An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

Finite-time economic model predictive control for optimal load dispatch and frequency regulation in interconnected power systems

This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.

Finite-time robust control of uncertain fractional-order Hopfield neural networks via sliding mode control

The finite-time control of uncertain fractional-order Hopfield neural networks is investigated in this paper. A switched terminal sliding surface is proposed for a class of uncertain fractional-order Hopfield neural networks. Then a robust control law is designed to ensure the occurrence of the sliding motion for stabilization of the fractional-order Hopfield neural networks. Besides, for the unknown parameters of the fractional-order Hopfield neural networks, some estimations are made. Based on the fractional-order Lyapunov theory, the finite-time stability of the sliding surface to origin is proved well. Finally, a typical example of three-dimensional uncertain fractional-order Hopfield neural networks is employed to demonstrate the validity of the proposed method.

Heating load interval forecasting approach based on support vector regression and error estimation

As the existing heating load forecasting methods are almostly point forecasting,an interval forecasting approach based on Support Vector Regression (SVR) and interval estimation of relative error is proposed in this paper.The forecasting output can be defined as energy saving control setting value of heating supply substation;meanwhile,it can also provide a practical basis for heating dispatching and peak load regulating operation.By means of the proposed approach,SVR model is used to point forecasting and the error interval can be gained by using nonparametric kernel estimation to the forecast error,which avoid the distributional assumptions.Combining the point forecasting results and error interval,the forecast confidence interval is obtained.Finally,the proposed model is performed through simulations by applying it to the data from a heating supply network in Harbin,and the results show that the method can meet the demands of energy saving control and heating dispatching.

Adaptive Bit Loading Scheme with Semi-Blind Channel Estimation for OFDMSystems

An adaptive bit loading and power-allocation scheme is proposed in order to augment the performance of the system based on orthogonal frequency division multiplexing （OFDM）, which is based on the maximum power margin. Coinciding with the adaptive loading scheme, a semi-blind channel estimation algorithm using subspace decomposition method is proposed, which uses the information in the cyclic prefix. An initial channel state information is estimated by using the training sequences with the method of interpolation filtering. The proposed adaptive scheme is simulated on an OFDM wireless local area network（WLAN） system in a time-varying channel. The performance is compared to the constant loading scheme.

State Estimation of Regional Power Systems with Source-Load Two-Terminal Uncertainties

The development and utilization of large-scale distributed power generation and the increase of impact loads represented by electric locomotives and new energy electric vehicles have brought great challenges to the stable operation of the regional power grid.To improve the prediction accuracy of power systems with source-load twoterminal uncertainties,an adaptive cubature Kalman filter algorithm based on improved initial noise covariance matrix Q0 is proposed in this paper.In the algorithm,the Q0 is used to offset the modeling error,and solves the problem of large voltage amplitude and phase fluctuation of the source-load two-terminal uncertain systems.Verification of the proposed method is implemented on the IEEE 30 node system through simulation.The results show that,compared with the traditional methods,the improved adaptive cubature Kalman filter has higher prediction accuracy,which verifies the effectiveness and accuracy of the proposed method in state estimation of the new energy power system with source-load two-terminal uncertainties.

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with Predictive Machine Learning Approach

Virtual Machines are the core of cloud computing and are utilized toget the benefits of cloud computing. Other essential features include portability,recovery after failure, and, most importantly, creating the core mechanismfor load balancing. Several study results have been reported in enhancing loadbalancingsystems employing stochastic or biogenetic optimization methods.It examines the underlying issues with load balancing and the limitationsof present load balance genetic optimization approaches. They are criticizedfor using higher-order probability distributions, more complicated solutionsearch spaces, and adding factors to improve decision-making skills. Thus, thispaper explores the possibility of summarizing load characteristics. Second,this study offers an improved prediction technique for pheromone level predictionover other typical genetic optimization methods during load balancing.It also uses web-based third-party cloud service providers to test and validatethe principles provided in this study. It also reduces VM migrations, timecomplexity, and service level agreements compared to other parallel standardapproaches.

Finite-time decentralized event-triggered state estimation for coupled neural networks under unreliable Markovian network against mixed cyberattacks

This article investigates the issue of finite-time state estimation in coupled neural networks under random mixed cyberattacks,in which the Markov process is used to model the mixed cyberattacks.To optimize the utilization of channel resources,a decentralized event-triggered mechanism is adopted during the information transmission.By establishing the augmentation system and constructing the Lyapunov function,sufficient conditions are obtained for the system to be finite-time bounded and satisfy the H∞ performance index.Then,under these conditions,a suitable state estimator gain is obtained.Finally,the feasibility of the method is verified by a given illustrative example.

基于盲源分离的工业谐波源负荷分类识别方法

针对传统谐波源辨识方法无法实现工业用户谐波源具体类型的非侵入式识别的问题,该文提出一种基于盲源分离的工业谐波源负荷分类识别方法。该方法仅依据工业用户进线处电压、电流数据即可实现谐波源负荷具体类型的非侵入式识别。首先,从负荷等值阻抗模型入手,建立工业用户多负荷等值阻抗并联电路模型;其次,采用集合经验模态分解与奇异值分解相结合的方法确定构成用户进线处监测点综合等值阻抗信号的源阻抗信号数目;然后,采用快速独立分量分析实现将谐波源负荷等值阻抗信号从综合负荷等值阻抗信号中分离;最后,将分离出谐波源负荷等值阻抗信号频率特征与典型谐波源负荷进行匹配,进而实现分类识别。仿真与实测实验结果均表明,所提方法能够准确识别工业用户所含多种谐波源负荷的具体类型,具有较好的可行性和实用性。

地震荷载作用下土体变形试验研究

为研究不规则地震荷载对土体变形特性的影响,对砂土试样开展了系统的动三轴试验。试验中分别对3种不同密度砂土试样施加了4条具有不同峰值的不规则地震应力时程及其循环次数为20周的等幅正弦荷载,以便开展对比研究。结果表明:在地震荷载及其等幅循环荷载输入下,砂土试样的应变增长曲线差异显著,荷载类型及荷载波形是影响土单元应变时程发展特征的主要因素。考虑荷载不规则性得到的修正系数受荷载类型和砂土相对密实度的影响,不受输入荷载动应力幅值大小及砂土类型的影响。最后针对地震荷载下土单元变形的计算,给出了以常规的等幅三轴变形试验为依据的估算方法。

基于输入−状态联合估计的漂浮式风机塔架结构载荷反演及状态分析

[目的]旨在研究波浪作用下漂浮式风机塔架的结构载荷反演方法及动力响应状态。[方法]以半潜型风机为研究对象,通过时域仿真模拟测量数据,并且基于漂浮式风机的频域仿真模型计算塔架的质量归一化模态,将二者作为输入−状态联合估计(joint input-state estimation,JIS)算法的输入,对漂浮式风机塔架开展结构载荷反演以及实时状态分析。[结果]结果表明:基于JIS算法,以塔架节点的仿真数据作为算法的输入,估计的塔根载荷以及塔架节点响应与仿真结果具有良好的一致性,实现了高精度的反演。[结论]反演结果验证了JIS算法的有效性,可以用于漂浮式风机的实时状态分析,保障漂浮式风机长期安全稳定运行。

飞行器结构热声疲劳问题研究进展

高超声速飞行器表面结构,如发动机叶片、内涵道及其排气道等暴露在高温强噪声复合环境中,容易出现热声疲劳损伤问题,因此,结构热声疲劳问题引起了广泛关注。热声疲劳研究对增强这类结构的耐久性和可靠性具有重要意义。本文总结了国内外在飞行器结构热声疲劳领域的研究现状,阐述了在热声疲劳理论研究、仿真分析和试验技术方面所取得的进步。从时间维度回顾了国外自20世纪70年代至今在结构热声疲劳领域的研究情况;按研究单位分类介绍了国内科研院所和高等院校在该领域所开展的工作。在此基础上分析了飞行器结构热声疲劳研究所面临的技术难点问题,指出了尚需进一步研究的方向。

基于负荷预测和无迹粒子滤波的配电网动态状态估计

随着汽车充电成为新型重要负荷,为确保此时配电网运行与控制安全,对其进行实时准确的态势感知,提出一种基于卷积神经网络和门控循环单元的短期负荷预测与无迹粒子滤波算法自适应混合的配电网动态状态估计方法。结合使用卷积神经网络和门控循环单元进行短期负荷预测,将预测得到的有功与无功功率进行潮流计算,再与无迹粒子滤波量测估计值自适应加权得到电压幅值和相角状态估计结果。以IEEE33节点配电网为例,验证了所提状态估计方法的准确性与面对不良数据时的鲁棒性。

基于参数估计-滑模阻抗控制的SEA性能分析

重载四足机器人的足部与地面接触过程和步态转换过程中会受到不确定的冲击载荷作用,易导致足部机构载荷过大从而造成结构的冲击损坏。因此,针对使用液压串联弹性执行器(series elastic actuators,SEA)作为足部末端在非结构环境下动态性能差的问题,提出了基于环境参数估计的滑模阻抗控制方法(environmental parameter estimation sliding mode,EPESM)。以阀控液压缸的活塞位移传递函数为基础建立了基于位置内环的SEA阻抗控制模型,并以PID作为基础控制器;为改善SEA阻抗控制的动态性能,根据Lyapunov第二法构建稳定的自适应环境参数估计方法对SEA期望位置进行前馈补偿;为提升自适应环境参数估计方法在SEA工作过程中不同阶段的动态性能和环境变化适应性,使用模糊控制方法对自适应环境参数估计方法中的自适应参数进行寻优;以SEA状态方程为基础构建滑模控制器与PID控制器进行动态性能对比分析。仿真结果表明:在变SEA弹簧刚度工况和变环境刚度下,EPESM阻抗控制的响应速度明显更快,可将调节时间从平均5 s缩短到1 s内,能更快地达到预期位移和预期接触力,且能略微降低稳态误差,使接触力误差保持在±6 N内。在动态跟踪工况下,EPESM阻抗控制的动态性能更好,在快速进入跟踪状态后,可以长时间保持0.2 s以内的相位滞后和5.2%的幅值误差。

基于Vogit力学模型的照明系统节能控制研究

采用Vogit力学模型,构建照明系统离散动力学方程,分析照明系统状态空间变化特征,估计照明系统负载;引入电流灵敏度参量,结合照明系统电磁耦合特性,构建照明系统节能控制模型;通过自适应迭代处理实现自适应寻优,获取照明系统内环控制输出增益;根据照明系统节能控制带恒功率负载等效原理消除复阻抗,达到节能控制稳定的目的。以照明系统作为实验对象进行实验,结果表明该方法的节能控制效果较好,能够有效确保照明系统节能控制的稳定性。

工业用户连续参与需求响应的用户基线负荷精准计算方法

提出了一种将K-means聚类分析与长短期记忆神经网络算法结合,通过工业同源组信息进行迁移学习优化的计算方法。该方法实现了对长时间连续参与需求响应的工业用户基线负荷的精准计算,提高了工业用户需求响应效果评价的准确性。通过城市级虚拟电厂平台采集的参与需求响应实践的工业用户电力负荷数据,验证了该方法的有效性。

基于弹性网络回归的实际采暖热指标估算方法

为满足热电联产企业估算区域热负荷的需求,提出一种使用弹性网络回归模型的估算方法。首先对实际采暖热指标的影响因素进行分析,确定模型的输入参数;然后以西安市123个小区2022—2023年采暖季的实际运行数据为基础建立估算模型,并证明该模型相较于Lasso回归和岭回归模型的拟合优度分别提高了3.88%和4.22%;最后从多角度选取西安市部分小区相关数据构成验证集对弹性网络回归模型进行验证。验证结果表明:弹性网络回归模型综合了Lasso回归和岭回归的优点,模型的均方根误差和拟合优度分别为1.150和0.953,相较于传统模型能在符合热负荷需求的同时降低4%的能源消耗。说明该方法能准确估算不同参数条件下的实际采暖热指标,可以满足热电联产企业的实际需求。

河流水质采样策略与频率对氮磷负荷估算的影响

为探究适合的流域水质监测频率与采样策略,提高氮磷污染物负荷估算的精度,基于云南省洱源县凤羽河流域2011~2013年间逐日流量与总氮､总磷浓度的每日数据,在7d/次,14d/次,21d/次,28d/次4种采样频率下设定了3种不同的采样情景(情景a:普通随机取样､情景b:随机取样的基础上增加降水量超过10mm的水样样品采集,情景c:随机取样的基础上增加降水量超过25mm的水样样品采集).基于采样频率与采样情景的组合,利用LOADEST模型估算的总氮与总磷负荷,并与逐日连续水量水质数据计算的实测值进行比较,评估了不同取样频率和取样情景下LOADEST模型的负荷估算精度.结果表明:在LOADEST模型的估算过程中,同种采样频率下,增加降雨时期的浓度数据将会提高LOADEST模型的模拟精度;在降雨事件期间进行加密采样,不仅加大了工作量,且增加的采样次数过多(如情景b)使总氮的RMSE值由1.92增至2.26,总磷的RMSE值由0.08增至0.19,高估氮磷负荷,模型模拟精度降低.在四种不同的采样频率下,情景c中总氮的RMSE值范围为1.92~2.07,总磷的RMSE值范围为0.06~0.13,均取得了该频率下最低的RMSE值,模型估算精度最高;从不同频率与采样情景对模型估算结果的影响来看,对总氮负荷估算结果(RMSE值范围为1.92~2.52)影响要大于总磷(RMSE值范围为0.06~0.18).其中,情景a与情景b的负荷估算结果精度随着采样频率降低而降低.情景c的估算结果受现有取样频率降低的影响不明显.本研究表明,在常规固定频率水质监测基础上结合气象预报适量增加降雨时的采样可在28d/次的较低取样频率下得到较高的氮磷负荷估算精度,符合我国各地区环境水质采样频率,可为相关研究的水质采样策略提供一定的参考作用.

冲击工况下行星齿轮系统点蚀故障评估方法

行星齿轮系统作为工业减速机的关键结构,在运转过程中受到循环载荷和冲击载荷的作用,易使齿轮表面金属疲劳剥落,形成点蚀故障。当系统受到冲击载荷作用时,振动信号的剧烈波动会干扰系统的故障识别。针对该问题,基于冲击工况下含点蚀故障的行星齿轮系统动态特性分析,开发了诊断指标。首先,基于解析几何方法及Hertz接触理论建立了齿轮副接触刚度模型;其次,引入冲击载荷,利用冲击函数法求解了太阳轮-行星轮啮合力;最后,基于系统动态响应信号的频谱分布规律,提出了边带质心能量指标,实现了故障冲击与载荷冲击的准确辨识。

基于KANN-DBSCAN带宽优化的核密度估计载荷谱外推

针对核密度估计载荷外推全局固定带宽的局限性,提出一种基于KANN-DBSCAN(K-average nearest neighbor density-based spatial clustering of applications with noise)改进带宽取值的核密度估计(kernel density estimation, KDE)载荷外推方法。通过KANN-DBSCAN聚类算法对载荷数据进行分组聚类,采用拇指法求得不同簇间的最优带宽,然后进行核密度估计,再采用蒙特卡洛模拟进行外推。以某电动汽车在用户道路的实测载荷数据为应用对象,对外推方法的合理性进行检验。从统计参数检验量、拟合度检验和伪损伤检验3个指标对外推效果进行评估。结果表明:相比固定带宽的核密度估计外推方法,基于KANN-DBSCSN核密度估计的外推方法获得的外推载荷在统计参数上与实测载荷更为接近,均值、标准差和最大值的误差分别仅为1.9%、 4.3%和1.9%;幅值累计频次曲线拟合度R2均大于0.99,伪损伤均接近1。结果验证了该聚类方法在核密度估计载荷外推的有效性,有助于编制汽车在用户道路上的载荷谱,为具有相似载荷分布特点的机械零部件载荷外推提供了参考。