Algorithm for identifying wind power ramp events via novel improved dynamic swinging door
With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce e...
        Saved in:
      
    
          | Published in | Renewable energy Vol. 171; pp. 542 - 556 | 
|---|---|
| Main Authors | , , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
            Elsevier Ltd
    
        01.06.2021
     | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 0960-1481 1879-0682  | 
| DOI | 10.1016/j.renene.2021.02.123 | 
Cover
| Abstract | With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems.
•A novel wind power ramp event detection algorithm is presented.•The algorithm is based on swing door algorithm(SDA) and sliding window(SW).•The optimal ‘door width’ of SDA is obtained.•The algorithm shows good performance both in accuracy and efficiency.•Enable auxiliary decision-making for power systems. | 
    
|---|---|
| AbstractList | With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. •A novel wind power ramp event detection algorithm is presented.•The algorithm is based on swing door algorithm(SDA) and sliding window(SW).•The optimal ‘door width’ of SDA is obtained.•The algorithm shows good performance both in accuracy and efficiency.•Enable auxiliary decision-making for power systems.  | 
    
| Author | Xiong, Xiong Xu, Taotao Chen, Zhenghong He, Yingjie Li, Fen Zhang, Fanghong Cui, Yang  | 
    
| Author_xml | – sequence: 1 givenname: Yang surname: Cui fullname: Cui, Yang email: qhcuiyang@126.com organization: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China – sequence: 2 givenname: Yingjie surname: He fullname: He, Yingjie email: hyj202411@sina.com organization: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China – sequence: 3 givenname: Xiong surname: Xiong fullname: Xiong, Xiong email: nxgxiong@163.com organization: Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China – sequence: 4 givenname: Zhenghong surname: Chen fullname: Chen, Zhenghong email: chenzh64@126.com organization: Hubei Meteorological Service Center, Wuhan, 430205, China – sequence: 5 givenname: Fen surname: Li fullname: Li, Fen email: 17027036@qq.com organization: School of Electrical Engineering, Shanghai University of Electric Power, Shanghai, 200090, China – sequence: 6 givenname: Taotao surname: Xu fullname: Xu, Taotao organization: Central China Branch of China Resources Power Investment Co., Ltd., Wuhan, 430000, China – sequence: 7 givenname: Fanghong surname: Zhang fullname: Zhang, Fanghong organization: Chongqing Hai Zhuang Wind Power Equipment Co., Ltd., Chongqing, 401122, China  | 
    
| BookMark | eNqFkE1LAzEQhoNUsK3-Aw85etk1yX5k14NQil9Q8KIHTyHNztYpu0lNti3996bUkwdlDjMw7zMMz4SMrLNAyDVnKWe8vF2nHmysVDDBUyZSLrIzMuaVrBNWVmJExqwuWcLzil-QSQhrxnhRyXxMPmbdynkcPnvaOk-xATtge0C7onu0Dd24PXjqdb-hsIu7QHeoqXU76Cj2Gx-HhjYHq3s0NERkdUQb5_wlOW91F-Dqp0_J--PD2_w5Wbw-vcxni8RkWT0kdSs4l7nMGNSVlI2EZVGCaAsmjeTatMvStEVh-FICGJ0JxqQWuTQF5xXUkE3JzelufOZrC2FQPQYDXactuG1QoszKvBAlK2I0P0WNdyF4aNXGY6_9QXGmjibVWp1MqqNJxYSKJiN29wszOOgBnR28xu4_-P4EQ3SwQ_AqGARroEEPZlCNw78PfAPQi5Uc | 
    
| CitedBy_id | crossref_primary_10_3390_en16031166 crossref_primary_10_1049_gtd2_70003 crossref_primary_10_1007_s11042_022_12467_1 crossref_primary_10_1007_s11814_024_00286_z crossref_primary_10_1016_j_renene_2023_03_131 crossref_primary_10_1016_j_renene_2024_120581 crossref_primary_10_1016_j_energy_2023_128075 crossref_primary_10_3390_solar4010005 crossref_primary_10_1049_rpg2_70002 crossref_primary_10_1109_JSEN_2024_3499365 crossref_primary_10_3390_en15072676 crossref_primary_10_1049_gtd2_12423 crossref_primary_10_1088_1742_6596_2806_1_012014 crossref_primary_10_1080_02286203_2022_2147044 crossref_primary_10_1007_s11356_022_19595_z crossref_primary_10_1016_j_enconman_2024_118767 crossref_primary_10_3390_electronics12214443 crossref_primary_10_1016_j_egyr_2024_07_023 crossref_primary_10_1016_j_compeleceng_2024_109305 crossref_primary_10_1016_j_energy_2022_125888 crossref_primary_10_1016_j_gloei_2023_10_003 crossref_primary_10_1016_j_egyr_2021_08_137 crossref_primary_10_1016_j_solener_2024_112866 crossref_primary_10_1080_00051144_2023_2241772 crossref_primary_10_3390_en15165850 crossref_primary_10_1007_s10479_024_06236_6  | 
    
| Cites_doi | 10.1109/TSTE.2015.2477244 10.1109/TPWRS.2009.2016364 10.1016/j.rser.2015.07.154 10.1109/TSTE.2014.2386870 10.1016/j.ijepes.2019.05.033 10.1016/j.energy.2017.01.104 10.1002/we.2347 10.1016/j.renene.2017.05.046 10.1002/we.526 10.1109/TPWRS.2013.2266378 10.1002/we.1753 10.1016/j.renene.2017.08.071 10.1175/WAF-D-15-0144.1 10.3390/en13236449 10.1016/j.renene.2017.04.005 10.1049/iet-rpg.2017.0144 10.1260/030952409789685681  | 
    
| ContentType | Journal Article | 
    
| Copyright | 2021 Elsevier Ltd | 
    
| Copyright_xml | – notice: 2021 Elsevier Ltd | 
    
| DBID | AAYXX CITATION 7S9 L.6  | 
    
| DOI | 10.1016/j.renene.2021.02.123 | 
    
| DatabaseName | CrossRef AGRICOLA AGRICOLA - Academic  | 
    
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic  | 
    
| DatabaseTitleList | AGRICOLA | 
    
| DeliveryMethod | fulltext_linktorsrc | 
    
| Discipline | Engineering | 
    
| EISSN | 1879-0682 | 
    
| EndPage | 556 | 
    
| ExternalDocumentID | 10_1016_j_renene_2021_02_123 S0960148121003025  | 
    
| GeographicLocations | China | 
    
| GeographicLocations_xml | – name: China | 
    
| GroupedDBID | --K --M .~1 0R~ 123 1B1 1RT 1~. 1~5 29P 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARJD AAXUO ABFNM ABMAC ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHIDL AHJVU AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BELTK BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HMC HVGLF HZ~ IHE J1W JARJE JJJVA K-O KOM LY6 LY9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAC SDF SDG SDP SEN SES SET SEW SPC SPCBC SSR SST SSZ T5K TN5 WUQ ZCA ~02 ~G- AAHBH AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEGFY AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7S9 L.6  | 
    
| ID | FETCH-LOGICAL-c339t-9f21174730e9877d7eb56e2f507c71acfb6cf55c1b7eeca32007a247c5118e9e3 | 
    
| IEDL.DBID | .~1 | 
    
| ISSN | 0960-1481 | 
    
| IngestDate | Sun Sep 28 09:46:43 EDT 2025 Thu Apr 24 23:07:17 EDT 2025 Sat Oct 25 05:19:15 EDT 2025 Fri Feb 23 02:46:27 EST 2024  | 
    
| IsPeerReviewed | true | 
    
| IsScholarly | true | 
    
| Keywords | Power system Dynamic programming Swinging door algorithm (SDA) Wind power Sliding window (SW) Wind power ramp events (WPREs)  | 
    
| Language | English | 
    
| LinkModel | DirectLink | 
    
| MergedId | FETCHMERGED-LOGICAL-c339t-9f21174730e9877d7eb56e2f507c71acfb6cf55c1b7eeca32007a247c5118e9e3 | 
    
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23  | 
    
| PQID | 2636452605 | 
    
| PQPubID | 24069 | 
    
| PageCount | 15 | 
    
| ParticipantIDs | proquest_miscellaneous_2636452605 crossref_primary_10_1016_j_renene_2021_02_123 crossref_citationtrail_10_1016_j_renene_2021_02_123 elsevier_sciencedirect_doi_10_1016_j_renene_2021_02_123  | 
    
| PublicationCentury | 2000 | 
    
| PublicationDate | June 2021 2021-06-00 20210601  | 
    
| PublicationDateYYYYMMDD | 2021-06-01 | 
    
| PublicationDate_xml | – month: 06 year: 2021 text: June 2021  | 
    
| PublicationDecade | 2020 | 
    
| PublicationTitle | Renewable energy | 
    
| PublicationYear | 2021 | 
    
| Publisher | Elsevier Ltd | 
    
| Publisher_xml | – name: Elsevier Ltd | 
    
| References | Qu, Xu, Sun (bib24) 2019; 112 Bossavy, Girard, Kariniotakis (bib28) 2015; 18 Zhukov, Sidorov, Foley (bib13) 2017 Yang, Ma, Li (bib9) 2018; 46 Kamath (bib6) 2011 Tomin, Zhukov, Sidorov (bib14) 2015; 13 Gallego-Cristobal, Cuerva-Tejero, Lopez-Garcia (bib16) 2015; 52 Bristol (bib21) 1990 Cui, Chen, Liu (bib30) 2017 Niu, Guo, Jin (bib2) 2017; 11 Santhanagopalan, Rotea, Iungo (bib29) 2018; 116 Sun, Jiang, Cheng (bib32) 2018; 115 Florita, Hodge, Orwig (bib23) 2013; 8770 Akish, Bianco, Djalalova (bib11) 2019; 22 Sevlian, Rajagopal (bib15) 2013; 28 Harsh, Dipankar, Josep (bib7) 2019; 108 Ela, Kirby, Navid (bib10) 2012 Li, Li, Yan (bib25) 2019; 40 Cui, Ke, Sun (bib31) 2015; 6 Ren, Wang, Zhang (bib20) 2018; 34 Bossavy, Girard, Kariniotakis (bib3) 2013; 16 Han, Qiao, Li (bib8) 2020; 13 Cui, Zhang, Feng (bib27) 2017; 111 Greaves, Collins, Parkes (bib17) 2009; 33 Cui, Zhang, Florita (bib18) 2016; 7 Makarov, Loutan, Ma (bib22) 2009; 24 (bib1) 2019 Kamath (bib19) 2010; 13 Zhang, Florita, Hodge (bib26) 2017; 122 Freedman, Markus, Penc (bib5) 2008 Cornejo-Bueno, Camacho-Gómez, Aybar-Ruiz (bib4) 2018; 32 Bianco, Djalalova, Wilczak (bib12) 2016; 31 Gallego-Cristobal (10.1016/j.renene.2021.02.123_bib16) 2015; 52 Freedman (10.1016/j.renene.2021.02.123_bib5) 2008 Kamath (10.1016/j.renene.2021.02.123_bib6) 2011 Li (10.1016/j.renene.2021.02.123_bib25) 2019; 40 Florita (10.1016/j.renene.2021.02.123_bib23) 2013; 8770 Zhang (10.1016/j.renene.2021.02.123_bib26) 2017; 122 Akish (10.1016/j.renene.2021.02.123_bib11) 2019; 22 Bristol (10.1016/j.renene.2021.02.123_bib21) 1990 Kamath (10.1016/j.renene.2021.02.123_bib19) 2010; 13 Tomin (10.1016/j.renene.2021.02.123_bib14) 2015; 13 Qu (10.1016/j.renene.2021.02.123_bib24) 2019; 112 Zhukov (10.1016/j.renene.2021.02.123_bib13) 2017 Makarov (10.1016/j.renene.2021.02.123_bib22) 2009; 24 Santhanagopalan (10.1016/j.renene.2021.02.123_bib29) 2018; 116 Cui (10.1016/j.renene.2021.02.123_bib31) 2015; 6 Niu (10.1016/j.renene.2021.02.123_bib2) 2017; 11 Sevlian (10.1016/j.renene.2021.02.123_bib15) 2013; 28 Sun (10.1016/j.renene.2021.02.123_bib32) 2018; 115 Cornejo-Bueno (10.1016/j.renene.2021.02.123_bib4) 2018; 32 Bossavy (10.1016/j.renene.2021.02.123_bib3) 2013; 16 Han (10.1016/j.renene.2021.02.123_bib8) 2020; 13 Ela (10.1016/j.renene.2021.02.123_bib10) 2012 Bossavy (10.1016/j.renene.2021.02.123_bib28) 2015; 18 Harsh (10.1016/j.renene.2021.02.123_bib7) 2019; 108 Cui (10.1016/j.renene.2021.02.123_bib30) 2017 Cui (10.1016/j.renene.2021.02.123_bib18) 2016; 7 Yang (10.1016/j.renene.2021.02.123_bib9) 2018; 46 Bianco (10.1016/j.renene.2021.02.123_bib12) 2016; 31 Ren (10.1016/j.renene.2021.02.123_bib20) 2018; 34 Greaves (10.1016/j.renene.2021.02.123_bib17) 2009; 33 Cui (10.1016/j.renene.2021.02.123_bib27) 2017; 111  | 
    
| References_xml | – volume: 24 start-page: 1039 year: 2009 end-page: 1050 ident: bib22 article-title: Operational impacts of wind generation on California power systems publication-title: IEEE Trans. Power Syst. – volume: 115 start-page: 575 year: 2018 end-page: 584 ident: bib32 article-title: Short-term wind power forecasts by a synthetical similar time series data mining method publication-title: Renew. Energy – volume: 46 start-page: 62 year: 2018 end-page: 68 ident: bib9 article-title: Ultra-short-term wind power climbing event detection and statistical analysis publication-title: Power Syst. Protect. Contr. – volume: 52 start-page: 1148 year: 2015 end-page: 1157 ident: bib16 article-title: A review on the recent history of wind power ramp forecasting publication-title: Renew. Sustain. Energy Rev. – volume: 122 start-page: 528 year: 2017 end-page: 541 ident: bib26 article-title: Ramp forecasting performance from improved short-term wind power forecasting publication-title: Energy – volume: 11 start-page: 1667 year: 2017 end-page: 1678 ident: bib2 article-title: Dynamic reactive power optimal allocation to decrease wind power curtailment in a large-scale wind power integration area publication-title: IET Renew. Power Gener. – volume: 16 start-page: 51 year: 2013 end-page: 63 ident: bib3 article-title: Forecasting ramps of wind power production with numerical weather prediction ensembles publication-title: Wind Energy – volume: 34 start-page: 109 year: 2018 end-page: 114 ident: bib20 article-title: Sliding window detection and case analysis of wind power power hill climb event publication-title: Power System and Clean Energy – volume: 111 start-page: 227 year: 2017 end-page: 244 ident: bib27 article-title: Characterizing and analyzing ramping events in wind power, solar power, load, and net load publication-title: Renew. Energy – volume: 112 start-page: 393 year: 2019 end-page: 403 ident: bib24 article-title: A parameter and resolution adaptive algorithm for rapid detection of ramp events in different timescale databases of the power system publication-title: Int. J. Electr. Power Energy Syst. – volume: 28 start-page: 3610 year: 2013 end-page: 3620 ident: bib15 article-title: Detection and statistics of wind power ramps publication-title: IEEE Trans. Power Syst. – volume: 32 year: 2018 ident: bib4 article-title: Wind power ramp event detection with a hybrid neuro-evolutionary approach publication-title: Neural Comput. Appl. – volume: 33 start-page: 309 year: 2009 end-page: 319 ident: bib17 article-title: Temporal forecast uncertainty for ramp events publication-title: Wind Eng. – start-page: 1 year: 2011 end-page: 8 ident: bib6 article-title: Associating weather conditions with ramp events in wind power generation publication-title: IEEE Power Syst. Confer. Exposition – start-page: 661 year: 2017 ident: bib13 article-title: Random forest based approach for concept drift handling: analysis of images, social networks and texts publication-title: Commun. Comput. Infor. Sci. – volume: 13 start-page: 1 year: 2010 end-page: 6 ident: bib19 article-title: Understanding wind ramp events through analysis of historical data publication-title: Proc. IEEE PES Trans. Distribut. Confer. Expo, New Orleans. – volume: 13 start-page: 211 year: 2015 end-page: 228 ident: bib14 article-title: Random forest based model for preventing large-scale emergencies in power systems publication-title: Int. J. Artif. Intell. – volume: 108 start-page: 369 year: 2019 end-page: 379 ident: bib7 article-title: Hybrid machine intelligent SVR variants for wind forecasting and ramp events publication-title: Renew. Sustain. Energy Rev. – volume: 31 start-page: 1157 year: 2016 end-page: 1177 ident: bib12 article-title: A wind energy ramp tool and metric for measuring the skill of numerical weather prediction models publication-title: Weather Forecast. – start-page: 749 year: 1990 end-page: 756 ident: bib21 article-title: Swinging door trending: adaptive trend recording? publication-title: Proc. ISA Nat. Conf. – volume: 40 start-page: 3289 year: 2019 end-page: 3298 ident: bib25 article-title: PV power ramp events probability modeling and assessment in multiple time scales publication-title: Acta Energiae Solaris Sin. – volume: 18 start-page: 1169 year: 2015 end-page: 1184 ident: bib28 article-title: An edge model for the evaluation of wind power ramps characterization approaches publication-title: Wind Energy – start-page: 3376 year: 2017 end-page: 3384 ident: bib30 article-title: Short-term wind power prediction analysis of complicated topography in the condition of wind power brownouts publication-title: Acta Energiae Solaris Sin. – year: 2008 ident: bib5 article-title: Analysis of West Texas Wind Plant Ramp-Up and Ramp-Down Events – year: 2019 ident: bib1 article-title: Global wind energy council report 2019 – volume: 7 start-page: 150 year: 2016 end-page: 162 ident: bib18 article-title: An optimized swinging door algorithm for identifying wind ramping events publication-title: IEEE Trans. Sustain. Energy – volume: 22 start-page: 1165 year: 2019 end-page: 1174 ident: bib11 article-title: Measuring the impact of additional instrumentation on the skill of numerical weather prediction models at forecasting wind ramp events during the first Wind Forecast Improvement Project (WFIP) publication-title: Wind Energy – volume: 13 year: 2020 ident: bib8 article-title: Wind power ramp event forecasting based on feature extraction and deep learning publication-title: Energies – start-page: 1 year: 2012 end-page: 8 ident: bib10 article-title: Effective Ancillary Services Market Designs on High Wind Power Penetration Systems – volume: 8770 start-page: 147 year: 2013 end-page: 152 ident: bib23 article-title: Identifying wind and solar ramping events publication-title: Green Technol. Confer. – volume: 6 start-page: 422 year: 2015 end-page: 433 ident: bib31 article-title: Wind power ramp event forecasting using a stochastic scenario generation method publication-title: IEEE Trans Sustain Energy – volume: 116 start-page: 232 year: 2018 end-page: 243 ident: bib29 article-title: Performance optimization of a wind turbine column for different incoming wind turbulence publication-title: Renew. Energy – start-page: 749 year: 1990 ident: 10.1016/j.renene.2021.02.123_bib21 article-title: Swinging door trending: adaptive trend recording? publication-title: Proc. ISA Nat. Conf. – volume: 13 start-page: 1 issue: 4 year: 2010 ident: 10.1016/j.renene.2021.02.123_bib19 article-title: Understanding wind ramp events through analysis of historical data publication-title: Proc. IEEE PES Trans. Distribut. Confer. Expo, New Orleans. – volume: 7 start-page: 150 issue: 1 year: 2016 ident: 10.1016/j.renene.2021.02.123_bib18 article-title: An optimized swinging door algorithm for identifying wind ramping events publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2015.2477244 – volume: 13 start-page: 211 issue: 1 year: 2015 ident: 10.1016/j.renene.2021.02.123_bib14 article-title: Random forest based model for preventing large-scale emergencies in power systems publication-title: Int. J. Artif. Intell. – volume: 34 start-page: 109 issue: 1 year: 2018 ident: 10.1016/j.renene.2021.02.123_bib20 article-title: Sliding window detection and case analysis of wind power power hill climb event publication-title: Power System and Clean Energy – volume: 24 start-page: 1039 issue: 2 year: 2009 ident: 10.1016/j.renene.2021.02.123_bib22 article-title: Operational impacts of wind generation on California power systems publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2009.2016364 – volume: 52 start-page: 1148 year: 2015 ident: 10.1016/j.renene.2021.02.123_bib16 article-title: A review on the recent history of wind power ramp forecasting publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2015.07.154 – volume: 6 start-page: 422 issue: 2 year: 2015 ident: 10.1016/j.renene.2021.02.123_bib31 article-title: Wind power ramp event forecasting using a stochastic scenario generation method publication-title: IEEE Trans Sustain Energy doi: 10.1109/TSTE.2014.2386870 – year: 2008 ident: 10.1016/j.renene.2021.02.123_bib5 – volume: 8770 start-page: 147 year: 2013 ident: 10.1016/j.renene.2021.02.123_bib23 article-title: Identifying wind and solar ramping events publication-title: Green Technol. Confer. – volume: 112 start-page: 393 issue: 11 year: 2019 ident: 10.1016/j.renene.2021.02.123_bib24 article-title: A parameter and resolution adaptive algorithm for rapid detection of ramp events in different timescale databases of the power system publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2019.05.033 – volume: 122 start-page: 528 year: 2017 ident: 10.1016/j.renene.2021.02.123_bib26 article-title: Ramp forecasting performance from improved short-term wind power forecasting publication-title: Energy doi: 10.1016/j.energy.2017.01.104 – volume: 22 start-page: 1165 issue: 9 year: 2019 ident: 10.1016/j.renene.2021.02.123_bib11 article-title: Measuring the impact of additional instrumentation on the skill of numerical weather prediction models at forecasting wind ramp events during the first Wind Forecast Improvement Project (WFIP) publication-title: Wind Energy doi: 10.1002/we.2347 – volume: 116 start-page: 232 issue: PT.B year: 2018 ident: 10.1016/j.renene.2021.02.123_bib29 article-title: Performance optimization of a wind turbine column for different incoming wind turbulence publication-title: Renew. Energy doi: 10.1016/j.renene.2017.05.046 – start-page: 3376 issue: 12 year: 2017 ident: 10.1016/j.renene.2021.02.123_bib30 article-title: Short-term wind power prediction analysis of complicated topography in the condition of wind power brownouts publication-title: Acta Energiae Solaris Sin. – volume: 16 start-page: 51 issue: 1 year: 2013 ident: 10.1016/j.renene.2021.02.123_bib3 article-title: Forecasting ramps of wind power production with numerical weather prediction ensembles publication-title: Wind Energy doi: 10.1002/we.526 – start-page: 1 year: 2012 ident: 10.1016/j.renene.2021.02.123_bib10 – volume: 28 start-page: 3610 issue: 4 year: 2013 ident: 10.1016/j.renene.2021.02.123_bib15 article-title: Detection and statistics of wind power ramps publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2013.2266378 – volume: 32 year: 2018 ident: 10.1016/j.renene.2021.02.123_bib4 article-title: Wind power ramp event detection with a hybrid neuro-evolutionary approach publication-title: Neural Comput. Appl. – volume: 46 start-page: 62 issue: 6 year: 2018 ident: 10.1016/j.renene.2021.02.123_bib9 article-title: Ultra-short-term wind power climbing event detection and statistical analysis publication-title: Power Syst. Protect. Contr. – volume: 18 start-page: 1169 issue: 7 year: 2015 ident: 10.1016/j.renene.2021.02.123_bib28 article-title: An edge model for the evaluation of wind power ramps characterization approaches publication-title: Wind Energy doi: 10.1002/we.1753 – volume: 115 start-page: 575 year: 2018 ident: 10.1016/j.renene.2021.02.123_bib32 article-title: Short-term wind power forecasts by a synthetical similar time series data mining method publication-title: Renew. Energy doi: 10.1016/j.renene.2017.08.071 – volume: 31 start-page: 1157 year: 2016 ident: 10.1016/j.renene.2021.02.123_bib12 article-title: A wind energy ramp tool and metric for measuring the skill of numerical weather prediction models publication-title: Weather Forecast. doi: 10.1175/WAF-D-15-0144.1 – volume: 13 issue: 23 year: 2020 ident: 10.1016/j.renene.2021.02.123_bib8 article-title: Wind power ramp event forecasting based on feature extraction and deep learning publication-title: Energies doi: 10.3390/en13236449 – volume: 111 start-page: 227 issue: 10 year: 2017 ident: 10.1016/j.renene.2021.02.123_bib27 article-title: Characterizing and analyzing ramping events in wind power, solar power, load, and net load publication-title: Renew. Energy doi: 10.1016/j.renene.2017.04.005 – start-page: 1 year: 2011 ident: 10.1016/j.renene.2021.02.123_bib6 article-title: Associating weather conditions with ramp events in wind power generation publication-title: IEEE Power Syst. Confer. Exposition – volume: 11 start-page: 1667 issue: 13 year: 2017 ident: 10.1016/j.renene.2021.02.123_bib2 article-title: Dynamic reactive power optimal allocation to decrease wind power curtailment in a large-scale wind power integration area publication-title: IET Renew. Power Gener. doi: 10.1049/iet-rpg.2017.0144 – start-page: 661 year: 2017 ident: 10.1016/j.renene.2021.02.123_bib13 article-title: Random forest based approach for concept drift handling: analysis of images, social networks and texts publication-title: Commun. Comput. Infor. Sci. – volume: 108 start-page: 369 issue: 7 year: 2019 ident: 10.1016/j.renene.2021.02.123_bib7 article-title: Hybrid machine intelligent SVR variants for wind forecasting and ramp events publication-title: Renew. Sustain. Energy Rev. – volume: 33 start-page: 309 issue: 4 year: 2009 ident: 10.1016/j.renene.2021.02.123_bib17 article-title: Temporal forecast uncertainty for ramp events publication-title: Wind Eng. doi: 10.1260/030952409789685681 – volume: 40 start-page: 3289 issue: 11 year: 2019 ident: 10.1016/j.renene.2021.02.123_bib25 article-title: PV power ramp events probability modeling and assessment in multiple time scales publication-title: Acta Energiae Solaris Sin.  | 
    
| SSID | ssj0015874 | 
    
| Score | 2.528402 | 
    
| Snippet | With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and... | 
    
| SourceID | proquest crossref elsevier  | 
    
| SourceType | Aggregation Database Enrichment Source Index Database Publisher  | 
    
| StartPage | 542 | 
    
| SubjectTerms | algorithms China Dynamic programming electric power Power system Sliding window (SW) Swinging door algorithm (SDA) wind Wind power Wind power ramp events (WPREs)  | 
    
| Title | Algorithm for identifying wind power ramp events via novel improved dynamic swinging door | 
    
| URI | https://dx.doi.org/10.1016/j.renene.2021.02.123 https://www.proquest.com/docview/2636452605  | 
    
| Volume | 171 | 
    
| hasFullText | 1 | 
    
| inHoldings | 1 | 
    
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Baden-Württemberg Complete Freedom Collection (Elsevier) customDbUrl: eissn: 1879-0682 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0015874 issn: 0960-1481 databaseCode: GBLVA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier Science Direct Journals customDbUrl: eissn: 1879-0682 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0015874 issn: 0960-1481 databaseCode: ACRLP dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier ScienceDirect customDbUrl: eissn: 1879-0682 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0015874 issn: 0960-1481 databaseCode: .~1 dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: ScienceDirect Journal Collection customDbUrl: eissn: 1879-0682 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0015874 issn: 0960-1481 databaseCode: AIKHN dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVLSH databaseName: Elsevier Journals customDbUrl: mediaType: online eissn: 1879-0682 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0015874 issn: 0960-1481 databaseCode: AKRWK dateStart: 19910101 isFulltext: true providerName: Library Specific Holdings  | 
    
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1NT4QwEG2MXvRg_IzfqYlXXKCFwnFjNKtGL2qip4aWQTG7sNld9eZvd6aAURNj4olAWiDTMsxrZ95j7AiSCIy1wvNzhQAFIyLPCFBeodIkNkYa5dYhr67jwZ28uI_u59hJVwtDaZWt7298uvPW7ZVea83euCx7NxR8YzBPDFg4U0MqNJdSkYrB8ftnmkcQJQ0TMzb2qHVXPudyvIg1siKyzNAxdwah-O339MNRu7_P2QpbbsNG3m_ebJXNQbXGlr6QCa6zh_7wsUao_zTiGIjy0lXguiom_obAm49JD41PstGYO9amKX8tM17VrzDkpVtagJznjUA9n76RkBF2zet6ssHuzk5vTwZeK5zgWSHSmZcWCOsQJwgf0kSpXIGJYggLjP2sCjJbmNgWUWQDowBsJmi9MgulsgQ3IAWxyearuoItxoUJIPCNzGOZyEIGBo-G1OYjfJSfqW0mOntp27KKk7jFUHfpY8-6sbImK2s_1GjlbeZ99ho3rBp_tFfdUOhvs0Oj4_-j52E3cho_HNoNySqoX6Y6jGkHluDczr_vvssW6axJHdtj87PJC-xjkDIzB24WHrCF_vnl4PoDn7_naQ | 
    
| linkProvider | Elsevier | 
    
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Nb9QwELWWcoAeUPlSS2kxEtewSWzHyXG1arWUbS_sSuVkxc4EgrbJar9647d3xkmqgoQqcYqU2Ek0dibv2TNvGPsEqQLrnAjCQiNBQUQUWAE6KHWWJtZKq_065OVVMpnLi2t1PWDjPheGwio739_6dO-tuzPDzprDZVUNvxH4RjBPClg4U2P1hD2VKtbEwD7_vo_ziFTaSjFj64Ca9_lzPsiLZCNrUsuMvXRnFIt__Z_-8tT-93N-wF50uJGP2ld7yQZQv2L7D9QEX7Pvo8WPBrn-zxuOSJRXPgXXpzHxW2TefEkF0fgqv1lyL9u05rsq53WzgwWv_NoCFLxoK9Tz9S1VMsKuRdOs3rD5-dlsPAm6ygmBEyLbBFmJvA6JggghS7UuNFiVQFwi-HM6yl1pE1cq5SKrAVwuaMEyj6V2xDcgA_GW7dVNDYeMCxtBFFpZJDKVpYwsHi2Vm1f4qDDXR0z09jKukxWn6hYL08eP_TKtlQ1Z2YSxQSsfseC-17KV1Xikve6HwvwxPQx6_kd6fuxHzuCXQ9sheQ3Ndm3ihLZgic-9---7f2DPJrPLqZl-ufp6zJ7TlTaO7D3b26y2cIKIZWNP_Yy8Awtu6P4 | 
    
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Algorithm+for+identifying+wind+power+ramp+events+via+novel+improved+dynamic+swinging+door&rft.jtitle=Renewable+energy&rft.au=Cui%2C+Yang&rft.au=He%2C+Yingjie&rft.au=Xiong%2C+Xiong&rft.au=Chen%2C+Zhenghong&rft.date=2021-06-01&rft.issn=0960-1481&rft.volume=171+p.542-556&rft.spage=542&rft.epage=556&rft_id=info:doi/10.1016%2Fj.renene.2021.02.123&rft.externalDBID=NO_FULL_TEXT | 
    
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0960-1481&client=summon | 
    
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0960-1481&client=summon | 
    
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0960-1481&client=summon |