{"title":"考虑时频分析和收敛效应的基于 Informer 的光伏簇功率预测新方法","authors":"","doi":"10.1016/j.epsr.2024.111049","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate prediction of photovoltaic (PV) cluster power is crucial for the reliable and cost-effective operation of PV high penetration power systems. This paper introduces a method that utilizes time-frequency correlation. Firstly, the cluster power is decomposed using Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise Algorithm (CEEMDAN) to extract more time-frequency information. Then, Kendall correlation coefficients are used to assess the consistency of time-frequency information across individual power plants and clusters within each frequency band. These coefficients are weighted according to the energy distribution in each frequency band to select the PV reference power station. Additionally, factors influencing PV power generation are taken into account to develop the PV impact factor. An Informer neural network is employed to predict the power output of the PV reference power plant. A trend inconsistency factor is introduced to adjust the PV cluster power variance. The final cluster prediction value is determined by correcting the linearly scaled variance using the adjusted variance. The method's feasibility and effectiveness are validated using real operational data from the PV cluster power plant in Alice Springs, Australia. This method offers a novel and highly accurate approach for forecasting future PV cluster power.</p></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378779624009350/pdfft?md5=9f719c3a2073e4207cccd1275545a2c0&pid=1-s2.0-S0378779624009350-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A new method of photovoltaic clusters power prediction based on Informer considering time-frequency analysis and convergence effect\",\"authors\":\"\",\"doi\":\"10.1016/j.epsr.2024.111049\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate prediction of photovoltaic (PV) cluster power is crucial for the reliable and cost-effective operation of PV high penetration power systems. This paper introduces a method that utilizes time-frequency correlation. Firstly, the cluster power is decomposed using Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise Algorithm (CEEMDAN) to extract more time-frequency information. Then, Kendall correlation coefficients are used to assess the consistency of time-frequency information across individual power plants and clusters within each frequency band. These coefficients are weighted according to the energy distribution in each frequency band to select the PV reference power station. Additionally, factors influencing PV power generation are taken into account to develop the PV impact factor. An Informer neural network is employed to predict the power output of the PV reference power plant. A trend inconsistency factor is introduced to adjust the PV cluster power variance. The final cluster prediction value is determined by correcting the linearly scaled variance using the adjusted variance. The method's feasibility and effectiveness are validated using real operational data from the PV cluster power plant in Alice Springs, Australia. This method offers a novel and highly accurate approach for forecasting future PV cluster power.</p></div>\",\"PeriodicalId\":50547,\"journal\":{\"name\":\"Electric Power Systems Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0378779624009350/pdfft?md5=9f719c3a2073e4207cccd1275545a2c0&pid=1-s2.0-S0378779624009350-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electric Power Systems Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378779624009350\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electric Power Systems Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378779624009350","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A new method of photovoltaic clusters power prediction based on Informer considering time-frequency analysis and convergence effect
Accurate prediction of photovoltaic (PV) cluster power is crucial for the reliable and cost-effective operation of PV high penetration power systems. This paper introduces a method that utilizes time-frequency correlation. Firstly, the cluster power is decomposed using Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise Algorithm (CEEMDAN) to extract more time-frequency information. Then, Kendall correlation coefficients are used to assess the consistency of time-frequency information across individual power plants and clusters within each frequency band. These coefficients are weighted according to the energy distribution in each frequency band to select the PV reference power station. Additionally, factors influencing PV power generation are taken into account to develop the PV impact factor. An Informer neural network is employed to predict the power output of the PV reference power plant. A trend inconsistency factor is introduced to adjust the PV cluster power variance. The final cluster prediction value is determined by correcting the linearly scaled variance using the adjusted variance. The method's feasibility and effectiveness are validated using real operational data from the PV cluster power plant in Alice Springs, Australia. This method offers a novel and highly accurate approach for forecasting future PV cluster power.
期刊介绍:
Electric Power Systems Research is an international medium for the publication of original papers concerned with the generation, transmission, distribution and utilization of electrical energy. The journal aims at presenting important results of work in this field, whether in the form of applied research, development of new procedures or components, orginal application of existing knowledge or new designapproaches. The scope of Electric Power Systems Research is broad, encompassing all aspects of electric power systems. The following list of topics is not intended to be exhaustive, but rather to indicate topics that fall within the journal purview.
• Generation techniques ranging from advances in conventional electromechanical methods, through nuclear power generation, to renewable energy generation.
• Transmission, spanning the broad area from UHV (ac and dc) to network operation and protection, line routing and design.
• Substation work: equipment design, protection and control systems.
• Distribution techniques, equipment development, and smart grids.
• The utilization area from energy efficiency to distributed load levelling techniques.
• Systems studies including control techniques, planning, optimization methods, stability, security assessment and insulation coordination.