Electric Power Systems Research最新文献_第6页

Voltage harmonic suppression powering non-linear load of single-phase inverter with impedance method 用阻抗法抑制单相逆变器非线性负载的电压谐波

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-01-19 DOI: 10.1016/j.epsr.2026.112733

Jian Huang , Fei Wang , Chen Wang , Huiqin Xu , Lixin Liu

This paper proposes a novel control strategy that integrates the load current feed-forward method (LCFFM) with an impedance notch filter to mitigate output-voltage distortion in single-phase inverters supplying non-linear loads. The study emphasizes the synergistic combination of LCFFM and the notch filter as its core innovation, which significantly improves both steady-state accuracy and harmonic suppression. Through output impedance modeling, the characteristics of non-linear loads and their impact on output voltage are analyzed. The design of LCFFM and its influence on output impedance are elaborated, along with an analysis of sampling and PWM-update delays. Furthermore, the impedance notch filter is designed and implemented to enhance harmonic rejection around critical frequencies. The proposed integrated approach is also evaluated in terms of dynamic and steady-state performance, accompanied by stability analysis. Experimental results demonstrate that the combined use of LCFFM and the impedance notch filter reduces the output voltage total harmonic distortion (THD) from (7.7 ± 0.2)% to (4.3 ± 0.2)% and improves steady-state voltage accuracy from 99.39% to 99.71%, validating the effectiveness and innovation of the proposed method.

本文提出了一种将负载电流前馈法（LCFFM）与阻抗陷波滤波器相结合的新型控制策略，以减轻提供非线性负载的单相逆变器输出电压畸变。该研究强调LCFFM与陷波滤波器的协同组合是其核心创新，在稳态精度和谐波抑制方面都有显著提高。通过输出阻抗建模，分析了非线性负载的特性及其对输出电压的影响。阐述了LCFFM的设计及其对输出阻抗的影响，并分析了采样和pwm更新延迟。此外，设计并实现了阻抗陷波滤波器，以增强临界频率附近的谐波抑制能力。本文还从动态性能和稳态性能两方面对所提出的综合方法进行了评价，并进行了稳定性分析。实验结果表明，LCFFM与阻抗陷波滤波器的结合使用将输出电压总谐波失真（THD）从（7.7±0.2）%降低到（4.3±0.2）%，将稳态电压精度从99.39%提高到99.71%，验证了所提方法的有效性和创新性。

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引用次数: 0

Total harmonic distortion and unbalance factor estimator by three-phase quadrature signal generator based on multiresonant linear oscillator 基于多谐振线性振荡器的三相正交信号发生器的总谐波失真和不平衡因子估计

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-01-20 DOI: 10.1016/j.epsr.2026.112730

Victor Aviña-Corral , Daniel Clemente-Lopez , Jose de Jesus Rangel-Magdaleno , Oscar Martínez-Fuentes , Jose Hugo Barron-Zambrano

This paper presents an advanced real-time framework for analyzing p-phase electrical networks by examining symmetric components across multiple harmonic levels. The proposed method is based on a linear oscillator and a bank of multiple resonant linear oscillators serving as quadrature signal generators, each tuned to specific harmonic frequencies, allowing simultaneous estimation of amplitude, phase angle, and phase shift for positive, negative, and homopolar sequences. A novel metric, the Total Harmonic Distortion and Unbalance Factor (THD-UF and THD-UF*), is introduced to quantify distortion and asymmetry per symmetrical sequence, providing a comprehensive indicator of power quality. The effectiveness of the proposed framework is demonstrated through simulations, embedded implementations, and measurements obtained from a real electrical grid. Specifically, the theoretical and practical analysis was carried out for a three-phase electrical network, focusing on the estimation of the fundamental component and multiple harmonic orders. From a practical perspective, the methodology was implemented on two embedded platforms to support experimental validation. In a first stage, a Raspberry Pi was used to characterize processing latency and timing behavior under constrained hardware conditions. Later, a Zynq-7000 system-on-chip was used for real-time acquisition and processing of the three-phase electrical network system, demonstrating the suitability of the proposed framework for real-time execution requirements in power-system monitoring applications.

本文提出了一种先进的实时框架，通过检查跨多个谐波水平的对称分量来分析p相电网。所提出的方法是基于一个线性振荡器和一组多个谐振线性振荡器作为正交信号发生器，每个调谐到特定的谐波频率，允许同时估计振幅，相位角，相移为正，负，和同极序列。引入了一种新的度量，总谐波失真和不平衡因子（THD-UF和THD-UF*），用于量化每个对称序列的失真和不对称，提供了一个全面的电能质量指标。通过仿真、嵌入式实现和从实际电网中获得的测量结果，证明了所提出框架的有效性。具体而言，对三相电网进行了理论和实践分析，重点是基波分量和多次谐波阶数的估计。从实践的角度来看，该方法在两个嵌入式平台上实现，以支持实验验证。在第一阶段，树莓派被用来表征在受限硬件条件下的处理延迟和定时行为。随后，Zynq-7000片上系统被用于三相电网系统的实时采集和处理，证明了所提出的框架适合电力系统监控应用的实时执行要求。

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引用次数: 0

Robust probabilistic photovoltaic power forecasting via multitask representation and cross-attention 基于多任务表示和交叉注意的鲁棒概率光伏功率预测

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-01-20 DOI: 10.1016/j.epsr.2026.112739

Boxiao Luo, Shenglei Pei, Jinchang Cheng, Shoupeng Zhang, Tao Luo

Photovoltaic (PV) power forecasting is essential for integrating high-penetration renewable energy into power grids. However, the inherent volatility of PV power and the occurrence of extreme values pose significant challenges. These issues not only degrade the accuracy of traditional models but also hinder reliable uncertainty quantification. To address these problems, this paper proposes DD-MDN, a robust probabilistic forecasting model. Unlike conventional methods that rely on linear feature selection, our model effectively captures nonlinear dependencies among meteorological features via deep nonlinear representation learning; this module is trained with uncertainty-weighted multitask learning to obtain robust, regime-aware features. Furthermore, a cross-attention mechanism is employed to capture the dynamic influence of time-varying meteorological conditions on power fluctuations, thereby further enhancing model robustness. Through a mixture density network, DD-MDN provides a robust probabilistic characterization of the uncertainty associated with extreme power output values. Compared with various baselines, our approach demonstrates superior performance under these challenging conditions: deterministic metrics show notable improvements, and it achieves the best overall probabilistic forecasting performance. Its robustness is validated through noise-injection tests. Experimental results on both public and industrial PV datasets confirm the model’s effectiveness and reliability.

光伏发电功率预测是实现高渗透可再生能源并网的关键。然而，光伏发电固有的波动性和极值的出现带来了重大挑战。这些问题不仅降低了传统模型的准确性，而且阻碍了不确定性的可靠量化。为了解决这些问题，本文提出了一种鲁棒概率预测模型DD-MDN。与依赖线性特征选择的传统方法不同，我们的模型通过深度非线性表示学习有效地捕获了气象特征之间的非线性依赖关系；该模块使用不确定性加权多任务学习进行训练，以获得鲁棒的、状态感知的特征。此外，采用交叉关注机制捕捉时变气象条件对电力波动的动态影响，从而进一步增强模型的鲁棒性。通过混合密度网络，DD-MDN提供了与极端功率输出值相关的不确定性的鲁棒概率表征。与各种基线相比，我们的方法在这些具有挑战性的条件下表现出优越的性能：确定性度量显示出显着的改进，并且它实现了最佳的总体概率预测性能。通过噪声注入试验验证了该方法的鲁棒性。在公共和工业光伏数据集上的实验结果验证了该模型的有效性和可靠性。

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引用次数: 0

Global power sharing and virtual inertia control for the interlinking modular universal converter in standalone hybrid ac/dc microgrid 独立交直流混合微电网中互连模块化通用变换器的全局功率共享和虚拟惯性控制

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-01-20 DOI: 10.1016/j.epsr.2026.112764

Javier Gutiérrez-Escalona , Carlos Roncero-Clemente , Oleksandr Matiushkin , Mohammadreza Azizi , João Martins

Hybrid ac/dc microgrids (HMGs) have gained significant attention in recent years for their efficient integration of distributed energy resources and their capability to operate independently from the grid during islanded mode. The bidirectional interlinking converter (BIC) is the key element that coordinates power exchange between sub-grids to achieve precise power sharing. This work introduces a hierarchical control strategy providing adaptive bidirectional virtual inertia (BVI) support and precise global power sharing in a standalone HMG. Besides, the proposed control strategy was designed and validated for the recently introduced modular universal converter (MUC) topology with voltage boosting capability, demonstrating its effectiveness in performing advanced BIC functionalities such as inertia support. The hardware-in-the-loop tests conducted in real time show that the proposed strategy achieves zero power sharing error while enhancing the voltage and frequency stability through BVI transferring. Furthermore, the performance of the MUC as BIC with advanced functionalities is demonstrated, reaffirming its strong potential as a highly modular and standardized power converter solution for standalone HMGs.

近年来，交直流混合微电网因其高效集成分布式能源和在孤岛模式下独立于电网运行的能力而受到广泛关注。双向互联变换器（BIC）是协调各子电网间电力交换以实现精确电力共享的关键部件。本文介绍了一种在独立HMG中提供自适应双向虚拟惯性（BVI）支持和精确全局功率共享的分层控制策略。此外，针对最近推出的具有升压能力的模块化通用变换器（MUC）拓扑设计并验证了所提出的控制策略，证明了其在执行高级BIC功能（如惯性支持）方面的有效性。实时硬件在环测试表明，该策略在通过BVI传输提高电压和频率稳定性的同时，实现了零功率共享误差。此外，还展示了MUC作为具有先进功能的BIC的性能，重申了其作为独立hmg的高度模块化和标准化功率转换器解决方案的强大潜力。

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引用次数: 0

Predictive cooperative terminal agents for decentralised self-healing in multi-terminal HVDC grids 多终端高压直流电网分散自愈的预测合作终端代理

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-01-20 DOI: 10.1016/j.epsr.2026.112747

Sami Astan , Amin Hajizadeh

The large-scale integration of offshore wind generation demands protection systems for multi-terminal high-voltage direct current (MT-HVDC) grids that can operate reliably under stochastic power injections, converter-dominated fault dynamics, and asynchronous communication. This paper proposes a Predictive Cooperative Terminal Agent (PCTA) framework, a fully decentralised and self-healing protection architecture in which each voltage source converter terminal operates as an autonomous intelligent agent. Using locally measured electrical states, predictive threshold adaptation, and event-triggered peer-to-peer coordination, agents detect faults, isolate affected components, and restore power flow in real time. A centralised-training, decentralised-execution multi-agent actor–critic reinforcement learning algorithm optimises fault detection latency, selectivity, and renewable utilisation. Robustness is enhanced through Kalman-based denoising and validated using systematic noise-sweep and high-resistance fault studies. High-fidelity digital-twin and hardware-in-the-loop simulations demonstrate sub-8 ms fault detection, selectivity exceeding 93 %, stable post-fault self-healing, and scalability to dense meshed networks.

海上风力发电的大规模集成要求多端高压直流（MT-HVDC）电网的保护系统能够在随机电力注入、变流器主导的故障动态和异步通信下可靠运行。本文提出了一种预测合作终端代理（PCTA）框架，它是一种完全分散和自修复的保护体系结构，其中每个电压源变换器终端都作为一个自主智能代理运行。利用局部测量的电状态、预测阈值适应和事件触发的点对点协调，代理可以检测故障，隔离受影响的组件，并实时恢复潮流。一种集中训练、分散执行的多智能体actor-critic强化学习算法优化了故障检测延迟、选择性和可再生利用率。鲁棒性通过卡尔曼去噪增强，并通过系统噪声扫描和高阻故障研究进行验证。高保真数字孪生和硬件在环仿真表明，故障检测低于8毫秒，选择性超过93%，故障后自修复稳定，可扩展到密集的网状网络。

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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

Electric Power Systems Research

Pub Date : 2026-06-01 Epub 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

SVC-assisted power factor optimization: A dual-benefit strategy for grid efficiency and pipeline integrity management svc辅助功率因数优化：电网效率和管道完整性管理的双重效益策略

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-02-05 DOI: 10.1016/j.epsr.2026.112819

M’hamed Ouadah , Omar Touhami , Rachid Ibtiouen , Ahlem Chahinez Kadri , Sofiane Chabane

This study pioneers a proactive source-control strategy for mitigating electromagnetic interference (EMI) on buried pipelines through Static Var Compensator (SVC)-assisted power factor optimization of adjacent high-voltage transmission lines. An integrated framework couples electromagnetic modeling with electrochemical validation, establishing a quantitative correlation between induced current density (J_ind) and corrosion rates of API X70 steel (0.119→0.404 mm/year as J_ind increases 0→200 A/m²). For a 400 kV, 900 MW transmission line, the iterative optimization algorithm achieves an optimal power factor of 0.966, reducing J_ind from 24.14 to 19.99 A/m² maintaining compliance with the 20 A/m² safety thresho ldwhile simultaneously providing 434 MVAR reactive power savings. These results demonstrate three key contributions: (1) a novel cross-infrastructure application of SVC technology beyond traditional economic optimization, (2) a validated quantitative J_ind -corrosion relationship enabling risk assessment, and (3) a dual-benefit solution that enhances grid efficiency while safeguarding pipeline integrity in shared energy corridors.

本研究率先提出了一种主动源控制策略，通过静态无功补偿器（SVC）辅助的相邻高压输电线路功率因数优化，减轻埋地管道上的电磁干扰（EMI）。一个集成的框架将电磁建模与电化学验证相结合，建立了API X70钢的感应电流密度（Jind）与腐蚀速率之间的定量相关性（当Jind增加0→200 a /m²时，为0.119→0.404 mm/年）。对于400kv， 900mw输电线路，迭代优化算法的最优功率因数为0.966，使金德从24.14 a /m²降低到19.99 a /m²，保持了20 a /m²的安全阈值，同时节省了434 MVAR的无功功率。这些结果展示了三个关键贡献：(1)超越传统经济优化的SVC技术的新型跨基础设施应用；(2)验证的定量腐蚀关系，实现风险评估；(3)双重效益解决方案，提高电网效率，同时保护共享能源走廊中的管道完整性。

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引用次数: 0

Distributed economic dispatch for islanded microgrids under asynchronous and random communication conditions 异步随机通信条件下孤岛微电网的分布式经济调度

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-02-06 DOI: 10.1016/j.epsr.2026.112823

Wei Sun, Mengjie Ding, Lei Huang, Qiyue Li, Weitao Li

In islanded microgrids, the shared communication channel among multiple nodes introduces inherent randomness and asynchrony, which can severely impair the convergence of conventional distributed dispatch algorithms. To overcome these challenges, this paper proposes an asynchronous distributed optimization framework that guarantees reliable convergence under such uncertain communication conditions. First, to accurately capture the random nature of the communication process, the network is modeled as an asynchronous randomly time-varying directed graph. Based on this model, a state-offset-based asynchronous consensus algorithm is developed, enabling each agent to update its local control immediately upon receiving new information without relying on global synchronization. A key feature of the proposed algorithm is the introduction of an auxiliary variable that records state offsets during asynchronous updates, thereby enhancing the system’s fault tolerance and ensuring coordinated optimization across agents. Subsequently, a rigorous theoretical analysis is conducted to establish the convergence, stability, and feasibility of the proposed method. Finally, simulation results validate the effectiveness and robustness of the algorithm under asynchronous and random communication conditions, while also demonstrating stable performance in the presence of dynamic load variations.

在孤岛微电网中，多节点间的共享通信信道引入了固有的随机性和非同步性，严重影响了传统分布式调度算法的收敛性。为了克服这些挑战，本文提出了一种异步分布式优化框架，保证了在这种不确定通信条件下的可靠收敛。首先，为了准确捕捉通信过程的随机性，将网络建模为异步随机时变有向图。在此模型的基础上，提出了一种基于状态偏移的异步共识算法，使每个agent在接收到新信息后能够立即更新其本地控制，而无需依赖全局同步。该算法的一个关键特征是引入了一个辅助变量，用于记录异步更新期间的状态偏移，从而增强了系统的容错性并确保跨代理的协调优化。随后进行了严格的理论分析，证明了该方法的收敛性、稳定性和可行性。最后，仿真结果验证了该算法在异步和随机通信条件下的有效性和鲁棒性，同时也证明了该算法在动态负载变化下的稳定性能。

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引用次数: 0

Towards a performance index for distribution transformer prioritization and management 建立配电变压器优先排序和管理的性能指标

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-02-04 DOI: 10.1016/j.epsr.2026.112806

Luís H.T. Bandória , Ricardo Torquato , Madson C. Almeida , Renato M. Monaro

Differentiating the operational condition of distribution transformers is essential for their effective management. Traditional approaches based on overloading or equivalent aging capture only limited aspects of operation and offer restricted support for management decisions. This paper proposes a performance index that prioritizes distribution transformers by quantifying key aspects of equipment operation through comprehensive feature engineering applied to data commonly available to utilities. Using information such as rated capacity, active power demand, monthly energy, and consumer connections, 13 features were derived and incorporated into a two-step learning framework based on dimensionality reduction that produces a single valued and interpretable performance index. Results show that the index effectively separates units according to their utilization patterns and provides a practical decision support tool for targeted management interventions.

对配电变压器的运行状态进行判别是对配电变压器进行有效管理的必要条件。基于过载或等效老化的传统方法只能捕获操作的有限方面，并为管理决策提供有限的支持。本文提出了一种性能指标，通过综合特征工程应用于公用事业公司常用的数据，量化设备运行的关键方面，从而对配电变压器进行优先排序。利用诸如额定容量、有功电力需求、每月能源和消费者连接等信息，推导出13个特征，并将其合并到基于降维的两步学习框架中，从而产生单一值和可解释的性能指数。结果表明，该指标能有效地根据利用模式对单位进行划分，为有针对性的管理干预提供了实用的决策支持工具。

引用次数: 0

A novel cascade model for power quality detection and classification based on histogram of oriented gradient and long short-term memory network 基于定向梯度直方图和长短期记忆网络的电能质量检测与分类级联模型

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-06-01 Epub Date: 2026-02-04 DOI: 10.1016/j.epsr.2026.112816

Ganesh Kumar Budumuru , Papia Ray , Manish Kumar Babu

Distributed generation sources such as wind and photovoltaic are being developed effectively to meet the current energy demands. If the Power Quality Disturbances (PQDs) are correctly identified and properly classified by both producers and consumers, these issues can be resolved. One of the most important aspects of Power Quality (PQ) problem handling is the automatic classification of PQDs. To achieve intelligent PQD classification, we present a particular kind of recurrent neural network (RNN) called Long Short-Term Memory (LSTM) network is made specially to learn long-term dependencies and solve sequence prediction tasks. This research implements the powerful Histogram of Oriented Gradient (HOG) technique for PQD feature extraction. To implement the proposed method, twenty different PQDs are generated in the MATLAB/SIMULINK environment. The HOG technique is then applied to extract key features such as amplitude, phase, energy, variance, entropy, and mean deviation. The HOG data is subsequently fed to LSTM model for classification of PQDs. The proposed model (HOG-LSTM) gives 99.69 % classification testing accuracy with less training time, and it is compared with other classification models. This model is validated with real time signals which are generated with laboratory hardware setup. The main advantages of the suggested method include quick detection, less computational time, and good classification efficiency.

为了满足当前的能源需求，风能和光伏等分布式发电资源正在得到有效开发。如果电能质量干扰（pqd）被生产者和消费者正确识别和正确分类，这些问题就可以解决。电能质量问题处理的一个重要方面是电能质量的自动分类。为了实现PQD的智能分类，我们提出了一种特殊的递归神经网络（RNN），称为长短期记忆（LSTM）网络，专门用于学习长期依赖关系和解决序列预测任务。本研究实现了强大的面向梯度直方图（HOG）技术用于PQD特征提取。为了实现所提出的方法，在MATLAB/SIMULINK环境中生成了20个不同的pqd。然后应用HOG技术提取关键特征，如振幅、相位、能量、方差、熵和平均偏差。然后将HOG数据输入LSTM模型进行pqd的分类。该模型（HOG-LSTM）在训练时间较短的情况下，分类测试准确率达到99.69%，并与其他分类模型进行了比较。利用实验室硬件装置产生的实时信号对该模型进行了验证。该方法具有检测速度快、计算时间短、分类效率高等优点。

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引用次数: 0