IET Renewable Power Generation最新文献

{"title":"The Diagnosis of Wind Turbine Blade Imbalance Using Dual-Input Signals in Parallel CNN-Transformer","authors":"Shu Cheng,&nbsp;Jingming Li,&nbsp;Chaoqun Xiang,&nbsp;Xizhuo Yu,&nbsp;Hongwen Liu,&nbsp;Ruirui Zhou","doi":"10.1049/rpg2.70071","DOIUrl":"https://doi.org/10.1049/rpg2.70071","url":null,"abstract":"<p>Prolonged exposure of wind turbine blades to wind forces can lead to blade twisting and structural loosening. These defects result in uneven mass distribution, causing severe vibrations in wind turbines, which reduce energy efficiency and increase operational costs. To address the challenges of weak vibration signal feature extraction and poor diagnostic model performance caused by blade mass imbalance, this paper proposes a dual-signal parallel CNN-transformer model based on Fast Fourier Transform (FFT) and Variational Mode Decomposition (VMD). A convolutional neural network (CNN) is employed to extract spatial features from the fused time-frequency domain signals, while the time-domain signals are input into a transformer encoder to capture long-term temporal dependencies. A cross-attention mechanism integrates temporal and spatial features by computing attention weights, allowing the model to focus on critical features while reducing computational complexity. Experiments using the vibration data of the wind turbine nacelle collected through the SCADA system show that when the stacked time-frequency signals are used as input, the accuracy of the model is increased by 36.04%, 7.34% and 5.41% compared with the original signal, FFT-processed signal and VMD-processed signal, respectively. The proposed method achieves a diagnostic accuracy of 97.5% under full-sample conditions and 95% under low-sample conditions.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weak Coupling Effects in the PLL Modes of Grid-Following PMSG Wind Power Systems","authors":"Xumeng Cui,&nbsp;Lei Chen,&nbsp;Kaiyuan Hou,&nbsp;Yong Min,&nbsp;Kefei Wang,&nbsp;Fei Xu","doi":"10.1049/rpg2.70073","DOIUrl":"https://doi.org/10.1049/rpg2.70073","url":null,"abstract":"<p>Small-signal synchronisation stability is a critical issue of grid-connected permanent magnet synchronous generator (PMSG) wind power systems employing grid-following voltage source converters (VSCs). While most studies simplify the system by modelling multiple VSCs as a single unit to analyse oscillations caused by VSC-grid interactions, few have examined interactions among VSCs. This paper proposes the cross-synchronising coefficient to quantify these interactions and applies it to analyse small-signal synchronisation stability in multi-PMSG wind power systems. When considering only PLL, the cross-synchronising coefficient is analytically derived and generally small, or even zero, indicating weak coupling between the synchronisation dynamics of different grid-side converters. Unlike strong synchronising interactions among nearby synchronous generators, the interaction between the PLL dynamics of PMSG converters remains weak even at short electrical distances. When outer control loops are included, the interaction increases but remains moderate. These findings suggest that the weak coupling of PLL modes in PMSG wind power systems allow for independent stability analysis and control design. Simulation results validate the theoretical analysis, offering insights into the small-signal synchronisation stability of PMSG wind power systems.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Task Deep Reinforcement Learning with Scenario Clustering for Real-Time Scheduling of Wind-Solar-Hydro Complementary Generation Systems","authors":"Yuanyu Ge,&nbsp;Jun Xie,&nbsp;Shuo Feng,&nbsp;Jiaqi Chang,&nbsp;Zhangwei Wang","doi":"10.1049/rpg2.70070","DOIUrl":"https://doi.org/10.1049/rpg2.70070","url":null,"abstract":"<p>Real-time scheduling of wind-solar-hydro complementary power generation systems (WSHCPGS) is crucial for enhancing energy utilization efficiency and power supply quality. However, WSHCPGS encounter challenges stemming from the complexity of multi-energy coupling systems and the inherent uncertainty of renewable energy sources. Traditional scheduling methods struggle to quickly and accurately adapt to the dynamic environment. Therefore, this paper proposes a multi-task deep reinforcement learning (DRL) method with scenario clustering for real-time scheduling of WSHCPGS. Conventional single-task DRL methods suffer from low learning efficiency and insufficient generalization ability. Their scheduling strategies may not be robust enough when facing uncertain environments. To address these challenges, this paper divides typical scenarios using the t-distributed stochastic neighbor embedding (t-SNE) and density-based spatial clustering of applications with noise (DBSCAN) methods and identifies scenario categories based on the stacking ensemble learning (SEL) algorithm. Then, a multi-task soft actor–critic (MTSAC) algorithm is proposed for real-time scheduling. The proposed method enables targeted training for specific scenarios to ensure the optimality of scheduling strategies. Simulation results indicate that the multi-task method can handle uncertainty better and converge faster than conventional single-task DRL algorithms. Unlike single-task DRL methods, MTSAC with scenario clustering enhances adaptability and robustness. Furthermore, compared to traditional methods such as model predictive control (MPC) and particle swarm optimization (PSO), the proposed method achieves significant increases of 6.92% and 30.21% in reservoir energy storage, all while maintaining decision-making times below 0.1 s.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operational and Planning Strategy for Hydrogen Energy Storage in Distribution Networks Under Dynamic Transformer Capacity Expansion Scenarios","authors":"Jin Zhu,&nbsp;Jianfeng Zhao,&nbsp;Yuan Gao,&nbsp;Xiaodong Yuan,&nbsp;Lucheng Hong,&nbsp;Ai Du","doi":"10.1049/rpg2.70067","DOIUrl":"https://doi.org/10.1049/rpg2.70067","url":null,"abstract":"<p>The large-scale integration of distributed generation has significantly increased the complexity of distribution network operation optimization, leading to issues such as voltage violations and reverse power flows. To address these challenges, this paper proposes an operational and planning strategy for hydrogen energy storage in distribution networks under dynamic transformer capacity expansion scenarios. First, the impact of reverse power flow on transformer losses in distribution networks with high penetration of renewable energy is analyzed, clarifying the advantages of hydrogen energy storage in conjunction with dynamic transformer capacity expansion scenarios. Second, a collaborative optimization strategy for the operation of the distribution network that integrates PV and hydrogen energy is proposed for scenarios with dynamic transformer capacity expansion. Next, the two-level planning strategy for hydrogen energy storage in distribution networks under dynamic transformer capacity expansion scenarios is established. Meanwhile, an improved generative adversarial network is used to account for the uncertainty in renewable energy output, and a heuristic algorithm is applied to solve the two-level configuration model in the hydrogen energy storage planning. Finally, the effectiveness of the hydrogen energy storage operational planning strategy is validated through the study of the IEEE 33-bus and IEEE 118-bus distribution network. In the IEEE 33-bus distribution network, the proposed strategy reduces the maximum voltage from 1.07 to 1.05 and decreases the maximum reverse power flow by 78.95%. After integrating hydrogen energy storage, the electricity purchase cost for the hydrogen production system is 1.9 × 10⁶ ¥, the annual total maintenance cost is 4.4 × 10⁴ ¥, and the hydrogen sales revenue reaches 6.6 × 10⁶ ¥, demonstrating significant economic benefits of hydrogen energy storage operation. Similar results are also observed in the 118-bus system, further validating the effectiveness of the proposed strategy.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilient Distributed Predefined Time Secondary Control for Cyber-Physical Microgrids","authors":"Junfeng Tan,&nbsp;Fan Zhang,&nbsp;Yanlu Huang,&nbsp;Shuai Zhao,&nbsp;Hongyu Su","doi":"10.1049/rpg2.70055","DOIUrl":"https://doi.org/10.1049/rpg2.70055","url":null,"abstract":"<p>This paper proposed a resilient distributed predefined-time sliding mode control for islanded AC microgrids with external disturbances caused by noisy circumstances or cyber-attacks. By utilizing the predefined-time convergence theory, the voltage regulation and frequency restoration as well as active power sharing can be achieved within a predefined time, which is directly equal to an adjustable parameter. Furthermore, based on the integral sliding mode control approach, the proposed method can completely compensate the external disturbance. Different from some voltage control methods based on complex second-order consensus, a novel secondary controller is designed by adopting the virtual control technique, such that the voltage regulation can be achieved under a first-order consensus with a corresponding tracking controller. In addition, the direct Lyapunov method is utilized to prove the stability of islanded AC microgrids under the proposed controller, and the analysis of predefined-time convergence is also given. Finally, case studies on a microgrid test system with four distributed generator is built in the MATLAB/SimPowerSystems software environment are conducted to demonstrate the effectiveness and superior performance of the proposed control scheme.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Transient Stability in Hybrid DC/AC Microgrids: Robust Composite Control Strategy With Virtual Capacitors Integration Using ANFIS-Optimized Control Gain Parameters","authors":"Md. Saiful Islam,&nbsp;Israt Jahan Bushra,&nbsp;Tushar Kanti Roy,&nbsp;Amanullah Maung Than Oo","doi":"10.1049/rpg2.70066","DOIUrl":"https://doi.org/10.1049/rpg2.70066","url":null,"abstract":"<p>Fluctuations in renewable energy generation due to unpredictable weather pose major challenges to power balance in hybrid DC/AC microgrids (HDAMGs). The inclusion of bio-renewable energy sources further complicates operational stability. This paper proposes a robust composite control strategy integrating a non-singular integral terminal sliding mode controller with a nonlinear backstepping controller. The scheme is enhanced by an adaptive fractional-order reaching law, ensuring dynamic stability, chattering elimination, and finite-time convergence. To maximize renewable energy utilization, an artificial neural network-based global power point tracking algorithm optimizes energy extraction from solar PV and wind turbines. An adaptive neuro-fuzzy inference system further tunes control parameters in real time. A virtual capacitor is employed to enhance inertia, transient response, and power-sharing accuracy. System stability is validated through control Lyapunov functions and the complete strategy is implemented on the Simulink platform. Extensive simulations demonstrate that the proposed method eliminates overshoots and improves settling time by 72% compared to the same controller without the virtual capacitor. Compared to existing controllers, it achieves up to 86% overshoot reduction and 78% faster settling. Under <span></span><math>\u0000 <semantics>\u0000 <mo>±</mo>\u0000 <annotation>$pm$</annotation>\u0000 </semantics></math>15% parameter variation, it maintains robustness, delivering 58–76% improved settling time and 75–81% overshoot reduction, thereby ensuring reliable HDAMG performance under dynamic conditions.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic Security-Constrained Optimal Power Flow (PSCOPF) Considering a Wide Range of Uncertainties Based on Data Clustering","authors":"Vahid Askari Ghourttapeh,&nbsp;Reza Ghanizadeh,&nbsp;Mojtaba Beiraghi","doi":"10.1049/rpg2.70065","DOIUrl":"https://doi.org/10.1049/rpg2.70065","url":null,"abstract":"<p>The optimal power flow (OPF) problem plays a critical role in power system operation and planning. However, the optimal solution derived from OPF may lead to operating limit violations under certain credible contingencies. Enhancing OPF by incorporating additional security constraints to ensure system reliability and security is known as security-constrained OPF (SCOPF). Meanwhile, the increasing integration of wind power generation (WPG) has introduced significant uncertainties into power system operations due to its variable nature. As a result, solutions obtained for the SCOPF problem within a deterministic framework may become invalid when WPG output fluctuates. In such cases, employing an appropriate probabilistic method is essential to effectively account for these uncertainties. This paper introduces a probabilistic framework for solving the SCOPF problem using the data clustering method. Compared to Monte Carlo simulation, this approach offers high speed and good accuracy, making it a practical solution. The primary objective of this study is to balance system security—measured by the expected power not served index—and the expected operational cost. The effectiveness of the proposed framework is validated through IEEE 14-bus and IEEE 57-bus test systems.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of a Combined Heat Store and Heat Exchanger for CAES Systems","authors":"Bruno Cardenas,&nbsp;Seamus Garvey,&nbsp;James Rouse,&nbsp;Zahra Baniamerian,&nbsp;Daniel Pottie,&nbsp;Edward Barbour","doi":"10.1049/rpg2.70064","DOIUrl":"https://doi.org/10.1049/rpg2.70064","url":null,"abstract":"<p>A combined heat store and heat exchanger unit (HSX) intended for compressed air energy storage (CAES) is presented. The unit is directly charged by the pressurised air emerging from the compression train, which removes the need for a secondary low-pressure air circuit. Salt is used as the thermal storage medium due to its good heat capacity, thermal conductivity, and its ability to accommodate the thermal expansion of the stainless-steel pipes. This paper uses a CAES system (15 MW, 12-h discharge) driven by an offshore wind turbine as a case study. There are not many commercial CAES systems in operation; however, the levelized cost of the heat storage subsystem of a CAES system (i.e. heat store, set of heat exchangers and ancillary low-pressure circuit) ranges between 45 and 48 £/MWh. Findings show that the most cost-effective design for a HSX has a capital cost of ∼£55k. This translates into a levelized cost of storage of ∼31.5 £/MWh. The roundtrip exergy efficiency of this design is 93.7 %. This accounts for heat-exergy and pressure-exergy losses; losses to ambient are not considered. A HSX unit can considerably reduce the overall cost of a CAES system.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Online Learning Algorithm for Ultra-Short-Term Load Forecasting in Real-Time Electricity Spot Market Based on Deep Extreme Learning Machine","authors":"Yi Ding,&nbsp;Chao Pang,&nbsp;Liyong Wei,&nbsp;Jiaqi Shi,&nbsp;Xinzhi Li,&nbsp;Qi Gao,&nbsp;Wenyu Bian,&nbsp;Qiqi Guo,&nbsp;Nian Liu","doi":"10.1049/rpg2.70057","DOIUrl":"https://doi.org/10.1049/rpg2.70057","url":null,"abstract":"<p>In the context of the deregulated electricity market, ultra-short-term load forecasting is crucial for market pricing and trading. Accurate forecasting outputs effectively aid market participants in making rational bidding and purchasing decisions. In our paper, an advanced online learning algorithm is introduced for ultra-short-term load forecasting under the background of the real-time electricity spot market, by leveraging an online deep extreme learning machine (DELM) and a heuristic algorithm. Firstly, the abnormal data type in the smart grid is considerably classified into several typical scenarios, and a Fourier residual sequence is deployed to restore incorrect data to the original form. Additionally, DELM is employed as the core online training algorithm to map the relationship between input features and forecasting output by feeding newly generated data. Subsequent optimization of the model hyper parameter is achieved through the lion swarm optimization (LSO) algorithm, which effectively improves the training efficiency and generalization of DELM. The case study shows the superiority of the LSO-DELM over traditional machine learning models on the real-world data in the electricity spot market. The integration of these advanced methodologies significantly enhances the precision and efficiency of the load forecasting task; participants in the electricity spot market are empowered to optimize resource allocation and minimize operational costs.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robustly Optimal Operation in Grid-Interactive Efficient Buildings Facilitating Small-Signal Stability","authors":"Jun Wang,&nbsp;Yunhe Hou,&nbsp;Xiaodong Zheng,&nbsp;Nengling Tai,&nbsp;Wei Bao,&nbsp;Weibin Li,&nbsp;Qiyu Lu","doi":"10.1049/rpg2.70056","DOIUrl":"https://doi.org/10.1049/rpg2.70056","url":null,"abstract":"<p>Grid-interactive efficient buildings (GEBs) have garnered global attention for their ability to achieve flexible, resilient, and environmentally friendly objectives. However, the increasing integration of renewable energy sources (RESs) introduces challenges which can compromise power system stability. Traditional robust energy management approaches fall short as they fail to address the adverse impacts on small-signal stability. Additionally, the complexity of coordinating diverse devices and their intricate interactions leave the concept of co-optimization in GEBs in its nascent stages. To address these challenges, this paper proposes a robust optimization model for GEBs that minimizes costs while ensuring system stability. The model integrates adjustable droop gains in inverters connected to distributed energy resources (DERs). First, dynamic models for various GEB devices are developed. Next, an hourly optimal power flow problem is formulated using interval predictions for RESs to ensure robustness against uncertainties. Leveraging a polyhedral uncertainty set, the model is solved via a Benders decomposition-based method, incorporating analytical stability sensitivity cuts. Simulations on a 33-bus GEB demonstrate that the proposed model significantly enhances small-signal stability at a relatively low cost, outperforming benchmark models in handling uncertainties. This approach marks a significant step forward in advancing the co-optimization of energy management and stability in GEBs.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}