This paper introduces a new optimization method to determine the optimal allocation of Unified Power Quality Conditioner (UPQC) in the distribution systems. UPQC is a versatile Custom Power Device (CPD) to solve problems related to voltage and current by the series and shunt compensator in the distribution systems. The task of UPQC highlighted in this paper is the required load reactive power is provided by both the series and shunt compensators. The UPQC’s steady state compensation capability has given a solution for providing reactive power compensation in large distribution systems. The optimization method adopted is Moth Flame Optimization (MFO). The best location and series compensator voltage are determined using MFO. The voltage injected by the series compensator and reactive power injected by the shunt compensator is incorporated in the load flow method. The effectiveness of the proposed method is validated with standard distribution systems.
More than 66 million people in India speak Telugu, a language that dates back thousands of years and is widely spoken in South India. There has not been much progress reported on the advancement of Telugu text Optical Character Recognition (OCR) systems. Telugu characters can be composed of many symbols joined together. OCR is the process of turning a document image into a text-editable one that may be used in other applications. It saves a great deal of time and effort by not having to start from scratch each time. There are hundreds of thousands of different combinations of modifiers and consonants when writing compound letters. Symbols joined to one another form a compound character. Since there are so many output classes in Telugu, there’s a lot of interclass variation. Additionally, there are not any Telugu OCR systems that take use of recent breakthroughs in deep learning, which prompted us to create our own. When used in conjunction with a word processor, an OCR system has a significant impact on real-world applications. In a Telugu OCR system, we offer two ways to improve symbol or glyph segmentation. When it comes to Telugu OCR, the ability to recognise that Telugu text is crucial. In a picture, connected components are collections of identical pixels that are connected to one another by either 4- or 8-pixel connectivity. These connected components are known as glyphs in Telugu. In the proposed research, an efficient deep learning model with Interrelated Tagging Prototype with Segmentation for Telugu Text Recognition (ITP-STTR) is introduced. The proposed model is compared with the existing model and the results exhibit that the proposed model’s performance in text recognition is high.
Transmission line is a main portion of power system owing to its capacity of increasing power in a power grid. Nonetheless, due to increasing complexity, faulty detection in power line has been always a potential issue. Parallel incomplete journey transmission lines (PIJTL) frequently subject a variety of technical issues in the view of power system protection. This study presents artificial neural networks (ANN) based inter circuit fault classification of PIJTL using MATLAB Software. Although different approaches have been addressed for ordinary shunt faults in PIJTL, nonetheless, determining the inter circuit faults in PIJTL hasn't been focused so far. When fault occurs in the PIJTL current waveforms are distorted due to transients and its pattern changes according to the fault type in the line. The ANN approach finds the inter circuit faults by means of currents. ANN takes a reduced set of feature inputs, i.e., the fundamental components of six phase currents of the two parallel lines at source of parallel incomplete journey only. The result performed that proposed ANN is capability of right tripping action then type of fault at high speed as a result can be applied in practical application. The main feature of ANN is that it acceptably estimates finds the inter circuit faults and also ordinary shunt faults, thus making it more accurate and reliable when compared to other approaches. Several fault case studies have conformed the effectiveness of ANN technique. Further, fuzzy based inter circuit fault locator and classifier for PIJTL we can design.
This paper presents optimal placement of Static Series Voltage Regulator (SSVR), for voltage profile improvement and power loss reduction in radial distribution systems under steady state condition. SSVR consists of a series compensator. The series compensator injects the series voltage in quadrature with the branch current in such a way that the receiving end voltage is maintained at desired value (up to 1 p.u). The criteria for selection of optimum location of SSVR are under voltage problem mitigation and loss reduction in the network under steady sate condition. Particle Swam Optimization (PSO) technique is used to find the rating of the device. The proposed model is tested using standard distribution system consisting of 33 nodes.
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