Identification of Power Quality Disturbances in Electrical Systems - A Signal Processing Approach

Abstract

The Detection and Classification of Power Quality Disturbances (PQD) is important for quick diagnosis and mitigation disturbances. Poor power quality could have serious effects on sensitive electric devices. Consumers face difficulty to quantify the cost of failure equipment. There is a need to recognize and mitigate PQD to supply clean power to the consumer. In this Project PQDs simulated with MATLAB R2022b to be validated experimentally on a test bench using stepdown transformer

Introduction

V. VOLTAGE TRANSIENT

Fig. 10(a) and Fig. 10(b) illustrate a snapshot and voltage signal captured in the presence of sampling noise for a duration of 5 seconds, with a root-mean-square (rms) value of 18 volts. In this case, a transient occurs at 0.8 seconds by deliberately shorting a few turns of the secondary of a transformer. The complex-valued analytic signal output of the input signal is used to obtain the proposed FFT and CWT, as shown in Fig. 10(c) and Fig. 10(d), respectively.  

Sampling noise can introduce unwanted artifacts into voltage signals, making it challenging to detect and diagnose power quality disturbances. However, real-time analysis of voltage signals using FFT and CWT can provide valuable insights into the frequency and time-frequency characteristics of the signal, even in the presence of noise.  

The transient disturbance caused by the short circuit is visible in both the FFT and CWT. In the FFT, the magnitude of the spike at the fundamental frequency (50 Hz) is reduced, indicating a reduction in the signal strength during the transient. Additionally, a sharp peak is observed at the time of the transient in the CWT, highlighting the time-frequency characteristics of the signal.  It is essential to consider the impact of sampling noise on voltage signals when studying power quality disturbances. The use of advanced signal processing techniques, such as FFT and CWT, can provide valuable insights into the behaviour of voltage signals, aiding in the early detection and diagnosis of potential issues.  

Conclusion

This research paper demonstrated the role of power signal identification in detecting and analysing power quality disturbances (PQD) in a step-down transformer. An experimental test bed was created to simulate PQD events, and the acquired signals were processed using Fast Fourier Transform and Continuous Wavelet Transform based Signal Processing techniques. The results obtained from this study showed that these techniques were effective in identifying the frequency and magnitude of PQD events and providing more detailed information about their characteristics in the time-frequency domain. The study highlights the importance of power quality monitoring and maintenance in power systems. By using the techniques presented in this study, power system engineers can identify and analyse PQD events, ultimately leading to more reliable and efficient power systems. Future research can build upon these findings to develop more sophisticated techniques for power quality analysis, leading to even better power system performance.

References

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Copyright

Copyright © 2023 S. D. Mohammed Vaseem, Shaik Mahammed Yaseen, S. Karim Basha, S. Mohammed Yousuf. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.