IET Renewable Power Generation最新文献_第2页

An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence 基于多策略随机加权灰狼优化器与蜂群智能的巨浪高度预报综合方法

IET Renewable Power Generation Pub Date : 2024-02-07 DOI: 10.1049/rpg2.12961

Emrah Dokur, N. Erdogan, Mahdi Ebrahimi Salari, Ugur Yuzgec, Jimmy Murphy

{"title":"An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence","authors":"Emrah Dokur, N. Erdogan, Mahdi Ebrahimi Salari, Ugur Yuzgec, Jimmy Murphy","doi":"10.1049/rpg2.12961","DOIUrl":"https://doi.org/10.1049/rpg2.12961","url":null,"abstract":"While wave energy is regarded as one of the prominent renewable energy resources to diversify global low‐carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current experience in wave energy systems demonstrates that operation and maintenance costs are dominant in their cost structure due to unplanned maintenance resulting in energy production loss. Accurate and high performance simulation forecasting tools are required to improve the efficiency and safety of wave converters. This paper proposes a new methodology for significant wave height forecasting. It is based on incorporating swarm decomposition (SWD) and multi‐strategy random weighted grey wolf optimizer (MsRwGWO) into a multi‐layer perceptron (MLP) forecasting model. This approach takes advantage of the SWD approach to generate more stable, stationary, and regular patterns of the original signal, while the MsRwGWO optimizes the MLP model parameters efficiently. As such, forecasting accuracy has improved. Real wave datasets from three buoys in the North Atlantic Sea are used to test and validate the forecasting performance of the proposed model. Furthermore, the performance is evaluated through a comparison analysis against deep‐learning based state‐of‐the‐art forecasting models. The results show that the proposed approach significantly enhances the model's accuracy.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel wasserstein generative adversarial network for stochastic wind power output scenario generation 用于随机风电输出情景生成的新型 Wasserstein 生成式对抗网络

IET Renewable Power Generation Pub Date : 2024-02-01 DOI: 10.1049/rpg2.12932

Xiurong Zhang, Daoliang Li, Xueqian Fu

引用次数: 0

Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction 基于 BIGRU 网络和误差辨别校正的风能估算混合模型

IET Renewable Power Generation Pub Date : 2024-02-01 DOI: 10.1049/rpg2.12956

Yalong Li, Ye Jin, Yangqing Dan, Wenting Zha

{"title":"Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction","authors":"Yalong Li, Ye Jin, Yangqing Dan, Wenting Zha","doi":"10.1049/rpg2.12956","DOIUrl":"https://doi.org/10.1049/rpg2.12956","url":null,"abstract":"Accurate estimation of wind power is essential for predicting and maintaining the power balance in the power system. This paper proposes a novel approach to enhance the accuracy of wind power estimation through a hybrid model integrating neural networks and error discrimination‐correction techniques. In order to improve the accuracy of estimation, a bidirectional gating recurrent unit is developed, forming an initial wind power estimation curve through training. Additionally, a sequential model‐based algorithmic configuration optimizes bidirectional gating recurrent unit's network hyperparameters. To tackle estimation errors, a multi‐layer perceptron combined with sequential model‐based algorithmic configuration is employed to create a classification model that automatically discerns the quality of estimates. Subsequently, an innovative correction model, based on grey relevancy degree and relevancy errors, is devised to rectify erroneous estimates. The final estimates result from a summation of the initial estimates and the values derived from error corrections. By analysing the real data from a wind farm in northwest China, a simulation test validates the proposed hybrid model. Experimental results demonstrate a substantial improvement in modelling accuracy when compared to the initial model.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"77 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139824675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated control strategy of BESS in primary frequency modulation considering SOC recovery 考虑 SOC 恢复的初级频率调制 BESS 综合控制策略

IET Renewable Power Generation Pub Date : 2024-02-01 DOI: 10.1049/rpg2.12959

Yiqi Liu, Meiru Chen, Bing Xie, Mingfei Ban

{"title":"Integrated control strategy of BESS in primary frequency modulation considering SOC recovery","authors":"Yiqi Liu, Meiru Chen, Bing Xie, Mingfei Ban","doi":"10.1049/rpg2.12959","DOIUrl":"https://doi.org/10.1049/rpg2.12959","url":null,"abstract":"This paper proposes a comprehensive control strategy for a battery energy storage system (BESS) participating in primary frequency modulation (FM) while considering the state of charge (SOC) recovery. On the one hand, when the frequency fluctuations are outside the dead zone of the FM, variable coefficient virtual sag control and variable coefficient virtual inertia control are coordinated for more accurate primary FM. Also, an assisted FM strategy is proposed, where the temporal alignment of assisted FM is determined by the regional segmentation of the frequency deviation and SOC, and it enables the BESS to recover SOC concurrently during its involvement in primary FM. On the other hand, when the frequency fluctuations are inside the dead zone of the FM, typical S‐type functions are deployed to dynamically establish the mutual constraints relationships of SOC recovery demand coefficient and frequency deviation. Subsequently, the recovery control coefficients are derived, and accordingly, a SOC recovery strategy is proposed. Finally, simulation results verify the efficacy of the proposed control strategy in the grid primary FM system and demonstrate that the strategy expedites the SOC recovery and concurrently diminishes the conventional unit output.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"95 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139872789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel DC‐link voltage synchronous control with enhanced inertial capability for full‐scale power conversion wind turbine generators 用于全功率转换风力涡轮发电机的具有增强惯性能力的新型直流链路电压同步控制装置

IET Renewable Power Generation Pub Date : 2024-02-01 DOI: 10.1049/rpg2.12936

Yao Qin, Han Wang, Dangsheng Zhou, Zhen-Quan Deng, Jianwen Zhang, Xu Cai

{"title":"A novel DC‐link voltage synchronous control with enhanced inertial capability for full‐scale power conversion wind turbine generators","authors":"Yao Qin, Han Wang, Dangsheng Zhou, Zhen-Quan Deng, Jianwen Zhang, Xu Cai","doi":"10.1049/rpg2.12936","DOIUrl":"https://doi.org/10.1049/rpg2.12936","url":null,"abstract":"The new power system is characterized by high penetration of renewable energy sources and a high proportion of power electronics (namely, double‐high). The grid‐forming control is an effective method to improve the grid‐connected stability of wind turbine generators (WTGs) in the “double‐high” grid. The control method based on the DC‐link voltage can effectively realize the grid‐forming control for WTGs. However, there is a disadvantage that the DC‐link voltage cannot be maintained at the given value. To address this, the grid synchronization mechanism of DC‐link voltage is explored and the specific implementation of a novel DC‐link voltage synchronous control applicable to full‐scale power conversion WTGs is proposed. Then, the boundary of the inertial coefficient is probed through the state‐space method. And a compensation control is proposed to enlarge the inertial response capability based on the mechanism of damping characteristics. Finally, the PSCAD/EMTDC simulation and RTLAB hardware‐in‐loop experiment show that the synchronization frequency can accurately map the grid frequency changes in real‐time under the premise that the DC‐link voltage remains constant. In addition, the inertial coefficient can be increased by more than five times with the compensation strategy, which can enhance the support capability of the WTGs to the power grid.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"361 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139828318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0