Transmission system restoration with co-optimization of repairs,load pickups,and generation dispatch

This paper studies the restoration of a transmission system after a significant disruption such as a natural disaster. It considers the co-optimization of repairs, load pickups, and generation dispatch to produce a sequencing of the repairs that minimizes the size of the blackout over time. The core of this process is a Restoration Ordering Problem (ROP), a non-convex mixed-integer nonlinear program that is outside the capabilities of existing solver technologies. To address this computational barrier, the paper examines two approximations of the power flow equations: The DC model and the recently proposed LPAC model. Systematic, large-scale testing indicates that the DC model is not sufficiently accurate for solving the ROP. In contrast, the LPAC power flow model, which captures line losses, reactive power, and voltage magnitudes, is sufficiently accurate to obtain restoration plans that can be converted into AC-feasible power flows. An experimental study also suggests that the LPAC model provides a robust and appealing tradeoff between accuracy and computational performance for solving the ROP.     

Reactive pulsed laser deposited N–C codoped TiO2 thin films

TiO₂ is a large bandgap chemically stable oxide useful for several applications that involve photo-activated processes, including photocatalysis, photovoltaics, photoelectrolysis, etc. However, the large band gap renders this material not a very efficient absorber of the solar spectrum. Various schemes of cation and anion doping have been utilized that reduce this deficiency to a certain extent. In this paper we present the results of N–C codoping of TiO₂ thin films deposited by a reactive pulsed laser deposition technique. These films were compared for their optical and structural properties with undoped, N doped and C doped TiO₂ films prepared by the same technique. While all samples contained polycrystalline anatase phase, varying N₂ and CH₄ partial pressures resulted in change in TiO₂ lattice parameters due to codoping. X-ray diffraction high-resolution scans show the evidence of C incorporation into TiO₂ lattice by 2θ shift in (101) reflections due to large ionic radius of C. N doping was confirmed by XPS analyses. Direct relationship between oxygen vacancies and doping concentration was established by the deconvolution of XPS peaks. Considerable bandgap reduction occurred that was measured by using UV–vis diffuse reflectance spectroscopy. Results show that reactive pulsed laser deposition is indeed a useful method for the synthesis of codoped TiO₂ thin films as bandgap reduction of ~1.00 eV via N–C codoping was successfully achieved.     

Electronic structure and absolute band edge position of tetragonal AgInS2 photocatalyst: A hybrid density functional study

Energy bands, effective mass of carriers, absolute band edge positions and optical properties of tetragonal AgInS₂ were calculated using a first-principles approach with the exchange correlation described by B3LYP hybrid functional. The results indicate that tetragonal AgInS₂ has a direct band gap of 1.93 eV, which reproduce well experimental value. Calculated effective masses of electrons and holes are both small which are beneficial to separation and migration of electron and hole pairs. This implies that AgInS₂ has good photocatalytic performance. The calculated optical characteristics indicate that AgInS₂ has a slight anisotropy for both the real and imaginary parts of the dielectric function and exhibits large optical absorption in the visible light region. Furthermore, the calculated band edge positions in (100), (010) and (001) surfaces indicate that tetragonal AgInS₂ is beneficial to the reduction and oxidation of water to hydrogen and oxygen under visible light irradiation.     

Fabrication,structural and vibrational properties,and physical and optical properties tailoring of nanocrystalline MoS2 films

The modification and tuning features of nanostructured films are of great interest because of controllable and distinctive inherent properties in these materials. Here, nanocrystalline MoS₂ films were fabricated on the stainless steels by a radio frequency magnetron sputtering at ambient temperature. X-ray photoelectron spectroscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction and Raman scattering spectroscopy were used to study the chemical state, chemical composition, crystal structure and vibrational properties of the fabricated MoS₂ films. The bias voltage dependent structural evolution and its influence on the optical properties of MoS₂ nanocrystalline films were systematically investigated. Besides, the residual stresses of MoS₂ nanocrystalline films were explored by employing sin²ψ approach. X-ray diffraction demonstrates that the nanocrystalline MoS₂ films have single-phase hexagonal crystal structure. All MoS₂ films are polycrystalline in nature. The bandgap values are found to be intensively dependent on bias voltage. Our findings show that the nanocrystalline MoS₂ films with different physical properties and intense quantum confinement effect can be realized through adjusting bias voltages. This work may provide deep insight for realizing transitional metal dichalcogenide-based nanostructured film optoelectronic devices with tunable physical properties through a traditional, very cost-effective, and large-scale fabrication method.     

Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.     

Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.     

Decomposition of first-order hybrid Petri nets for hierarchical control of manufacturing systems

Although hybrid Petri net (HPN) is a popular formalism in modelling hybrid production systems, the HPN model of large scale systems gets substantially complicated for analysis and control due to large dimensionality of such systems. To overcome this problem, a typical approach is to decompose the net into subnets and then control the plant through hierarchical or decentralized structures. Although this concept has been widely discussed in the literature for discrete PNs, there is a lack of research for HPNs. In this paper, a new method of decomposition of first-order hybrid Petri nets (FOHPNs) is proposed first and then the hierarchical control of the subnets through a coordinator is introduced. The advantage of using the proposed approach is validated by an existing example. A sugar milling case study is analysed by using a decomposed FOHPN model and the optimization results are compared against the results of the approaches presented in other papers. Simulation results show not only an improvement in production rate, but also show the ability to control the plant online. In addition, by using the hierarchical control structure for an FOHPN model, it is possible to reduce the cost of communication links, improve the reliability of the system, maintain the plant locally, and partially redesign the system.     

Integrated circuit and system design for renewable energy inverters

This study proposes that a novel integrated circuit (IC) and system design for renewable energy inverters can harvest renewable energy to power direct current (DC) and alternating current (AC) loads. In addition, an intelligent synthesis and management tool is developed to design the proposed system and to judge the system’s operational maintenance decisions. Finally, a renewable energy inverter’s information is posted to an online system. Users can obtain the proposed system’s information at any time and place. The accurate and superior performance of the proposed IC and system is confirmed by computer simulations and experimental results.     

Novel organic dyes containing N-bridged oligothiophene coplanar cores for dye-sensitized solar cells

Three novel organic dyes adopting fully-fused coplanar heteroarene as the donor moieties end-capped with two cyanoacrylic acids as acceptors and anchoring groups have been synthesized, characterized, and used as the sensitizers for dye-sensitized solar cells (DSSCs). The photophysical and electrochemical properties of the novel dyes and the characteristics of the DSSCs based on the novel organic dyes were investigated. The incorporation of the coplanar cores with electron-donating N-bridges are beneficial for the better intramolecular charge transfer (ICT), giving these new dyes good light-harvesting capability. The LUMO energy levels of these coplanar heteroacene-based dyes are sufficiently high for the efficient electron injection to TiO₂ upon photo-excitation, while the suitable HOMOs allow the regeneration of oxidized dyes with the electrolyte redox (I⁻/I₃⁻). The structural features of the coplanar cores (penta vs. hexa heteroarene) as well as the alkyl substitutions play crucial roles in governing the physical properties and device performance. Among these three novel organic sensitizers, the EHTt dye composed of a fully fused hexa-arene core and less bulky N-alkyl groups caused the DSSC to show the best photovoltaic performance with an open-circuit voltage (V_OC) of 0.58 V, a short-circuit photocurrent density (J_SC) of 13.72 mA/cm², and a fill factor (FF) of 0.69, yielding an overall power conversion efficiency (PCE) of 5.52% under AM 1.5G solar irradiation.