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Impact of different reserve cost allocation mechanisms on market participants’ revenues: a quantitative analysis 不同储备成本分配机制对市场参与者收入的影响:定量分析
IF 3.4 4区 工程技术 Q3 ENERGY & FUELS Pub Date : 2024-09-06 DOI: 10.3389/fenrg.2024.1413297
Xu Wen, Quan Zhou, Baosong Luo, Yang Yang, Rui Mao, Dong Fan
Insufficient flexibility is a major barrier to the development of new power systems. Leveraging the resource allocation function of the electricity market is a promising way to enhance the flexibility of power systems and promote the consumption of renewables. The reasonable allocation of ancillary service costs plays a pivotal role in this function. Towards the target of “who causes, who shares,” various research related to cost allocation has been conducted. However, there is a lack of quantitative analysis of the impact of different cost allocation mechanisms on the market participants’ revenues. Whether various cost allocation mechanisms can alleviate the insufficient flexibility problem of power systems needs to be validated. With this in mind, taking operating reserve ancillary services as an example, a long-term market operation simulation model with energy-reserve joint clearing is established in this paper based on the time series production simulation. According to this, the revenues of market participants under different reserve cost allocation mechanisms are quantified. Besides, a self-dispatch model for the energy storage (ES) equipped by renewables is established, based on which the impact of ES on the revenues of renewables under different cost allocation mechanisms is analyzed. Case studies based on practical data from a provincial power grid in China demonstrate that with the well-designed reserve cost allocation mechanism, the revenues of flexible resources can be ensured. Meanwhile, renewables are incentivized to reduce their fluctuations and uncertainties by equipping the ES. Hence, the insufficient flexibility problem of power systems can be alleviated from both supply and requirements perspectives.
灵活性不足是新型电力系统发展的主要障碍。发挥电力市场的资源配置功能,是提高电力系统灵活性、促进可再生能源消纳的有效途径。在这一功能中,辅助服务成本的合理分配起着举足轻重的作用。为了实现 "谁造成、谁分担 "的目标,人们开展了与成本分配相关的各种研究。然而,不同成本分配机制对市场参与者收益的影响缺乏定量分析。各种成本分配机制能否缓解电力系统灵活性不足的问题还需要验证。鉴于此,本文以运行储备辅助服务为例,基于时间序列生产仿真,建立了能量储备联合清算的长期市场运行仿真模型。据此,量化了不同储备成本分配机制下市场参与者的收益。此外,本文还建立了可再生能源配备的储能装置(ES)的自我调度模型,并在此基础上分析了不同成本分配机制下储能装置对可再生能源收益的影响。基于中国某省级电网实际数据的案例研究表明,通过精心设计的储备成本分配机制,可确保灵活资源的收益。同时,可再生能源可以通过配备 ES 来减少其波动和不确定性。因此,电力系统灵活性不足的问题可以从供应和需求两个角度得到缓解。
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引用次数: 0
MMC parameter selection and stability control for flexible direct transmission converter station of energy storage power station 储能电站柔性直流换流站的 MMC 参数选择与稳定性控制
IF 3.4 4区 工程技术 Q3 ENERGY & FUELS Pub Date : 2024-09-06 DOI: 10.3389/fenrg.2024.1445383
Ji Xiaotong, Jiang Kezheng, Wang Chenyu, Ye Chang, Liu Dan
With the continuous advancement of science and technology, there is a growing global focus on new energy sources. Despite the rapid progress of offshore wind power generation systems, they are still plagued by issues such as significant transmission loss, limited transmission distance, and low-frequency oscillation, which hinder further development. To address these challenges, the Flexible Direct Current Transmission System (VSC-HVDC) has emerged as a widely studied solution. The integration of energy storage power stations presents new opportunities for enhancing offshore wind power transmission systems. These power stations not only serve as energy buffer pools to reduce transmission loss but also improve transmission efficiency through intelligent regulation and control, effectively mitigating low-frequency oscillation. This article introduces an optimization control parameter design method based on sensitivity analysis to enhance the stability of MTDC based on MMC. It outlines the topology structure of the offshore VSC-HVDC system, covering the main circuit and control system. Additionally, the article delves into the derivation of the small signal stability model of the system and investigates the selection of control parameters based on the eigenvalue objective function. Lastly, it analyzes the impact of the control system on the stability of the wind power flexible direct output converter station, highlighting the significant influence of control system parameters on the small signal stability of MTDC systems based on MMC. The MMC parameter selection strategy proposed in this paper is shown to effectively enhance system stability.
随着科学技术的不断进步,全球对新能源的关注度越来越高。尽管海上风力发电系统发展迅速,但仍存在传输损耗大、传输距离有限、低频振荡等问题,阻碍了其进一步发展。为应对这些挑战,柔性直流输电系统(VSC-HVDC)已成为一种被广泛研究的解决方案。储能电站的集成为加强海上风电传输系统提供了新的机遇。这些电站不仅可以作为能量缓冲池,减少输电损耗,还能通过智能调节和控制提高输电效率,有效缓解低频振荡。本文介绍了一种基于灵敏度分析的优化控制参数设计方法,以增强基于 MMC 的 MTDC 的稳定性。文章概述了海上 VSC-HVDC 系统的拓扑结构,包括主电路和控制系统。此外,文章还深入探讨了系统小信号稳定性模型的推导,并研究了基于特征值目标函数的控制参数选择。最后,文章分析了控制系统对风电柔性直接输出换流站稳定性的影响,强调了控制系统参数对基于 MMC 的 MTDC 系统小信号稳定性的重要影响。结果表明,本文提出的 MMC 参数选择策略能有效提高系统稳定性。
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引用次数: 0
Predicting short-term energy usage in a smart home using hybrid deep learning models 利用混合深度学习模型预测智能家居的短期能源使用情况
IF 3.4 4区 工程技术 Q3 ENERGY & FUELS Pub Date : 2024-09-05 DOI: 10.3389/fenrg.2024.1323357
Imane Hammou Ou Ali, Ali Agga, Mohammed Ouassaid, Mohamed Maaroufi, Ali Elrashidi, Hossam Kotb
The forecasting of home energy consumption is a crucial and challenging topic within the realm of artificial intelligence (AI)-enhanced energy management in smart grids (SGs). The primary goal of this study is to provide accurate energy consumption forecasts for a smart home. Two deep learning models are implemented: ConvLSTM, which combines convolutional operations with Long Short-Term Memory (LSTM), and the CNN-LSTM model, which synergizes Convolutional Neural Networks (CNN) and LSTM networks. Both hybrid models offer a comprehensive approach to modeling complex relationships in spatial and temporal patterns. Additionally, two baseline models—LSTM and CNN—are employed for comparative analysis. Utilizing real data from a smart home in Houston, Texas, the results demonstrate that both the hybrid models deliver highly accurate predictions for energy consumption. However, the ConvLSTM model outperforms all proposed models, improving predictions in terms of mean absolute percentage error by 4.52%, 9.59%, and 10.53% for 1 day, 3 days, and 6 days in advance, respectively.
家庭能源消耗预测是智能电网(SG)中人工智能(AI)增强型能源管理领域的一个重要而又具有挑战性的课题。本研究的主要目标是为智能家居提供准确的能耗预测。本研究采用了两种深度学习模型:ConvLSTM 结合了卷积操作和长短期记忆(LSTM),而 CNN-LSTM 模型则协同了卷积神经网络(CNN)和 LSTM 网络。这两种混合模型都为空间和时间模式中的复杂关系建模提供了一种全面的方法。此外,还采用了 LSTM 和 CNN 两种基线模型进行比较分析。利用德克萨斯州休斯顿一个智能家居的真实数据,结果表明这两种混合模型都能提供高度准确的能耗预测。不过,ConvLSTM 模型的表现优于所有建议的模型,提前 1 天、3 天和 6 天预测的平均绝对百分比误差分别提高了 4.52%、9.59% 和 10.53%。
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引用次数: 0
Optimal planning strategy for charging and discharging an electric vehicle connected to the grid through wireless recharger 通过无线充电器为接入电网的电动汽车充放电的优化规划策略
IF 3.4 4区 工程技术 Q3 ENERGY & FUELS Pub Date : 2024-09-05 DOI: 10.3389/fenrg.2024.1453711
Asma Boukhchana, Aymen Flah, Abdulaziz Alkuhayli, Rahmat Ullah, Claude Ziad El-Bayeh
The increasing number of electric Vehicles (EVs) and their influence on the power grid present difficulties that this article addresses by suggesting optimal planning methods for EV charging and discharging. EV charging and discharging operations are effectively managed by creating both locally and globally optimal planning schemes. Future transportation could be changed by the widespread adoption of dynamic wireless power transfer systems in conjunction with EVs, as they would enable speedier travel and continuous EV battery recharging. Dynamic wireless power transfer is a practical answer to problems with electric vehicles. The electrification of automobiles will have a significant influence on the power infrastructure due to the increase in demand for electricity. In this study, we provide an optimal planning method worldwide and a locally optimal strategy for EV charging and discharging. To minimize the total cost of all EVs that charge and discharge during the day, we propose an optimization problem for global planning in which the charging powers are optimized. The simulation results demonstrate that the proposed planning schemes can effectively reduce the total electricity cost for EV owners while also minimizing the impact on the power grid. The globally optimal planning scheme achieves the lowest electricity cost, while the locally optimal scheme provides a good balance between cost reduction and computational complexity.
随着电动汽车(EV)数量的不断增加及其对电网的影响,本文提出了电动汽车充放电的最优规划方法,从而解决了这一难题。通过创建局部和全局最优规划方案,可有效管理电动汽车充放电操作。动态无线电力传输系统与电动汽车的广泛应用将改变未来的交通状况,因为它们将实现更快的旅行和持续的电动汽车电池充电。动态无线电力传输是解决电动汽车问题的一个切实可行的办法。由于电力需求的增加,汽车电气化将对电力基础设施产生重大影响。在这项研究中,我们为电动汽车充放电提供了一种全球最优规划方法和局部最优策略。为了使一天中所有电动汽车充放电的总成本最小化,我们提出了一个优化充电功率的全局规划问题。模拟结果表明,所提出的规划方案能有效降低电动汽车车主的总电费,同时也能最大限度地减少对电网的影响。全局最优规划方案实现了最低的电力成本,而局部最优方案则在降低成本和计算复杂度之间取得了良好的平衡。
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引用次数: 0
Simulation of low-load operation for a 350 MW supercritical unit 350 兆瓦超临界机组低负荷运行模拟
IF 3.4 4区 工程技术 Q3 ENERGY & FUELS Pub Date : 2024-09-05 DOI: 10.3389/fenrg.2024.1448416
Shiming Xu, Bo Yu, Qiang Zhou, Xiangyu Zhang, Fujun Wang, Huaichun Zhou
Currently, there is a scarcity of studies exploring the safe operating parameters for coal-fired power units at loads below 30%.To accurately understand the operating characteristics of coal-fired units under low load conditions, and to provide a design basis for flexibility modifications, a simulation model coupled with boiler and turbine was established, which includes the flue gas and air system, steam and water system, steam turbine, and steam extraction heat recovery system, and the iterative calculation strategy for low load conditions was proposed. The simulation calculation was performed on a 350 MW supercritical coal-fired unit, with the model results showing a high degree of alignment with the unit’s design and operational parameters. Under the condition of 269MW, the maximum calculation error between the model’s predicted exit flue gas temperature of the air preheater and the actual operational results was 8.84%. This discrepancy was due to a sudden increase in the operating flue gas temperature, which may be associated with a blockage in the air preheater. And the simulation results under low load conditions indicate that when the unit load is below 20%, the furnace total airflow is controlled to no less than 30% of the airflow at Maximum Continuous Rating (BMCR) and the minimum feedwater flow rate can be reduced to 20% of that in Turbine Heat Acceptance (THA) load, and the unit switches to wet state operation around 20% load. As the unit load decreases, the coal consumption rate for power generation and steam turbine heat consumption rate both increase significantly. The coal consumption rate for power generation at 30% load is increased by 13.3% compared to BMCR load, and it is increased by 32.5% at 15% load which is operated in wet state. Under low load conditions, the coal consumption rate of the unit can be reduced by decreasing the oxygen content in the flue gas, reducing the minimum feedwater flow rate, and implementing boiler water recirculation.
为准确了解燃煤机组在低负荷条件下的运行特性,为灵活性改造提供设计依据,建立了包括烟气和空气系统、蒸汽和水系统、汽轮机和蒸汽抽余热系统在内的锅炉和汽轮机耦合仿真模型,并提出了低负荷条件下的迭代计算策略。模拟计算以 350 MW 超临界燃煤机组为对象,模型结果与机组设计和运行参数高度吻合。在 269MW 条件下,模型预测的空气预热器出口烟气温度与实际运行结果之间的最大计算误差为 8.84%。造成这一误差的原因是运行烟气温度突然升高,这可能与空气预热器的堵塞有关。而低负荷条件下的模拟结果表明,当机组负荷低于 20% 时,炉膛总风量控制在不低于最大连续额定值 (BMCR) 时风量的 30%,最小给水流量可降至汽轮机热量接受 (THA) 负荷时的 20%,机组在 20% 负荷左右切换至湿态运行。随着机组负荷的降低,发电耗煤量和汽轮机耗热量都会显著增加。与 BMCR 负荷相比,30% 负荷时的发电耗煤量增加了 13.3%,而在湿态运行的 15% 负荷时,发电耗煤量增加了 32.5%。在低负荷条件下,可以通过降低烟气中的含氧量、减少最小给水流量和实施锅炉水再循环来降低机组的煤耗率。
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引用次数: 0
nth-order feature adjoint sensitivity analysis methodology for response-coupled forward/adjoint linear systems: II. Illustrative application to a paradigm energy system 响应耦合正向/反向线性系统的 nth 阶特征积分灵敏度分析方法:II.范例能源系统的示例应用
IF 3.4 4区 工程技术 Q3 ENERGY & FUELS Pub Date : 2024-09-05 DOI: 10.3389/fenrg.2024.1421519
Dan Gabriel Cacuci
This work presents a representative application of the newly developed “nth-order feature adjoint sensitivity analysis methodology for response-coupled forward/adjoint linear systems” (abbreviated as “nth-FASAM-L”), which enables the most efficient computation of exactly obtained mathematical expressions of arbitrarily high-order (nth-order) sensitivities of a generic system response with respect to all of the parameters (including boundary and initial conditions) underlying the respective forward/adjoint systems. The nth-FASAM-L has been developed to treat responses of linear systems that simultaneously depend on both the forward and adjoint state functions. Such systems cannot be considered particular cases of nonlinear systems, as illustrated in this work by analyzing an analytically solvable model of the energy distribution of the “contributon flux” of neutrons in a mixture of materials. The unparalleled efficiency and accuracy of the nth-FASAM-L stem from the maximal reduction in the number of adjoint computations (which are “large-scale” computations) for determining the exact expressions of arbitrarily high-order sensitivities since the number of large-scale computations when applying the nth-FASAM-N is proportional to the number of model features as opposed to the number of model parameters (which are considerably more than the number of features). Hence, the higher the order of computed sensitivities, the more efficient the nth-FASAM-N becomes compared to any other methodology. Furthermore, as illustrated in this work, the probability of encountering identically vanishing sensitivities is much higher when using the nth-FASAM-L than other methods.
这项工作介绍了新开发的 "响应耦合正向/副线性系统 nth 阶特征副灵敏度分析方法"(缩写为 "nth-FASAM-L")的代表性应用,该方法能够最高效地计算通用系统响应的任意高阶(nth 阶)灵敏度的精确数学表达式,这些灵敏度与各自正向/副系统的所有基础参数(包括边界和初始条件)相关。nth-FASAM-L 的开发是为了处理同时取决于正向和负向状态函数的线性系统响应。此类系统不能被视为非线性系统的特殊情况,本研究通过分析混合物料中中子 "贡献通量 "能量分布的可分析求解模型来说明这一点。nth-FASAM-L 无与伦比的效率和准确性源于最大限度地减少了用于确定任意高阶敏感性精确表达式的邻接计算(即 "大规模 "计算)的次数,因为应用 nth-FASAM-N 时大规模计算的次数与模型特征的数量成正比,而不是与模型参数的数量成正比(模型参数的数量远远多于特征的数量)。因此,计算灵敏度的阶数越高,nth-FASAM-N 与其他方法相比就越有效。此外,如本研究所示,与其他方法相比,使用 nth-FASAM-L 时,遇到等效消失敏感度的概率要高得多。
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引用次数: 0
Electricity consumption forecasting using a novel homogeneous and heterogeneous ensemble learning 利用新型同质和异质集合学习预测用电量
IF 3.4 4区 工程技术 Q3 ENERGY & FUELS Pub Date : 2024-09-04 DOI: 10.3389/fenrg.2024.1442502
Hasnain Iftikhar, Justyna Zywiołek, Javier Linkolk López-Gonzales, Olayan Albalawi
In today’s world, a country’s economy is one of the most crucial foundations. However, industries’ financial operations depend on their ability to meet their electricity demands. Thus, forecasting electricity consumption is vital for properly planning and managing energy resources. In this context, a new approach based on ensemble learning has been developed to predict monthly electricity consumption. The method divides electricity consumption time series into deterministic and stochastic components. The deterministic component, which consists of a secular long-term trend and an annual seasonality, is estimated using a multiple regression model. In contrast, the stochastic part considers the short-run random fluctuations of the consumption time series. It is forecasted by four different time series, four machine learning models, and three novel proposed ensemble models: the time series homogeneous ensemble model, the machine learning ensemble model, and the heterogeneous ensemble model. The study analyzed data on Pakistan’s monthly electricity consumption from 1991-January to 2022-December. The evaluation of the forecasting models is based on three criteria: accuracy metrics (including the mean absolute percent error (MAPE), the mean absolute error (MAE), the root mean squared error (RMSE), and the root relative squared error (RRSE)); an equality forecast statistical test (the Diebold and Mariano’s test); and a graphical assessment. The heterogeneous ensemble model’s forecasting results show lower error values compared to the homogeneous ensemble models and the singles models, with accuracy metrics measured by MAPE, MAE, RMSE, and RRSE at 5.0027, 460.4800, 614.5276, and 0.2933, respectively. Additionally, the heterogeneous ensemble model is statistically significant (p < 0.05) and superior to the rest of the models. Also, the heterogeneous ensemble model demonstrates considerable performance with the least mean error, which is comparatively better than the individual and best models reported in the literature and are considered baseline models. Further, the forecast values’ monthly behavior depicts that electricity consumption is higher during the summer season, and this demand will be highest in June and July. The forecast model and graph reveal that electricity consumption rapidly increases with time. This indirectly indicates that the government of Pakistan must take adequate steps to improve electricity production through different energy sources to restore the country’s economic status by meeting the country’s electricity demand. Despite several studies conducted from various perspectives, no analysis has been undertaken using an ensemble learning approach to forecast monthly electricity consumption for Pakistan.
在当今世界,国家经济是最重要的基础之一。然而,各行各业的财务运营取决于其满足电力需求的能力。因此,预测用电量对于正确规划和管理能源资源至关重要。在此背景下,我们开发了一种基于集合学习的新方法来预测月度用电量。该方法将用电量时间序列分为确定性和随机性两部分。确定性部分由长期趋势和年度季节性组成,采用多元回归模型进行估算。相比之下,随机部分考虑了用电量时间序列的短期随机波动。它由四个不同的时间序列、四个机器学习模型和三个新提出的集合模型进行预测:时间序列同质集合模型、机器学习集合模型和异质集合模型。研究分析了巴基斯坦 1991 年 1 月至 2022 年 12 月的月度用电量数据。预测模型的评估基于三个标准:准确度指标(包括平均绝对百分比误差(MAPE)、平均绝对误差(MAE)、均方根误差(RMSE)和相对平方根误差(RRSE));相等预测统计检验(Diebold 和 Mariano 检验);以及图形评估。与同质集合模型和单一模型相比,异质集合模型的预测结果显示出较低的误差值,用 MAPE、MAE、RMSE 和 RRSE 衡量的准确度指标分别为 5.0027、460.4800、614.5276 和 0.2933。此外,异构集合模型的统计意义显著(p < 0.05),优于其他模型。同时,异质集合模型也表现出相当高的性能,平均误差最小,相对优于文献中报道的单个模型和最佳模型,被认为是基准模型。此外,预测值的月度行为表明,夏季用电量较高,6 月和 7 月的需求量最大。预测模型和图表显示,用电量随着时间的推移迅速增加。这间接表明,巴基斯坦政府必须采取适当措施,通过不同能源提高电力生产,以满足国家的电力需求,从而恢复国家的经济地位。尽管从不同角度进行了多项研究,但还没有人使用集合学习方法对巴基斯坦的月度用电量进行预测分析。
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引用次数: 0
Design and implementation of online battery monitoring and management system based on the internet of things 基于物联网的在线电池监测和管理系统的设计与实施
IF 3.4 4区 工程技术 Q3 ENERGY & FUELS Pub Date : 2024-09-04 DOI: 10.3389/fenrg.2024.1454398
Kena Chen, Lei Luo, Wei Lei, Pinlei Lv, Liang Zhang
Battery pack provides the backup power supply for DC system of power substations. In the event of an AC power outage or other accidents, it is an important guarantee for the reliable operation of power substation. To prevent possible failures, batteries usually require careful maintenance. Common methods are online monitoring, condition assessments, and health management. Among these, model-based techniques are widely used for battery monitoring and prognostics optimization. Data-driven methods are a good alternative solution when no mathematical models are available. As substations develop towards intelligent and unmanned modes, this paper proposes an online battery monitoring and management system based on the “cloud-network-edge-end” Internet of Things (IoT) architecture. Firstly, advanced battery monitoring system based on IoT architecture is reviewed in depth. It provides basis for later designing. Secondly, the battery online monitoring and management system is designed considering functional requirements and data link. Designing functions include ledger management, basic battery information display, real-time display of battery monitoring data, and the visualization of battery alarm information. It can implement online monitoring and intelligent maintenance management for battery operating status. Finally, the designed and developed system is applied in a 110 kV offshore substation, mainly providing battery maintenance suggestions and fault alarm prompts. Typical results of ledger information management, key parameter monitoring and alarm prompt are presented. This verifies the effectiveness and convenience of IoT-based system for the monitoring and management of batteries.
蓄电池组为变电站直流系统提供备用电源。在交流断电或发生其他事故时,它是变电站可靠运行的重要保证。为防止可能出现的故障,通常需要对蓄电池进行精心维护。常见的方法有在线监测、状态评估和健康管理。其中,基于模型的技术被广泛用于电池监测和预报优化。在没有数学模型的情况下,数据驱动方法是一种很好的替代解决方案。随着变电站向智能化和无人值守模式发展,本文提出了一种基于 "云-网-端 "物联网架构的在线电池监测和管理系统。首先,对基于物联网架构的先进电池监控系统进行了深入探讨。为后期设计提供依据。其次,考虑到功能需求和数据链路,设计了电池在线监测和管理系统。设计功能包括台账管理、电池基本信息显示、电池监测数据实时显示、电池报警信息可视化等。该系统可实现对电池运行状态的在线监测和智能维护管理。最后,将设计开发的系统应用于 110 kV 海上变电站,主要提供电池维护建议和故障报警提示。系统在台账信息管理、关键参数监控和报警提示方面取得了典型成果。这验证了基于物联网的系统在电池监测和管理方面的有效性和便利性。
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引用次数: 0
Busbar fault diagnosis method based on multi-source information fusion 基于多源信息融合的母线故障诊断方法
IF 3.4 4区 工程技术 Q3 ENERGY & FUELS Pub Date : 2024-09-04 DOI: 10.3389/fenrg.2024.1443570
Xuebao Jiang, Haiou Cao, Chenbin Zhou, Xuchao Ren, Jiaoxiao Shen, Jiayan Yu
Against the backdrop of smart grid development, the electric power system demands higher accuracy and comprehensiveness in fault analysis. Establishing a digital twin platform for multiple equipment faults represents the future direction of power system development. Presently, while many researchers employ artificial intelligence algorithms to diagnose faults in key equipment such as transmission lines and transformers, intelligent diagnostic methods for busbar faults remain insufficient. Therefore, this paper proposes a busbar fault diagnosis method based on multi-source information fusion. Initially, the diagnostic method for busbar faults is explored, conducting both time-domain and frequency-domain analyses on simulated fault data. The data of this model are optimized using Dempster-Shafer evidence theory to enhance algorithm training speed. Subsequently, BP neural network training is implemented. Finally, validation testing of fault data demonstrates a fault recognition accuracy of 99.1% for this method. Experimental results illustrate the method’s feasibility and low computational costs, thereby advancing the development of digital twin platforms for power system fault diagnosis.
在智能电网发展的背景下,电力系统对故障分析的准确性和全面性提出了更高的要求。建立多设备故障数字孪生平台是未来电力系统发展的方向。目前,许多研究人员采用人工智能算法诊断输电线路和变压器等关键设备的故障,但母线故障的智能诊断方法仍然不足。因此,本文提出了一种基于多源信息融合的母线故障诊断方法。首先,探讨了母线故障诊断方法,对模拟故障数据进行了时域和频域分析。利用 Dempster-Shafer 证据理论对该模型的数据进行了优化,以提高算法训练速度。随后,实施了 BP 神经网络训练。最后,故障数据的验证测试表明,该方法的故障识别准确率达到 99.1%。实验结果表明了该方法的可行性和低计算成本,从而推动了用于电力系统故障诊断的数字孪生平台的发展。
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引用次数: 0
Reactive power regulation strategy for WTGs based on active disturbance rejection control 基于主动干扰抑制控制的风电机组无功功率调节策略
IF 3.4 4区 工程技术 Q3 ENERGY & FUELS Pub Date : 2024-09-04 DOI: 10.3389/fenrg.2024.1447094
Shuilian Xue, Zhilong Yin, Zhiguo Wang, Feng Yu, Hailiang Chen
With the large-scale interconnection of wind power generation, the voltage problem of the power system becomes more and more prominent. Compared with adding external reactive power compensation devices, it is more economical and responsive for fans to adjust their control strategies to provide reactive power support. To make full use of reactive power supported by wind turbines, a mathematical model of doubly fed induction generator (DFIG) wind turbines is constructed to characterize the reactive power boundary of wind turbines. Then, active disturbance rejection control (ADRC) is used to generate a voltage control signal to effectively improve the unit’s reactive response speed; in addition, a variable gain coefficient is used to adjust the reactive power output of the unit, which effectively improves the reactive power response speed and its control adaptability and robustness under changing power grid conditions. Finally, a wind turbine generator (WTG) simulation model is built using MATLAB/Simulink simulation software, different fault locations are perturbed, and the effectiveness of reactive power support of the proposed ADRC-based strategy is simulated and verified. The proposed ADRC-based strategy could inject more reactive power to the grid to improve the voltage.
随着风力发电的大规模并网,电力系统的电压问题日益突出。与增加外部无功补偿装置相比,风机通过调整控制策略来提供无功功率支持更为经济和灵敏。为了充分利用风力发电机的无功功率支持,本文构建了双馈异步发电机(DFIG)风力发电机的数学模型,以描述风力发电机的无功功率边界。然后,利用有源干扰抑制控制(ADRC)产生电压控制信号,有效提高机组的无功响应速度;此外,利用可变增益系数调节机组的无功输出,有效提高无功响应速度以及在电网条件变化时的控制适应性和鲁棒性。最后,利用 MATLAB/Simulink 仿真软件建立了风力发电机(WTG)仿真模型,对不同故障位置进行扰动,仿真并验证了基于 ADRC 的无功功率支持策略的有效性。所提出的基于 ADRC 的策略可以向电网注入更多的无功功率,从而改善电压。
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引用次数: 0
期刊
Frontiers in Energy Research
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