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Centralised MPC for Long-term Voltage Stability Control of Power System
KTH, School of Electrical Engineering and Computer Science (EECS).
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Centraliserad MPC för långsiktig spänningsstabilitetskontroll av kraftsystem (Swedish)
Abstract [en]

In a power system it is important to keep voltages at specific levels at network buses. Deviations from that can lead to reduced efficiency of transferred power or, in more severe cases, widespread power outages affecting large parts of society. There exists a variety of power system devices that have the ability to regulate the voltage levels. These devices have maximum and minimum control capacities and may have additional operational constraints. It is desired to keep the control capacity of these actuators close to neutral operation so that they have the ability to respond to future disturbances. Due to the nature of such a control problem, a suitable tool is Model Predictive Control. In this thesis, a centralised model predictive control is designed for long-term voltage stability control of a power system. The system model employed is a two-area power system model, where each area includes a network of generators and loads. The model predictive control regulates the tap position of a tap-changing transformer and the reactive power compensation provided by two capacitor banks. In this thesis, it is shown that a centralised model predictive controller successfully maintains voltages within the desired range for a 3.5 % longer duration compared to a decentralised control approach when facing a voltage collapse scenario. Additionally, thanks to its predictive capabilities, it efficiently dampened oscillations in the post-transient steadystate scenario, leading to a 6.6 % shorter settling time than that observed with the decentralised control approach.

Abstract [sv]

I ett kraftsystem är det viktigt att hålla spänningen på specifika nivåer vid nätverksbussarna. Avvikelser från detta kan leda till nedsatt effektöverföringseffektivitet eller, i allvarligare fall, omfattande strömavbrott som påverkar stora delar av samhället. Det finns en mängd olika kraftsystemsenheter som har förmågan att reglera spänningsnivåerna. Dessa enheter har maximala och minimala kontrollkapaciteter och kan ha ytterligare driftbegränsningar. Det är önskvärt att hålla kontrollkapaciteten hos dessa enheter nära neutral drift så att de har förmågan att svara på framtida störningar. På grund av arten av ett sådant kontrollproblem är ett lämpligt verktyg Model Predictive Control. I den här avhandlingen är en centraliserad modellprediktiv reglering utformad för långsiktig spänningsstabilitetskontroll av ett kraftsystem. Systemmodellen som används är en två-area kraftsystemmodell, där varje område inkluderar ett nätverk av generatorer och belastningar. Kontrollen reglerar varvtalet hos en lindningskopplare och den reaktiva effektkompensationen som tillhandahålls av två kondensatorbanker. I denna avhandling visas det att en centraliserad modell-prediktiv reglering framgångsrikt kan upprätthålla spänningar inom det önskade intervallet under en 3.5 % längre varaktighet jämfört med en decentraliserad styrmetod under ett spänningskollapsscenario. Dessutom, tack vare dess prediktiva kapacitet, dämpade den effektivt svängningar i det post-transienta steady-state-scenariot, vilket ledde till en 6.6 % kortare insvängningstid än den som observerades med den decentraliserade styrmetoden.

Place, publisher, year, edition, pages
2023. , p. 64
Series
TRITA-EECS-EX ; 2023:947
Keywords [en]
Voltage stability, Model predictive control, Tap-changing transformer, Capacitor bank
Keywords [sv]
Spänningsstabilitet, Model prediktiv reglering, Lindningskopplare, Kondensatorbank
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-344447OAI: oai:DiVA.org:kth-344447DiVA, id: diva2:1845159
Subject / course
Systems Engineering
Educational program
Master of Science - Systems, Control and Robotics
Supervisors
Examiners
Available from: 2024-03-20 Created: 2024-03-18 Last updated: 2024-03-20Bibliographically approved

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