lin Zequan; Hao Qiangyan; Wang Yunyun; Pei Gang

doi:10.19912/j.0254-0096.tynxb.2024-2130

PDF(2143 KB)

Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (4) : 644-652. DOI: 10.19912/j.0254-0096.tynxb.2024-2130

RESEARCH ON INTELLIGENT ENERGY PREDICTION AND CONTROL FOR PHOTOVOLTAIC MONITORING DEVICES OF TRANSMISSION LINES

Wang Chuanyu¹, Liu Ziyan¹, Lin Zequan¹, Hao Qiangyan¹, Wang Yunyun², Pei Gang¹

Author information +

History +

Abstract

Photovoltaic monitoring devices for power supply and transmission lines often experience significant weather-related issues and high offline rates. To mitigate these problems in isolated PV systems, an intelligent energy prediction and control technology is introduced. This technology uses historical and forecasted weather data, combined with a novel VMD-RIME-LSTM neural network model, to predict future PV output. It intelligently adjusts device operation modes based on energy information and monitoring needs, ensuring high-quality monitoring and efficient solar resource utilization. This enhances device stability and reduces offline rates. An experimental platform and a full-year simulation model were developed to verify the technology’s effectiveness in lowering offline rates and improving operational quality.

Key words

PV power / neural network model / energy management / transmission line monitoring device / power prediction / photovoltaic isolated energy system

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Wang Chuanyu, Liu Ziyan, Lin Zequan, Hao Qiangyan, Wang Yunyun, Pei Gang. RESEARCH ON INTELLIGENT ENERGY PREDICTION AND CONTROL FOR PHOTOVOLTAIC MONITORING DEVICES OF TRANSMISSION LINES[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 644-652 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2130

References

[1] 中国能源新闻网. 2024年全国电网工程建设投资创新高[EB/OL]. (2025-11-04). https://www.cpnn.com.cn/news/dianli2023/202511/t20251103_1843061.html
China Energy News Network. National power grid engineering construction investment hits a record high in2024[EB/OL]. (2025-11-04). https://www.cpnn.com.cn/news/dianli2023/202511/t20251103_1843061.html
[2] 谢景海, 袁敬中, 郭嘉, 等. 一种输电线路多参数可视化物联监测系统[J]. 科学技术与工程, 2021, 21(12): 4975-4981.
XIE J H, YUAN J Z, GUO J, et al.A multi-parameter visual internet of things monitoring system for transmission lines[J]. Science technology and engineering, 2021, 21(12): 4975-4981.
[3] AHMED R, SREERAM V, MISHRA Y, et al.A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization[J]. Renewable and sustainable energy reviews, 2020, 124: 109792.
[4] 周育才, 肖添, 谢七月, 等. 基于聚类的HPO-BILSTM光伏功率短期预测[J]. 太阳能学报, 2024, 45(4): 512-518.
ZHOU Y C, XIAO T, XIE Q Y, et al.Clustering-based HPO-BILSTM short-term prediction of PV power[J]. Acta energiae solaris sinica, 2024, 45(4): 512-518.
[5] GAO Y S.PID-based search algorithm: a novel metaheuristic algorithm based on PID algorithm[J]. Expert systems with applications, 2023, 232: 120886.
[6] 王印松, 吕率豪. 基于改进时间卷积网络的微电网超短期负荷预测[J]. 太阳能学报, 2024, 45(6): 255-263.
WANG Y S, LYU S H.Ultra-short-term power load prediction of micro-grid based on improved temporal convolutional network[J]. Acta energiae solaris sinica, 2024, 45(6): 255-263.
[7] 王晓霞, 艾兴成, 王涛. 基于实例迁移学习的小样本光伏功率短期预测[J]. 太阳能学报, 2024, 45(6): 325-333.
WANG X X, AI X C, WANG T.Few-shot photovoltaic power short-term forecasting based on instance transfer learning[J]. Acta energiae solaris sinica, 2024, 45(6): 325-333.
[8] LIMOUNI T, YAAGOUBI R, BOUZIANE K, et al.Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model[J]. Renewable energy, 2023, 205: 1010-1024.
[9] ZHAO H, ZHU D L, YANG Y L, et al.Study on photovoltaic power forecasting model based on peak sunshine hours and sunshine duration[J]. Energy science & engineering, 2023, 11(12): 4570-4580.
[10] BRUSCO G, MENNITI D, PINNARELLI A, et al.Renewable energy community with distributed storage optimization to provide energy sharing and additional ancillary services[J]. Sustainable energy, grids and networks, 2023, 36: 101173.
[11] 邢毓华, 任甜甜. 改进MOPSO在微电网优化调度中的应用研究[J]. 太阳能学报, 2024, 45(6): 191-200.
XING Y H, REN T T.Application research of improved MOPSO in microgrid optimal dispatch[J]. Acta energiae solaris sinica, 2024, 45(6): 191-200.
[12] 王海燕. 独立运行模式下微电网的能量管理与协调控制策略研究[D]. 西安: 西安理工大学, 2017.
WANG H Y.Research on the energy management and coordinated control strategy of microgrid in the autonomous mode[D]. Xi’an: Xi’an University of Technology, 2017.
[13] SREEKANTH R, BABU M R.Energy management of renewable-based micro-grids with electric vehicle aggregators: COA-CANN approach[J]. Environment, development and sustainability, 2024.
[14] ALNEJAILI T, LABDAI S, CHRIFI-ALAOUI L.Predictive management algorithm for controlling PV-battery off-grid energy system[J]. Sensors, 2021, 21(19): 6427.
[15] 董存, 王铮, 白捷予, 等. 光伏发电功率超短期预测方法综述[J]. 高电压技术, 2023, 49(7): 2938-2951.
DONG C, WANG Z, BAI J Y, et al.Review of ultra-short-term forecasting methods for photovoltaic power generation[J]. High voltage engineering, 2023, 49(7): 2938-2951.
[16] SANTHAKUMARI M, SAGAR N.A review of the environmental factors degrading the performance of silicon wafer-based photovoltaic modules: failure detection methods and essential mitigation techniques[J]. Renewable and sustainable energy reviews, 2019, 110: 83-100.
[17] 鞠泽慧. 光伏微电网发电预测及优化调度研究[D]. 南昌: 南昌大学, 2022.
JU Z H.Research on power generation prediction and optimal scheduling of photovoltaic microgrid[D]. Nanchang: Nanchang University, 2022.
[18] 梁亚峰, 马立红, 邱剑洪, 等. 基于CEEMD-LSTM光伏短期功率预测[J]. 科学技术与工程, 2024, 24(13): 5396-5405.
LIANG Y F, MA L H, QIU J H, et al.Short-term photovoltaic power forecasting based on CEEMD-LSTM[J]. Science technology and engineering, 2024, 24(13): 5396-5405.
[19] CHOI G, OH H S, KIM D.Enhancement of variational mode decomposition with missing values[J]. Signal processing, 2018, 142: 75-86.
[20] SU H, ZHAO D, HEIDARI A A, et al.RIME: a physics-based optimization[J]. Neurocomputing, 2023, 532: 183-214.
[21] LANDI F, BARALDI L, CORNIA M, et al.Working memory connections for LSTM[J]. Neural networks, 2021, 144: 334-341.
[22] LIU C C, LI M, YU Y J, et al.A review of multitemporal and multispatial scales photovoltaic forecasting methods[J]. IEEE access, 2022, 10: 35073-35093.
[23] QING X Y, NIU Y G.Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM[J]. Energy, 2018, 148: 461-468.
[24] 赵东生, 周原, 范亚洲, 等. 输电线路在线监测设备供能技术研究综述[J]. 广东电力, 2024, 37(7): 107-117.
ZHAO D S, ZHOU Y, FAN Y Z, et al.Overview of energy supply technology for online monitoring equipment of transmission lines[J]. Guangdong electric power, 2024, 37(7): 107-117.
[25] 侯祖锋, 王超, 徐春华, 等. 考虑负荷重要程度的配电网韧性提升策略及评估方法[J]. 电力科学与技术学报, 2024, 39(3): 78-85.
HOU Z F, WANG C, XU C H, et al.Promotion strategy and evaluation method of distribution network resilience considering load importance[J]. Journal of electric power science and technology, 2024, 39(3): 78-85.