Principal Component Analysis (PCA)-supported underfrequency load shedding algorithm

Škrjanc, Tadej (avtor), Mihalič, Rafael (avtor), Rudež, Urban (avtor)

This research represents a conceptual shift in the process of introducing flexibility into power system frequency stability-related protection. The existing underfrequency load shedding (UFLS) solution, although robust and fast, has often proved to be incapable of adjusting to different operating conditions. It triggers upon detection of frequency threshold violations, and functions by interrupting the electricity supply to a certain number of consumers, both of which values are decided upon beforehand. Consequently, it often does not comply with its main purpose, i.e., bringing frequency decay to a halt. Instead, the power imbalance is often reversed, resulting in equally undesirable frequency overshoots. Researchers have sought a solution to this shortcoming either by increasing the amount of available information (by means of wide-area communication) or through complex changes to all involved protection relays. In this research, we retain the existing concept of UFLS that performs so well for fast-occurring frequency events. The flexible rebalancing of power is achieved by a small and specialized group of intelligent electronic devices (IEDs) with machine learning functionalities. These IEDs interrupt consumers only when the need to do so is detected with a high degree of certainty. Their small number assures the fine-tuning of power rebalancing and, at the same time, poses no serious threat to system stability in cases of malfunction.

• Jezik: Angleški jezik
• Ključne besede: machine learning, power system frequency stability, load shedding, power system protection
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2020
• Št. strani: 9 str.
• Številčenje: Vol. 13, iss. 22, art. 5896
• PID: 20.500.12556/RUL-134402
• UDK: 621.31:004
• ISSN pri članku: 1996-1073
• DOI: 10.3390/en13225896
• COBISS.SI-ID: 37324291
• Datum objave v RUL: 13.01.2022

Gradivo je del revije

• Naslov: Energies
• Skrajšan naslov: Energies
• Založnik: Molecular Diversity Preservation International
• ISSN: 1996-1073
• COBISS.SI-ID: 518046745

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 12.11.2020

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojno učenje, frekvenčna stabilnost elektroenergetskega sistema, razbremenjevanje, zaščita elektroenergetskega sistema