A Bayesian Network Method for Quantitative Evaluation of Defects in Multilayered Structures from Eddy Current NDT Signals

Accurate evaluation and characterization of defects in multilayered structures from eddy current nondestructive testing (NDT) signals are a difficult inverse problem. There is scope for improving the current methods used for solving the inverse problem by incorporating information of uncertainty in the inspection process. Here, we propose to evaluate defects quantitatively from eddy current NDT signals using Bayesian networks (BNs). BNs are a useful method in handling uncertainty in the inspection process, eventually leading to the more accurate results. The domain knowledge and the experimental data are used to generate the BN models. The models are applied to predict the signals corresponding to different defect characteristic parameters or to estimate defect characteristic parameters from eddy current signals in real time. Finally, the estimation results are analyzed. Compared to the least squares regression method, BNs are more robust with higher accuracy and have the advantage of being a bidirectional inferential mechanism. This approach allows results to be obtained in the form of full marginal conditional probability distributions, providing more information on the defect. The feasibility of BNs presented and discussed in this paper has been validated

The diversity and structure of diazotrophic communities in the rhizosphere of coastal saline plants is mainly affected by soil physicochemical factors but not host plant species

The diversity and community structure of rhizospheric microbes are largely affected by soil physicochemical properties and plant species. In this work, high throughput sequencing and quantitative real-time PCR targeting nifH gene were used to assess the abundance and diversity of diazotrophic community in the coastal saline soils of Yellow River Delta (YRD). We demonstrated that the copy number of nifH gene encoding the Fe protein subunit of the nitrogenase in the nitrogen fixation process was significantly affected by soil physiochemical factors, and the abundance of diazotrophs in the rhizospheric soil samples collected from different locations was positively related with soil physicochemical properties. Soil salinity (P=0.003) and moisture (P=0.003) were significantly co-varied with the OTU-based community composition of diazotrophs. Taxonomic analysis showed that most diazotrophs belonged to the Alphaproteobacteria, Gammaproteobacteria and Deltaproteobacteria. Linear discriminant analysis (LDA) effect size (LEfSe) and canonical correspondence analysis (CCA) showed that diazotrophic community structure significantly varied with soil salinity, moisture, pH and total nitrogen, carbon, sulphur and nitrite (NO2–N) content. Our findings provide direct evidence toward the understanding of different effects of soil physicochemical properties and host plant traits such as halophytes types, life span and cotyledon type, on the community composition of diazotrophic populations in the rhizosphere of plants grown in coastal saline soils

Identification of overhead line fault traveling wave and interference clutter based on convolution neural network and random forest fusion

High-speed traveling wave acquisition devices often use a mutation start algorithm with a low threshold value, which can collect a large number of interference clutters. If the devices automatically screen out a flashover fault traveling wave from a massive traveling wave, the traveling wave fault location performance could be improved. In this paper, a fault and interference classification method based on the random forest algorithm, which uses a convolutional neural network as a supervised feature extractor of traveling wave data, is proposed. First, one-dimensional traveling wave data are mapped to a two-dimensional matrix by dividing the information section, and a gray image is used to characterize it. Next, a two-dimensional convolution neural model of traveling wave data is constructed to realize the self-learning of traveling wave data characteristics, and the waveform feature sequence of traveling wave data is obtained. Then, on this basis, a random forest algorithm is used to realize automatic identification and screening of flashover fault traveling waves. Finally, a large number of tests on the simulation and measured data show that the proposed combined algorithm based on the feature extraction and classification of stochastic forest algorithm has better recognition effect of fault and interference than the traditional support vector machine, single stochastic forest algorithm, and convolutional neural network

Inertia estimation of power system with new energy considering with high renewable penetrations

The emerging energy technologies, such as wind energy and photovoltaic (PV), will gradually replace the traditional synchronous generator in wide-area power system. As the wind, PV and energy storage equipment are all controlled by power electronic inverters, which are decoupled from the system and cannot provide effective inertial support to the power system with new energy (PS-NE), resulting in stability problems caused by the low inertia of the PS-NE. In particular, this paper investigates the inertia response of synchronous generator and PS-NE, and the inertia model of PS-NE considering with high renewable penetrations. Then the relation between inertia and frequency of PS-NE with high renewable penetrations are explored in this paper. Besides, the real-time inertia of the PS-NE is estimated by using the statistical algorithm based on the historical data of the PS-NE, and the range of the inertia and synchronous generator start-up capacity of the PS-NE can be estimated by using the statistical algorithm based on the renewable penetrations when we cannot obtain the historical data of the PS-NE. In addition, IEEE39 system of PS-NE is used to verify the relation between inertia and frequency of PS-NE with high renewable penetrations, and the inertia estimation of PS-NE with high renewable penetrations is analyzed in this paper

Parameter identification of photovoltaic discrete-time equivalent model using the bat algorithm

As the development of photovoltaic (PV) power generation continuously accelerated the total installed capacity of PV, the traditional static load model is difficult to meet the needs of the power grid with the increasing penetration of PV. And the dynamic model of grid-connected PV power generation is complicated and there are plenty parameters need to be identified, so the dynamic model of grid-connected PV power generation are extremely challenging to apply in wide-area power system. In this paper, a discrete-time equivalent model of PV (PDEM) is established based on the third-order dynamic differential equation of the PV power generation system and the parameters of the PDEM are identified using the least squares (LS) and the bat algorithm (BA). Besides, the dynamic characteristics of the PV power generation grid connected system with different permeability and the fitting residuals of the two methods is analyzed in the IEEE14-bus system incorporated into the PV system. The applicability of the PDEM is verified by setting short circuit grounding fault and changing the PV permeability and voltage dip. The simulation results demonstrate that the PDEM has a strong adaptability and good applicability in the case of high PV permeability with a wide application. And the applicability of the BA in identification of PDEM are given in this paper

Wave energy converter array layout optimization : a critical and comprehensive overview

The production efficiency and optimal control of wave energy converter (WEC) array are mainly based on array layout, thus it is crucial to establish a reliable mathematical model for the WEC array layout optimization. So far, a lot of research has been done on the modeling and methods of WEC array layout optimization. However, the existing reviews are either incomplete in the classification of modeling methods, or incomplete in a collection of optimization methods, especially lacking detailed evaluation. This paper aims to comprehensively summarize various WEC models and related approaches for WEC array layout optimization. Note that over 80 related pieces of literature have been carefully analyzed and summarized, and they are divided into three categories: meta-heuristic-based, machine learning-based, and mathematics-based methods. In particular, the advantages/disadvantages, variables, evaluation indices, parameters, complexity, and objective functions are thoroughly discussed. Finally, potential research directions and recommendations are proposed in future in-depth research for both researchers and engineers

Thermodynamically Stable Pickering Emulsion Configured with Carbon-Nanotube-Bridged Nanosheet-Shaped Layered Double Hydroxide for Selective Oxidation of Benzyl Alcohol

A simple strategy to configure a high thermodynamically stable Pickering emulsion with 2D sheet-shaped layered double hydroxide (LDH) coupled carbon nanotube (CNT) nanohybrid (LDH–CNT) is reported. With the benefit of a unique 2D sheet-shaped structure of the LDH, the as-made LDH–CNTs with amphiphilicity as solid emulsifiers have a good capability for assembling and stabilizing at the water–oil interface, and a superior thermostability emulsion is delivered, indicative of an increased catalytic performance for selective oxidation of benzyl alcohol to benzaldehyde. Such a unique and excellent thermodynamic stability characteristic makes high reaction interfacial areas well-kept during the reaction process, yielding high catalytic performance. The present strategy provides a simple method for configuration and design of solid nanoparticle emulsifiers with high thermodynamic stability, which will make such a material be of great potential in many important applications such as catalysis and emulsifiers