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A Rapid Single-Phase Blackout Detection Algorithm Based on Clarke-Park Transformations
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DOI：10.3390/electricity7010008.png)
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Abstract
En 中文
This paper presents a detection algorithm for identifying when a sinusoidal signal becomes zero, which can provide information about its amplitude. This method can be used to detect voltage interruptions in a single-phase sinusoidal waveform, which may be applied in the rapid recognition of power outages in single-phase electrical systems. The method requires the measurement of a voltage signal. Other analysis methods, like calculating the Root Mean Square (RMS), are based on window sampling and require storing a relatively larger amount of samples in the system memory; an advantage of the proposed method is that it does not require as many samples, but its main advantage is its ability to reduce the detection time compared to other approaches. Techniques like the RMS value or amplitude detection through FFT typically require one full AC cycle to change from a 100% to 0% output signal and then detect a blackout, whereas the proposed method achieves detection within only a quarter cycle without considering additional rate-of-change enhancements, which can be further applied. The algorithm treats the measured single-phase voltage as the alpha component of an alpha beta Clarke pair and generates the beta component by introducing a 90 degrees electrical delay through a delayed replica of the original signal. The resulting alpha beta signals are then transformed into the dq reference frame in which the d component is used for outage detection, as it rapidly decreases from 100% to 0% within a quarter cycle following an interruption. This rapid response makes the proposed method suitable for applications that demand minimal detection latency, such as battery backup systems. Both simulation and experimental results validate the effectiveness of the approach.
Keywords:
single-phase power interruption
rapid blackout detection
quarter-cycle detection
energy storage systems
renewable energy
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Journal
E
IF:
1.8
Papers:
92
Citations:
205
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