Voltage Sag Management in Power Systems

Voltage sags, or short-duration reductions in supply voltage, pose significant challenges to the reliability and economic performance of modern power networks. Their causes range from asymmetrical or symmetrical faults and transformer energisation to the dynamic behaviour of large motors and distributed generators. Unaddressed, sags can lead to equipment malfunction, industrial process interruptions and large economic losses. Management strategies encompass accurate detection and localisation of sag events, implementation of ride-through capabilities in sensitive loads and development of voltage sag emulators for controller testing. Real-time algorithms based on phasor- and instantaneous-value analysis help operators identify fault origins, while machine learning techniques enhance source-localisation precision. On the mitigation side, voltage sag generators enable laboratory validation of low-voltage ride-through (LVRT) performance for renewable generators, and optimal placement of monitoring devices ensures widespread observability. Together, these approaches form a holistic framework that supports network resilience, facilitates regulatory compliance and underpins the transition to more complex grids with high penetrations of renewable energy.

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Voltage Sag Management in Power Systems publication trend

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Technical terms

Voltage sag: A short-duration drop in RMS voltage, typically lasting 0.5–30 cycles, caused by faults or heavy load switching.

Ride-through capability: The ability of electrical equipment or generators to remain connected and operational during temporary voltage deviations.

Source localisation: The process of identifying the origin of a voltage sag event within a power network.

Phasor-based methods: Analytical techniques that use positive‐sequence voltage or current phasors to infer fault direction or magnitude.

Instantaneous-based methods: Approaches that analyse raw time-domain waveform samples for rapid detection of voltage disturbances.

Voltage sag emulator (VSG): A test apparatus that reproduces controlled voltage sag waveforms for validating equipment ride-through performance.

Convolutional neural network (CNN): A class of deep learning model that extracts hierarchical features from time-series data for pattern recognition.

References

1. Most influential feature form for supervised learning in voltage sag source localization. Engineering Applications of Artificial Intelligence (2024).
2. Cause, Classification of Voltage Sag, and Voltage Sag Emulators and Applications: A Comprehensive Overview. IEEE Access (2019).
3. A BPSO-Based Method for Optimal Voltage Sag Monitor Placement Considering Uncertainties of Transition Resistance. IEEE Access (2020).
4. Modified methods for voltage-sag source detection using transient periods. Electric Power Systems Research (2022).
5. Cost of Industrial Process Shutdowns Due to Voltage Sag and Short Interruption. Energies (2021).