Search Results (58)

The purpose of this paper is to present a techno-economic methodology for assessing the economic sustainability of offshore wind energy development within the Romanian exclusive economic zone (EEZ) of the Black Sea. The methodology illustrates nine key cases in this area that are grouped into three classes, each positioned at a greater distance from the Romanian coast and thus generating different environments given the water depth and wind climate. The data used for the analysis came from the ERA5 database and covered a 20-year span. Six types of wind turbines with capacities ranging from 5 to 9.5 MW were considered. In determining the levelized cost of energy (LCOE), the turbine with the highest production was considered, which turned out to be the Seimens Gamesa 8 MW, and for the economic model, the components related to both capital and operating costs were considered. Following the analysis, it was observed that the B2 site presents the best wind resources, also leading to the highest energy production of x. Regarding the LCOE analysis, values between 66.86 EUR/MWh and 87.39 EUR/MWh were obtained if the entire energy production is considered. Following the simulation with losses, the LCOE increases to values between 92.19 EUR/MWh and 121.85 EUR/MWh. Finally, an optimization calculation was also performed for the site with the highest LCOE considering another foundation time, after which the LCOE decreased to approximately 111.09 EUR/MWh, if we refer to energy production with losses. The results contribute to the economic sustainability evaluation of offshore wind projects in the Romanian Black Sea and influence future investment plans, sustainable energy planning, and renewable energy infrastructure development. Full article

Accurate impedance modeling of wind turbines (WTs) is essential for assessing the small-signal stability of power systems with high penetration of renewable energy. Existing approaches face a fundamental trade-off: physics-based “white-box” models require proprietary manufacturer parameters that are rarely disclosed, while purely data-driven “black-box” models often lack physical interpretability and exhibit poor generalization under unseen operating conditions. To address this gap, this paper proposes a gray-box framework—the Physics-Informed Hybrid Model (PIHM)—that integrates a simplified physical impedance branch with a Bidirectional Long Short-Term Memory (Bi-LSTM) network in a novel parallel architecture. The physical branch, systematically parameterized via a constrained phase-error minimization method, captures the dominant baseline dynamics and decouples the learning task, allowing the Bi-LSTM to focus exclusively on the complex nonlinear residual. The framework is validated on a high-fidelity simulation platform of a doubly fed induction generator (DFIG) wind farm. Quantitative results demonstrate that the PIHM achieves an average coefficient of determination ($R^2$) of 0.989 and a mean squared error (MSE) of $1.24\times {10}^{-4}$ on unseen test data, while producing smooth, physically consistent impedance profiles that generalize across four distinct wind speed conditions. These results establish the PIHM as a reliable, parameter-free tool for impedance-based stability analysis of modern wind power systems. Full article

Remote communities in Canada face high electricity costs, energy insecurity, and significant greenhouse gas emissions due to heavy dependence on diesel generation. This study proposes and evaluates an AC-coupled hybrid distribution microgrid for remote off-grid communities, using Black Tickle, Newfoundland and Labrador as a representative case study. The system integrates two 200 kW wind turbines, a 200 kW diesel backup generator, a 16 MWh lithium-ion battery storage system, and a bidirectional converter, modeled and optimized in HOMER Pro 3.18.3 using local meteorological data, community load profiles, and a cycle-charging dispatch strategy. The optimized configuration achieves 86.7% wind penetration and 100% supply reliability with zero unmet load, yielding a total net present cost of USD 13.6 million and a levelized cost of energy of 0.999 USD/kWh over a 25-year horizon. Battery storage accounts for 73.5% of annualized costs, representing the primary economic challenge for wider deployment. Sensitivity analyses show that diesel price fluctuations exert approximately 4.1 times greater influence on system economics than equivalent carbon pricing changes, while the optimal configuration remains robust across all tested policy scenarios. These findings demonstrate that AC-coupled wind–diesel–battery microgrids offer a viable pathway for reducing fossil fuel dependence and supporting clean energy transition in remote, harsh-climate communities. Full article

The transition to sustainable energy systems requires the effective integration of offshore wind energy with hydrogen production. In this context, the paper assesses the potential for offshore hydrogen production in eight locations, three of which are located in the Black Sea, using data from the ERA5 database (period 2016–2025) at a height of 10 m and then extrapolated to a height of 150 m. The methodology includes estimating the annual energy production for four types of offshore turbines (Siemens Gamesa (Zamudio, Spain) SG 14-236 DD, Vestas (Aarhus, Denmark) V236-15.0, GE (Rotterdam, The Netherlands) Haliade-X 13, and MingYang (Guangdong, China) MySE12-242) and correlating it with six electrolyzer configurations (PEM and AWE) in gross and net scenarios, as well as analyzing the energy compatibility related to the number of electrolyzers. The novelty of the study lies in the integrated multi-site approach and in the direct quantification of the relationship between wind production and electrolysis requirements for different turbine–electrolyzer combinations. The results indicate a variation in gross annual energy production (AEP) in the range of 45.65 to 81.11 GWh/year, while the net scenario, accounting for operational losses, ranged from 37.75 to 67.05 GWh/year, and hydrogen production between 327 and 1075 t/year, highlighting that the optimal performance is determined by the compatibility between turbine and electrolyzer and the specific energy consumption rather than the nominal power. The ${\mathrm{N}}_{\mathrm{net}}$ analysis shows that, in most cases, the energy produced by a single turbine is insufficient for the full operation of large capacity electrolyzers, resulting in a sub-unit utilization rate and necessitating the use of multiple turbines to reach the nominal operating regime. The analysis is limited to a technical assessment based on historical climatological data, excluding economic aspects, grid constraints, and variations in equipment performance over time. The results underscore the importance of integrating the sizing of offshore wind–hydrogen systems with local resources and energy conversion efficiency. Full article

Floating offshore wind turbines (FOWTs) exhibit complex motions under marine environmental loads and frequently undergo coupled oscillations across multiple degrees of freedom (DOFs). Accurate short-term motion prediction of these responses is crucial for operational safety and maintenance. To overcome the limitations of traditional “black-box” models under complex aero-hydrodynamic loads, this study proposes STL–TimesNet–NLinear, a novel physics-informed deep learning framework. The framework utilizes STL decomposition to explicitly decouple motion signals: NLinear captures non-stationary low-frequency slow drifts, while TimesNet extracts multi-periodic wave-frequency responses. The model was evaluated across different platform typologies—a 5 MW semi-submersible and a larger-scale 15 MW Spar-type platform—under various typical operational and extreme environmental conditions. Model performance was evaluated using comparative and ablation experiments. At a prediction-ahead time (PAT) of 5 s, the proposed model achieves coefficients of determination (R²) exceeding 0.95. Even at longer PATs, the R² remains above 0.90, consistently outperforming multiple benchmark models. Compared to traditional recurrent neural networks (e.g., LSTM), it decreases the Mean Absolute Error (MAE) for pitch motion under extreme sea states by 54.7% and increases the R² to 0.9573. Furthermore, the inference latency is only 2.4 ms per step. These findings confirm that the proposed STL–TimesNet–NLinear model provides fast and accurate solutions for the short-term motion response prediction of FOWTs, demonstrating valuable potential applications for enhancing the safety planning of offshore wind turbine operation and maintenance. Full article

Under extreme fault conditions or during maintenance restarts, DC collection wind farms may experience a total blackout due to protective isolation. Addressing the black-start challenges arising from the unidirectional power flow structure and weak damping characteristics inherent to DC step-up collection wind farms, this paper proposes a sequential black-start control scheme predicated on grid-source coordination. A representative topology and an equivalent black-start model of the DC collection system are established to analyze the start-up mechanism and to design an active voltage build-up strategy with virtual impedance for the grid-side Modular Multilevel Converter (MMC). Meanwhile, generator-side permanent-magnet direct-drive wind turbines exploit their self-excitation capability and optimized pitch control to realize islanded self-bootstrapping and stable rotational speed. In addition, we develop a two-stage soft cut-in strategy that combines open-loop voltage scanning for pre-synchronization with closed-loop constant-current ramping of DC/DC converters, together with control logic for sequentially connecting multiple units to the DC grid. Simulation results show that the proposed approach smoothly restores the system from a zero-energy state to the rated operating point without external power sources, confirming the feasibility of full-farm start-up using the grid-side converter station and unit self-bootstrapping. Full article

The smart grid concept is based on the full integration of different types of energy sources and intelligent devices. Due to the short- and long-term volatility of these sources, new flexibility measures are necessary to ensure the smart grid operates stably and reliably. One option is to convert renewable energy into hydrogen, especially during periods of generation overcapacity, in order that the hydrogen that is produced can be stored effectively and used “just in time” to stabilize the power system by undergoing a reverse conversion process in gas turbines or fuel cells which then supply power to the network. On the other hand, in order to achieve a sustainable general energy system (GES), it is necessary to replace other forms of fossil energy use, such as that used for heating and other industrial processes. Research indicates that a comprehensive hydrogen supply infrastructure is required. This infrastructure would include electrolyzers, conversion stations, pipelines, storage facilities, and hydrogen gas turbines and/or fuel cell power stations. Some studies in Germany suggest that the existing gas infrastructure could be used for this purpose. Further, nuclear and coal power plants are not considered reserve power plants (as in the German case), and an additional 20–30 GW of generation capacity in H₂-operated gas turbines and strong H₂ transportation infrastructure will be required over the next 10 years. The novelty of the approach presented in this article lies in the development of a unified modeling framework that enables the simultaneous and coherent representation of both economic and technical aspects of hydrogen production systems which will be used for planning and pre-decision making. From the technical perspective, the model, based on the black box approach, captures the key operational characteristics of hydrogen production, including energy consumption, system efficiency, and operational constraints. In parallel, the economic layer incorporates capital expenditures (CAPEX), operational expenditures (OPEX), and cost-related performance indicators, allowing for a direct linkage between technical operation and economic outcomes. This paper describes the systematic transformation from today’s power system to one that includes a hydrogen economy, with a particular focus on practical experiences and developments, especially in the German energy system. It discusses the components of this new system in depth, focusing on current challenges and applications. Some scaled current applications demonstrate the state of the art in this area, including not only technical requirements (reliability, risks) and possibilities, but also economic aspects (cost, business models, impact factors). Full article

This study presents a comparative analysis of the potential for combined use of wind and solar energy in nine key coastal settlements on the Bulgarian Black Sea coast—Shabla, Balchik, Varna, Byala, Obzor, Nesebar, Burgas, Primorsko, and Tsarevo—selected for their diverse geographical and meteorological characteristics. The study evaluates the feasibility of implementing hybrid renewable energy systems by analyzing the average annual solar radiation and wind velocity for each location. A methodology based on physical and technical parameters is applied to determine the required installed capacity of photovoltaic systems to meet the average annual household electricity consumption of 6000 kWh. Concurrently, wind energy potential is assessed through theoretical and practical models using two turbine sizes (3 m and 6 m in diameter), which represent small-scale residential wind applications. Full article

Wind turbines serve as core equipment for renewable energy development and utilization. Tower high-strength bolts are critical load-bearing components of wind turbines, making precise detection and early identification of their failures of significant importance. By addressing the challenges of extracting failure features from tower high-strength bolts and the insufficient generalization capability of traditional intelligent diagnostic models, this study achieves the precise detection and early identification of bolt-loosening failures. This study independently constructed a simulation platform to collect bolt vibration data and proposed a diagnostic model—BKA-CNN-GRU—based on a CNN-GRU architecture enhanced by the Black-winged Kite Algorithm (BKA). This approach enables precise detection and early identification of high-strength bolt-loosening failures. The specific research approach involved first establishing experimental conditions with varying bolt tightening levels to capture time-domain vibration signals. Subsequently, Fast Fourier Transform (FFT) was applied to convert these signals into frequency-domain data. Building upon this, BKA optimization was employed to refine key hyperparameters of the CNN-GRU model, thereby enhancing diagnostic performance. Experimental results demonstrate that, under the same dataset, the BKA-CNN-GRU model achieves an average accuracy of 99.31%, significantly outperforming comparison models such as CNN-GRU. Furthermore, the model exhibits robust performance and strong generalization capabilities under varying sample proportions and small-sample conditions. Full article

With the increasing penetration of renewable energy generation, analysis of the transient characteristics of doubly fed induction generators, as the mainstream wind turbine configuration, is made highly significant both theoretically and practically. However, manufacturers treat the control parameters as confidential commercial secrets, rendering them a “black box”. Parameter identification is fundamental for studying transient characteristics and system stability. Existing identification methods achieve accurate results only under moderate or severe voltage dip faults. To address this limitation, this paper proposes a control parameter identification method based on the adaptive differential evolution algorithm, suitable for DFIG time-domain simulation models. This method enables accurate parameter identification even during mild voltage dips. Firstly, a trajectory sensitivity analysis is employed to evaluate the difficulty of identifying each parameter, establishing the identification sequence accordingly. Secondly, based on the control loop where each parameter resides, the time-domain expressions are discretized to formulate the fitness function. Finally, the identified control parameters are compared against their true values. The results demonstrate that the proposed identification method achieves high accuracy and robustness while maintaining a rapid identification rate. Full article

Wind turbine power forecasting is crucial for optimising energy production, planning maintenance, and enhancing grid stability. This research focuses on predicting the output of a Senvion MM92 wind turbine at the Kelmarsh wind farm in the UK using SCADA data from 2020. Two approaches are explored: a hybrid model combining Stochastic Differential Equations (SDEs) with Neural Networks (NNs) and Deep Learning models, in particular, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and the Combination of Convolutional Neural Networks (CNNs) and LSTM. Notably, while SDE-NN models are well suited for predictions in cases where data patterns are chaotic and lack consistent trends, incorporating stochastic processes increases the complexity of learning within SDE models. Moreover, it is worth mentioning that while SDE-NNs cannot be classified as purely “white box” models, they are also not entirely “black box” like traditional Neural Networks. Instead, they occupy a middle ground, offering improved interpretability over pure NNs while still leveraging the power of Deep Learning. This balance is precious in fields such as wind power prediction, where accuracy and understanding of the underlying physical processes are essential. The evaluation of the results demonstrates the effectiveness of the SDE-NNs compared to traditional Deep Learning models for wind power prediction. The SDE-NNs achieve slightly better accuracy than other Deep Learning models, highlighting their potential as a powerful alternative. Full article

This paper presents a dynamic model for full-power converter permanent magnet synchronous wind turbines based on Physics-Informed Neural Networks (PINNs). The model integrates the physical dynamics of the wind turbine directly into the loss function, enabling high-accuracy equivalent modeling with limited data and overcoming the typical “black-box” constraints and large data requirements of traditional data-driven approaches. To enhance the model’s real-time adaptability, we introduce an online update mechanism leveraging Bayesian Neural Networks (BNNs) combined with a clustering-guided strategy. This mechanism estimates uncertainty in the neural network weights in real-time, accurately identifies error sources, and performs local fine-tuning on clustered data. This improves the model’s ability to track real-time errors and addresses the challenge of parameter-specific adjustments. Finally, the data-driven model is integrated into the CloudPSS platform, and its multi-scenario modeling accuracy is validated across various typical cases, demonstrating the robustness of the proposed approach. Full article

Ice formation on critical infrastructure such as wind turbine blades can lead to severe performance degradation and safety hazards. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning to detect and classify ice on various coated and uncoated surfaces. Hyperspectral reflectance data were acquired using a push-broom HSI system under controlled laboratory conditions, with ice and rime ice generated using a thermoelectric cooling setup. Support Vector Machine (SVM) and Random Forest (RF) classifiers were trained on uncoated aluminum samples and evaluated on surfaces with different coatings to assess model generalization. Both models achieved high classification accuracy, though performance declined on black-coated surfaces due to increased absorbance by the coating. The study further examined the impact of spectral band reduction to simulate different sensor types (e.g., NIR vs. SWIR), revealing that model performance is sensitive to wavelength range, with SVM performing optimally in a reduced band set and RF benefiting from the full spectral range. A multiclass classification approach using RF successfully distinguished between glaze and rime ice, offering insights into more targeted mitigation strategies. The results confirm the potential of HSI and machine learning as robust tools for surface ice monitoring in safety-critical environments. Full article

With the increasing proportion of grid-connected capacity of new energy units, such as wind power and photovoltaics, accurately constructing simulation models of these units is of great significance to the study of new power systems. However, the actual control strategies and parameters of many new energy units are often unavailable due to factors like outdated equipment or commercial confidentiality. This unavailability creates modeling challenges that compromise accuracy, ultimately affecting grid-connected power generation performance. Aiming at the problem of accurate modeling of fault ride-through control of new energy turbine “black box” controllers, this paper proposes an accurate identification method of fault ride-through control characteristics of doubly fed wind turbines based on hardware-in-the-loop testing. Firstly, according to the domestic and international new energy turbine fault ride-through standards, the fault ride-through segmentation control characteristics are summarized, and a general structured model for fault ride-through segmentation control of doubly fed wind turbines is constructed; Secondly, based on the measured hardware-in-the-loop data of the doubly fed wind turbine black box controller, the method of data segmentation preprocessing and structured model identification of the doubly fed wind turbine is proposed by utilizing statistical modal features and genetic Newton’s algorithm, and a set of generalized software simulation platforms for parameter identification is developed by combining Matlab and BPA; lastly, using the measured data of the doubly fed wind turbine in the black box and the software platform, the validity and accuracy of the proposed parameter identification method and software are tested in the simulation. Finally, the effectiveness and accuracy of the proposed parameter identification method and software are simulated and tested by using the measured data of black box doubly fed wind turbine and the software platform. The results show that the method proposed in this paper has higher recognition accuracy and stronger robustness, and the recognition error is reduced by 2.89% compared with the traditional method, which is of high value for engineering applications. Full article

The series-connected DRU-MMC hybrid converter, with its compact size and cost-effectiveness, presents an attractive solution for long-distance offshore wind power transmission. However, its application is limited by the DRU’s unidirectional power flow and the voltage mismatch between the auxiliary MMC and the onshore MMC during black-start operations. To overcome these challenges, a four-stage black-start strategy utilizing an auxiliary step-down transformer connected to the onshore MMC is proposed. The proposed strategy operates as follows: The onshore MMC first lowers its valve-side voltage via an auxiliary transformer, enabling reduced DC-side voltage. With the DRU bypassed, the offshore MMC draws startup power through the DC link, then switches to V/f mode with wind turbine curtailment to reduce DC current below the DRU bypass threshold. After stable, low-power operation, the DRU is integrated. The onshore MMC then restores rated DC voltage and disconnects the transformer, allowing gradual wind turbine reconnection to complete black-start. The simulation results confirm the approach’s feasibility under conditions where all wind turbines operate in grid-following mode. Full article