Advancing Electrical Losses Assessment Methods in Power Systems

• Autorzy: Rexhepi Vezir , Rexhepi Shaqir

Identyfikatory

DOI

10.12912/27197050/182923

• Języki publikacji: EN

Abstrakty

EN

The operation of modern power systems requires a sophisticated technological infrastructure to effectively manage and evaluate their parameters and performance. This infrastructure includes the generation, transmission and distribution power system components. This paper provides an overview of the loss evaluation to a part of Kosovo’s power system, substation with wind and photovoltaic (PV) energy sources integrated (SS Mramori, SS Kitka, and SS Kamenica) and the analysis of the loss assessment methods. One the assessment method in the research encompass simulated loss scenarios and their corresponding values in network components, employing the simulation based on the respective software tools. In current trends, power systems are visualized through the Supervisory Control and Data Acquisition (SCADA) platform. However, in Kosovo, although losses are integral to the SCADA system, they are represented as a overall value in the online mode, not encompassed depict losses per-components in real-time. This limitation hinders effective online power system optimization regarding the losses. As consequence, the purpose of this study is proposal a logical method developed through neural networks. The methodology incorporates various parameters, including as inputs variables; voltages, currents, active and reactive powers, and their computed values for extracting losses (X(x1, x2, ..., xn)). These parameters undergo systematic processing through hidden layers (Y(x1, x2, ..., xn)), leading to the classification of components within the power system. Finally, at the output stage (A(x1, x2, ..., xn)), an assessment is conducted based on the level of losses observed in the components of the power system. This implementation method promises significant benefits for transmission systems, impacting not only reducing losses, power quality but also yielding economic advantages.

Słowa kluczowe

EN

electrical loss; power systems; SCADA; neural network; classification; assessment; monitoring

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2024
• Tom: Vol. 25, iss. 3
• Strony: 326--335
• Opis fizyczny: Bibliogr. 26 poz., rys.

Twórcy

autor

Rexhepi Vezir

• Department of Power Engineering, Faculty of Electrical and Computer Engineering, University of Prishtina “Hasan Prishtina” 10000, Prishtina, Kosovo

• https://orcid.org/0000-0002-3975-6927

autor

Rexhepi Shaqir

• Department of Finance Accounting, Faculty of Business, University of Durres “Aleksander Moisiu”, 2000, Durres, Albania

• https://orcid.org/0000-0002-7023-5142

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).