A data‐driven transient stability‐based approach for out‐of‐step prediction in power systems
This paper presents a prediction‐based algorithm designed to address out‐of‐step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi‐machine power system is evaluated using the equal‐area criterion (EAC)....
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| Published in | IET generation, transmission & distribution Vol. 18; no. 21; pp. 3407 - 3423 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Wiley
01.11.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1751-8687 1751-8695 1751-8695 |
| DOI | 10.1049/gtd2.13290 |
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| Summary: | This paper presents a prediction‐based algorithm designed to address out‐of‐step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi‐machine power system is evaluated using the equal‐area criterion (EAC). The proposed algorithm calculates the characteristics of the P‐δ curves within the EAC framework after a large disturbance. The critical P‐δ trace is determined by analysing the cumulative energy in the acceleration area following fault clearance. The stability margin of the rotor angle is then computed based on the actual active power and its relationship with the critical curve. The algorithm predicts the occurrence of OOS by comparing the measured active power with the corresponding value on the critical curve. Furthermore, a complementary strategy is proposed to predict the OOS condition in integrated inverter‐based power systems. The effectiveness of the proposed algorithm is validated through simulations conducted on the 73‐bus IEEE test power system.
This research paper offers a fresh data‐driven‐based method to predict out‐of‐step conditions in power systems using the data acquisition from PMUs. To this end, the equal‐area criterion is used to figure out the characteristics of during and post‐fault for the prediction aim. |
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| ISSN: | 1751-8687 1751-8695 1751-8695 |
| DOI: | 10.1049/gtd2.13290 |