Article Preview
Top1. Introduction
The paper presents research based on the textile factory production line. For textile manufacturers, the production line's power quality and motor monitoring are essential to operational stability. The purpose of power quality and motor status monitoring is to conduct maintenance in advance to avoid system downtime or malfunctions. New long-distance transmission lines are required to harness these resources to provide an ever-growing load centre. Presumably, these new lines could be high-voltage DC (HVDC) transmission systems owned by companies that break away the standard utilities, which need small and native AC to DC converters. In recent years, many renewable energy sources are wont to generate DC (DC); during this situation, DC to AC inverters must be to interconnect the DC electricity generators to AC systems. Additionally, new power electronics-based controllers like those that the Flexible AC Transmission Systems (FACTS) will install for improving power transfer and providing voltage support are vital factors for reliable operations. Thus, the transmission during a future Smart Grid is going to be far more controllable, and therefore the benefits of HVDC systems and FACTS controllers will be maximized (IEEE, 2011; IEEE, 2013; IEEE, 2016).
This research also implements a monitoring system based on smart grid interoperability. The basic principle of a smart grid is to deliver a secure and robust electric system. To accomplish seamless operation for electricity generation and permit two-way power flow with communication, assimilation of energy with technology and information communication is essential. Moving towards the next generation of Smart Grid is a crucial task and requires robust design and consistent infrastructure of networks through which communication is possible to overcome flaws related to the existing system. (Amin, 2011; Germany Trade and Invest, 2014; IEEE, 2011)
This research also complies with industry 4.0 standards and implements an essential structure for a smart factory. To realize the more intelligent manufacturing processes fourth technological revolution encapsulates development trends for the future industries(Rüßmann et al., 2015), which will adapt on-board information and advanced communication technology for evolution within the supply chain and assembly line, by real-time data monitoring, tracking the status and positions of the product also by sticking to the instructions to regulate production processes.(Radziwona, 2014). Recent research specializing in the role of Industry 4.0 on the assembly and services sector of Pakistan conducted a survey questionnaire from a complete 224 employees of textile and logistics companies. Therefore, the findings revealed that Industry 4.0 has significant importance in overcoming the varied challenges of Pakistan's textile and logistics industry (Imran et al., n.d.). Industry 4.0 connects embedded system production technologies and smart production processes to pave, thanks to a replacement technological age, which radically transforms industry and production value chains and business models, e.g., Smart Factory. Some key features of Industry 4.0 are digitization, optimization, customization of production and automation, adaptation, Etc. (Burke, 2017; German Standardization Roadmap, 2018; Raafi, 2019).
One of the essential factors in the smart grid is signal processing. This factor helps the engineer and researcher figure out the exact plan, design, and complex operations in the smart grid. Since the monitoring station for the inverter can provide users with the real-time waveform, wavelet transformation is useful for analysing power quality. A wavelet transform within which waves are separate samples referred to as a discrete wavelet model. The DWT (Discrete Wavelet Transform) gives a multi-goal portrayal of the signal, which is valuable in analysing real-world signals. DWT is a fast computation wavelet transform that is based on sub-band coding.
To improve the capability of the WT (wavelet transforms) based on power quality monitoring system, many researchers proposed a de-noising approach to detecting transient disturbances in a noisy environment. Moreover, the modified approach is known as S–transform, which is useful for localization, detection, and visually classify the types of disturbances. (Dash et al., 2003; Yang & Liao, 2001)