Robust Fault Detection of Markovian Jump Systems

This paper deals with the problem of observer-based robust fault detection for both continuous- and discrete-time linear Markovian jump systems with an unknown input. By using a fault detection filter as residual generator and a proposed performance index as objective function, the robust fault detection filter design is formulated as an optimization problem. A sufficient condition to solve this problem is established in terms of the feasibility of certain matrix inequalities, which can be solved via iterative linear matrix inequality (LMI) algorithms. The residual evaluation problem is also considered. A numerical example is given to illustrate the effectiveness of the proposed techniques.     

基于特征结构配置的飞控系统故障检测与诊断

针对具有未知扰动输入的飞行控制系统,运用特征值配置设计了一种用于故障检测和诊断的观测器,他通过对观测器进行左特征向量的配置使得残差与干扰分布方向正交,故障检验残差r与未知输入干扰d之间的传递函数阵为零,从而使残差与干扰直接解耦.通过这种方法,残差信号得以对干扰具有鲁棒性,使FDI算法不受系统不确定性干扰的影响,提高系统故障诊断的可靠性和精度.同时通过残差信号估计故障,能在线辨识故障的形态,仿真结果验证了该方法的有效性.     

A Fault Tolerant Doubly Fed Induction Generator Wind Turbine Using a Parallel Grid Side Rectifier and Series Grid Side Converter

With steadily increasing wind turbine penetration, regulatory standards for grid interconnection have evolved to require that wind generation systems ride-through disturbances such as faults and support the grid during such events. Conventional modifications to the doubly fed induction generation (DFIG) architecture for providing ride-through result in compromised control of the turbine shaft and grid current during fault events. A DFIG architecture in which the grid side converter is connected in series as opposed to parallel with the grid connection has shown improved low voltage ride through but poor power processing capabilities. In this paper, a unified DFIG wind turbine architecture which employs a parallel grid side rectifier and series grid side converter is presented. The combination of these two converters enables unencumbered power processing and robust voltage disturbance ride through. A dynamic model and control structure for this architecture is developed. The operation of the system is illustrated using computer simulations.     

Health assessment and management of wind turbine blade based on the fatigue test data

With the quality problem becoming increasingly prominent, it is necessary to assess and manage the health condition of the wind turbine blade. The health degree is defined and calculated by the Grey Relation (GR) model to assess the health condition of the wind turbine blade quantitatively. The availability, reliability and artificial test result are taken as three indexes of the health degree. The availability is defined according to the stiffness degradation affected by the environmental temperature. Based on the health assessment results, after determining the decision objectives and the management strategies, the weights of the decision objectives and the health management decision are determined by the Analytic Hierarchy Process and Fuzzy (AHP-Fuzzy) decision method. This process is shown in a practical example of the 3 MW wind turbine blade. As a result, an approach and a certain theoretical guidance for the health assessment and management of wind turbine blade are proposed.     

Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype

In this paper, a model-based procedure exploiting analytical redundancy for the detection and isolation of faults on a gas turbine process is presented. The main point of the present work consists of exploiting system identification schemes in connection with observer and filter design procedures for diagnostic purpose. Linear model identification (black-box modelling) and output estimation (dynamic observers and Kalman filters) integrated approaches to fault diagnosis are in particular advantageous in terms of solution complexity and performance. This scheme is especially useful when robust solutions are considered for minimise the effects of modelling errors and noise, while maximising fault sensitivity. A model of the process under investigation is obtained by identification procedures, whilst the residual generation task is achieved by means of output observers and Kalman filters designed in both noise-free and noisy assumptions. The proposed tools have been tested on a single-shaft industrial gas turbine prototype model and they have been evaluated using non-linear simulations, based on the gas turbine data.     

基于集员未知输入观测器的风力机桨距执行器故障诊断

风力机一般放置在恶劣的环境中,其桨距执行器极易出现故障。文中针对一类含有未知但有界干扰和噪声的风力机系统的桨距执行器故障问题,设计了集员未知输入观测器对桨距的执行器故障进行检测并分离。采用气动机理和现代辨识原理建立风力机系统模型,通过优化未知输入观测器设计对系统中的干扰解耦,基于中心对称多胞体估计不考虑故障时残差的区间包络,并将其作为残差估计的上下动态阈值,实现状态估计。在上述基础上提出了利用一组集员未知输入观测器进行故障诊断的策略。仿真结果表明,在实验过程中,文中所设计的集员未知输入观测器准确地诊断出了风力机桨距执行器的3阶和5阶线性系统在发生突变故障和缓慢时变故障的时间和位置,证明了所提故障诊断策略的有效性。     

Automatic Feature Selection of Hardware Layout: A Step toward Robust Hardware Trojan Detection

Recently, the problem of hardware Trojan detection has gained a tangible significance in academia and industry. That problem, by its nature, is complex, time consuming and error prone due to design and fabrication outsourcing of hardware circuits to external untrusted foundries. Researchers have proposed different approaches, either destructive or non-destructive, to overcome that problem. The destructive approach depends on reverse engineering via decapsulation, delayering and layout identification. This paper presents a first trial of a new approach that can afford an automatic and robust solution for the step of layout identification. The proposed technique takes the underlying digital circuit as input, and automatically determines its basic features using Haar feature extractor. Based on that features, a decision tree is trained to act as a weak classifier, which is later boosted, by making use of AdaBoost learning algorithm, to produce a strong classifier in a chain of cascaded classifiers. Accordingly, a classification model is built up to provide an automatic hardware Trojan location and detection tool. To evaluate the proposed model, ISCAS89 benchmark dataset was used for training and testing. The hardware dataset has been altered deliberately to show different Trojan examples –namely, Trojan insertion, Trojan deletion and Trojan parametric- inside hardware circuits. By investigating the underlying experimental results, the capabilities of the proposed model are evaluated, and the evaluation shows that the approach can detect different hardware Trojan types in different circuit layouts, with high accuracy rate. The proposed approach is not only automatic, but also robust and promising.     

基于数值微分的树突状细胞故障检测方法

针对现有树突状细胞算法（dendritic cell algorithm,DCA）在不同类型设备的故障检测中严重依赖人工经验定义输入信号,缺乏适应性和完备性,提出了一种基于数值微分的树突状细胞故障检测模型——NDDC-FD.首先,引入变化是系统危险发生的征兆和外在表现的思想,提出了一种基于变化危险感知的信号自适应提取方法,采用数值微分描述数据的变化,再从变化中提取输入信号.其次,原DC模型中异常抗原的评价方式对突变性故障能有效检测,却无法及时发现渐变性故障,提出了采用T细胞浓度作为故障评价指标.最后,在DAMADICS和TE两个基准平台上,将本文方法与原DCA算法和传统主元分析法（principal component analysis,PCA）进行比较测试.实验结果表明NDDC-FD方法不仅提高了原DCA算法的适应性,且和DCA、PCA相比具有较高检测率的同时,更能较早地检测到渐变性故障.因此,本文提出的故障检测方法NDDC-FD具有一般性,且性能良好.     

Robust Adaptive Fault Estimation for a Class of Nonlinear Systems Subject to Multiplicative Faults

In this paper, a fault estimation problem for a class of nonlinear systems subject to multiplicative faults and unknown disturbances is investigated. Multiplicative faults usually mixed with system states and inputs can cause additional complexity in the design of fault estimator due to parameter changes within process. Especially for the nonlinear system corrupted with unknown disturbances, it is not an easy work to distinguish the real fault factor from the mixed term. Under the nonlinear Lipschitz condition, the proposed robust adaptive fault estimation approach not only estimates the multiplicative faults and system states simultaneously, but also extracts the real effect of the faults. Meanwhile, the effect of disturbances is restricted to an L ₂ gain performance criteria which can be formulated into the basic feasibility problem of a linear matrix inequality (LMI). In order to reduce the conservatism of the proposed method, a relaxing Lipschitz matrix is introduced. Finally, an illustrative example is applied to verify the efficiency of the proposed robust adaptive estimation scheme.     

Fault Detection for Uncertain Fuzzy Systems Based on the Delta Operator Approach

This paper investigates the problem of designing a robust fault-detection for uncertain T-S fuzzy models based on the delta operator approach. By means of the T-S fuzzy delta operator systems, a fuzzy fault detection filter system is constructed via the delta operator approach. The worst case fault sensitivity has been formulated in terms of linear matrix inequalities. The proposed fault-detection filter not only ensures the H _?-gain from a fault signal to a residual signal greater than a prescribed value, but also guarantees the H _∞-gain from an exogenous input to a residual signal less than a prescribed value in terms of the solvability of linear matrix inequalities. The linear matrix inequalities can be solved by an effective algorithm. A numerical example is provided to illustrate the effectiveness of the proposed design techniques.     

Diagnosis of multiple sensor and actuator failures in automotiveengines

Unlike cases where only a single failure occurs, fault detection and isolation of multiple sensor and actuator failures for engines are difficult to achieve because of the interactive effects of the failed components. If faults all appear either in sensors only or in actuators only, many existing residual generators which provide decoupled residual signals can be employed directly to obtain proper fault detection and isolation. However, when both sensor and actuator failures occur at the same time, their mutual effects on residuals make fault isolation particularly difficult. Under such circumstances, further decision logic is required. In the paper, the authors propose a hexadecimal decision table to relate all possible failure patterns to the residual code. The residual code is obtained through simple threshold testing of the residuals, which are the output of a general scheme of residual generators. The proposed diagnostic system incorporating the hexadecimal decision table has been successfully applied to automotive engine sensors and actuators in both simulation and experimental analyses. Enhancement of the present diagnostic performance by implementing an additional sensor is also described     

A Semiautomatic Approach to Deriving Turbine Generator Diagnostic Knowledge

Condition monitoring of turbine generators, housed at British Energy nuclear power stations throughout the U.K., is implemented to diagnose incipient faults at an early stage, so corrective action can be taken to avoid the associated high costs of an unplanned shutdown. A prototype expert system has been developed that provides decision support to condition monitoring experts who monitor British Energy turbine generators. The expert system automatically interprets data from strategically positioned sensors and transducers on the turbine generator by applying expert knowledge in the form of heuristic rules. This paper reviews the application domain and describes the work undertaken in developing the prototype expert system. The paper also outlines a learning module design that uses an approach based on an analytical symbolic machine learning technique, explanation-based generalization, to semiautomatically derive heuristic rules for turbine generator fault diagnosis. The approach adopted by the learning module is explained in detail and a worked example demonstrates how the learning module can derive a fault heuristic from a single training example. The modular approach to capturing the causal fault and behavioral models is described, and the method in which the module will be integrated with the existing expert system has been outlined. A preliminary evaluation of the learning module design is discussed.     

Generator speed regulation in the presence of structural modes through adaptive control using residual mode filters

Wind turbines operate in highly turbulent environments resulting in aerodynamic loads that can easily excite turbine structural modes, potentially causing component fatigue and failure. Two key technology drivers for turbine manufacturers are increasing turbine up time and reducing maintenance costs. Since the trend in wind turbine design is towards larger, more flexible turbines with lower frequency structural modes, manufacturers will want to develop control paradigms that properly account for the presence of these modes. Accurate models of the dynamic characteristics of new wind turbines are often not available due to the complexity and expense of the modeling task, making wind turbines ideally suited to adaptive control approaches. In this paper, we develop theory for adaptive control with rejection of disturbances in the presence of modes that inhibit the controller. A residual mode filter is introduced to accommodate these modes and restore important properties to the adaptively controlled plant. This theory is then applied to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine. The adaptive pitch controller is compared in simulations with a baseline classical proportional integrator (PI) collective pitch controller.     

High-Order Sliding-Mode Control of Variable-Speed Wind Turbines

This paper deals with the power generation control in variable-speed wind turbines. These systems have two operation regions which depend on wind turbine tip speed ratio. A high-order sliding-mode control strategy is then proposed to ensure stability in both operation regions and to impose the ideal feedback control solution in spite of model uncertainties. This control strategy presents attractive features such as robustness to parametric uncertainties of the turbine. The proposed sliding-mode control approach has been validated on a 1.5-MW three-blade wind turbine using the National Renewable Energy Laboratory wind turbine simulator FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code. Validation results show that the proposed control strategy is effective in terms of power regulation. Moreover, the sliding-mode approach is arranged so as to produce no chattering in the generated torque that could lead to increased mechanical stress because of strong torque variations.      

Fault Detection for Networked Nonlinear Systems with Time Delays and Packet Dropouts

This paper addresses the problem of fault detection (FD) for networked systems with global Lipschitz nonlinearities and incomplete measurements, including time delays and packet dropouts which are described by a more general model using Markov jump system approach. We aim to design a mode-dependent fault detection filter (FDF) such that, for all external disturbances and incomplete measurements, the error between the residual and fault is made as small as possible. The addressed FD problem is then converted into an auxiliary H _∞ filtering problem of Markov jump system with time-varying delay. By applying Lyapunov–Krasovskii approach, a sufficient condition for the existence of the FDF is derived in terms of certain linear matrix inequalities (LMIs). When these LMIs are feasible, the explicit expression of the desired FDF can also be characterized. A numerical example is exploited to show the effectiveness of the results obtained.     

关于风电场风电机组中风力发电机的运行维护分析

本文通过风电机组中风力发电机的常见故障分析,针对故障诊断难点问题,优化风力发电机的运行维护环节,重点加强故障难点的维护工作,为风电场风电机组的运行维护提供新的思路。     

基于压电陶瓷的风机叶片模型覆冰主动监测

南方山区高湿低温严酷环境易导致冰灾,风机叶片覆冰会导致发电效率降低甚至停机,因此发展高效可靠的风机叶片覆冰监测技术对防冰、除冰具有重要意义。该文提出了一种基于压电陶瓷应力波测量的风机叶片覆冰主动监测方法。开展叶片模型进行模拟覆冰试验,在叶片表面布置伸缩型和剪切型两种压电陶瓷片,根据波动法原理对驱动器输入正弦扫频电压信号作为激励,记录不同传感器在不同结冰厚度下的响应信号,并对测量信号进行小波包能量分析。结果表明,同一压电陶瓷片接收信号的小波包能量与结冰厚度存在明显关系。     

基于LVQ神经网络风电机组齿轮箱故障诊断研究

针对风电机组齿轮箱故障诊断技术的不足,提出一种基于LVQ神经网络的故障诊断方法,利用小波分析方法对某风电机组齿轮箱正常状态、磨损故障和断齿故障状态下的振动信号进行降噪处理,在时域和频域内提取了5个特征参数对所建立的模型进行训练。为了检验模型的实际诊断能力,与标准BP神经网络的诊断结果进行对比。仿真结果表明:基于LVQ神经网络的故障诊断速度更快、准确率更高、泛化能力更强,验证了所提出方法的实用性和有效性。     

Current Source Inverter Based Grid Connected Hybrid PV-Wind Power Generation Unit

ABSTRACT

This paper presents a current source inverter (CSI)-based hybrid power generation system, which uses wind turbine and photovoltaic cells (PVs). A permanent magnet synchronous generator (PMSG) is connected to the CSI using a diode rectifier and a buck converter that is used to control the speed of the rotor. Another buck converter is used to control the maximum power point tracking of PVs. The operation of proposed system is studied under normal and grid voltage dip conditions. According to new grid codes, most power generating units are supposed to remain connected to the grid during voltage sag conditions and inject reactive current to grid as defined by grid codes. The CSI has fault current limiting capability that makes it appropriate to use in grid-connected applications and during voltage sag conditions in particular. The proposed system tracks the maximum power point of wind turbine and PVs under normal mode and injects required reactive current to the grid during voltage drop. However, incorporation of CSI with the inherent behaviour of wind turbine and PVs causes fault current to be within the tolerable range for power electronic devices. Simulations are carried out by using PSCAD/EMTDC software to verify the proposed system.