Abstract
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.
Highlights
The worst-case scenario for an electric power system (EPS) is a long-lasting blackout [1]
Due to different local frequency conditions, intelligent electronic devices (IEDs) detections and actions are not synchronized, nor are their triggering actions. This may be perceived as problematic, we believe it is an advantage. This is because tripping in this manner is more widely dispersed over time, and the power adjustment is more continuous compared to a coordinated approach, for which the underfrequency load shedding (UFLS) actions are synchronized throughout the EPS
This paper introduces a small and specialized group of IEDs with machine learning functionalities, forming a so-called libero UFLS stage that includes a feature missing in existing UFLS
Summary
The worst-case scenario for an electric power system (EPS) is a long-lasting blackout [1]. When extreme situations occur, underfrequency load shedding (UFLS) and overfrequency protection for the generating units, as aspects of SIPS, are required to support frequency control The former temporarily curtails consumption in cases of large power deficits, while the latter curtails generation in cases of large power surpluses in order to restore the balance in due time [2,3]. The L-UFLS IEDs are equipped with a special pattern recognition functionality, which makes them capable of efficiently recognizing the need for intervention and adjusting their own triggering parameters The significance of this solution is that it keeps the existing UFLS intact, leaving it fully capable of handling most cases of power imbalance efficiently and quickly by slowing down the fast-occurring frequency drop. We avoid potential overshedding of the conventional UFLS, as our strategy allows for the disconnection of consumers in smaller bundles
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