Least Squares Estimation-Based Synchronous Generator Parameter Estimation Using PMU Data

In this paper, least square estimation (LSE)-based dynamic generator model parameter identification is investigated. Electromechanical dynamics related parameters such as inertia constant and primary frequency control droop for a synchronous generator are estimated using Phasor Measurement Unit (PMU) data obtained at the generator terminal bus. The key idea of applying LSE for dynamic parameter estimation is to have a discrete \underline{a}uto\underline{r}egression with e\underline{x}ogenous input (ARX) model. With an ARX model, a linear estimation problem can be formulated and the parameters of the ARX model can be found. This paper gives the detailed derivation of converting a generator model with primary frequency control into an ARX model. The generator parameters will be recovered from the estimated ARX model parameters afterwards. Two types of conversion methods are presented: zero-order hold (ZOH) method and Tustin method. Numerical results are presented to illustrate the proposed LSE application in dynamic system parameter identification using PMU data.Comment: 5 pages, 6 figures, accepted by IEEE PESGM 201

Efficient synthesis of Vitamin D3 in a 3D ultraviolet photochemical microreactor fabricated using an ultrafast laser

Large-scale, high-precision, and high-transparency microchannels hold great potential for developing high-performance continuous-flow photochemical reactions. We demonstrate ultrafast laser-enabled fabrication of 3D microchannel reactors in ultraviolet (UV) grade fused silica which exhibit high transparency under the illumination of UV light sources of wavelengths well below 300 nm with excellent mixing efficiency. With the fabricated glass microchannel reactors, we demonstrate continuous-flow UV photochemical synthesis of vitamin D3 with low power consumption of the UV light sources

An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue Functions

We present an approximate nonsmooth algorithm to solve a minimization problem, in which the objective function is the sum of a maximum eigenvalue function of matrices and a convex function. The essential idea to solve the optimization problem in this paper is similar to the thought of proximal bundle method, but the difference is that we choose approximate subgradient and function value to construct approximate cutting-plane model to solve the above mentioned problem. An important advantage of the approximate cutting-plane model for objective function is that it is more stable than cutting-plane model. In addition, the approximate proximal bundle method algorithm can be given. Furthermore, the sequences generated by the algorithm converge to the optimal solution of the original problem

Reaction of Hexagonal Boron Nitride Nano-crystals under Mild Hydrothermal Conditions

The reaction between hexagonal boron nitride (hBN) nano-crystals and water at low temperature and low pressure has been investigated. The results reveal that this reaction can be greatly promoted by increasing the hot-pressing temperature. However, when the temperature is above 280 • C, the reaction is too fast to be controlled by varying the hot-pressing pressure and time. On the other hand, stress and defects are induced in hBN nano-crystals by the hydrothermal hot-pressing process, resulting in a shift of the IR absorption bands and a deterioration of crystalline perfection. These results may be useful for synthesizing cBN by the hydrothermal method and converting hBN nanocrystals into cBN under moderate conditions

Decreased Functional Connectivity in Insular Subregions in Depressive Episodes of Bipolar Disorder and Major Depressive Disorder

Objective: Clinically, it is very difficult to distinguish between major depressive disorder (MDD) and bipolar disorder (BD) in the period of depression. Increasing evidence shows that the insula plays an important role in depression. We aimed to compare the resting-state functional connectivity (rsFC) of insular subregions in patients with MDD and BD in depressive episodes (BDD), who had never experienced manic or hypomanic episodes when they were scanned to identify biomarkers for the identification of two diseases.Methods: We recruited 21 BDD patients, 40 MDD patients and 70 healthy controls (HC). Resting-state functional magnetic resonance imaging (rs-fMRI) was performed. BDD patients had never had manic or hypomanic episodes when they were scanned, and the diagnoses were determined by follow-up. We divided the insula into three parts including the ventral anterior insular cortex (v-AIN), dorsal anterior insular cortex (d-AIN), and posterior insula (PI). The insular-based rsFC was compared among the three groups, and an analysis of the correlation between the rsFC value and Hamilton depression and anxiety scales was carried out.Results: BDD and MDD patients demonstrated decreased rsFC from the v-AIN to the left superior/middle frontal gyrus compared with the HC group. Versus MDD and HC groups, BDD patients exhibited decreased rsFC from the v-AIN to the area in the left orbital frontal gyrus and left superior temporal gyrus (included temporal pole), from the PI to the right lateral postcentral gyrus and from all three insular subregions to the somatosensory and motor cortex. Meanwhile, a correlation between the rsFC value of the PI-right lateral postcentral gyrus and anxiety score was observed in patients.Conclusion: Our findings show BDD and MDD patients have similar decreases in insular connectivity in the dorsal lateral frontal regions, and BDD patients have specific decreased insular connectivity, especially in the somatosensory and motor cortex, which may be used as imaging evidence for clinical identification