共查询到18条相似文献,搜索用时 203 毫秒
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针对目前电力负荷数据随机性强,影响因素复杂,传统单一预测模型精度低的问题,结合卷积神经网络(Convolutional neural network,CNN)、双向门控循环单元(Bi-directional gated recurrent unit,BiGRU)以及注意力机制(Attention)在短期电力负荷预测上的不同优点,提出一种基于CNN-BiGRU-Attention的混合预测模型。该方法首先通过CNN对历史负荷和气象数据进行初步特征提取,然后利用BiGRU进一步挖掘特征数据间时序关联,再引入注意力机制,对BiGRU输出状态给与不同权重,强化关键特征,最后完成负荷预测。试验结果表明,该模型的平均绝对百分比误差(Mean absolute percentage error,MAPE)、均方根误差(Root mean square error,RMSE)、判定系数(R-square,R2)分别为0.167%、0.057%、0.993,三项指标明显优于其他模型,具有更高的预测精度和稳定性,验证了模型在短期负荷预测中的优势。 相似文献
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高压输电线路的发展性故障往往受运行状态和故障发展过程的影响,给故障甄别和保护决策造成困难。为了准确识别高压输电线路的发展性故障,保证继电保护装置动作的正确性,将全卷积神经网络(fully convolutional network, FCN)与卷积注意力模块(convolutional block attention module, CBAM)相结合,提出一种基于CBAM-FCN的发展性故障识别方法,通过在传统全卷积网络中引入CBAM模块,使神经网络能够聚焦于故障波形的突变、幅值等重要特征,忽略无关干扰。此外,所提方法能够输出表征故障状态变化的一维时序序列,实现对输电线路发展性故障的全过程识别。最后大量仿真验证了所提方法的抗噪性能和泛化能力,并通过可视化技术展示了网络模型的可解释性。 相似文献
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针对基于卷积神经网络(convolutional neural network, CNN)的柴油机故障诊断方法在训练样本匮乏时易过拟合、诊断准确率低的问题,提出一种基于改进残差卷积网络的“端到端”柴油机故障诊断方法。该方法采用连续可微指数线性单元(continuously differentiable exponential linear units, CELU)作为CNN激活函数并采取小批次训练方法,提高模型提取特征能力的同时加速其收敛;在模型中加入残差结构将深层网络提取到的抽象特征与表层特征相融合,避免深层网络导致的特征信息丢失与梯度消失问题。经柴油机故障模拟试验验证,该方法在仅使用20个样本进行训练时,能实现95.5%的故障诊断准确率;与CNN相比,该方法在不同类型及规模的训练集下,故障诊断准确率均有显著提升。 相似文献
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基于Attention-GRU的短期光伏发电功率预测 总被引:1,自引:0,他引:1
针对传统长短时记忆神经网络(LSTM)参数量较多以及在处理长时间序列时容易忽略重要时序信息的不足,提出一种结合注意力机制(attention)与门控循环单元(GRU)的Attention-GRU短期光伏发电功率预测模型。首先,基于改进相似日理论建立新的数据集;然后,利用门控循环单元提取光伏发电功率的时序特征,引入注意力机制加强对时序输入中重要信息的关注;最终构建针对不同天气类型的预测模型。仿真结果表明,提出的模型与对比模型相比,预测精度更高。 相似文献
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锂离子电池的健康状态(state of health,SOH)是电池管理系统的核心问题,对其精确的评估能够保障电池的安全可靠运行.然而在实际应用中,容量较难直接测得,导致SOH估算困难.为了获得准确的SOH,本文提出一种基于注意力改进双向门控循环单元(BiGRU)的锂离子电池SOH估计方法.首先提取电池充放电曲线中的电压、电流与阻抗等参数,通过自编码器(auto encoder,AE)对其降维,提取特征量并减少数据间的冗余性.其次,引入注意力机制(attention mechanism,AM)对输入变量分配权重,突出对SOH估计起到关键作用的特征量.最后,利用BiGRU学习输入变量与容量之间的映射关系,捕获容量衰减下的长期依赖性.在不同充电倍率的电池数据集上的结果表明,该方法对不同类型电池的SOH皆可以实现高精度估计,均方根误差在1.1%以下. 相似文献
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随着电力用户规模不断增长、业务量不断扩大,传统客服模式已不能满足繁重的用户业务咨询需求,在此背景下设计一种精准的客户服务模式就显得至关重要。针对这一需求,文章首先提出了一种融合注意力机制+双向长短期记忆网络+双向门控循环单元(Attention+BiLSTM+BiGRU)的序列到序列(Seq2Seq)模型;然后通过对上下文语义信息的深层时序特征提取及赋权,有效提高了模型对话性能;最后通过算例仿真,实验结果从定性和定量的角度验证了所述方法具有回答效果更佳、问题识别能力更高的特点,对在线电力智能客服系统的设计和实现具有一定的参考价值。 相似文献
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针对目前燃气轮机基于数据驱动的故障诊断技术诊断精度有待提升的问题,建立某型号燃气轮机的热力学模型并植入故障特征构造训练样本,在此基础上训练一种基于注意力机制的卷积神经网络与长短期记忆网络结合的神经网络模型。卷积层和注意力机制模块提取燃气轮机多维度的故障特征,长短期网络层进行时序动态故障参数处理。研究表明:相比于典型卷积神经网络,这种神经网络模型不仅能够识别多种故障的动态特征,对于各类故障的诊断能力均可达到93%以上,且加入注意力机制模块后对于不同的故障类型诊断准确率最高提升约3%。 相似文献
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针对风电机组轴承故障诊断中经典一维卷积神经网络和二维卷积神经网络准确率低的问题,将一维原始振动信号和二维时频图相融合,构建基于CBAM-InceptionV2-双流CNN的滚动轴承故障诊断方法。首先,通过快速傅里叶变换和小波变换,将原始振动信号转化为一维数据和二维时频图;其次,构建基于CBAMInceptionV2-双流CNN模型;最后,将提取到的双层特征信息进行融合,并输入到Softmax完成故障分类。实验结果表明,所提模型能够显著地提升轴承故障诊断的准确性。 相似文献
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针对滚动轴承运行环境复杂,传统故障诊断方法难以从强非线性信号中提取有效故障特征,且无法充分利用信号自身特征的问题,提出CNN-LSTM-SVM故障诊断方法。以滚动轴承加速度寿命实验数据为研究对象,基于卷积神经网络(Convolutional Neural Network, CNN)与长短期记忆网络(Long Short Term Memory, LSTM)技术提取信号特征并结合支持向量机(Support Vector Machine, SVM)完成故障分类。结果显示:该方法具有良好外推性能,在变演变阶段下的平均准确率达到95.92%,与现有方法相比,至少高出11.34%,且在噪声环境下的诊断准确率均高于现有方法,稳定性更佳,体现良好的鲁棒性与泛化性。 相似文献
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《International Journal of Hydrogen Energy》2023,48(50):19262-19278
Data-driven fault diagnosis methods require huge amounts of expensive experimental data. Due to the irreversible damage of severe fault embedding experiments to proton exchange membrane fuel cell (PEMFC) systems, rare available data can be obtained. In view of this issue, a fault diagnosis method based on an auxiliary transfer network (ATN) is proposed. This method uses two parallel neural networks (main and auxiliary neural network) and a prediction fusion module to realize fault diagnosis. The auxiliary neural network is a fault diagnosis classifier pretrained based on both slight and severe fault simulative data, and its weights are transmitted into the ATN structure and frozen. After that, the main neural network is trained based on a large number of slight fault experimental data and a small number of severe fault experimental data. Through ATN, the main neural network learns the abstract features of severe faults under the guidance of auxiliary neural network, and realizes the transfer learning from simulation-based fault diagnosis classifier to experiment-based fault diagnosis classifier. Through testing, the accuracy and precision of ATN-based fault diagnosis classifier with LSTM as both main and auxiliary neural network reaches 0.993 and 1.0 respectively, which is higher than the common data-driven methods. 相似文献
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With multiple microgrids (MGs) integrated into power distribution networks in a distributed manner, the penetration of renewable energy like photovoltaic (PV) power generation surges. However, the operation of power distribution networks is challenged by the issues of multiple power flow directions and voltage security. Accordingly, an efficient voltage control strategy is needed to ensure voltage security against ever-changing operating conditions, especially when the network topology information is absent or inaccurate. In this paper, we propose a novel data-driven voltage profile improvement model, denoted as system-wide composite adaptive network (SCAN), which depends on operational data instead of network topology details in the context of power distribution networks integrated with multiple MGs. Unlike existing studies that realize topology identification and decision-making optimization in sequence, the proposed end-to-end model determines the optimal voltage control decisions in one shot. More specifically, the proposed model consists of four modules, Pre-training Network and modified interior point methods with adversarial networks (Modified IPMAN) as core modules, and discriminator generative adversarial network (Dis-GAN) and Volt convolutional neural network (Volt-CNN) as ancillary modules. In particular, the generator in SCAN is trained by the core modules in sequence so as to form an end-to-end mode from data to decision. Numerical experiments based on IEEE 33-bus and 123-bus systems have validated the effectiveness and efficiency of the proposed method. 相似文献
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弹片是解决翼型流动分离的重要技术手段,合理的弹片参数对翼型表面压力分布尤为重要。基于数据驱动的深度学习方法与计算流体力学(Computational Fluid Dynamics, CFD)相结合,可快速有效地完成对复杂流场特征的识别与提取。本文提出一种基于卷积神经网络(Convolutional Neural Network, CNN)的翼型表面压力分布预测方法,通过提取流场的尾流速度、压力等流动特征构建翼型表面压力分布的预测模型。首先,通过数值模拟计算了8种不同抬起角度的NACA 0012弹片翼型的流场;其次,采用提取的流场数据建立CNN预测模型;最后,将预测值和CFD计算值进行对比。结果表明:基于CNN的预测模型对翼型表面压力系数分布有较高的预测精度,其中尾流速度模型在弹片抬起角度为15°时的预测均方根误差仅为0.1,说明尾流速度中包含丰富的流场信息。 相似文献
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《International Journal of Hydrogen Energy》2023,48(78):30532-30555
Estimating the remaining useful life (RUL) of proton exchange membrane fuel cells (PEMFCs) is key to predicting and increasing their durability. This paper investigates six deep learning techniques – long short-term memory (LSTM) networks, gated recurrent units (GRU), 1-D convolutional neural network (CNN)-LSTMs, 1D–CNN–GRUs, 1D–CNN–Bidirectional-LSTMs, and 1D–CNN–Bidirectional-GRUs – for RUL and long-term degradation trend prediction for a five-cell PEMFC stack operated with and without current ripples. The hyperparameters of all models are optimized for minimum root-mean-square error (RMSE) of the stack voltage using tree-structured Parzen estimators (TPE) with hyperband pruning. The obtained prediction results are benchmarked using the coefficient of determination (R2), RMSE, and mean absolute percentage error (MAPE) in stack voltage predictions and relative error (RE) in RUL estimates. The comparative analysis for models trained on 50% datasets reveals that the best R2, RMSE, MAPE, and RE of 98.87E-2, 1.258E-3, 0.285E-1, and 0.0425E-1 are offered by the LSTM model for the stack without current ripples. Further, for the stack with current ripples, the best R2, RMSE, MAPE, and RE of 99.482E-2, 1.77E-3, 0.444E-1, and 0.089E-1 are exhibited by the 1D–CNN–Bidirectional-GRU model. The LSTM model also reveals excellent transfer learning performance on the stack with current ripples with the best R2, RMSE, MAPE, and RE of 98.91E-2, 2.455E-3, 0.362E-1, and 0.044E-1, respectively. 相似文献
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核动力装置是一个高度复杂并具有高度安全性要求的结构体系,其故障检测方法一般采用传统的阈值方法。为克服阈值方法的不足,提出了基于RBF(radial basis function)神经网络的核动力装置故障诊断方法。该方法选择对核动力装置安全具有重要影响的运行参数作为神经网络的输入,并利用核动力装置正常运行模式及典型故障模式的监测数据作为训练样本,网络训练采用正交最小二乘算法(orthogonal least square,OLS)。为了验证所提方法的可行性,利用核动力装置运行监测数据进行检验。结果表明,RBF神经网络成功地诊断出了故障,具有良好的诊断效果。 相似文献