Research on fault diagnosis of airborne fuel pump based on EMD and probabilistic neural networks

Airborne fuel pump is a key component of the airborne fuel system, which once fails will bring a huge negative impact on aircraft safety. Therefore, accurate, reliable and effective fault diagnosis must be performed. However, the current airborne fuel pump has several difficulties: fault samples shortage, high maintenance costs and low diagnostic efficiency. In this paper, after Failure Mode, Effects and Criticality Analysis (FMECA) of airborne fuel pump, an experimental platform of airborne fuel transfusion system is developed and then a fault diagnosis model based on empirical mode decomposition (EMD) and probabilistic neural networks (PNN) is established. Meanwhile, the diagnosis model is verified by practical experiments, and the sensor layout of the experimental platform is optimized. Firstly, the vibration signals and pressure signals under normal state and six types of typical fuel pump faults are acquired on the experimental platform. Then EMD method is applied to decompose the original vibration signals into a finite Intrinsic Mode Functions (IMFs) and a residual. Secondly, the energy of first four IMFs is extracted as vibration signals fault feature, combined with the mean outlet pressure to construct fault feature vectors. Then feature vectors are divided into training samples and testing samples. Training samples are used to train PNN fault diagnosis model and testing samples are used to verify the model. Finally, the experimental results show that only one pressure sensor and one y-axis vibration sensor are needed to achieve 100% fault diagnosis. Furthermore, compared with SVM and GA-BP, the PNN fault diagnosis model has fast convergence, high efficiency and a higher performance and recognition for the typical faults of airborne fuel pump.     

基于多源协同的区域分层能源综合调度优化

为解决区域综合能源的调度优化问题,提出了基于多级能源协同的区域分层能源综合调度方案。分析了多级能源区域协调系统特点以及分层优化需求;建立了双层优化模型,上层优化输电网经济成本,下层优化微网运行成本,二者通过联络线交换功率形成信息流,利用典型算法对本文提出模型进行求解。通过仿真分析了所提方法的有效性,对比了微网联网运行和孤岛运行模式下的系统经济性,说明了双层优化的合理性,即在不同的可响应负荷水平下,相比孤岛模式系统成本下降在11%~17%之间。     

高压大容量电力电子变压器中高频变压器研究现状和发展趋势

高频变压器是DC/DC变换器的核心部件,其激励波形一般为非正弦波,工作频率可达数千赫兹以上,导致铁心损耗显著增加,准确模拟非正弦激励下高频变压器铁心动态磁滞特性,有助于铁心损耗计算以及变压器的电磁暂态分析。首先,该文考虑铁心磁化物理过程,利用系统级电力电子仿真软件PLECS中底层模块和语言编写程序,建立基于静态J-A磁滞模型和磁导-电容类比法的铁心静态磁滞磁导模型;然后,考虑高频条件下涡流效应与弛豫效应对铁心动态磁滞特性的影响,根据损耗统计理论建立铁心动态磁滞磁导模型;最后,基于模拟退火与Levinberg-Marquardt混合算法提取静态J-A磁滞模型参数,根据铁心损耗特性曲线采用最小二乘法提取动态损耗系数。对比分析不同占空比、高频方波电压激励下电流波形以及磁滞回线的仿真结果与实验测量结果,验证了模型的有效性。

     

基于新能源出力比例的大规模系统惯量估计

随着大量分布式电源的接入,微电网惯性水平显著降低,易导致频率失稳。而充电站(charging station,CS)内入网的电动汽车(electric vehicle,EV)可为电网提供辅助调频服务,基于此该文提出了一种基于CS调频能力的虚拟同步发电机(virtual synchronous generator,VSG)控制策略。首先,提出了基于CS调频功率的VSG控制架构,并基于用户充电需求,提出了反映EV调频能力的调频参与度因子计算方法;其次,提出了CS调频功率可控边界评估方法和基于EVs分类方法的功率响应模型;进一步,设计了自适应转动惯量、阻尼系数以及基于CS调频功率边界的频率-调频功率控制器;最后,在MATLAB/Simulink上搭建的CS VSG模型上进行仿真,验证了所提出的控制策略能减小频率的偏移量,提高系统的频率稳定性和动态调节能力,并基于CS调频能力和频率偏移量决定响应调频功率,实现其在CS内EVs间的合理分配。

     

海上风电机组三脚架支撑结构疲劳分析方法

为实现海上风电机组支撑结构抗疲劳设计,提出基于整体-局部方式建模的疲劳分析方法。利用该方法对热点局部区域进行详细有限元建模,基于IIW规范采用高精度六面体实体单元离散,其余非重点区域采用梁单元离散,可克服以往运用应力影响矩阵研究中忽略整体结构位移、边界条件不易施加等缺点。基于IEC 61400规范进行整机动力学和载荷计算,对某海上风电机组三脚架支撑结构进行疲劳分析。对比分析不同载荷工况、波高和水深下的累积损伤值,结果验证了所提出的整体-局部建模方法在海上风电机组三脚架支撑结构疲劳分析中的可行性和优越性。     

基于W-BiLSTM的可再生能源超短期发电功率预测方法

针对现有新能源超短期预测方法难以有效挖掘和分析数据的固有波动规律,且当时序过长时易丢失重要信息等问题,提出了一种基于注意力(Attention)机制的小波分解-双向长短时记忆网络(W-BiLSTM)超短期风、光发电功率预测方法.首先,利用小波分解提取输入时间序列的时域信息和频域信息.随后,考虑双向信息流,采用双向长短时...     

基于馈线树动态拓扑的高容错故障定段方法

开关的投切和分布式电源的渗透使得配电网拓扑和运行方式复杂多变,给馈线故障定段带来挑战。提出把馈线拓扑分解为树枝和分叉区两种连接单元的馈线树故障定段法。通过智能馈线终端单元识别馈线上各开关的状态变化以实时更新馈线拓扑信息,计算树枝状态函数。根据节点共识容错判据进行故障区段可信度判断,进一步结合分叉区状态进行交叉验证。对节点信息正方向的定义以过流前潮流信息为基准,解决有源配电网故障电流的方向定义问题。最后通过算例验证所提方法的优越性。     

基于预测滚动的有源低压配电网三相不平衡治理方法

针对传统治理模式存在的控制时效性低以及与有源配电网不兼容等问题,该文提出了一种将负荷换相预决策和滚动优化相结合的三相不平衡治理方法。首先,分析了分布式电源对低压三相不平衡的影响机理。在此基础上,提出三相电流不平衡治理的预决策方法,在负荷换相预决策模块中采用降噪回归模型进行短期负荷预测,并利用启发式算法进行馈线电流平衡优化。进而,考虑到预测模型的学习能力有限,提出了三相电流不平衡治理的滚动优化的原理与方法。最后实例仿真分析表明,所提方法不仅能在新能源分布式接入的台区中获得较好的平衡效果,而且在没有历史数据或实时数据的情况下,预决策模块或滚动优化模块的独立工作仍能具有三相不平衡抑制能力。     

基于负荷分类的低压台区线损率自动测算

针对低压台区线损率异常原因难以辨析的问题,提出了一种基于统计偏差和技术偏差的线损率分析方法。首先,通过分析统计线损率和理论线损计算模型,结合Matlab数值仿真引入理想线损率的概念。在此基础上,对比分析统计线损率、理论线损率、理想线损率之间的关系得到统计偏差指数及技术偏差指数。再通过比较偏差指数与设定阈值,确定线损率异常原因并制定相应降损措施。最终,通过工程实践验证了该方案可有效辨析线损率异常成因,采取对应降损措施后,试点台区技术偏差指数由1.2降低至1.0,同等供出电量时线损率由3.1%下降至2.5%。

     

Improved meta-heuristic techniques for simultaneous capacitor and DG allocation in radial distribution networks

The active and reactive power flow in distribution networks can be effectively controlled by optimally placing Shunt Capacitors (SCs) and Distributed Generators (DGs). This paper presents improved versions of three evolutionary or swarm-based search algorithms, namely, Improved Genetic Algorithm (IGA), Improved Particle Swarm Optimization (IPSO) and Improved Cat Swarm Optimization (ICSO) to efficiently handle the problem of simultaneous allocation of SCs and DGs in radial distribution networks while considering variable load scenario. The benefit of network reconfiguration has also been taken into account after optimal allocation of these devices. Several algorithm specific modifications are suggested in the standard forms of GA, PSO and CSO to overcome their inherent drawbacks. In addition, an intelligent search approach is proposed to enhance overall performance of proposed algorithms. The proposed methods are investigated on IEEE 33-bus and 69-bus test distribution systems showing promising results when compared with other recently established methods. Application results also show a marked improvement in the performance of these algorithms while compared with their respective standard counterparts.