Impacts of Life Distributions on Reliability Analysis of Smart Substations

Most of the existing reliability analysis is based on exponential distribution which is convenient to calculation, whereas most intelligent electronic devices exhibit Weibull distribution. What kind of influence will life distribution have on the reliability and availability of a smart substation. This paper compares the reliability and availability of systems with different life distributions based on reliability block diagram and Monte-Carlo simulation, by treating the smart substations as non-repairable and repairable systems respectively. Simulation results proves the feasibility of substituting Weibull distribution with exponential distribution in engineering practice when the smart substations are treated as repairable systems, thus the life data estimation as well as the reliability analysis and calculation will be greatly simplified. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.483

Comprehensive Evaluation of Reliability of Protection System in Smart Substation

The reliability of smart substations has a great significance on the safety and stability of smart grid operation. Taking the protection system in smart substation as an example, this paper constructs comprehensive reliability models to evaluate the reliability of smart substations with different architectures. The paper first illustrates two important aspects which affect the reliability of the protection system, namely the network architecture and the maintenance strategy. To satisfy these two aspects, the paper then adopt the Monte Carlo simulation combined with the Reliability Block Diagram method to make quantitative reliability analysis. At last, reliability of four power transformer protection systems applying different maintenance strategies with alternative architectures is evaluated. The simulation results show clearly that advanced maintenance strategies such as conditional maintenance will play a critical role in enhancing the reliability and availability of smart substation. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.510

Improved magnetoelectric effect in magnetostrictive/piezoelectric composite with flux concentration effect for sensitive magnetic sensor

The magnetoelectric (ME) composite with the flux concentration effect is designed, fabricated, and characterized for detecting weak ac magnetic-field. The high-permeability Fe73.5Cu1Nb3Si13.5B9 (FeCuNbSiB) foils act as flux concentrators and are bonded at the free ends of traditional ME laminates. With the improved ME responses in the proposed ME composite based on the flux concentration effect, the output sensitivities under zero-biased magnetic field can reach 7 V/Oe and 15.8 mV/Oe under the resonance frequency of 177.36 kHz and the off-resonance frequency of 1 kHz, respectively. The results indicate that the proposed ME composites show promising applications for high-sensitivity self-biased magnetic field sensors and ME transducers

Nanoscratch-Induced Formation of Metallic Micro/Nanostructures With Resin Masks

Metallic micro/nanostructures present a wide range of applications due to the small size and superior performances. In order to obtain high-performance devices, it is of great importance to develop new methods for preparing metallic micro/nanostructures with high quality, low cost, and precise position. It is found that metallic micro/nanostructures can be obtained by scratch-induced directional deposition of metals on silicon surface, where the mask plays a key role in the process. This study is focused on the preparation of keto-aldehyde resin masks and their effects on the formation of scratch-induced gold (Au) micro/nanostructures. It is also found that the keto-aldehyde resin with a certain thickness can act as a satisfactory mask for high-quality Au deposition, and the scratches produced under lower normal load and less scratching cycles are more conducive to the formation of compact Au structures. According to the proposed method, two-dimensional Au structures can be prepared on the designed scratching traces, providing a feasible path for fabricating high-quality metal-based sensors

An efficient YOLO v3-based method for the detection of transmission line defects

The UAV inspection method is gradually becoming popular in transmission line inspection, but it is inefficient only through real-time manual observation. Algorithms are available to achieve automatic image identification, but the detection speed is slow, and video image processing is not possible. In this paper, we propose a fast detection method for transmission line defects based on YOLO v3. The method first establishes a YOLO v3 target detection model and obtains the a priori size of the target candidate region by clustering analysis of the training sample library. The training process of the model is accelerated by adjusting the loss function to adjust the learning direction of the model. Finally, transmission line defect detection was achieved by building a transmission line defect sample library and conducting training. The test results show that compared with other deep learning models, such as Faster R-CNN and SSD, the improved model based on YOLO v3 has a huge speed advantage and the detection accuracy is not greatly affected, which can meet the demand for automatic defect recognition of transmission line inspection videos

International alliance of urolithiasis guideline on shockwave lithotripsy

Abstract Different international associations have proposed their own guidelines on urolithiasis. However, the focus is primarily on an overview of the principles of urolithiasis management rather than step-by-step technical details for the procedure. The International Alliance of Urolithiasis (IAU) is releasing a series of guidelines on the management of urolithiasis. The current guideline on shockwave lithotripsy (SWL) is the third in the IAU guidelines series and provides a clinical framework for urologists and technicians performing SWL. A total of 49 recommendations are summarized and graded, covering the following aspects: indications and contraindications; preoperative patient evaluation; preoperative medication; prestenting; intraoperative analgesia or anesthesia; intraoperative position; stone localization and monitoring; machine and energy settings; intraoperative lithotripsy strategies; auxiliary therapy following SWL; evaluation of stone clearance; complications; and quality of life. The recommendations, tips, and tricks regarding SWL procedures summarized here provide important and necessary guidance for urologists along with technicians performing SWL. PATIENT SUMMARY: For kidney and urinary stones of less than 20 mm in size, shockwave lithotripsy (SWL) is an approach in which the stone is treated with shockwaves applied to the skin, without the need for surgery. Our recommendations on technical aspects of the procedure provide guidance for urologists and technicians performing SWL

TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT

Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made it possible to interact with tables using natural language input, bringing this capability closer to reality. In this paper, we present TableGPT, a unified fine-tuned framework that enables LLMs to understand and operate on tables using external functional commands. It introduces the capability to seamlessly interact with tables, enabling a wide range of functionalities such as question answering, data manipulation (e.g., insert, delete, query, and modify operations), data visualization, analysis report generation, and automated prediction. TableGPT aims to provide convenience and accessibility to users by empowering them to effortlessly leverage tabular data. At the core of TableGPT lies the novel concept of global tabular representations, which empowers LLMs to gain a comprehensive understanding of the entire table beyond meta-information. By jointly training LLMs on both table and text modalities, TableGPT achieves a deep understanding of tabular data and the ability to perform complex operations on tables through chain-of-command instructions. Importantly, TableGPT offers the advantage of being a self-contained system rather than relying on external API interfaces. Moreover, it supports efficient data process flow, query rejection (when appropriate) and private deployment, enabling faster domain data fine-tuning and ensuring data privacy, which enhances the framework's adaptability to specific use cases.Comment: Technical Repor

Deflator selection and generalized linear modelling in market-based regression analyses

The scale factor refers to an unknown size variable which affects some or all observed variables in a multiplicative fashion. The scale effect studied by several researchers in market-based regression analyses is defined here as the intriguing combination of coefficient bias and heteroscedasticity caused by the scale. Deflation is the most popular technique used in previous market-based studies to mitigate the scale effect. Selection of a suitable deflator, however, remains as a difficult and challenging task due to the lack of a general statistical framework for this type of research. In this article, we establish a general statistical framework for deflator and model selection. We argue and show that the existence and severity of the scale effect can be identified and measured using the Average Absolute Values of Studentized Residuals and the Relative Total Prediction Error for stratified firm groups. The proposed framework consists of five major components. Results from our simulation studies and sensitivity analyses show that if the true scale variable is used as a deflator to produce one of the deflated candidate models, this model can be correctly identified using the proposed strategy, even if the working model is mildly misspecified. In addition, our studies show that the generalized linear modelling method can be very useful for mitigating the scale effect when the unknown true scale variable is related to the whole set of independent variables through the so-called mean function.