巨型浮吊起重船稳性安全的解耦控制方法

针对巨型起重船起重臂相对船体进行起吊、回转、变幅运动存在非线性强耦合等问题,在建立了起重船运动姿态数学模型的基础上,提出一种神经网络解耦控制策略。应用神经网络自适应解耦反馈控制方法,实现起重机回转、变幅的解耦,最终控制起吊执行电机、船舶压载泵及锚泊定位电机,将起重船起重过程的船舶重心控制在稳定区域内,从而使起重船的操控安全高效。仿真结果表明,起重机的回转和变幅运动的动态解耦控制效果良好。     

基于神经网络自适应PID控制的船舶操纵研究

本文针对船舶操纵这种非线性、时变参数控制对象,提出了一种采用神经网络自适应ＰＩＤ控制方案。该控制结构有两上子神经网络组成,一个三层ＢＰ神经网络用于对被控对象进行在线辨只,另一个两层线性网络构成具有ＰＩＤ结构的控制器。文中给出了神经网络在线训练学习方法,并进行船舶操纵控制仿真研究。     

FPSO: lessons learned

This article describes about the design of various electrical systems and installation issues of typical FPSOs (Floating, Production, Storage, and Offloading) system and provide some lessons learned. FPSO consists of a vessel or hull and a topsides facility creates many electrical interface issues between the two major parts of the completed vessel. These vessels typically have more extensive electrical systems than the typical fixed platforms. Electrical operating loads may total 40-50 MW or more depending on the vessel configuration. For the purpose of this typical FPSO design, it is assumed that the selected drivers are all electric motors. It is assumed that the total connected load is 70 MW with an operating load of 40 MW. Based on the electrical load requirements, the FPSO has an extensive electrical distribution system. The distribution system for a FPSO are very similar to those of any large distribution system. Lessons learned can only be effective if the are collected on current projects, reviewed for accuracy and sufficient detail to describe the issue, distributed to future project personnel, and incorporated into the new project design. Many of the lessons learned presented in this article are applicable to other types of electrical work and not limited to FPSOs.     

电力调度软交换技术组网的研究

目前,电力调度通信专网主要基于电路交换技术,通过调度程控交换机及相应的模拟中继线路、数字中继线路,组成多级汇接交换的电话网络。交换机网络业务扩展不再具备优势,以此引入软交换技术,该技术具备更多功能,如视频、图像、多媒体会议等。通过IP中继网关可连接2个网络,同时终端也需要支持接入融合,这样能较好地扩展交换机网络的单一性,加强业务的多样性。目前已经验证方案的有效性,为进一步优化系统打下了坚实基础。     

基于支持向量机的船舶电力负荷预测

船舶电力系统是一个独立的电力系统，需要根据准确的负荷预测来控制多台发电机组的运行。本文提出了一种基于支持向量机的船舶电力负荷短期预测方法。对某大型集装箱船舶在不同工况下的电力负荷数据，分别用基于径向基核函数的支持向量机方法、多层BP网络和RBF网络方法进行训练和预测计算，仿真结果表明支持向量机具有更高的预测精度，是船舶电力负荷预测的一种有效方法。     

Static VAR compensator using recurrent neural network

In this paper, an internal model control recurrent neural network method is used to control the switching of thyristor-controlled reactor in a static VAR compensator (SVC) system for regulating the voltage. The novel controller scheme contains several feedback loops instead of only a feed-forward loop as in the conventional recurrent neural network (RNN). In the proposed controller model, the RNN identifier creates a sample of the connected system and its output generates a part of inputs for the RNN controller which then sends the control signal to the SVC system. Three types of non-linear conditions are chosen to test the operational capability of the new control system to perform the voltage regulation satisfying the IEEE Std 519-1992. The test cases contain a three-phase fault power system, opening of one of the transmission lines in a double line transmission system and sudden changes in the load demand. Results show that the proposed control model is capable of regulating the voltage of the system in a desired range.     

抽油机节能的模糊神经网络控制研究

依据对油田石油开采中普遍存在的电能浪费以及近年来人们采用各种节能控制方案的分析，提出“抽油机连续工作变为间歇工作的运行方式”。为解决抽油机节电的停机时间这一关键问题，采用了模糊神经网络(FNN)智能控制方案。结合抽油机抽油过程的特点及节能需要，用T-S模糊系统构造FNN的模型框架。为了工程实现，对基本FNN进行了改进和简化，按该算法编制的软件在工程中取得了显著的节能效果。     

Identification of power system load dynamics using artificialneural networks

Power system loads are important in the planning and operation of an electric power system. Load characteristics can significantly influence the results of synchronous stability and voltage stability studies. This paper presents a methodology for the identification of power system load dynamics using neural networks. Input-output data of a power system dynamic load is used to design a neural network model which comprises delayed inputs and feedback connections. The developed neural network model can predict the future power system dynamic load behavior for arbitrary inputs. In particular, a third-order induction motor load neural network model is developed to verify this methodology. Neural network simulation results are illustrated and compared with the actual induction motor load response     

500 kV充油海底电缆运行状态综合在线监测系统研究

海南联网系统500 kV海底电缆运行环境复杂多变,会直接影响到海底电缆的运行状态.为了有效掌握海南联网系统三相31 km海底电缆的运行状态,研究充油海底电缆运行状态综合在线监测技术至关重要.文中根据海底电缆实际运行监测要求,结合充油海底电缆捆绑通信光缆的这一特殊结构,开展了基于布里渊光时域分析的分布式光纤温度传感技术研究,建立了基于IEC 60287热路模型的充油海底电缆的温度热场分布的热路模型,研制了500 kV充油海底电缆运行状态综合在线监测系统,完成了系统的现场安装及挂网运行.该系统能实时监测海底电缆的温度、油流以及油压等运行参数,通过海底电缆温度、油流以及油压历史数据分析,表明系统可实时监测海底电缆的运行状态及稳定可靠运行.     

A decomposition method for the solution of the network equations of short-term dynamics of connected power systems,taking into consideration the axial asymmetry of the machines

In the numerical simulation of the short-term dynamics of power systems, mutual angles of machine rotors and other relevant state variables are observed for a period up to a few seconds after sudden and forced switching on and tripping of the machines, transmission lines, transformers and consumer buses. On the one hand the set of differential equations, which are associated with the machines, is numerically integrated, and on the other the set of algebraic equations, which are associated with the network, is solved. This paper presents a decomposition method for solving the network equations of short-term dynamics of connected power systems, taking into consideration the axial asymmetry of the machines.     

Robust adaptive regulation of dynamically positioned ships with unknown dynamics and unknown disturbances

We develop a robust adaptive regulating control law for dynamically positioned ships subject to unknown dynamics and bounded unknown disturbances incorporating the radial basis function (RBF) neural network (NN), the dead zone adaptive technique, and a robust control term into the vectorial backstepping approach. The RBF NNs with the dead zone adaptive laws approximate the ship unknown dynamics. The adaptive law‐based robust control term compensates for unknown disturbances, NN approximation errors, and undesirable errors arising from the design procedures. The developed dynamic positioning (DP) control law regulates the ship position and heading to the desired values with arbitrarily small errors, while guaranteeing the uniform ultimate boundedness of all signals in the DP closed‐loop control system of ships. High‐fidelity simulations on two supply ships and comparisons demonstrate the effectiveness and the superiority of the developed DP control law.     

基于AdaBoost-Elman算法的锂离子电池荷电状态估计方法

工作状态下的电池是一个动态的非线性系统,基于数据驱动的机器学习是锂离子电池SOC估计建模的一类重要方法,其中基于神经网络的学习方法是典型代表.针对单一前馈型神经网络(如BP神经网络)预测过程中存在泛化能力低、局部极小化、预测精度低及动态性不足等问题,提出基于AdaBoost-Elman算法的锂离子电池SOC估计方法.该...     

采用NSGA-Ⅱ混合智能算法的风电场多目标电网规划

风电并网在实现节约化石能源和减少有害气体排放等效益的同时,也将对电力系统的可靠性造成一定的负面影响。为达到投资经济性、系统可靠性、环保效果的整体最优,构建了多目标风电场接入的输电线路与电网的联合优化规划模型;针对目标权重未知、人工神经网络(artificial neural network,ANN)收敛困难、无法合理决策等问题,采用方差最大化决策和分类逼近理想解的排序方法(technique for order preference by similarity to an ideal solution,TOPSIS)缩小最优解的范围,并在此基础上提出了随机模拟、神经元网络和非劣排序遗传算法Ⅱ(non-dominated sorting genetic algorithm Ⅱ,NSGA-Ⅱ)相结合的混合智能算法;对增加风电场的改进IEEE Garver-6系统进行计算分析,结果表明该方法具有较高的决策效率和计算精度,从而验证了所提出模型和方法的合理性和有效性。     

BP神经网络PID控制器在热油锅炉温控中的应用

介绍了基于BP神经网络PID控制算法,结合多模态控制理论,提出将BP神经网络PID控制器应用于热油锅炉温控中,并将其与普通PID控制进行比较。结果表明,该方法具有超调量小、过渡时间短、鲁棒性好等特点,弥补了常规PID在锅炉控温中参数难以整定等缺点。将此控制策略应用于热油锅炉温控中,构建了锅炉的自控系统,使锅炉处于最佳的燃烧状态,保证锅炉的安全经济运行。     

Application of neural-network modules to electric power system fault section estimation

This paper presents a neural system intended to aid the control center operator in the task of fault section estimation. Its analysis is based on information about the operation of protection devices and circuit breakers. In order to allow the diagnosis task, the protection system philosophy of busbars, transmission lines, and transformers are modeled with the use of two types of neural networks: the general regression neural network and the multilayer perceptron neural network. The tool described in this paper can be applied to real bulk power systems and is able to deal with topological changes without having to retrain the neural networks.     

基于微分代数神经网络的含新能源区域电网端口动态特性学习方法

高比例新能源渗透背景下,建立能够准确描述复杂环境因素影响下含新能源的区域电网端口特性动态模型,对于新型电力系统动态分析至关重要。为此,提出了一种基于微分代数神经网络的含新能源区域电网端口动态特性学习方法。该方法利用微分代数神经网络,基于区域电网接入点的时序量测以及光照强度、温度等环境量测数据,学习以神经网络表达的端口特性模型。所得模型由初始状态提取模块、微分神经网络模块、代数神经网络模块组成,可直接接入电力系统暂态仿真器中,用于分析电力系统整体动态特性。在IEEE-39节点系统中对该方法进行仿真验证,测试结果表明：所得模型能够适应不同环境场景,准确率高,验证了方法的有效性。该建模方法仅依赖端口时序量测,在新型电力系统动态分析中具有较大的应用潜力。     

变压器油中水分在线监测的神经网络计算模型

为了对变压器油中的微水含量进行在线监测和建模计算,介绍了变压器油纸绝缘中水分的分布和利用聚酰亚胺薄膜电容式湿度传感器在线监测变压器油中微水含量的原理并在变压器油中微水含量监测的相关理论基础上,提出了一种基于神经网络的在线监测计算模型以评估变压器油中的微水含量。比较油中微水含量的计算值与测量值证明,采用该模型能够发现变压器中导致油中水分异常变化的故障。实际测试结果表明该模型能很好地反映变压器油中水分的实际情况。     

基于TensorFlow框架的有源配电网深度学习故障定位方法

随着大规模分布式电源(DG)接入配电网,配电网的结构由传统的辐射型变为多端电源结构,传统的故障定位方法不再完全满足含DG的配电网系统,对此提出一种基于深度学习的有源配电网故障定位方法。首先通过馈线监控终端采集过电流故障数据与节点电压数据,结合各电源出力数据,形成故障数据向量;然后使用Tensorflow构建基于全连接网络的深度神经网络模型,挖掘故障数据向量与故障支路之间的映射联系,形成故障定位模型;最后利用该模型在线定位故障并验证其有效性。模型测试结果表示,与反向传播神经网络、学习向量量化神经网络模型相比,深度学习模型收敛速度更快,故障定位准确率更高,同时在数据畸变或缺失时,模型具有较高的容错性。     

Current trajectory image-based protection algorithm for transmission lines connected to MMC-HVDC stations using CA-CNN

In the presence of an MMC-HVDC system, current differential protection (CDP) has the risk of failure in operation under an internal fault. In addition, CDP may also incur security issues in the presence of current transformer (CT) saturation and outliers. In this paper, a current trajectory image-based protection algorithm is proposed for AC lines connected to MMC-HVDC stations using a convolution neural network improved by a channel attention mechanism (CA-CNN). Taking the dual differential currents as two-dimensional coordinates of the moving point, the moving-point trajectories formed by differential currents have significant differences under internal and external faults. Therefore, internal faults can be identified using image recognition based on CA-CNN. This is improved by a channel attention mechanism, data augmentation, and adaptive learning rate. In comparison with other machine learning algorithms, the feature extraction ability and accuracy of CA-CNN are greatly improved. Various fault conditions like different network structures, operation modes, fault resistances, outliers, and current transformer saturation, are fully considered to verify the superiority of the proposed protection algorithm. The results confirm that the proposed current trajectory image-based protection algorithm has strong learning and generalizability, and can identify internal faults reliably.     

基于预警单元的逆变器并联系统分层故障诊断系统

随着逆变器并联系统规模的增大,电源系统对故障诊断提出了新的要求.结合逆变器并联系统故障诊断的特点,对所存在的问题进行了分析,并通过新的应用思路,将神经网络和专家系统应用到逆变器并联系统的故障诊断中.提出并定义了故障预警的一系列相关概念,设计了预警单元的工作结构.在此基础上提出了一种新的基于故障预警单元的分层故障诊断专家系统,详细阐述了故障预警单元的实现以及诊断系统的工作原理.通过原理分析及仿真结果可以看出,提出的方法对神经网络和专家系统在故障诊断中的有效应用具有很好的推动作用和一定的指导意义.