Showing papers by "Zhiwei Gao published in 2021"

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

Abstract: Wind energy is contributing to more and more portions in the world energy market However, one deterrent to even greater investment in wind energy is the considerable failure rate of turbines In particular, large wind turbines are expensive, with less tolerance for system performance degradations, unscheduled system shut downs, and even system damages caused by various malfunctions or faults occurring in system components such as rotor blades, hydraulic systems, generator, electronic control units, electric systems, sensors, and so forth As a result, there is a high demand to improve the operation reliability, availability, and productivity of wind turbine systems It is thus paramount to detect and identify any kinds of abnormalities as early as possible, predict potential faults and the remaining useful life of the components, and implement resilient control and management for minimizing performance degradation and economic cost, and avoiding dangerous situations During the last 20 years, interesting and intensive research results were reported on fault diagnosis, prognosis, and resilient control techniques for wind turbine systems This paper aims to provide a state-of-the-art overview on the existing fault diagnosis, prognosis, and resilient control methods and techniques for wind turbine systems, with particular attention on the results reported during the last decade Finally, an overlook on the future development of the fault diagnosis, prognosis, and resilient control techniques for wind turbine systems is presented

Fault Diagnosis and Fault Tolerant Control for T–S Fuzzy Stochastic Distribution Systems Subject to Sensor and Actuator Faults

Abstract: The problem of fault diagnosis (FD) and fault tolerant control for a class of Takagi–Sugeno (T–S) fuzzy stochastic distribution control systems subject to sensor and actuator faults is discussed in this article. First, fuzzy logic models are used to approximate the output probability density function (PDF). Next, an adaptive augmented state/FD observer is proposed to estimate the system state, sensor and the actuator faults simultaneously. New expected weights based on the sensor fault estimation information and a PI-type fuzzy feedback fault tolerant controller are designed to compensate the effect of sensor fault and actuator fault simultaneously. When the sensor fault occurs, the expected objective is redesigned to compensate the sensor fault. Meanwhile, the PI controller can compensate the effect of actuator fault, and the output PDF of the system can still track the desired PDF after the fault occurs. Finally, an example of quality distribution control in chemical reaction process is given to confirm the effectiveness of the algorithm.

Resilient Delayed Impulsive Control for Consensus of Multiagent Networks Subject to Malicious Agents

Abstract: Impulsive control is widely applied to achieve the consensus of multiagent networks (MANs). It is noticed that malicious agents may have adverse effects on the global behaviors, which, however, are not taken into account in the literature. In this study, a novel delayed impulsive control strategy based on sampled data is proposed to achieve the resilient consensus of MANs subject to malicious agents. It is worth pointing out that the proposed control strategy does not require any information on the number of malicious agents, which is usually required in the existing works on resilient consensus. Under appropriate control gains and sampling period, a necessary and sufficient graphic condition is derived to achieve the resilient consensus of the considered MAN. Finally, the effectiveness of the resilient delayed impulsive control is well demonstrated via simulation studies.

Proportional–Integral Synchronization for Nonidentical Wireless Packet-Coupled Oscillators With Delays

Abstract: Precise timing among wireless sensor nodes is a key enabling technology for time-sensitive industrial wireless sensor networks (WSNs). However, the accuracy of timing is degraded by manufacturing tolerance, aging of crystal oscillators, and communication delays. This article develops a framework of packet-coupled oscillators (PkCOs) to characterize the dynamics of communication and time synchronization of clocks in WSNs. The nonidentical clock is derived to describe the embedded clock's behavior accurately. A proportional–integral (PI) packet coupling scheme is proposed for synchronizing networked embedded clocks, while, scheduling wireless Sync packets to different slots for transmission. It also possesses the feature of automatically eliminating the effects of unknown processing delay, which further improves the synchronization performance. The rigorous theoretical analysis of PI-based PkCOs is presented via studying a closed-loop time synchronization system. The performance of PI-based PkCOs is evaluated on a hardware testbed of IEEE 802.15.4 WSN. The experimental results show that the precision of the proportional-integral PkCOs protocol is as high as 60 $\mu s$ (i.e., 2 ticks) for 32.768 kHz crystal oscillator-based clocks.

Special Issue on “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”

Abstract: Complex industrial automation systems and processes, such as chemical processes, manufacturing systems, wireless network systems, power and energy systems, smart grids and so forth, have greatly contributed to our daily life [...]

Iterative learning fault diagnosis and fault tolerant control for stochastic repetitive systems with Brownian motion.

Abstract: In this paper, the issue of iterative learning fault diagnosis (ILFD) and fault tolerant control (FTC) is studied for stochastic repetitive systems with Brownian motion. Different from existing fault diagnosis (FD) methods, a state/fault simultaneous estimation observer based on iterative learning method is designed. The convergence condition of the ILFD algorithm is given. By employing the fault estimation information, the FTC algorithm is proposed to compensate for the fault effect on the system and to keep the stochastic input-to-state stability of the control system. Finally, the simulation results of an induction motor system and a single-link robotic flexible manipulator system are given to show that the proposed method is validated.

Data-Driven Parameter Fault Classification for A DC–DC Buck Converter

Abstract: DC–DC power converters play an important role in renewable energy systems, electrical vehicles, and battery chargers and so forth. DC–DC Buck converters are prone to faults due to age and unexpected accidents. As a result, there is a high demand to improve the operation reliability and safety of power converters by using condition monitoring and fault diagnosis techniques. In this paper, data-driven and machine learning-based fault detection and fault classification strategies are addressed for DC–DC Buck converters under disparate faulty scenarios of the parameters. A variety of algorithms such as principal component analysis, multi-linear principal component analysis, uncorrelated multi-linear principal component analysis, and Fast Fourier Transformation pre-processing based multi-linear principal component analysis and uncorrelated multi-linear principal component analysis techniques are applied for fault classification and diagnosis of the parameter faults in the DC–DC Buck converters. The effectiveness is demonstrated and discussed with details.

Non-existence of the asymptotic flocking in the Cucker-Smale model with short range communication weights

Abstract: For the long range communicated Cucker $-$ Smale model, asymptotic flocking exists for any initial data. It is noted that, for the short range communicated Cucker-Smale model, asymptotic flocking only holds for very restricted initial data. In this case, the non-existence of the asymptotic flocking has been frequently observed in numerical simulations, however, the theoretical results are far from perfect. In this note, we first point out that the non-existence of the asymptotic flocking is equivalent to the unboundedness of the second order space moment, i.e. $\sup_t\sum|x_i(t)-x_j(t)|^2=\infty$ . Furthermore, by taking the second derivative and then integrating, we establish a new and key equality about this moment. At last, we use this equality and some technical lemmas to deduce a general sufficient condition to the non-existence of asymptotic flocking.

Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies

Abstract: Data-driven fault diagnosis and classification for wind turbine systems have received much attention due to a large amount of data available recorded by supervisory control and data acquisition (SCADA) systems and smart meters. It is of interest but challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a data-driven and supervised machine learning-based fault diagnosis and classification algorithm is addressed by the combination and consolidation among Hilbert-Huang Transformation (HHT), Multi-Linear Principal Component Analysis (MPCA), and Support Vector Machine (SVM) to enhance the feasibility and capability of fault diagnosis and classification for systems subjected to multiple faults. The algorithm proposed is applied to the 4.8 MW wind turbine benchmark model, where multiple actuator faults are taken into considerations. The effectiveness of the methodology is demonstrated by using intensive simulations and comparison studies.

Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

Abstract: It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed for forging machines to achieve the optimal model parameters by applying the raw data directly instead of observation window. This approach is an online parameter identification algorithm in one period without the need of the labelled samples as training database. It has an excellent ability against unknown distributed disturbances in a dynamic process, especially capable of adapting to a new process without historical data. The effectiveness of the algorithm is demonstrated and validated by a simulation of acquiring the parameter values of a forging machine.

Fault reconstruction and resilient control for discrete-time stochastic systems.

Abstract: In this paper, a novel resilient control technique is proposed for discrete-time stochastic Brownian systems with simultaneous unknown inputs and unexpected faults. Prior to previous work, the stochastic Brownian system under consideration is quite general, where stochastic perturbations exist in states, control inputs, uncertainties, and faults. Moreover, the unknown input uncertainties concerned cannot be fully decoupled. Innovative observer by employing augmented system approach, decomposition observer, and optimization algorithms is proposed to achieve simultaneous estimates of both states and faults. Furthermore, fault reconstruction-based signal compensation is formulated to alleviate the effects from actuator faults and sensor faults. An observer-based controller is eventually constructed to enhance the stability and robustness of the closed-loop dynamic system. The integrated resilient control technique can ensure the system has reliable output even under faults. Both linear systems and Lipschitz nonlinear systems are investigated and the design procedures are addressed, respectively. Finally, the proposed resilient control techniques are validated via an electromechanical servo-system, and an aircraft system.

An Ensemble Approach for Fault Diagnosis via Continuous Learning

Abstract: The great success of deep neural network (DNN) in image field stimulates its application in fault detection and diagnose. However due to the limitation of system security, it is impossible to obtain complete fault data as the training database for neural network, so that it is challenging to identify a fault that never occurred before. In this paper, an ensemble approach is proposed to adapt to a new fault by adding output branches of the neural network. Firstly, the time series are transferred to numerous imaging matrixes. The intrinsic characteristics of the matrixes are then extracted using deep neural network which are used to judge whether it is a new fault according to the distance criterion. For a new fault, the DNN will retrain by transferring learning in order to reduce the computation and training time. The effectiveness of the algorithm is demonstrated by a numerical simulation example based on a wind turbine benchmark model.

A study of Qatar’s Network Reliability During the Forthcoming FIFA 2022 World Cup

Abstract: The effect of increased load, due to the use of eight air-conditioned football stadiums during the forthcoming FIFA 2022 world cup, on Qatar’s electricity network is investigated with the aid of PowerWorld simulator. The bowl areas of the stadiums currently under construction are estimated according to their seating capacity, and their air-conditioning loads are calculated using the air-conditioning requirement for a Qatari villa as a reference. Data regarding Qatar’s electricity network are compiled with the aid of three publicly available sources. Power flow studies and contingency analyses are performed with and without the stadiums’ load. An arbitrary correction factor is introduced to account for the uncertain nature of air-conditioning in open environment as well as cooling of other facilities. Results indicate that the addition of the stadiums’ load introduces supply security violations. These are identified and appropriate remedial actions (involving generation and static VAr control) are devised and implemented.