Predictive control approaches to fault tolerant control of wind turbines

This thesis focuses on active fault tolerant control (AFTC) of wind turbine systems. Faults in wind turbine systems can be in the form of sensor faults, actuator faults, or component faults. These faults can occur in different locations, such as the wind speed sensor, the generator system, drive train system or pitch system. In this thesis, some AFTC schemes are proposed for wind turbine faults in the above locations. Model predictive control (MPC) is used in these schemes to design the wind turbine controller such that system constraints and dual control goals of the wind turbine are considered. In order to deal with the nonlinearity in the turbine model, MPC is combined with Takagi-Sugeno (T-S) fuzzy modelling. Different fault diagnosis methods are also proposed in different AFTC schemes to isolate or estimate wind turbine faults.The main contributions of the thesis are summarized as follows:A new effective wind speed (EWS) estimation method via least-squares support vector machines (LSSVM) is proposed. Measurements from the wind turbine rotor speed sensor and the generator speed sensor are utilized by LSSVM to estimate the EWS. Following the EWS estimation, a wind speed sensor fault isolation scheme via LSSVM is proposed.A robust predictive controller is designed to consider the EWS estimation error. This predictive controller serves as the baseline controller for the wind turbine system operating in the region below rated wind speed.T-S fuzzy MPC combining MPC and T-S fuzzy modelling is proposed to design the wind turbine controller. MPC can deal with wind turbine system constraints externally. On the other hand, T-S fuzzy modelling can approximate the nonlinear wind turbine system with a linear time varying (LTV) model such that controller design can be based on this LTV model. Therefore, the advantages of MPC and T-S fuzzy modelling are both preserved in the proposed T-S fuzzy MPC.A T-S fuzzy observer, based on online eigenvalue assignment, is proposed as the sensor fault isolation scheme for the wind turbine system. In this approach, the fuzzy observer is proposed to deal with the nonlinearity in the wind turbine system and estimate system states. Furthermore, the residual signal generated from this fuzzy observer is used to isolate the faulty sensor.A sensor fault diagnosis strategy utilizing both analytical and hardware redundancies is proposed for wind turbine systems. This approach is proposed due to the fact that in the real application scenario, both analytical and hardware redundancies of wind turbines are available for designing AFTC systems.An actuator fault estimation method based on moving horizon estimation (MHE) is proposed for wind turbine systems. The estimated fault by MHE is then compensated by a T-S fuzzy predictive controller. The fault estimation unit and the T-S fuzzy predictive controller are combined to form an AFTC scheme for wind turbine actuator faults

Detecting outlier patterns with query-based artificially generated searching conditions

In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE

Detecting Outlier Patterns with Query-based Artificially Generated Searching Conditions

In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, national security, etc. However, sub-graph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this work, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined on a real-world academic network, using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs, and is robust to the choice of similarity measures

Light-activated ferroelectric transition in layer dependent Bi2O2Se films

Bi2O2Se has attracted intensive attention due to its potential in electronics, optoelectronics, as well as ferroelectric applications. Despite that, there have only been a handful of experimental studies based on ultrafast spectroscopy to elucidate the carrier dynamics in Bi2O2Se thin films, Different groups have reported various ultrafast timescales and associated mechanisms across films of different thicknesses. A comprehensive understanding in relation to thickness and fluence is still lacking. In this work, we have systematically explored the thickness-dependent Raman spectroscopy and ultrafast carrier dynamics in chemical vapor deposition (CVD)-grown Bi2O2Se thin films on mica substrate with thicknesses varying from 22.44 nm down to 4.62 nm at both low and high pump fluence regions. Combining the thickness dependence and fluence dependence of the slow decay time, we demonstrate a ferroelectric transition in the thinner (< 8 nm) Bi2O2Se films, influenced by substrate-induced compressive strain and non-equilibrium states. Moreover, this transition can be manifested under highly non-equilibrium states. Our results deepen the understanding of the interplay between the ferroelectric phase and semiconducting characteristics of Bi2O2Se thin films, providing a new route to manipulate the ferroelectric transition

Protective effects of ferulic acid against ionizing radiation-induced oxidative damage in rat lens through activating Nrf2 signal pathway

AIM: To examine the protection of ferulic acid (FA) against ionizing radiation (IR)-induced lens injury in rats, as well as the underlying mechanisms. METHODS: FA (50 mg/kg) was administered to rats for 4 consecutive days before they were given 10 Gy γ-radiation, as well as for 3 consecutive days afterward. Two weeks after radiation, the eye tissues were collected. Histological alterations were evaluated by hematoxylin-eosin staining. Enzyme linked immunosorbent assay (ELISA) was utilized to assess the activities of glutathione reductase (GR) and superoxide dismutase (SOD), as well as the levels of glutathione (GSH) and malondialdehyde (MDA) in the lenses. The protein and mRNA levels of Bcl-2, caspase-3, Bax, heme oxygenase-1 (HO-1), and glutamate-cysteine ligase catalytic subunit (GCLC) were quantified using Western blot and quantitative reverse transcription polymerase chain reaction, respectively. With nuclear extracts, the nuclear factor erythroid-2 related factor (Nrf2) protein expressions in the nuclei were also measured. RESULTS: Rats exposed to IR showed lens histological alterations which could be alleviated by FA. FA treatment reversed apoptosis-related markers in IR-induced lens, as evidenced by lower levels of Bax and caspase-3 and higher level of Bcl-2. Furthermore, IR induced oxidative damage manifested by decreased GSH level, increased MDA level, and decreased SOD and GR activities. FA boosted nuclear translocation of Nrf2 and increased the expressions of HO-1 and GCLC to inhibit oxidative stress, as evidenced by an increase in GSH, a decrease in MDA, and an increase in GR and SOD activities. CONCLUSION: FA may work well in preventing and treating IR-induced cataract through promoting the Nrf2 signal pathway to attenuate oxidative damage and cell apoptosis

Effects of Intranasal Oxytocin on Pup Deprivation-Evoked Aberrant Maternal Behavior and Hypogalactia in Rat Dams and the Underlying Mechanisms

Oxytocin (OT), a hypothalamic neuropeptide, applied through nasal approach (IAO), could improve maternal health during lactation that is disrupted by mother–baby separation; however, the regulation of IAO effects on maternal behaviors and lactation as well as the underlying mechanisms remain unclear. Using lactating rats, we observed effects of intermittent pup deprivation (PD) with and without IAO on maternal behaviors and lactation as well as the activity of OT neurons in the supraoptic nucleus (SON) and the activity of hypothalamic pituitary-adrenal axis, key factors determining the milk-letdown reflex during lactation and maternal behaviors. The results showed that PD reduced maternal behaviors and lactation efficiency of rat dams as indicated by significantly longer latency to retrieve their pups and low litter’s body weight gains during the observation, respectively. In addition, PD caused early involution of the mammary glands. IAO partially improved these changes in rat dams, which was not as significant as IAO effects on control dams. In the SON, PD decreased c-Fos and increased glial fibrillary acidic protein (GFAP) filaments significantly; IAO made PD-evoked c-Fos reduction insignificant while reduced GFAP filament significantly in PD dams. IAO tended to increase the levels of phosphorylated extracellular signal-regulated kinases (pERK) 1/2 in PD dams. Moreover, PD+IAO significantly increased plasma levels of dam adrenocorticotropic hormone and corticosterone but not OT levels. Lastly, PD+IAO tended to increase the level of corticotropin-releasing hormone in the SON. These results indicate that PD disrupts maternal behaviors and lactation by suppressing the activity of hypothalamic OT-secreting system through expansion of astrocytic processes, which are partially reversed by IAO through removing astrocytic inhibition of OT neuronal activity. However, the improving effect of IAO on the maternal health could be compromised by simultaneous activation of hypothalamic pituitary-adrenocortical axis