利用consensus滤波器诊断HVDC系统故障

高压直流输电(HVDC)系统对运行时的稳定性要求比较严格,出现故障时应能及时分辨故障类型并快速恢复。传统的利用神经网络诊断HVDC故障一般都是将电压电流信号输入网路,没有实际测量过程中随机噪声的干扰。为此,针对长输电线路中实际测量的直流电压信号易引入随机噪声干扰的特点,提出了一种分布式故障诊断算法并研究了consensus滤波器在滤除直流电压信号噪声中的应用。最后的仿真结果表明,consensus滤波器可有效滤除测量噪声,从而可有效检测出HVDC系统中的故障。     

Distributed output estimation error observer-based adaptive fault-tolerant consensus tracking control of multi-agent systems

The purpose of this study is to discuss the fully distributed design of output estimation error observer and fault-tolerant consensus tracking control for a class of multi-agent systems with Lipschitz nonlinear dynamics and actuator faults. Firstly, based on the relative output measurements of neighboring agents, the distributed output estimation error observer is developed to adaptively estimate the state and fault information of each agent, and further overcome the difficulties of online updating the adaptive estimations of unknown hyper-parameters. Secondly, to achieve the state consensus tracking goal and compensate for the negative effects of actuator faults, the distributed fault-tolerant consensus tracking control scheme is proposed on the basis of the state estimation and adaptive fault estimation information, and has excellent robustness and consensus tracking control performance. Moreover, sufficient criteria can ensure that consensus tracking error of each agent converges to a small set near the origin. Finally, numerical simulations are provided to show the effectiveness of the proposed fully distributed algorithm.     

A distributed state estimator for electric power systems

The author develops a theoretically robust and computationally efficient distributed state estimator to solve the weighted least square state estimation problem by using distributed computation. This distributed state estimator is used in decentralized control and executes in a data communication network that is assumed to be topologically the same as and physically in parallel with the power network. Several attractive satellite functions can be obtained which include: (1) reduction of the time-skew problem; (2) freedom from the power network topological error; (3) easy identification of the unobservable states; and (4) bad data detection and identification. The computational complexity of this distributed state estimator was analyzed. This state estimator was simulated on several cases of the IEEE 30-bus system. The numerical accuracy of the simulation results is satisfactory, and the estimated computation time including the communication delay demonstrates the excellent computational performance of the distributed state estimator     

Novel Fault Diagnosis Scheme for HVDC System via ESO

A novel fault detection and identification(FDI)scheme for HVDC(High Voltage Direct Current Transmission)system was presented.It was based on the unique active disturbance rejection concept,where the HVDC system faults were estimated using an extended states observer(ESO).Firstly,the mathematical model of HVDC system was constructed,where the system states and disturbance were treated as an extended state.An augment HVDC system was established by using the extended state in rectify side and converter side,respectively.Then,a fault diagnosis filter was established to diagnose the HVDC system faults via the ESO theory.The evolution of the extended state in the augment HVDC system can reflect the actual system faults and disturbances,which can be used for the fault diagnosis purpose.A novel feature of this approach is that it can simultaneously detect and identify the shape and magnitude of the HVDC faults and disturbance.Finally,different kinds of HVDC faults were simulated to illustrate the feasibility and effectiveness of the proposed ESO based FDI approach.Compared with the neural network based or support vector machine based FDI approach,the ESO based FDI scheme can reduce the fault detection time dramatically and track the actual system fault accurately.What’s more important,it needs not do complex online calculations and the training of neural network so that it can be applied into practice.     

Hierarchical structure-based adaptive fault-tolerant consensus control for multiple 3-DOF laboratory helicopters

This study investigates the consensus problem of multiple 3-DOF laboratory helicopters modeled with system nonlinearity, uncertainty, and actuator faults. The simultaneous additives and partial loss of effectiveness actuator faults are considered. The fault detection hierarchy, the healthy control hierarchy, and the fault-tolerant control hierarchy constitute the hierarchical structure of multihelicopter systems. The fault-tolerant consensus protocol is switched from the healthy control hierarchy once the actuator fault is detected in the fault detection hierarchy. An adaptive fault-tolerant consensus control scheme is developed on the basis of the instantaneous and integral estimations to compensate simultaneously for system nonlinearity, uncertainty, and actuator faults and to guarantee the mean-square consensus in a completely distributed form. Simulation results are presented to validate the effectiveness of the proposed adaptive fault-tolerant consensus control algorithm.     

Average information‐weighted consensus filter for target tracking in distributed sensor networks with naivety issues

In the consensus‐based state estimation, multiple neighboring nodes iteratively exchange their local information with each other and the goal is to get more accurate and more convergent state estimation on each node. In order to improve network scalability and fault tolerance, the distributed sensor networks are desirable because the requirements of the fusion node are eliminated. However, the state estimation becomes challenging in the case of limited sensing regions and/or distinct measurement‐noise covariances. A novel distributed average information‐weighted consensus filter (AICF) is proposed, which does not require the knowledge of the total number of sensor nodes. Based on the weighted average consensus, AICF effectively addresses the naivety issues caused by unequal measurement‐noise covariances. Theoretical analysis and experimental verification show that AICF can approach the optimal centralized state estimation.     

Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions

This paper establishes a novel online fault detection and identification strategy for a class of continuous piecewise affine (PWA) systems, namely, bimodal and trimodal PWA systems. The main contributions with respect to the state‐of‐the‐art are the recursive nature of the proposed scheme and the consideration of parametric uncertainties in both partitions and in subsystems parameters. In order to handle this situation, we recast the continuous PWA into its max‐form representation and we exploit the recursive Newton‐Gauss algorithm on a suitable cost function to derive the adaptive laws to estimate online the unknown subsystem parameters, the partitions, and the loss in control authority for the PWA model. The effectiveness of the proposed methodology is verified via simulations applied to the benchmark example of a wheeled mobile robot.     

电力系统的集成保护

通过介绍一种新颖的配电网保护方案来阐述集成保护的优越性。这个基于暂态极性方向比较的保护方案以伴随故障出现的暂态电流信号的检测和处理为基础,在配电网的各个变电站安装有专门设计的保护继电器,继电器的暂态检测单元检测故障生成的故障分量电信号,并将暂态极性识别算法应用于叠加故障分量信号上,判断信号的极性。通过对来自连接到变电站的所有线路的信号极性进行比较,可以确定出故障的方向。通过处理来自各变电站的方向信息,判别出实际的故障线路,从而实现电网的集成保护。     

高压直流输电系统故障诊断新方法

提出了一种新的故障跟踪估计器来诊断高压直流输电(HVDC)系统中的故障。考虑到随机噪声对测量结果的影响,首先采用协同(Consensus)滤波器对HVDC系统的输出滤波,然后根据HVDC状态方程引入一个新的参数——虚拟故障,来构建故障跟踪估计器。根据预测控制中的滚动优化思想,选取优化时域,并根据故障跟踪估计器的输出电压和HVDC实际电压的差,通过反复迭代运算来调节虚拟故障,使虚拟故障逼近系统中实际发生的故障,从而对故障进行分类。与基于神经网络等方法的故障诊断不同的是,该设计方法不但可以检测出故障,还可以估计出故障函数,针对不同类型的故障也可以很好地进行诊断。最后针对不同类型故障的仿真结果表明了该算法的可行性和有效性。     

Fault diagnosis by interval-based adaptive thresholds and peak-to-peak observers

This article investigates actuator fault diagnosis for continuous-time systems with unknown but bounded uncertainties. A novel interval-based adaptive threshold computation approach is proposed for residual evaluation. Meanwhile, peak-to-peak performance is applied to generate robust residuals against system uncertainties. By integrating the designed peak-to-peak residual generator with adaptive thresholds, we can achieve promising fault detection results. Furthermore, based on the general observer scheme, the proposed residual generator and adaptive thresholds can be equally used for fault isolation. Simulation results are given to illustrate the effectiveness and superiority of the proposed fault detection and isolation method.     

Robust adaptive consensus tracking for higher‐order multi‐agent uncertain systems with nonlinear dynamics via distributed intermittent communication protocol

This paper considers the robust adaptive consensus tracking for higher‐order multi‐agent uncertain systems with nonlinear dynamics via distributed intermittent communication protocol. The main contribution of this work is solving the robust consensus tracking problem without the assumption that the topology among followers is strongly connected and fixed. The focus is the problem of actuator with occasional failure inputs and communication resources constraints. A novel distributed intermittent communication framework is proposed via adaptive approach. In this framework, the underlying communication topologies switch among several directed graphs with a limited directed spanning tree rooted at a leader agent. Furthermore, by introducing a strategy of actuator fault compensation inputs, a combination of robust consensus tracking protocol is designed by the different adaptive feedback controllers. It is proved that the robust adaptive consensus tracking can be achieved by using local states information of neighboring agents if the communication retention rate condition is satisfied. Two examples are presented to demonstrate the effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Distributed monitoring of the absorption column of a post-combustion CO2 capture plant

In this work, we consider the monitoring of the absorption column of a typical post-combustion capture plant within a distributed framework. The column is decomposed into a few subsystems, and a distributed moving horizon state estimator network is designed to estimate the state of the entire column. A state predictor is also designed for each subsystem to approximate the future evolution of the subsystem. These subsystem predictors form a distributed predictor network. Based on the distributed estimator and predictor networks, a fault diagnosis mechanism is proposed specifically for potential additive sensor faults. Extensive simulation results are provided to demonstrate the performance of the proposed approach.     

Iterative distributed fault detection and isolation for linear systems based on moving horizon estimation

In modern engineering systems, reliability and safety can be conferred by efficient automatic monitoring and fault detection algorithms, allowing for the early identification and isolation of incipient faults. In case of large-scale and complex systems, scalability issues and computational limitations make centralized monitoring and fault detection methods unapplicable. Research is therefore currently focusing on the development of distributed methods, where the computational complexity is divided among different units. In this paper, we propose a partition-based model-based fault detection and isolation scheme based on moving horizon estimation, able to estimate both the state variables and the possible faults, modeled as additive signals on the state and/or output equations. Its theoretical properties are analyzed, and numerical simulations are performed to witness its potentialities in a benchmark case study.     

基于负序故障附加网络模型识别的有源配电网保护

目前,大量分布式电源与不可测负荷分支接入配电网,原有保护方案不能满足新型配电网需求。通过分析含不可测负荷分支的有源配电网的不对称故障特点,建立区内、外负序故障附加网络等值模型。分析所得等值模型,提取出被保护线路在区内、外故障下不同的模型表达,以此设计基于模型识别的保护判据,构造一种基于负序故障附加网络模型识别的保护方案。利用PSCAD仿真软件搭建10 kV有源配电网模型,对所提方案进行验证。结果表明所提方案能准确实现故障区域识别。     

含分布式光伏及储能变流器的微网故障特征与保护

在并网时,微网中所含的分布式电源可能包括恒功率控制的光伏变流器或电流下垂控制的储能变流器,其中传统电流下垂控制的储能变流器在电网电压跌落时所输出的较低的故障电流,以及可能会出现的功角失稳现象都为其故障保护带来了困难,而基于滞环控制的改进电流下垂控制可以使其在电网故障时保持功角稳定,同时具备低压穿越能力,给故障电流带来新的特征,有利于故障检测以及保护判据的配置。本文考虑变流器的低压穿越特性,分析了含分布式光伏及储能变流器的微网内部不同位置发生故障时的电流特征,利用正序电流故障分量与母线正序电压故障分量的相位差来判断故障方向,并提出基于EtherCAT工业以太网的集中式保护方案。最后,通过仿真与实验验证了所提方案的可行性。     

Hybrid-triggered fault detection filter design for networked Takagi–Sugeno fuzzy systems subject to persistent heavy noise disturbance

In the network environment, the single time-triggered scheme wastes limited bandwidth resources due to all the sampled data are transmitted to the networks, and the single event-triggered scheme may increase system error because of ignoring factors such as changes in network utilization. To reduce the design conservatism, this paper is concerned with the hybrid-triggered L₁ fault detection filter design for a class of nonlinear networked control systems (NCSs) described by Takagi–Sugeno (T-S) fuzzy model. Taking the effects of time-triggered scheme and event-triggered scheme into consideration simultaneously, we construct a fuzzy fault detection system. New results on stability and L₁ performance are proposed for fuzzy fault detection system by exploiting the Lyapunov–Krasovskii functional and by means of the integral inequality method. Specially, attention is focused on the design of fault detection filter that guarantees a prescribed L₁ noise attenuation level . Finally, two examples are presented to demonstrate the effectiveness of the proposed method.     

基于深度卷积神经网络的输电线路防鸟刺部件识别与故障检测

电力铁塔上故障防鸟刺的及时检测,对于减少输电线路鸟害的发生,从而保障输电线路安全可靠运行具有重要意义。电力巡检图像中电力铁塔上的防鸟刺具有轮廓特征较不明显、部分重叠分布的特点给防鸟刺部件识别与故障检测研究带来一定挑战。针对所述防鸟刺特点,提出一种基于深度卷积神经网络的防鸟刺部件识别与故障检测方法。先利用锐化滤波器对电力巡检图像进行锐化处理;其次运用经过多尺度训练的YOLOv3目标检测网络框选并截取出经过锐化处理的电力巡检图像中的防鸟刺区域;最后利用基于Resnet152特征提取网络的防鸟刺故障检测器处理截取出的防鸟刺区域,实现防鸟刺故障检测。利用上述方法,实现电力巡检图像中的防鸟刺部件识别与故障防鸟刺检测,防鸟刺部件识别平均准确率为95.36%,故障防鸟刺检测准确率为92.3%。实验结果表明,所提方法能够有效实现电力巡检图像中防鸟刺部件识别与故障检测。     

基于限流电抗电压积分值的环状柔性直流配电网保护方案

直流故障电流上升速度快,故障影响范围广,传统的交流故障检测方法不再适用。为此,基于系统复频域模型提出一种基于直流线路限流电抗电压积分值的多端直流环网故障快速检测方法。首先,详细分析了多端环状柔性直流配电网中各元件的时域和复频域等效模型。在此基础上,给出了限流电抗电压的计算方法,其实现了系统等效模型在时域和复频域上的转化,求解简单,建模难度低,且对不同系统的建模有普适性。其次,提出一种基于限流电抗电压积分值的故障检测方法,其抗噪能力强且具有一定的耐受过渡电阻能力。最后,在PSCAD/EMTDC中搭建仿真模型,验证了所提计算方法的正确性和保护方案的可行性。     

考虑储能与动态重构的柔性互联配电系统两阶段鲁棒优化

强波动性分布式电源的大量接入给配电系统可靠安全运行带来了挑战。为此,提出了一种考虑储能与动态重构的柔性互联配电系统两阶段鲁棒优化调度方法。首先,建立了综合考虑柔性配电开关、储能与动态重构的柔性互联配电系统两阶段鲁棒优化数学模型,其中第一阶段考虑日前分布式电源出力预测的不确定性以及网络拓扑和储能的时序相关性进行全局优化,第二阶段基于第一阶段的决策方案和准确的超短期预测信息,在各时段对柔性配电开关进行实时调度;其次,采用改进的列与约束生成算法对模型进行求解,通过辅助变量和对偶变量交替迭代,显著提高子问题的求解效率;最后,通过33和69节点系统进行测试,验证了所提调度模型和求解算法的有效性。     

考虑储能与动态重构的柔性互联配电系统两阶段鲁棒优化

强波动性分布式电源的大量接入给配电系统可靠安全运行带来了挑战。为此,提出了一种考虑储能与动态重构的柔性互联配电系统两阶段鲁棒优化调度方法。首先,建立了综合考虑柔性配电开关、储能与动态重构的柔性互联配电系统两阶段鲁棒优化数学模型,其中第一阶段考虑日前分布式电源出力预测的不确定性以及网络拓扑和储能的时序相关性进行全局优化,第二阶段基于第一阶段的决策方案和准确的超短期预测信息,在各时段对柔性配电开关进行实时调度;其次,采用改进的列与约束生成算法对模型进行求解,通过辅助变量和对偶变量交替迭代,显著提高子问题的求解效率;最后,通过33和69节点系统进行测试,验证了所提调度模型和求解算法的有效性。