Electric Power Systems Research最新文献_第4页

An adaptive online monitoring system using NFSOGI-PLL for three-phase voltage unbalance in grids 使用 NFSOGI-PLL 的电网三相电压不平衡自适应在线监测系统

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-11-05 DOI: 10.1016/j.epsr.2024.111210

HangYu Guo , Jiafeng Ding , Ruyingjing Zhang , Guangwei Yang

The extensive utilization of unbalanced and nonlinear loads has significantly impacted power quality in modern grids. Among various metrics, the three-phase voltage unbalance factor (TPVUF) stands out as a crucial indicator. To mitigate the challenges posed by harmonics and DC components that interfere with accurate TPVUF measurements, this paper introduces the Notch Filter-based Second-Order Generalized Integrator Phase-Locked Loop (NFSOGI-PLL) for the first time and designs an adaptive online monitoring system for TPVUF based on NFSOGI-PLL. To validate its performance, the hardware design and rapid software development of the NFSOGI-PLL-based TPVUF online monitoring system are thoroughly implemented, and the system is tested under various conditions. The results indicate that the proposed system not only provides real-time performance and high accuracy, complying with the rigorous standards set by GB/T 15543-2008 and IEC 61000-4-27, but also demonstrates remarkable stability under diverse influences. These findings underscore the exceptional filtering and tracking abilities of the NFSOGI-PLL in power systems, as well as the accuracy and anti-interference capabilities of the NFSOGI-based TPVUF online monitoring system, which holds great potential for widespread application in power system control and research.

不平衡和非线性负载的广泛使用极大地影响了现代电网的电能质量。在各种指标中，三相电压不平衡因数（TPVUF）是一项重要指标。谐波和直流分量会干扰 TPVUF 的精确测量，为了缓解谐波和直流分量带来的挑战，本文首次引入了基于陷波滤波器的二阶广义积分锁相环（NFSOGI-PLL），并设计了基于 NFSOGI-PLL 的 TPVUF 自适应在线监测系统。为了验证其性能，对基于 NFSOGI-PLL 的 TPVUF 在线监控系统进行了全面的硬件设计和快速软件开发，并在各种条件下对系统进行了测试。结果表明，所提出的系统不仅具有实时性和高精确度，符合 GB/T 15543-2008 和 IEC 61000-4-27 规定的严格标准，而且在各种影响条件下都表现出卓越的稳定性。这些结果凸显了 NFSOGI-PLL 在电力系统中卓越的滤波和跟踪能力，以及基于 NFSOGI 的 TPVUF 在线监测系统的准确性和抗干扰能力，为电力系统控制和研究领域的广泛应用提供了巨大潜力。

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引用次数: 0

Switch monitoring algorithm for 220 kV terminal substation startup process based on multi time scale graph model 基于多时标图模型的 220 kV 终端变电站启动过程开关监控算法

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-11-05 DOI: 10.1016/j.epsr.2024.111181

Yin Wu , Jie Lin , Jintao Liu , De Li , Zhou Li , Yufu Lu

The switch monitoring in the startup process of 220 kV terminal substation is a dynamic and changeable process, which has obvious characteristics of diversified scales in time, so as to solve the problem of switch monitoring under multivariable and multi-scale interference. The switch monitoring framework for the startup process of 220KV terminal substation is built. The process layer uses the graphic configuration software and combines the internal and external models of the substation to build the objective function for the generation of the substation graphic model. The particle swarm optimization algorithm is used to solve it to generate the optimal substation graphic model. Through the online monitoring device, the signals such as sensors, normally open and normally closed contacts are collected in real time, and the status of the switchgear is obtained through processing, the reduced half trapezoidal cloud model multivariable multi-scale sample entropy similarity tolerance criterion is used for softening treatment, and the multivariable multi-scale cloud sample entropy is determined to achieve the extraction of multiple time scale switch fault feature vectors, which are used as the input of the SE-DSCNN fault diagnosis model. Combined with the substation diagram, the switch fault identification during the startup process of the substation is realized, and the fault switch position is located. The experimental results show that the algorithm can accurately generate the substation model, which includes all the equipment in the substation, accurately describes the connection relationship and operation status of the equipment, and has high accuracy, integrity and aesthetics; The algorithm can effectively extract and analyze the MMCES entropy characteristics of different types of switch faults; The algorithm can realize the switch monitoring during the startup of substation C, and determine the fault switch and fault location.

220 千伏终端变电站启动过程中的开关监测是一个动态多变的过程，具有明显的时间尺度多样化特征，为解决多变量、多尺度干扰下的开关监测问题。本文构建了 220KV 终端变电站启动过程的开关监控框架。过程层利用图形组态软件，结合变电站内外部模型，建立目标函数，生成变电站图形模型。利用粒子群优化算法对其进行求解，生成最优的变电站图形模型。通过在线监测装置，实时采集传感器、常开常闭触点等信号，经过处理得到开关柜的状态，采用还原半梯形云模型多变量多尺度样本熵相似性容限准则进行软化处理，确定多变量多尺度云样本熵，实现多时间尺度开关故障特征向量的提取，作为SE-DSCNN故障诊断模型的输入。结合变电站示意图，实现变电站启动过程中的开关故障识别，并定位故障开关位置。实验结果表明，该算法能准确生成变电站模型，该模型包含了变电站内的所有设备，准确描述了设备的连接关系和运行状态，具有较高的准确性、完整性和美观性；该算法能有效提取和分析不同类型开关故障的MMCES熵特征；该算法能实现变电站C启动过程中的开关监测，并确定故障开关和故障位置。

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引用次数: 0

Enhancing the performance of solar-powered EV charging stations using the TOSSI-based CTF technique 利用基于 TOSSI 的 CTF 技术提高太阳能电动汽车充电站的性能

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-11-05 DOI: 10.1016/j.epsr.2024.111206

Manasi Pattnaik , Manoj Badoni , Rajeev Kumar , Pavan Khetrapal , Pratibha Kumari

In this paper, the design and analysis of a novel solar-powered EV-charging system employing a third-order sinusoidal signal integrator (TOSSI) based-CTF (character of triangular function) is proposed. The TOSSI-based CTF is used to extract fundamental active components by eliminating harmonic distortions from the load currents. This control structure has the unique capability of active current separation, employing simple mathematical operations. Moreover, the response is further refined with the help of optimized gain parameters. The designed system is capable of handling various power quality issues, including harmonic mitigation, current balancing, and power factor improvement. The EV battery is primarily charged by solar-PV power employing a bidirectional DC-DC converter. Alternatively, the EV battery may be charged by the grid supply during the unavailability of sunlight by taking the power quality issues into due consideration. The suggested control topology is used to enhance the dynamic operation of solar-powered EV charging stations experiencing solar power intermittency and variation of load. Using MATLAB, the efficacy of the proposed control topology is tested for different operating scenarios. The suggested control topology is also verified and validated using prototype hardware developed in the laboratory, where the suggested controller has proven its utility over and above existing state-of-the-art controllers in the domain.

本文提出了一种新型太阳能电动汽车充电系统的设计和分析方法，该系统采用了基于三阶正弦信号积分器（TOSSI）的三角函数（CTF）。基于 TOSSI 的 CTF 通过消除负载电流中的谐波畸变来提取基本有功元件。这种控制结构采用简单的数学运算，具有独特的有功电流分离能力。此外，在优化增益参数的帮助下，还进一步完善了响应。所设计的系统能够处理各种电能质量问题，包括谐波缓解、电流平衡和功率因数改善。电动汽车电池主要由太阳能光伏电源充电，采用双向 DC-DC 转换器。此外，考虑到电能质量问题，电动汽车电池也可以在没有阳光的情况下由电网供电充电。建议的控制拓扑结构可用于提高太阳能电动汽车充电站在太阳能间歇性供电和负载变化情况下的动态运行。利用 MATLAB，针对不同的运行场景测试了所建议的控制拓扑的有效性。此外，还利用实验室开发的原型硬件对建议的控制拓扑结构进行了验证和确认，证明建议的控制器比该领域现有的最先进控制器更实用。

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引用次数: 0

Assessing the Impact of DC Bipole Configuration on the Lightning Performance of a HVDC Transmission Line in terms of Backflashover 从反闪络角度评估直流双极配置对高压直流输电线路雷电性能的影响

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-11-05 DOI: 10.1016/j.epsr.2024.111174

Amanda A.C. Moraes, Fernando H. Silveira, Silvério Visacro

This work presents a discussion on the influence of both the polarity and position of the phase conductors (DC poles) of a double circuit 500 kV HVDC transmission line (TL) on lightning overvoltages developed across their line insulator strings and the corresponding lightning performance in terms of backflashover, considering computational simulations with the Hybrid Electromagnetic Model (HEM) and the Leader Progression Model (LPM). Several polarity arrangements of the DC poles were considered and their influence on the probability of backflashover occurrence due to negative downward lightning was assessed for tower-footing grounding impedances varying from 10 to 100 Ω. The study indicates the worst lightning performances for configurations B, C1 and D2, with critical current and backflashover probability varying from 96 to 49 kA and from 4% to 26%, respectively. These results show the importance of both the position and polarity of the phase conductors and tower-footing grounding impedance to define the HVDC TL performance, indicating the TL configurations with the lower phases with positive polarity as the one with worst lightning performance in terms of backflashover.

本研究讨论了双回路 500 kV 高压直流输电线路 (TL) 相导体（直流极）的极性和位置对其线路绝缘子串上产生的雷电过电压以及相应的反闪络雷电性能的影响，并考虑了混合电磁模型 (HEM) 和导线进展模型 (LPM) 的计算模拟。研究表明，配置 B、C1 和 D2 的雷击性能最差，临界电流和反闪络概率分别为 96 至 49 kA 和 4% 至 26%。这些结果表明，相导体的位置和极性以及塔脚接地阻抗对确定高压直流 TL 性能非常重要，表明具有正极性的低相位 TL 配置在反闪络方面的雷电性能最差。

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引用次数: 0

A graph and diffusion theory-based approach for localization and recovery of false data injection attacks in power systems 基于图和扩散理论的电力系统虚假数据注入攻击定位和恢复方法

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-11-05 DOI: 10.1016/j.epsr.2024.111184

Yixuan He, Jingyu Wang, Chen Yang, Dongyuan Shi

False Data Injection Attacks (FDIAs) pose a serious threat to power systems by interfering with state estimation and jeopardizing their safety and reliability. Detecting and recovering from FDIAs is thus critical for maintaining power system integrity. The increasing integration of renewable energy sources and the extensive use of power electronic devices introduce significant randomness in both power generation and loads, leading to significant power fluctuations and dynamic changes in power flows. These variations challenge the accuracy of existing FDIA detection and recovery methods. To address these challenges, an innovative data recovery framework is proposed, comprising two key stages: the FDIA localization stage and the FDIA data recovery stage. In the first stage, a Line Message Passing Neural Network (LMPNN) based FDIA localization model is employed to precisely identify the attacked data and generate a mask input for the recovery stage. In the data recovery stage, an FDIA data recovery model, named Denoising Diffusion Graph Models (DDGM), is designed to recover data with minimal error while conforming to the physical laws of the grid. Both models utilize node graph and line graph representations to depict measurements on buses and branches. By leveraging an optimized graph neural network, and inviting a loop-structured framework that combines a denoising diffusion model with a graph neural network these models effectively extract data features and inherent dynamic properties, enabling superior localization of FDIAs both in node and edge spaces and ensuring accurate recovery of compromised data even in the presence of high uncertainty and significant power fluctuations. By incorporating physical laws through a customized loss function embedding Kirchhoff’s circuit laws into the training process of DDGM, the model ensures the recovered data to be physically consistent with power system dynamics. Experimental validations on IEEE 39-bus and 118-bus test systems, under conditions of high fluctuations in generation and loads, demonstrate that the proposed models outperform existing methods, achieving significant improvements in accuracy and robustness.

虚假数据注入攻击（FDIAs）会干扰状态估计，危及电力系统的安全性和可靠性，从而对电力系统构成严重威胁。因此，检测和恢复 FDIA 对维护电力系统的完整性至关重要。可再生能源的日益集成和电力电子设备的广泛使用为发电和负载带来了显著的随机性，从而导致电力的大幅波动和电力流的动态变化。这些变化对现有 FDIA 检测和恢复方法的准确性提出了挑战。为应对这些挑战，我们提出了一个创新的数据恢复框架，包括两个关键阶段：FDIA 定位阶段和 FDIA 数据恢复阶段。在第一阶段，采用基于线消息传递神经网络（LMPNN）的 FDIA 定位模型来精确识别受攻击数据，并为恢复阶段生成掩码输入。在数据恢复阶段，设计了一种名为去噪扩散图模型（DDGM）的 FDIA 数据恢复模型，以最小的误差恢复数据，同时符合网格的物理规律。这两种模型都利用节点图和线图表示法来描述总线和分支上的测量结果。这些模型利用优化的图神经网络，并采用循环结构框架，将去噪扩散模型与图神经网络相结合，从而有效提取数据特征和固有的动态特性，在节点和边缘空间中实现出色的 FDIA 定位，并确保即使在存在高不确定性和显著功率波动的情况下也能准确恢复受损数据。通过在 DDGM 的训练过程中嵌入基尔霍夫电路定律的定制损失函数，该模型结合了物理定律，确保恢复的数据在物理上与电力系统动态一致。在发电和负载波动较大的条件下，在 IEEE 39 总线和 118 总线测试系统上进行的实验验证表明，所提出的模型优于现有方法，在准确性和鲁棒性方面都有显著提高。

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引用次数: 0

Performance evaluation of a low-voltage SVC utilizing IoT streamed data for distribution systems 利用物联网流数据对配电系统低压 SVC 进行性能评估

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-11-05 DOI: 10.1016/j.epsr.2024.111187

Roman N. Krasnoperov , Dmitry I. Panfilov , Michael G. Astachev , Ahmed M. Elkholy

This paper presents the design, implementation, and performance evaluation of a novel 50 kvar Static Var Compensator (SVC) integrated with an Internet of Things (IoT) controller for a low-voltage power distribution system in Moscow. This integration enhances real-time monitoring and control capabilities over traditional SVC systems. The study focuses on advanced control systems for reactive power compensation, voltage stabilization, and overall system efficiency. Data collected over two months demonstrate the SVC’s effectiveness in maintaining a stable power factor close to unity and reducing reactive power demand. The device successfully kept voltage levels within acceptable limits across all phases, reducing fluctuations and ensuring balanced voltage levels. Key findings include enhanced reactive power compensation, voltage stabilization (minimum voltage not less than 218 V), improved system efficiency (reactive power demand from the source near zero most of the time), and significant improvements in power quality and grid stability. The case study at Kosinskaya Street, Moscow, confirms the device’s role in improving power quality and grid stability. These results support the broader deployment of IoT-integrated SVC technology in low-voltage power distribution systems.

本文介绍了一种新型 50 kvar 静变量补偿器（SVC）的设计、实施和性能评估，该补偿器与物联网（IoT）控制器集成，用于莫斯科的低压配电系统。与传统的 SVC 系统相比，这种集成增强了实时监测和控制能力。研究重点是无功补偿、电压稳定和整体系统效率的先进控制系统。两个月来收集的数据证明了 SVC 在维持接近统一的稳定功率因数和减少无功功率需求方面的有效性。该装置成功地将各相电压水平保持在可接受的范围内，减少了波动，确保了电压水平的平衡。主要发现包括无功功率补偿增强、电压稳定（最低电压不低于 218 V）、系统效率提高（大部分时间电源的无功功率需求接近于零）以及电能质量和电网稳定性显著改善。莫斯科 Kosinskaya 街的案例研究证实了该设备在改善电能质量和电网稳定性方面的作用。这些结果支持在低压配电系统中更广泛地部署物联网集成 SVC 技术。

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引用次数: 0

Mapping and assessment of harmonic voltage levels for railway traction supply stations in Sweden 瑞典铁路牵引供电站谐波电压水平测绘与评估

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-11-05 DOI: 10.1016/j.epsr.2024.111195

Rafael S. Salles , Rebecca Asplund , Sarah K. Rönnberg

Assessing harmonic distortion measurements in the electric railway power systems (ERPS) requires evaluating the time-varying behavior, interactions, and performance in different time scales. This paper aims to map and assess harmonic voltage levels in 13 traction converter stations for the Swedish railway power supply system, with findings that have direct practical implications. For that, measurements from the public and railway grid sides for 69 weeks are analyzed. Statistical values are explored for the harmonic voltage spectra and total harmonic distortion (THD) variation. The public grid side measurements are investigated using 95th percentile weekly values, and performance is evaluated by comparing the recommended planning levels of IEC 61,000–3–6. The intraweek variation complements the information about the time-varying behavior of the THD. The 95th percentile, minimum daily values, and intraday variation are explored to understand the time-based behavior since there are no reference limits from standards for comparison, looking to the railway grid side. Extended analysis is placed on the railway grid side to highlight some aspects of measurement time-aggregation based on 10-min values, and time-series trend analysis is used to confirm traffic planning impact. Discussion and findings regarding railway operation, the technology deployed at the traction converter station, time-varying behavior, traffic planning impact, measurement time-aggregation, and spectra patterns were presented.

评估铁路电力系统（ERPS）中的谐波畸变测量值需要评估不同时间尺度下的时变行为、相互作用和性能。本文旨在绘制和评估瑞典铁路供电系统 13 个牵引变流器站的谐波电压水平，其结果具有直接的实际意义。为此，本文分析了公共电网和铁路电网在 69 周内的测量数据。探讨了谐波电压频谱和总谐波失真 (THD) 变化的统计值。公共电网侧的测量值采用每周第 95 百分位数进行调查，并通过比较 IEC 61,000-3-6 推荐的规划水平来评估性能。周内变化补充了 THD 时变行为的信息。由于铁路电网方面没有标准参考限值可供比较，因此对第 95 百分位数、最小日值和日内变化进行了探讨，以了解基于时间的行为。对铁路网侧进行扩展分析，以突出基于 10 分钟值的测量时间聚集的某些方面，并使用时间序列趋势分析来确认交通规划的影响。讨论和结论涉及铁路运行、牵引变流站部署的技术、时变行为、交通规划影响、测量时间聚集和频谱模式。

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引用次数: 0

A black-start strategy for active distribution networks considering source-load bilateral uncertainty and multi-type resources✰ 考虑源-负载双边不确定性和多类型资源的主动配电网络黑启动策略✰

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-10-18 DOI: 10.1016/j.epsr.2024.111161

Fuxing Yao, Shihong Miao, Tingtao Wang, Jiaxu Wang, Baisheng Wang, Haoyu Tan

The integration of multi-type resources provides new ideas for the black-start of active distribution networks (ADNs). However, the inability to deal with uncertainty will lead to problems such as frequency/voltage crossing limits, scheduling difficulties, and even restoration failures. To this end, an ADN black-start strategy considering source-load bilateral uncertainty and multi-type resources is proposed. The forecast error uncertainties of renewable energy sources (RESs) and loads are characterized in intervals based on Copula theory, which are then introduced into the black-start model of ADNs and solved by the column-and-constraint generation algorithm. Case studies based on the improved IEEE 33-node system indicate that the proposed strategy can effectively cope with source-load bilateral uncertainty and realize robust restoration of ADNs. The system also achieves a 99.97 % RESs consumption ratio and a 67.36 % power utilization ratio of energy storage devices. Compared with existing methods, our strategy can give a more economical and faster restoration scheme while considering safety, which can be deployed in dispatch centers to help operators make full use of existing resources to achieve black-start safely and stably after outages. However, the computational time will increase if it is migrated to grids with large topologies, which needs to be further investigated.

多类型资源的整合为主动配电网（ADN）的黑启动提供了新思路。然而，无法应对不确定性将导致频率/电压越限、调度困难甚至恢复失败等问题。为此，本文提出了一种考虑到源负荷双边不确定性和多类型资源的 ADN 黑启动策略。基于 Copula 理论，对可再生能源（RES）和负荷的预测误差不确定性进行了区间表征，然后将其引入 ADN 的黑启动模型，并通过列约束生成算法进行求解。基于改进后的 IEEE 33 节点系统的案例研究表明，所提出的策略能有效应对源负载双边不确定性，并实现 ADN 的稳健恢复。该系统还实现了 99.97% 的 RES 消耗率和 67.36% 的储能设备功率利用率。与现有方法相比，我们的策略可以给出一种更经济、更快速的恢复方案，同时兼顾安全性，可以部署在调度中心，帮助运营商充分利用现有资源，安全稳定地实现停电后的黑启动。但是，如果将其迁移到具有大型拓扑结构的电网中，计算时间会增加，这需要进一步研究。

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引用次数: 0

AI-based remaining useful life prediction for transmission systems: Integrating operating conditions with TimeGAN and CNN-LSTM networks 基于人工智能的输电系统剩余使用寿命预测：利用 TimeGAN 和 CNN-LSTM 网络整合运行条件

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-10-17 DOI: 10.1016/j.epsr.2024.111151

Yeon-Sub Sim , Chun-Kwon Lee , Jae-Sang Hwang , Gu-Young Kwon , Seung Jin Chang

The remaining useful life (RUL) prediction is key for ensuring the stability of transmission power systems. However, there is no sufficient actual life-cycle, and no mature physics-of-failure model of the power transmission facilities, which make it difficult to predict RUL. In this paper, we propose an AI-based transmission line RUL prediction method which incorporates the measured operating conditions of each line. The proposed method sets the basic linear asset unit as one cable segment and joint boxes on both sides. A feature extraction and piecewise-based RUL model was designed using asset data from 1,458 actual transmission lines accumulated by measuring unit over a period of 44 years. Consequently, the RULs which depend on load operating conditions of target assets can be successfully predicted using CNN-LSTM. In addition, a data augmentation algorithm based on time-series generative adversarial networks was developed to address the issue of imbalanced failure data and further improve the accuracy of RUL prediction. The performance of the proposed RUL estimation method is further verified using real-world data. The proposed method shows an improvement in fault-healthy classification accuracy by 35.72%, 21.43%, and 7.14% compared to existing feature extraction methods, including deep neural networks (DNN), convolutional neural networks (CNN), and autoencoder (AE), respectively. Additionally, when compared to representative deep learning models for RUL estimation, it achieves the best performance with RMSE and Score of 0.074 and 0.066, respectively. Moreover, the proposed method is capable of accurately estimating RUL even for equipment in the early failure period, where the actual operating time is short.

剩余使用寿命（RUL）预测是确保输电系统稳定性的关键。然而，由于没有足够的实际生命周期，也没有成熟的输电设施故障物理模型，因此很难预测剩余使用寿命。本文提出了一种基于人工智能的输电线路 RUL 预测方法，该方法结合了每条线路的实测运行状况。该方法将基本线性资产单元设定为一个电缆段和两侧的接头盒。利用测量单元在 44 年间积累的 1,458 条实际输电线路的资产数据，设计了一个特征提取和基于片断的 RUL 模型。因此，利用 CNN-LSTM 可以成功预测取决于目标资产负载运行条件的 RULs。此外，还开发了一种基于时间序列生成对抗网络的数据增强算法，以解决故障数据不平衡的问题，进一步提高 RUL 预测的准确性。利用实际数据进一步验证了所提出的 RUL 估算方法的性能。与现有的特征提取方法（包括深度神经网络 (DNN)、卷积神经网络 (CNN) 和自动编码器 (AE)）相比，所提方法的故障健康分类准确率分别提高了 35.72%、21.43% 和 7.14%。此外，与用于 RUL 估计的代表性深度学习模型相比，该方法的 RMSE 和 Score 分别为 0.074 和 0.066，达到了最佳性能。此外，即使对于实际运行时间较短的早期故障设备，所提出的方法也能准确估计 RUL。

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引用次数: 0

Transformer fault diagnosis method based on the three-stage lightweight residual neural network 基于三级轻量级残差神经网络的变压器故障诊断方法

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-10-17 DOI: 10.1016/j.epsr.2024.111142

Hang Liu, Ben Niu, Zhijian Liu, Ming Li, Zhiyu Shi

The fault diagnosis method for dissolved gas in transformer oil based on deep learning has the problems of complex structure, over-parameterization, and high resolution in practical application. This paper presents a three-stage lightweight residual neural network method for transformer fault diagnosis. In the first stage, based on the 50-layer residual networks, the residual block is enhanced using the inverted bottleneck idea, and the Swish activation function and a simple, parameter-free attention module are incorporated to optimize the model structure and performance. In the second stage, an adaptive channel pruning method is proposed, selectively eliminating redundant filters and channels based on the fault data complexity during the training process, thereby realizing network lightweight. In the third stage, a quantization-aware method is introduced, which converts all 32-bit floating point parameters in the network to 8-bit integers, reduces the bit width of each parameter, and accomplishes a reduction in parameter size. The experimental results for the transformer oil dissolved gas fault dataset indicate that the three-stage lightweight model, sizing at 2.20 MB—only 1.51 % of the original—achieves a fault diagnosis accuracy of 97.64 %, 1.49 % higher than the original, achieving a well-balanced between accuracy and complexity.

基于深度学习的变压器油中溶解气体故障诊断方法在实际应用中存在结构复杂、参数化程度过高、分辨率高等问题。本文提出了一种用于变压器故障诊断的三阶段轻量级残差神经网络方法。第一阶段，在 50 层残差网络的基础上，利用倒置瓶颈思想增强残差块，并加入 Swish 激活函数和简单的无参数注意力模块，以优化模型结构和性能。在第二阶段，提出了一种自适应通道剪枝方法，在训练过程中根据故障数据复杂度选择性地消除冗余滤波器和通道，从而实现网络轻量化。第三阶段，引入量化感知方法，将网络中所有 32 位浮点参数转换为 8 位整数，减小每个参数的位宽，实现参数大小的减小。变压器油溶解气体故障数据集的实验结果表明，三阶段轻量级模型的大小为 2.20 MB，仅为原始模型的 1.51%，故障诊断准确率达到 97.64%，比原始模型高出 1.49%，实现了准确率和复杂度之间的良好平衡。

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引用次数: 0