Improved S-Transform for Time-Frequency Analysis for Power Quality Disturbances

Renewable energy sources will be more vigorously deployed under the global trend of carbon emission reduction. The connection of numerous renewable energy sources poses an increasingly critical challenge to power quality (PQ) issues in power systems. Time-frequency analysis (TFA) is a foundational technique for real-time monitoring and disturbance detection for power signals. This paper develops an improved S-transform (IST) to accurately detect the disturbances such as oscillatory transient, time-varying harmonics and interharmonics, flicker, swell, sag, interrupt, phase jump, and frequency variation. The proposed IST features the exploration of a designed Gaussian window as the kernel function, whose shape and frequency spectrum can be controlled using a standard deviation based detection frequency parameter. This ensures that the detection requirements at different detection frequencies can be easily met. The IST can be realized by fast Fourier transform (FFT) and its inverse, which ensures that it can be implemented quickly. The IST can accurately detect the amplitude and phase information of fundamental signal, which is beneficial to determine the start and end time, and the intensity of disturbance. With the increase of detection frequency, IST also has excellent energy concentration performance. Simulation and experimental results validated the effectiveness and feasibility of the proposed method.

Automated summary

With the global trend of carbon emission reduction, renewable energy sources will be deployed more vigorously. However, the connection of numerous renewable energy sources poses an increasingly critical challenge to power quality (PQ) issues. This paper proposes an improved S-transform (IST) method that can accurately detect various disturbances. The method can be implemented quickly using fast Fourier transform (FFT), and it has excellent energy concentration performance at different detection frequencies.