The accurate detection and classification of power quality (PQ) disturbances in power systems is a key step to determine the causes of these events before any proper countermeasure could be taken. This paper presents a new algorithm for detection and classification of PQ disturbances based on the combination of double-resolution S-transform (DRST) and directed acyclic graph support vector machines (DAG-SVMs). The proposed method first employs DRST for an effective feature extraction from power signals. Then, the DAG-SVMs are used to predict the classes of PQ disturbances. The DRST not only has better time-frequency localization and stronger robustness but also reduces the computational complexity without losing the useful information of the original signal in comparison with the traditional S-transform. Through the combined use of DRST and DAG-SVMs, the algorithm can be easily implemented in embedded real-time applications. Finally, the implementation of the proposed algorithm in a digital signal processor + advanced reduced instruction set computing machine-based hardware test platform is introduced. The effectiveness of the proposed method is demonstrated by means of computer simulations and practical experiments with single and combined PQ disturbances.

Electrocardiogram (ECG) acquisition is easily contaminated by interferences, and denoising is the most important task in ECG detection. Variational mode decomposition (VMD) algorithm is widely used in ECG denoising, which can overcome mode aliasing between intrinsic mode function components existed in the traditional empirical mode decomposition (EMD) algorithm, but the mode decomposition number K and penalty factor α in VMD must be optimized to obtain the best signal decomposition accuracy. This paper proposes an improved VMD denoising algorithm that overcomes the shortcomings of slow parameter selection and poor generalization in the traditional VMD algorithm. The algorithm presented firstly adopts EMD algorithm to remove the low-frequency baseline drift noise, and then employs the adaptive particle swarm optimization algorithm to optimize the parameter pair ( K , α) for VMD. To validate the denoising performance of the improved VMD algorithm, the No.103 record from the MIT arrhythmia database is first selected as the pure ECG signal, then both 20dB Gaussian white noises and 0.3 Hz baseline drift are added to simulate the noisy ECG signal. Second, the ECG signals of nine subjects are collected by a customized ECG detection platform based on AD8232 and ADALM1000. The ECG denoising results in simulation and actual experiments show that the improved VMD algorithm achieves the highest signal-to-noise ratio, correlation coefficient and minimum mean square error compared with the traditional EMD and VMD algorithm, which demonstrates the proposed denoising algorithm has stronger denoising ability and better retains morphological characteristics of the original ECG signals.

The accurate time-frequency (TF) positioning of power quality (PQ) disturbances is the basis of dealing with PQ problems in power systems. To accurately detect PQ disturbances, this article proposes a Kaiser window-based S-transform (KST) that provides better time resolution at fundamental frequency to detect the amplitude information for voltage swell, sag, interrupt, flicker, and better frequency resolution at higher frequencies to detect the frequency of time-varying harmonics and oscillatory transient. Based on short-time Fourier transform and S-transform, KST uses a Kaiser window with the characteristic of inherent optimal energy concentration as the kernel function. The Kaiser window can be adjusted adaptively according to the detection demand of PQ disturbances by the designed control function. This allows KST to easily accommodate different detection requirements at different frequencies. The utilization of Fourier transform ensures that KST can be realized quickly. The complex TF matrix is generated after a signal is transformed by KST, where the column vector is expressed as the distribution of amplitude and phase with time at a certain frequency, and the row vector represents the distribution of amplitude and phase with frequency at a certain sampling time. Experimental results demonstrate that the proposed KST significantly outperforms the state-of-the-art techniques in TF analysis of PQ signals, especially for the energy concentration and the detection of fundamental wave.

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.