Research and Design of Fault Indicator Using Comprehensive Detection and Identification Method

With the progress and development of society, the power supply quality of power system is required to be higher and higher. It is necessary to locate the fault and remove it quickly. Therefore, it is necessary to install fault indicator on distribution line to improve the efficiency of finding fault location. As an important part of distribution network, the 10kV overhead line has the characteristics of many branches, wide coverage area, time-consuming and labor-consuming in line inspection and maintenance. The fault indicators currently used have problems such as complex structure, high cost of installation and deployment, and inaccurate fault detection. In this paper, a new type of fault indicator is proposed, which uses the comprehensive fault detection method. DSP processor is used to collect, calculate and process the voltage and current information of power grid. Through the embedded programming language, the comprehensive fault detection and identification is realized. Finally, the acquisition accuracy and fault judgment accuracy of the fault indicator are tested by simulating the fault signal in the laboratory. The experimental results show that the proposed fault indicator has high accuracy and can meet the requirements of fault indication, location and alarm

Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects

The strengthening energy or embrittling potency of an alloying element is a fundamental energetics of the grain boundary (GB) embrittlement that control the mechanical properties of metallic materials. A data-driven machine learning approach has recently been used to develop prediction models to uncover the physical mechanisms and design novel materials with enhanced properties. In this work, to accurately predict and uncover the key features in determining the strengthening energies, three machine learning methods were used to model and predict strengthening energies of solutes in different metallic GBs. In addition, 142 strengthening energies from previous density functional theory calculations served as our dataset to train three machine learning models: support vector machine (SVM) with linear kernel, SVM with radial basis function (RBF) kernel, and artificial neural network (ANN). Considering both the bond-breaking effect and atomic size effect, the nonlinear kernel based SVR model was found to perform the best with a correlation of r2 ~ 0.889. The size effect feature shows a significant improvement to prediction performance with respect to using bond-breaking effect only. Moreover, the mean impact value analysis was conducted to quantitatively explore the relative significance of each input feature for improving the effective prediction