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Extended state observer-based primary load frequency controller for power systems with ultra-high wind-energy penetration 基于扩展状态观测器的一次负载频率控制器,适用于风能渗透率超高的电力系统
IF 1.5 Q4 ENERGY & FUELS Pub Date : 2024-01-11 DOI: 10.1177/0309524x231221242
Tummala Slv Ayyarao, Ramakrishna S. S. Nuvvula, Polamarasetty P. Kumar, Ilhami Colak, Hasan Koten, Ahmed Ali, Baseem Khan
In this paper, a novel extended state observer-based (ESO) load frequency control is implemented. Specifically, the proposed control law focuses on the incorporation of wind energy injection as one of the disturbances, treating it as an additional state within the system. The proposed ESO is designed to estimate both the system states and the net disturbance, thereby enhancing its ability to regulate the overall load frequency performance. The proposed control strategy hinges on the judicious selection of control gains and disturbance gain. The estimated disturbance is then effectively compensated to regulate the load frequency. To evaluate the efficacy of the proposed controller, tests are conducted on both single and three area systems. The results demonstrate superior performance, even under conditions involving load and parameter variations.
本文实现了一种新颖的基于扩展状态观测器(ESO)的负载频率控制。具体来说,所提出的控制法则侧重于将风能注入作为扰动之一,将其视为系统内的附加状态。拟议的 ESO 设计用于估算系统状态和净干扰,从而增强其调节整体负载频率性能的能力。建议的控制策略取决于对控制增益和干扰增益的明智选择。然后对估计的干扰进行有效补偿,以调节负载频率。为了评估所提出的控制器的功效,对单区和三区系统进行了测试。结果表明,即使在涉及负载和参数变化的条件下,也能实现卓越的性能。
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引用次数: 0
Quantifying the impact of sensor precision on power output of a wind turbine: A sensitivity analysis via Monte Carlo simulation study 量化传感器精度对风力涡轮机功率输出的影响:通过蒙特卡罗模拟研究进行敏感性分析
IF 1.5 Q4 ENERGY & FUELS Pub Date : 2024-01-03 DOI: 10.1177/0309524x231211315
Moein Sarbandi, Hamid Khaloozadeh
Wind turbines (WTs) are complex systems with multiple interacting components, posing challenges in identifying factors affecting power output (PO). Sensors play an important role; however, sensor precision can result in measured values differing from actual values. Analyzing the impact of sensor precision on PO is essential. In this study, we employ sensitivity analysis (SA) via Monte Carlo (MC) simulation, offering a novel approach to quantify the influence of sensor precision on the PO of a 4.8 MW WT. We focus on evaluations under 5–20 m/s wind profiles, representing partial and full load regions that portray normal operation. Based on mean squared error (MSE) and parameter sensitivity (PS) index analyses, findings show the generator speed sensor’s precision significantly impacts PO. Therefore, designers should prioritize high-impact sensors like the generator speed, while sensorless strategies may be considered as alternatives to low-impact sensors like the blade pitch angle sensor, where appropriate.
风力涡轮机 (WT) 是由多个相互作用的部件组成的复杂系统,这给确定影响功率输出 (PO) 的因素带来了挑战。传感器发挥着重要作用,但传感器精度可能导致测量值与实际值不同。分析传感器精度对功率输出的影响至关重要。在本研究中,我们通过蒙特卡洛(MC)模拟进行了灵敏度分析(SA),提供了一种新方法来量化传感器精度对 4.8 MW WT 功率输出的影响。我们重点评估了 5-20 米/秒风速下的情况,代表了正常运行的部分和全负荷区域。根据均方误差 (MSE) 和参数灵敏度 (PS) 指数分析,结果表明发电机转速传感器的精度对 PO 有显著影响。因此,设计人员应优先考虑发电机转速等影响较大的传感器,同时在适当的情况下,可以考虑采用无传感器策略来替代叶片俯仰角传感器等影响较小的传感器。
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引用次数: 0
Design and realization of a pre-production platform for wind turbine manufacturing 风力涡轮机制造预生产平台的设计与实现
IF 1.5 Q4 ENERGY & FUELS Pub Date : 2023-12-22 DOI: 10.1177/0309524x231216648
Xiaoju Yin, Qi Zheng Mu, Guo Ce Shao, Shiyu Lu
Addressing the challenge of rehearsing large-scale equipment assembly, particularly for oversized components like wind turbine towers, hubs, nacelles, and blades, which often face quality issues such as substandard workmanship and wide tolerances during on-site assembly, a system has been developed for simulating wind turbine assembly. This system enables digital wind turbine assembly by creating virtual and production process models and employing intelligent database analysis. It resolves the problem of the absence of pre-production for large-scale equipment, meets the batch production needs of the wind turbine manufacturing industry, enhances the safety of wind turbine operations, improves operator assembly skills, and boosts production efficiency. This platform has already been implemented in the wind turbine manufacturing industry, yielding significant economic benefits.
为了应对大型设备装配演练的挑战,特别是风力涡轮机塔架、轮毂、机舱和叶片等超大型部件的装配演练,这些部件在现场装配过程中经常面临工艺不达标和公差过大等质量问题。该系统通过创建虚拟和生产工艺模型以及采用智能数据库分析,实现了风力涡轮机的数字化装配。它解决了大型设备没有预生产的问题,满足了风机制造业批量生产的需求,增强了风机操作的安全性,提高了操作人员的装配技能,提高了生产效率。该平台已在风力涡轮机制造业实施,产生了显著的经济效益。
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引用次数: 0
Analysis of wind power curve modeling using multi-model regression 利用多模型回归分析风能曲线建模
IF 1.5 Q4 ENERGY & FUELS Pub Date : 2023-12-22 DOI: 10.1177/0309524x231214141
Vivek Kumar Patidar, Rajesh Wadhvani, Muktesh Gupta
Wind power prediction is vital in renewable energy. Correct forecasts enable utility companies to optimize production and minimize costs. However, due to the intricate nature of wind patterns, making precise predictions is challenging. This article introduces a novel model combining Quantile Regression and Decision Tree Regression for forecasting wind energy. Trained on historical wind speed and output data, the model’s efficacy is assessed using metrics like mean absolute error and root mean squared error. The model is evaluated using the SCADA Turkey dataset, a prominent benchmark in wind forecasting. Preliminary results demonstrate the combined model’s superior predictive accuracy over traditional regression models, highlighting its potential for enhanced wind energy forecasting.
风能预测对可再生能源至关重要。正确的预测可以帮助公用事业公司优化生产、降低成本。然而,由于风力模式错综复杂,进行精确预测具有挑战性。本文介绍了一种结合了定量回归和决策树回归的新型风能预测模型。通过对历史风速和输出数据进行训练,使用平均绝对误差和均方根误差等指标对模型的功效进行评估。该模型使用 SCADA 土耳其数据集进行评估,该数据集是风能预测的一个重要基准。初步结果表明,该组合模型的预测准确性优于传统回归模型,凸显了其在增强风能预测方面的潜力。
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引用次数: 0
On the aerodynamics of dual-stage co-axial vertical-axis wind turbines 双级同轴垂直轴风力涡轮机的空气动力学研究
IF 1.5 Q4 ENERGY & FUELS Pub Date : 2023-12-09 DOI: 10.1177/0309524x231212638
Muhammad Saif Ullah Khalid, Priscila Scarlet Portocarrero Mendoza, D. Wood, A. Hemmati
This study explored the aerodynamics of a new multi-stage co-axial vertical-axis wind turbine based on bio-inspiration from natural swimming habit of fish. The turbine was formed from a conventional straight-bladed vertical axis turbine (VAWT) with an additional small inner rotor, also of three blades. The azimuthal and radial locations of the inner rotor were varied. Using numerical simulations, performance of the proposed new design was evaluated over a range of tip-speed ratios. The preliminary results identified a 600% increase in power output for multi-stage VAWTs at tip-speed ratios [Formula: see text], and a substantial drop in power coefficient at [Formula: see text]. The wake dynamics analyses revealed that the increase was due to interactions between the blades of one rotor and the other. This reduced the unsteady separation from the outer rotor, which produced most of the power. A detailed parametric study was also completed, which showed the implications of geometric and kinematic details on the performance of the proposed multistage VAWT.
本研究探索了一种基于鱼类自然游泳习惯的生物灵感的新型多级同轴垂直轴风力涡轮机的空气动力学。该涡轮由传统的直叶垂直轴涡轮(VAWT)与一个额外的小内转子组成,也是三个叶片。内转子的方位和径向位置发生了变化。通过数值模拟,在一定的叶尖速比范围内对新设计的性能进行了评估。初步结果表明,在叶尖速比下,多级vawt的功率输出增加了600%,而在叶尖速比下,功率系数则大幅下降。尾迹动力学分析表明,增加是由于一个转子叶片和另一个转子叶片之间的相互作用。这减少了与产生大部分功率的外转子的非定常分离。详细的参数研究也完成了,这表明几何和运动学细节对所提出的多级VAWT性能的影响。
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引用次数: 0
Innovative maximum power point tracking technique for wind energy conversion system 风能转换系统的创新型最大功率点跟踪技术
IF 1.5 Q4 ENERGY & FUELS Pub Date : 2023-11-29 DOI: 10.1177/0309524x231201656
S. Tounsi
In this work, an optimal control scheme based on the Maximum Power Point tracking for the Wind Energy Conversion System using Permanent Magnet Synchronous Generator (PMSG) is proposed and also modeled. The system studied in this paper consists of wind energy system powering a battery using a buck-boost converter as an interface. By modifying the buck-boost duty cycle, we vary the reflected voltage at PMSG, and accordingly its speed. It is controlled with Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) approach. The overall system is simulated with MATLAB/SIMULINK.
本文提出了一种基于最大功率点跟踪的优化控制方案,适用于使用永磁同步发电机(PMSG)的风能转换系统,并对其进行了建模。本文所研究的系统包括使用降压-升压转换器作为接口为电池供电的风能系统。通过改变降压-升压占空比,我们可以改变 PMSG 的反射电压,并相应地改变其速度。该系统采用扰动和观测(P&O)最大功率点跟踪(MPPT)方法进行控制。整个系统通过 MATLAB/SIMULINK 进行仿真。
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引用次数: 0
Optimization and techno-economic analysis of hybrid renewable energy systems for the electrification of remote areas 用于偏远地区电气化的混合可再生能源系统的优化和技术经济分析
IF 1.5 Q4 ENERGY & FUELS Pub Date : 2023-11-27 DOI: 10.1177/0309524x231210266
Ameer Faisal, N. Anwer
The welfare of the villages is one of the primary objectives of the rural electrification programmes. Compared to electrifying urban regions, electrifying rural areas is more expensive. Energy requirements in rural areas can be met using hybrid energy technologies. This study proposes a cost-effective power solution to reduce the net present cost (NPC), cost of energy (COE), unmet loads and CO2 emissions. Grey Wolf Optimizer (GWO) and Homer Pro are used to optimize the size of the components of the system. The combination of solar, wind and biogas with a battery storage system is cost-effective with zero unmet loads. Of the three combinations considered, the values of COE and NPC for combination-1 were 0.156 ($/kWh) and $2.05 M respectively. The comparative analysis of optimization between the GWO technique and Homer Pro carried out shows that the value of COE and NPC are reduced by 5.45% and 3.30% respectively.
农村的福利是农村电气化计划的主要目标之一。与城市地区的电气化相比,农村地区的电气化成本更高。使用混合能源技术可以满足农村地区的能源需求。本研究提出了一种具有成本效益的电力解决方案,以降低净现值成本 (NPC)、能源成本 (COE)、未满足的负荷和二氧化碳排放量。灰狼优化器(GWO)和 Homer Pro 用于优化系统组件的大小。太阳能、风能和沼气与蓄电池储能系统的组合具有成本效益,未满足的负荷为零。在考虑的三种组合中,组合-1 的 COE 值和 NPC 值分别为 0.156(美元/千瓦时)和 205 万美元。GWO 技术与 Homer Pro 的优化对比分析表明,COE 和 NPC 值分别降低了 5.45% 和 3.30%。
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引用次数: 0
A variable-time day-ahead scheduling strategy considering spectrum analysis 考虑频谱分析的变时日前调度策略
IF 1.5 Q4 ENERGY & FUELS Pub Date : 2023-11-22 DOI: 10.1177/0309524x231206560
Yunlong Wang, Xinshuang Yao
To build a new type of power system, the proportion of renewable energy sources with wind and solar energy as the main body has increased into the power grid. Due to the strong randomness and volatility of renewable energy as a power source, thermal power units need to fluctuate frequently to respond to system power requirements. To ensure the smooth operation of the thermal power units, a variable period day scheduling strategy considering spectrum analysis is proposed. Firstly, different dispatching periods are divided according to the characteristics of system net load fluctuation, secondly, to minimize the total cost, the joint dispatching model of wind power, solar energy, thermal power, and energy storage is established to form a day-ahead dispatching plan with variable time period. Finally, an example shows that this strategy can increase the smooth operation of thermal power units and improve the utilization rate of pumped storage units.
为构建新型电力系统,以风能和太阳能为主体的可再生能源在电网中的比例不断增加。由于可再生能源作为电源具有很强的随机性和波动性,火电机组需要频繁波动以响应系统的电力需求。为确保火电机组平稳运行,本文提出了一种考虑频谱分析的变周期日调度策略。首先,根据系统净负荷波动的特点划分不同的调度时段;其次,为使总成本最小,建立风电、太阳能、火电和储能的联合调度模型,形成变时段的日前调度计划。最后,举例说明该策略可提高火电机组的平稳运行率,并提高抽水蓄能机组的利用率。
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引用次数: 0
A Bayesian deep learning framework for reliable fault diagnosis in wind turbine gearboxes under various operating conditions 贝叶斯深度学习框架,用于在各种运行条件下对风力涡轮机齿轮箱进行可靠的故障诊断
IF 1.5 Q4 ENERGY & FUELS Pub Date : 2023-11-18 DOI: 10.1177/0309524x231206723
Abdelrahman Amin, A. Bibo, Meghashyam Panyam, Phanindra Tallapragada
Vibration-based fault diagnostics combined with deep learning approaches has promising applications in detecting and diagnosing faults in wind turbine gearboxes. Specifically when time series vibration data is transformed to a 2-dimensional cyclic spectral coherence maps, the accuracy of deep neural networks in classifying faults increases. Nevertheless, standard deep learning techniques are vulnerable to inaccurate predictions when tested with new data originating from unseen faults or unusual operating conditions. To address some of these shortcomings in the context of wind turbine gearboxes, this paper investigates fault diagnostics using Bayesian convolutional neural network which provide accurate results with uncertainty bounds reducing wrong overconfident classifications. The performance of Bayesian and standard neural networks is compared using a simulation-based dataset of acceleration signals generated from a multibody dynamic model of a 5 MW wind turbine. The framework proposed in this paper has relevance to fault detection and diagnosis in other rotating machinery applications.
基于振动的故障诊断与深度学习方法相结合,在检测和诊断风力涡轮机齿轮箱故障方面具有广阔的应用前景。具体来说,当时间序列振动数据转换为二维周期频谱相干图时,深度神经网络对故障分类的准确性就会提高。然而,当使用来自未见故障或异常运行条件的新数据进行测试时,标准深度学习技术很容易出现预测不准确的问题。为了解决风力涡轮机齿轮箱方面的一些缺陷,本文研究了使用贝叶斯卷积神经网络进行故障诊断的方法,该方法可提供具有不确定性边界的准确结果,减少错误的过度自信分类。贝叶斯神经网络和标准神经网络的性能是通过一个 5 兆瓦风力涡轮机多体动态模型产生的加速度信号的仿真数据集进行比较的。本文提出的框架对其他旋转机械应用中的故障检测和诊断也有借鉴意义。
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引用次数: 0
High performance of variable-pitch wind system based on a direct matrix converter-fed DFIG using third order sliding mode control 利用三阶滑模控制,基于直接矩阵变换器供电的双馈变流器的高性能变桨距风力系统
IF 1.5 Q4 ENERGY & FUELS Pub Date : 2023-11-17 DOI: 10.1177/0309524x231199435
A. Dendouga, A. Dendouga, N. Essounbouli
In this paper, a full nonlinear control of a variable-pitch wind system (VPWS) based on the doubly fed induction generator (DFIG) fed by a direct matrix converter (DMC) has been presented. In this context, The MPPT has been implemented using the third order sliding mode control (TOSMC) in order to ensure maximum power provided by the wind turbine on the one side, on the other side the pitch control has been implemented in order to limit the power extracted to its nominal value. Moreover, a TOSMC has been incorporated into the direct flied-oriented control (DFOC) to ensure high-performance control of the active and reactive power of DFIG. To examine the performance of the TOSMC, a comparative study was performed between this last type and the first and second order sliding mode controllers. The obtained results affirmed the high performance provided by the TOSMC compared to lower order sliding mode controllers.
本文介绍了变桨距风力系统(VPWS)的全非线性控制,该系统基于由直接矩阵转换器(DMC)馈电的双馈感应发电机(DFIG)。在此背景下,MPPT 采用三阶滑模控制 (TOSMC),一方面确保风力涡轮机提供最大功率,另一方面实施变桨控制,将提取的功率限制在额定值内。为了检验 TOSMC 的性能,对其与一阶和二阶滑模控制器进行了比较研究。研究结果表明,与低阶滑模控制器相比,TOSMC 具有更高的性能。
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引用次数: 0
期刊
Wind Engineering
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