Study and analysis of power quality situation in electrical power network. Case study: Lattakia-Syria

Author(s):  
Rinat R. Nasyrov ◽  
Raseel I. Aljendy ◽  
Tarek Kherbek
Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3151
Author(s):  
Janusz Mindykowski ◽  
Tomasz Tarasiuk ◽  
Piotr Gnaciński

This paper deals with new challenges regarding power quality in ship technology resulting from the novelisation and implementation of IACS (International Association of Classification Societies) rules and requirements. These rules, known as IACS E24 2016/2018, address harmonic distortion for ship electrical distribution systems, including harmonic filters. The reasons for the legislative changes based on a short overview of power quality-related accidents are discussed, after which a brief presentation of the updated IACS rules illustrated by a related DNV GL (Det Norske Veritas Germanischer Lloyd) case study is shown. A key part of this paper includes proposals concerning harmonics and interharmonics, distortion indices and transient disturbances. The aim of these proposals is to unify power quality indices and measurement procedures to maintain effective and comparable criteria for monitoring distortion and establish requirements for ship owners, designers, shipbuilders, classifiers, and crew members of marine objects.


2017 ◽  
Vol 2 (4) ◽  
pp. 227 ◽  
Author(s):  
Amam Hossain Bagdadee

Characteristics of Power quality has been with us since the inception of the electrical Power system. However, the topic of power quality has attracted particular attention in recent years due to the increase of electronically controlled. Power quality problems caused disruptions to electrical or electronic equipment and the resulting consequences are very expensive. Ripple techniques will be studied in this paper for analysing power quality monitoring. In the case study based on the measurement of the site of the Asian Institute of Technology (AIT) and it was examined using the proposed ripple technique.


2019 ◽  
Vol 63 (2) ◽  
pp. 99-105 ◽  
Author(s):  
Michał Jasiński ◽  
Jacek Rezmer ◽  
Tomasz Sikorski ◽  
Jarosław Szymańda

The aim of the paper is to present possible using of monitoring systems associated with photovoltaic systems (PV) in point of its integration with electrical power system (EPS). Presented investigations is a case study of 15 kW Scientific Photovoltaic System. The paper contains a description of applied control and monitoring systems including monitoring of PV panels parameters, weather condition, PV DC/AC inverters as well as special monitoring systems dedicated to power quality (PQ) and shape of voltage and current. The aim of the paper is to exhibit a possibility to combine different monitoring systems of the PV in order to improve evaluation of integration of PV with EPS. Presented example contains selected elements of power quality assessment, power and energy production, weather conditions for selected period of PV system working time.


Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2407 ◽  
Author(s):  
Michał Jasiński ◽  
Tomasz Sikorski ◽  
Zbigniew Leonowicz ◽  
Klaudiusz Borkowski ◽  
Elżbieta Jasińska

This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (EPN) of the mining industry with distributed generation (DG). The obtained results indicate that the application of the Ward algorithm to PQ data assures the division with regards to the work of the distributed generation, and also to other important working conditions (e.g., reconfiguration or high harmonic pollution). The presented analysis is conducted for the area-related approach—all measurement point data are connected at an initial stage. The importance rate was proposed in order to indicate the parameters that have a high impact on the classification of the data. Another element of the article was the reduction of the size of the input database. The reduction of input data by 57% assured the classification with a 95% agreement when compared to the complete database classification.


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