Elektriğin üretim, iletim, dağıtım ve son kullanıcı alanında kullanımında güç kalitesi problemleri sistem performansını, ekipman ömrünü ve buna bağlı olarak enerjinin verimli kullanımını olumsuz yönde etkiler. Güç kalitesi problemlerini giderebilmek için öncelikle bu problemleri tespit etmek ve kayıt altına almak gereklidir. Araştırmanın amacı güç kalitesi problemlerinin en ekonomik şekilde tespit edilmesi ve büyük veri analizine uygun formatta depolanarak birçok iyileştirme çalışmasına hizmet etmesidir. Araştırma kapsamda belirli güç kalitesi problemlerinin tespitinde nesnelerin interneti altyapısını kullanarak problemlerin tespit edilmesi ve buluttaki sunucularda kayıt altına alınması işlemi gerçekleştirilmiştir. Bu çalışmada belirli güç kalitesi problemlerini tespit eden algoritma geliştirilmiştir. Tasarlanan algoritma ve yeni sistem ekipmanları ile işletmede bulunan geleneksel sistem ekipmanlarından daha düşük yatırım maliyeti ile belirli güç kalitesi problemleri tespit edilebilmektedir. Bu çalışma bir tesiste belirli ölçüm noktasında yeni tasarlanan sistemin kurulması ve 48 saatlik ölçüm alınması ile test edilmiştir. Test verileri incelendiğinde tasarlanan algoritma ve sistemin işletmede oluşan güç kalitesi parametrelerini ve problemlerini tespit ederek buluttaki sunuculara gönderdiği tespit edilmiştir. Yapılan maliyet analizi ile mevcut sisteme göre yeni tasarlanan sistemin %47,15 oranında daha az yatırım maliyeti ile yapılabildiği görülmüştür. Bu tip çalışmaların artırılması ve devamında büyük veri, yapay zeka uygulamaları ile daha derin analizler gerçekleştirilmesi sağlanabilmektedir. Böylece elektrik sistemlerinin performansı daha düşük yatırım maliyetleri ile optimize edilerek enerji verimliliği artırılabilmektedir.
Problems with power quality in the production, transmission, distribution and use of electricity in the end-user field negatively affect the system performance, equipment life and consequently the efficient use of energy. In order to resolve power quality problems, it is necessary to first identify and record these problems. The purpose of the research is to identify power quality problems in the most economical way and to serve many improvement work by storing in a format suitable for big data analysis. In the field of research, the process of detecting specific power quality problems using the Internet infrastructure of objects and recording the problems on the servers in the cloud was carried out. This study developed an algorithm that identifies specific power quality problems. Designed algorithms and new system equipment can be identified with a lower investment cost than the traditional system equipment in the operation, with specific power quality problems. This study was tested with the installation of a newly designed system at a specific measurement point in a facility and with a 48-hour measurement. When the test data is studied, the algorithm designed and the system has been found to send to the servers in the cloud by identifying the power quality parameters and problems that occur in the operation. The cost analysis found that according to the existing system, the newly designed system could be done with 47.15% lower investment costs. The increase in this type of studies and the continuation of big data can be provided with more profound analysis with the application of artificial intelligence. Thus, the performance of electrical systems can be optimized with lower investment costs and energy efficiency can be increased.
Power quality problems in production, distribution and end-user areas of the electricity affects negatively to the system performance, equipment lifetime and due to that usage of the energy consumption. To be able to eliminate the specific power quality problems, firstly these problems must be detected and recorded. The aim of this study is detecting the specific power quality problems economically and serving the improvement activities by collecting the specific power quality problems in accordance with the big data analysis. Within the scope of this study, specific power quality problems are detected by using the Internet of Things infrastructure and recorded at the servers in the cloud. In this study, an algorithm which detects the specific power quality problems is developed. With the developed algorithm and new system equipment, specific power quality problems can be detected with lower investment costs than traditional system equipment which is in the plant. This study is tested with the implementation of the developed system in the MCC-2 board of the plant and with taking 48 hours of measurement. When the test data were examined, it is observed that the developed algorithm and system detected the specific power quality parameters and problems in the plant and sent them to the servers in the cloud. With the cost analysis, it was confirmed that the newly developed system can be built with 47.15% less investment cost than the existing system. It is possible to perform in-depth analysis with increasing these kinds of studies and afterwards big data, artificial intelligence applications. Thus, energy efficiency can be increased by optimizing the performance of the electrical systems with lower investment costs.
Alan : Fen Bilimleri ve Matematik
Dergi Türü : Ulusal
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