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Two-stage optimization of appliance scheduling and BESS capacity with comfort level 带舒适度的家电调度和BESS容量两阶段优化
IF 4.2 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-24 DOI: 10.1016/j.epsr.2026.112771
Zhiling Ren , Xi Chen , Yun Dong
Residential photovoltaic (PV)–battery energy storage system (BESS) planning often neglects impact of comfort level. We propose a two-stage framework that jointly selects BESS capacity and appliance start-up schedules while accounting for appliance-use comfort, thermal comfort, and BESS degradation. Annual days are clustered by k-means to select a set of typical days. Stage 1 performs a discrete capacity scan (coarse grid plus local refinement). For each candidate, Stage 2 solves a typical day mixed-integer linear programming problem with a linear fractional cost–comfort objective via Dinkelbach’s method, maps the resulting binary schedules to all natural days by cluster membership, and then solves an annual rolling linear programming problem for dispatch and degradation-inclusive evaluation. In the baseline case, the selected capacity is 10.75 kWh with an annual total cost of 2623.2 CNY and comfort indices of 0.793 and 0.788; degradation cost is 404.3 CNY. Without the comfort term, the preferred capacity increases to 16.5 kWh and the annual total cost decreases to 2265.9 CNY, while degradation cost rises to 555.1 CNY. Sensitivity analyses show that outcomes vary with comfort settings and time-of-use prices. Overall, the framework quantifies annual cost-comfort-degradation trade-offs and recommends a feasible BESS size and appliance schedules.
住宅光伏-电池储能系统规划往往忽视了舒适性的影响。我们提出了一个两阶段的框架,共同选择BESS容量和设备启动时间表,同时考虑设备使用舒适性,热舒适性和BESS退化。通过k-means对全年天数进行聚类,以选择一组典型天数。阶段1执行离散容量扫描(粗网格加上局部细化)。对于每个候选项目,第二阶段通过Dinkelbach方法解决了一个典型的日混合整数线性规划问题,该问题具有线性分数成本舒适目标,通过聚类隶属度将得到的二进制调度映射到所有自然天数,然后解决了一个年度滚动线性规划问题,用于调度和包含退化的评估。基线情况下,选择容量为10.75 kWh,年总成本2623.2元,舒适度指数分别为0.793和0.788;降解成本为404.3元。去掉舒适项后,优选容量增加到16.5 kWh,年总成本下降到2265.9元,降解成本上升到555.1元。敏感性分析表明,结果因舒适度设置和使用时间价格而异。总体而言,该框架量化了年度成本-舒适-退化权衡,并建议了可行的BESS大小和设备时间表。
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引用次数: 0
Multi-time-scale optimal scheduling of DCP-IES: Low-carbon-economic synergy with high renewable penetration DCP-IES多时间尺度优化调度:高可再生能源渗透率的低碳经济协同效应
IF 4.2 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-23 DOI: 10.1016/j.epsr.2026.112751
Xu Wu , Lan Yu , Jingtao Hu , Ke Xing , Guo Wang
Against the backdrop of high-proportion renewable energy integration into the main power grid, the low-carbon and high-reliability operation of the Data Center Park (DCP) Integrated Energy System (IES) is crucial for energy transition. However, DCPs-characterized by intensive computing loads and multi-energy coupling-confront dual scheduling challenges: carbon emission reduction and risk management. To address these challenges, this paper proposes a "carbon-risk" dual-constrained dynamic scheduling model featuring day-ahead and intra-day stages. Specifically, the day-ahead stage aims to minimize total operational and carbon costs; it adopts scenario-based methods to construct multi-source uncertainty scenarios, integrates Conditional Value at Risk (CVaR) to quantify risks arising from renewable energy uncertainty, and optimizes the operational strategies of combined cooling, heating, and power (CCHP) systems, energy storage, and renewable energy units. The intra-day stage, by leveraging rolling wind-solar forecasts, mitigates tie-line power deviations and dynamically adjusts carbon emissions via real-time adjustments to equipment output, ensuring grid security and the timeliness of carbon emission constraints. A case study on a DCP in northwest China validates the model’s effectiveness in synergistically reducing carbon footprints, mitigating operational risks, and enhancing economic performance, thus providing an effective solution for the optimal operation of DCP-IES under high-proportion renewable energy integration.
在可再生能源高比例并入主电网的背景下,数据中心园区(DCP)综合能源系统(IES)的低碳、高可靠运行对能源转型至关重要。然而,dcp调度面临着碳减排和风险管理的双重挑战。为了解决这些挑战,本文提出了一种“碳风险”双约束动态调度模型,该模型具有日前和日内两个阶段。具体来说,提前一天阶段旨在将总运营成本和碳排放成本降至最低;采用基于场景的方法构建多源不确定性情景,整合CVaR (Conditional Value at Risk)量化可再生能源不确定性带来的风险,优化冷热电联产系统、储能系统和可再生能源机组的运行策略。在日间阶段,利用滚动的风能-太阳能预测,减轻了配线功率偏差,并通过实时调整设备输出动态调整碳排放,确保电网安全和碳排放约束的及时性。以西北地区某DCP为例,验证了该模型在协同减少碳足迹、降低运行风险、提高经济效益方面的有效性,从而为DCP- ies在高比例可再生能源整合下的优化运行提供了有效的解决方案。
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引用次数: 0
Electric vehicle charging forecasting: Performance comparison and mathematical analysis of LSTM and neural ODE models 电动汽车充电预测:LSTM与神经ODE模型的性能比较与数学分析
IF 4.2 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-23 DOI: 10.1016/j.epsr.2026.112761
Shakir Uzzaman, Sancoy Barua, Ahmad Afif Alam, Yuki Osone, Daisuke Kodaira
Electric vehicles (EVs) are crucial for a sustainable future, but their unpredictable charging patterns threaten power grid stability. As EV adoption grows, accurate EV charging forecasts are essential to prevent system failures and minimize financial losses. Traditional approaches are often limited, which has led to advanced data-driven solutions. Deep learning methods, particularly Long Short-Term Memory (LSTM) networks, have shown promise for sequential data but perform poorly when time intervals are irregular. Neural Ordinary Differential Equation (ODE) models overcome this limitation by treating temporal data as a continuous flow, improving the handling of irregular intervals. This is the first study to use Neural ODE for EV charging demand prediction, addressing the limitations of traditional recurrent models, including LSTM and its variants, and demonstrating the advantages of continuous-time modeling. Both architectures were compared using several open-access EV charging datasets. Neural ODE consistently achieved lower mean absolute error (MAE) values than LSTM across all three datasets (0.045, 0.024, and 0.121 vs. 0.072, 0.054, and 0.18), corresponding to improvements of 32 %–55 %. This performance difference arises from fundamental architectural distinctions. By modeling temporal data as a continuous flow, Neural ODE better captures irregular patterns in real-world charging behavior. Mathematical analysis and additional testing across hyperparameter settings confirmed the robustness of Neural ODE. These findings highlight the potential of continuous-time neural models for accurate and reliable EV charging demand forecasting in smart grid applications.
电动汽车(ev)对于可持续发展的未来至关重要,但其不可预测的充电模式威胁着电网的稳定性。随着电动汽车的普及,准确的电动汽车充电预测对于防止系统故障和最大限度地减少经济损失至关重要。传统的方法往往是有限的,这导致了先进的数据驱动的解决方案。深度学习方法,特别是长短期记忆(LSTM)网络,已经显示出对序列数据的希望,但当时间间隔不规则时表现不佳。神经常微分方程(ODE)模型克服了这一限制,将时间数据视为连续流,改进了对不规则区间的处理。这是首次将Neural ODE用于电动汽车充电需求预测的研究,解决了传统循环模型(包括LSTM及其变体)的局限性,并展示了连续时间建模的优势。使用几个开放访问的电动汽车充电数据集对两种架构进行了比较。在所有三个数据集上,Neural ODE始终比LSTM获得更低的平均绝对误差(MAE)值(0.045、0.024和0.121 vs. 0.072、0.054和0.18),相应的改进幅度为32% - 55%。这种性能差异源于基本的体系结构差异。通过将时间数据建模为连续流,Neural ODE可以更好地捕捉真实充电行为中的不规则模式。数学分析和跨超参数设置的附加测试证实了Neural ODE的鲁棒性。这些发现突出了连续时间神经模型在智能电网应用中准确可靠地预测电动汽车充电需求的潜力。
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引用次数: 0
Real-time performance evaluation and enhancement of industrial-grade overcurrent relay for feeder protection in inverter interfaced distribution network 变频器接口配电网馈线保护用工业级过流继电器的实时性能评价与改进
IF 4.2 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-23 DOI: 10.1016/j.epsr.2026.112760
Arjita Pal , Rakesh Shamrao Patekar , Rabindra Mohanty , Bijaya Ketan Panigrahi
This paper presents the performance evaluation of a commercially available overcurrent relay (OCR) in a distribution system with inverter-interfaced source. A practical situation of an inverter operating at unity power factor is considered for the study. The inverter is interfaced with the distribution grid through a delta (Δ)-star-grounded (Yg) transformer. The available OCR operations for different fault types, including ground and phase faults are analyzed. In such a system, it is found that the ground fault protection is not a challenge, which can be easily detected without a change in the settings of OCR. However, phase fault detection is a problem using OCR with existing settings. A method is proposed to detect phase faults using negative-sequence superimposed current. Different fault types are also classified using current phasors from the inverter side measurement. A Power Hardware-in-the-Loop (PHIL) test bed is developed with Typhoon HIL and Real-Time Digital Simulator (RTDS) to verify the performance of the OCR during ground faults and to validate the proposed algorithms for phase faults in a modified CIGRE low voltage distribution network.
本文介绍了一种市售过流继电器(OCR)在逆变器接口电源配电系统中的性能评价。考虑了逆变器在单位功率因数下工作的实际情况。逆变器通过三角(Δ)星接地(Yg)变压器与配电网连接。分析了不同故障类型(包括接地故障和相故障)可用的OCR操作。在该系统中,发现接地故障保护不是一个挑战,在不改变OCR设置的情况下可以很容易地检测到。然而,在现有设置下使用OCR进行相位故障检测是一个问题。提出了一种利用负序叠加电流检测相故障的方法。根据逆变器侧测量的电流相量对不同的故障类型进行分类。利用台风HIL和实时数字模拟器(RTDS)搭建了电力半在环(PHIL)试验台,在改进的CIGRE低压配电网中验证了OCR在接地故障时的性能,并验证了所提出的相位故障处理算法。
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引用次数: 0
Day-ahead coordinated operation strategy for a price-maker virtual power plant with multiple prosumers 具有多个生产用户的定价虚拟电厂日前协调运行策略
IF 4.2 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-23 DOI: 10.1016/j.epsr.2026.112777
Wei Wang , Shunfu Lin , Bo Zhou , Liang Qian , Yunwei Shen , S.M. Muyeen
With the advancement of electricity market reforms and the rapid development of distributed energy resources, virtual power plants (VPPs) and prosumers have emerged as new market participants. However, individual prosumers may fail to meet market entry thresholds and lack market competitiveness due to scale constraints. This paper proposes a coordinated operation strategy for a price-maker VPP aggregating multiple prosumers to participate in day-ahead energy and ancillary services markets, aiming to improve VPP profitability and reduce prosumers’ operational costs. First, a market clearing model based on price quota curves is established. Then, a mixed game framework is constructed to characterize the complex interest interactions between the VPP and prosumers. Furthermore, risk-averse and opportunity-seeking decision models are developed based on the information gap decision theory to address price uncertainties. Finally, a hybrid solution method is employed to solve the model while protecting prosumers' commercial privacy. Simulation results demonstrate that the VPP aggregating prosumers for market participation reduces total operational costs by 14.4 %, with the mixed game framework providing an additional 1.9 % reduction. Moreover, the risk-averse and opportunity-seeking decision models can accommodate different risk preferences of the VPP.
随着电力市场改革的推进和分布式能源的快速发展,虚拟电厂和产消者成为新的市场参与者。然而,由于规模的限制,个体生产消费者可能无法达到市场准入门槛,缺乏市场竞争力。为了提高VPP的盈利能力,降低生产消费者的运营成本,本文提出了一种聚集多个生产消费者参与日前能源和辅助服务市场的限价VPP协同运营策略。首先,建立了基于价格配额曲线的市场出清模型。然后,构建了一个混合博弈框架来表征VPP与产消者之间复杂的利益互动。在此基础上,基于信息缺口决策理论建立了风险规避和机会寻求决策模型。最后,在保护产消商商业隐私的前提下,采用混合求解方法对模型进行求解。仿真结果表明,VPP将产消者聚集在一起参与市场,使总运营成本降低了14.4%,混合博弈框架又使总运营成本降低了1.9%。风险厌恶和机会寻求决策模型能够适应VPP不同的风险偏好。
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引用次数: 0
Optimal ancillary frequency service with net-zero energy goal for microgrids 基于净零能耗目标的微电网最优辅助频率服务
IF 4.2 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-22 DOI: 10.1016/j.epsr.2026.112750
Dimitra G. Kyriakou , Fotios D. Kanellos , George J. Tsekouras
Maintaining frequency stability is a critical challenge in microgrid operation, particularly under high renewable energy penetration and fluctuating demand conditions. Frequency deviations arise from imbalances between production and demand and are exacerbated by the variability of renewable generation. In this context, plug-in electric vehicles (PEVs) and building prosumers play a pivotal role in supporting frequency through fast and sophisticated control mechanisms. In this paper, novel control strategies are developed to achieve optimal frequency regulation by microgrids. The proposed approach defines and utilizes flexibility indices for all associated microgrid components. Through the effective coordination of building prosumers and PEVs’ flexibility, this work enhances and optimizes frequency support through dynamic adjustment of energy consumption and storage. Additionally, the environmentally aware regulation of local, building-integrated generators further contributes to sustainable frequency stabilization. The proposed method aims to ensure computational efficiency by leveraging advanced, smart and real-time power dispatch techniques. The performance and applicability of the method are validated through simulations of a realistic, complex microgrid, demonstrating its efficiency in addressing frequency regulation challenges.
保持频率稳定性是微电网运行的一个关键挑战,特别是在可再生能源渗透率高和需求波动的情况下。频率偏差源于生产和需求之间的不平衡,并因可再生能源发电的可变性而加剧。在这种情况下,插电式电动汽车(pev)和建筑产消消费者通过快速复杂的控制机制在支持频率方面发挥着关键作用。本文提出了一种新的控制策略来实现微电网的最优频率调节。该方法定义并利用了所有相关微电网组件的灵活性指标。本工作通过建筑产消者和电动汽车灵活性的有效协调,通过动态调整能耗和储能来增强和优化频率支持。此外,当地的环保意识调节,建筑集成发电机进一步有助于可持续的频率稳定。该方法旨在通过利用先进、智能和实时的电力调度技术来保证计算效率。通过对一个现实的、复杂的微电网的仿真,验证了该方法的性能和适用性,证明了其在解决频率调节挑战方面的效率。
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引用次数: 0
A review of HVDC-based transmission congestion alleviation strategies for modern power systems 基于高压直流的现代电力系统输电拥塞缓解策略综述
IF 4.2 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-22 DOI: 10.1016/j.epsr.2026.112748
Yang Zhou , Yifeng Liu , Sunhua Huang , Chenyang Guo , Yijia Cao , Yong Li
The rapid integration of large-scale renewable energy sources causes significant variability and uncertainty into modern power systems. Extreme fluctuations and randomness in wind and solar outputs, including sudden wind ramp-downs or rapid solar irradiance drops, can cause severe power imbalances, line overloads, and insufficient reserve responses, thereby threatening grid security. Conventional congestion management methods, including generation redispatch and network topology reconfiguration, often fail to adapt to fast-changing and uncertain conditions due to computational complexity and limited responsiveness. In this context, high voltage direct current (HVDC) transmission systems enable decoupled and bi-directional power flow control, rapid response, and precise regulation of interregional exchanges, outperforming other control devices in long-distance, large-capacity power transmission and congestion alleviation. This comprehensive review systematically analyses mainstream transmission congestion mitigation strategies across multiple dimensions, including key frameworks, general techniques and HVDC-based strategies for transmission congestion alleviation, highlighting the pivotal role of HVDC technologies. Furthermore, it synthesizes insights on power flow security and proposes future research directions to leverage HVDC coordination with complementary technologies. Finally, key challenges and promising research directions are identified to advance the security and reliability of future power grids.
大规模可再生能源的快速整合给现代电力系统带来了显著的可变性和不确定性。风能和太阳能输出的极端波动和随机性,包括风力突然下降或太阳辐照度迅速下降,可能导致严重的电力不平衡、线路过载和备用响应不足,从而威胁电网安全。传统的拥塞管理方法,包括代重调度和网络拓扑重构,由于计算复杂性和有限的响应能力,往往不能适应快速变化和不确定的条件。在此背景下,高压直流(HVDC)输电系统具有解耦、双向的潮流控制、快速响应和区域间交流的精确调节等特点,在远距离、大容量输电和缓解拥堵方面优于其他控制设备。本文从多个维度系统分析了主流的输电拥塞缓解策略,包括关键框架、一般技术和基于HVDC的输电拥塞缓解策略,突出了HVDC技术的关键作用。此外,本文还综合了对潮流安全的见解,并提出了利用高压直流协调与互补技术的未来研究方向。最后,对未来电网的安全性和可靠性提出了关键挑战和有前景的研究方向。
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引用次数: 0
Influence of DC bias of step-up transformer on compensation performance of railway power conditioner 升压变压器直流偏置对铁路电力调节器补偿性能的影响
IF 4.2 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-21 DOI: 10.1016/j.epsr.2026.112762
Youhua Jiang, Mengxuan Ge
The railway static power conditioner (RPC) is typically connected to the traction power grid through step-up transformers. However, the DC component in the RPC output voltage introduces a DC bias in these transformers, degrading the RPC’s compensation performance. This paper investigates the mechanism of DC bias generation, the transformer current characteristics, and their coupling with the RPC. Through the analysis of the RPC topology, compensation principle, and equivalent circuit, the formation of DC bias is clarified. When DC bias causes core saturation, the transformer excitation current exhibits a sharp peak waveform, preventing the secondary current from effectively restoring the primary current. The study further examines the operational behavior of the step-up transformer and RPC under non-ideal conditions, revealing that the DC bias significantly affects the transformer’s no-load current ratio and, consequently, the RPC’s compensation capability. Moreover, the boundaries and constraints of DC bias for the step-up transformer are established. Results show that maintaining the RPC output current within 0.3 % of the transformer’s rated current ensures operation within the permissible DC bias region, where the excitation current remains nearly symmetrical and current recovery is achieved. Experimental results validate the proposed mechanism and theoretical analysis.
铁路静电力调压器通常通过升压变压器与牵引电网连接。然而,RPC输出电压中的直流分量在这些变压器中引入了直流偏置,降低了RPC的补偿性能。本文研究了直流偏置产生的机理、变压器电流特性及其与RPC的耦合。通过对RPC拓扑结构、补偿原理和等效电路的分析,阐明了直流偏置的形成。当直流偏置导致铁心饱和时,变压器励磁电流呈现出尖锐的峰值波形,阻止二次电流有效地恢复一次电流。该研究进一步考察了升压变压器和RPC在非理想条件下的运行行为,揭示了直流偏置显著影响变压器的空载电流比,从而影响RPC的补偿能力。建立了升压变压器直流偏置的边界和约束条件。结果表明,将RPC输出电流保持在变压器额定电流的0.3%以内,可以确保在允许的直流偏置区域内运行,在该区域内,励磁电流保持几乎对称,并且实现了电流恢复。实验结果验证了所提出的机理和理论分析。
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引用次数: 0
Sparse mixture of experts enhanced transformer architecture for short-term hydroelectric reservoir volume prediction 稀疏混合专家改进了短期水电库容预测的变压器结构
IF 4.2 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-21 DOI: 10.1016/j.epsr.2026.112754
Laio Oriel Seman , Kin-Choong Yow , Stefano Frizzo Stefenon
In hydroelectric-based systems, effective energy generation planning relies heavily on precise forecasting of reservoir water levels. This paper proposes a novel hybrid forecasting framework that integrates multiple preprocessing strategies with a sparse Mixture of Experts enhanced Transformer architecture for short-term reservoir volume prediction. When evaluated on 19 interconnected reservoirs across two major river basins in southern Brazil using real operational data from the Brazilian National System Operator, the proposed model achieves a mean squared error of 0.062 and a mean absolute error of 0.145. Comprehensive benchmarking against 18 state-of-the-art deep learning methods demonstrates that the proposed approach significantly outperforms existing methods while maintaining computational efficiency through sparse expert routing. Our results confirm that combining diverse preprocessing strategies with conditional computation mechanisms provides superior forecasting accuracy for reservoir management in hydroelectric power systems.
在以水力发电为基础的系统中,有效的发电规划在很大程度上依赖于对水库水位的精确预测。本文提出了一种新的混合预测框架,该框架将多种预处理策略与稀疏混合专家增强变压器结构相结合,用于短期储层体积预测。当使用巴西国家系统运营商的实际操作数据对巴西南部两个主要河流流域的19个相互连接的水库进行评估时,所提出的模型的均方误差为0.062,平均绝对误差为0.145。对18种最先进的深度学习方法的综合基准测试表明,该方法在通过稀疏专家路由保持计算效率的同时,显著优于现有方法。研究结果表明,将多种预处理策略与条件计算机制相结合,可以提高水电系统水库管理的预测精度。
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引用次数: 0
Wavelet transform-based adaptive single-pole auto-reclosing approach for power transmission lines considering shunt reactors 考虑并联电抗器的输电线路小波变换自适应单极自合闸方法
IF 4.2 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-21 DOI: 10.1016/j.epsr.2026.112758
Ahmed R. Adly , Mohamed A. Tolba , Mahmoud M. Elgamasy , Omar F. Fadl , Mahmoud A. Elsadd
Identifying the fault types is an essential mission of the transmission line protective scheme. This paper introduces an innovative wavelet-based adaptive reclosing technique for transmission systems equipped with shunt reactors. This approach helps prevent costly blackouts and ensures the reliability of the electrical grid. Significantly, even and odd harmonic components in the voltage signal become prominent after breaker operation, during the secondary arc, and following arc extinction in transient fault conditions. To address these challenges, the proposed method utilizes the wavelet packet transform (WPT) with a recursive and short-windowing approach to effectively extract harmonic components of various orders. Two distinct energy coefficient indices are developed to distinguish between transient and permanent faults and to determine the arc extinction instant. Different configurations of the power system are utilized to examine the proposed protective scheme. Software ATP-EMTP (Simulation experiments) verified the feasibility of the presented scheme. To verify the capability and accuracy of presented scheme, it is examined by many fault scenarios such as different fault location, different arc models, different compensation levels, power swing situation, varying source impedances, varying operating voltage, varying operating frequency and noise effect. In addition, the validity of the presented scheme is compared with other protection directional schemes.
故障类型识别是输电线路保护方案的一项重要任务。介绍了一种新颖的基于小波的输电系统自适应重合闸技术。这种方法有助于防止代价高昂的停电,并确保电网的可靠性。值得注意的是,在断路器运行后,在二次电弧期间,以及在瞬态故障条件下电弧熄灭后,电压信号中的偶、奇谐波分量变得突出。为了解决这些问题,该方法利用小波包变换(WPT)的递归短窗方法有效地提取了不同阶次的谐波分量。提出了两种不同的能量系数指标来区分暂态故障和永久故障,并确定消弧瞬间。利用不同的电力系统配置来检验所提出的保护方案。软件ATP-EMTP(仿真实验)验证了该方案的可行性。为了验证所提方案的能力和准确性,在不同的故障位置、不同的电弧模型、不同的补偿水平、功率摆幅情况、不同的源阻抗、不同的工作电压、不同的工作频率和噪声影响等多种故障场景下进行了测试。此外,还与其他保护定向方案进行了有效性比较。
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引用次数: 0
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Electric Power Systems Research
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