Sinusoidal oscillators, as excitation sources for impedance measurements, require high signal quality for accurate measurements. However, traditional amplitude control methods, such as automatic gain control (AGC) or Zener clipping amplitude control, introduce either system complexity or additional total harmonic distortion (THD). In this paper, a sinusoidal oscillator topology based on multiple feedback band-pass filter is proposed, which employs antiparallel diode pairs that exploit diodes’ forward characteristics to efficiently generate low-THD, amplitude-tunable sinusoidal signals at low amplitudes. The proposed topology is capable of generating low-distortion sinusoidal signals with specified amplitudes across a frequency range of 10Hz to 1MHz. Within the range of 1kHz to 1MHz, the THD is less than 0.0900%, and below 1kHz, the THD is less than 0.1694%. Methods for generating low-distortion sinusoidal signals are also analyzed. In complex impedance demodulation applications, compared to DDS solution, the improved demodulation accuracy of the proposed topology highlights the indispensable role of high-precision single-frequency excitation sources.

With the large-scale integration of renewable energy sources and widespread application of high-frequency power electronic devices, the issue of supraharmonics in power systems has become increasingly prominent, which imposes a significant challenge to power quality. To mitigate the impact of spectral leakage and the picket-fence effect on the accuracy of supraharmonic disturbance parameter measurements, and reduce the sampling pressure of supraharmonic signal, this article proposes a supraharmonic measurement method based on windowed compressed sensing (CS) and the orthogonal matching pursuit (OMP) algorithm. The four-term third-order Nuttall window function is integrated into CS technology, where a windowed sparse measurement matrix is constructed to enable windowed compressed sampling of supraharmonic signals. Subsequently, the OMP algorithm is used for frequency estimation of the supraharmonic signals, and the three-spectral-line interpolation technique is applied to reduce the picket-fence effect, improving the accuracy of supraharmonic disturbance frequency, amplitude, and phase measurements. Simulation experiments demonstrate that the proposed algorithm significantly addresses the spectral leakage and the picket-fence effect, effectively reduces the data required for supraharmonic detection, and improves the measurement accuracy while improving noise resistance. In practical experiments, the absolute errors in frequency and amplitude measurement for three sets of supraharmonic components are 0.55, 0.36, and 1.09 Hz and 0.0006, 0.0008, and 0.001 V, respectively, validating the effectiveness and practicality of the proposed method.

The extraction and filtering of electric network frequency (ENF) is significant for verifying the authenticity of digital audio. However, there are many challenges in accurately extracting ENF from digital audio, which makes it difficult to establish an effective matching relationship with the database. To address this problem, an improved matrix pencil (IMP) method is presented to extract ENF signals for phase measuring units. The power grid signal is constructed into a Hankel matrix, which is decomposed into singular values and filtered out the harmonics of the power grid using an adaptive order determination method. By embedding ENF as a watermark into digital audio through encryption technology, a quadratic box plot (QBP) is proposed to detect potential outliers caused by the bit error rate. Next, the empirical wavelet transform (EWT) is used to filter out Gaussian white noise between different power equipment to improve the similarity of database matching. Integrating the IMP and QBP-EWT, examples from the dataset demonstrate that the proposed ENF extraction and filtering framework has a higher assessment performance. Compared with several commonly used methods, our framework has profound outlier identification ability and effectively improves the accuracy of database matching.

Intelligent classification of multiple power quality disturbances (PQDs) is a top priority in pollution control of the power grid. However, the large-scale application of renewable energy introduces lots of nonlinear and impact loads, which makes the PQDs more complex and challenges the effectiveness of conventional detection frameworks. In this article, a novel framework based on optimized Kaiser-window-based$S$-transform (OKST) and multiple fusion convolutional neural network (MFCNN) is proposed to identify multiple complex PQDs. First, the OKST is used for the time–frequency positioning of PQDs, where an improved control function is proposed to meet different detection requirements of time–frequency. Additionally, the parameters of the control function are adjusted automatically using maximum energy concentration. Then, the MFCNN based on residual networks (ResNets) is further proposed to extract and classify these time–frequency features automatically. In MFCNN, feature information is fused using different convolution kernels at a two-dimensional level, which can effectively reduce information loss and improve classification performance. The network model is set up using the Pytorch platform, and the dataset containing 28 types of PQDs and 2 types of nonlinearly mixed PQDs is built to test our framework. The result shows that the proposed OKST-MFCNN obtains an average accuracy of 99.38% under the 20-dB noise level, which is more accurate and robust than some advanced PQDs detection frameworks. Moreover, the accuracy of 97.94% is achieved with satisfactory real-time performance in hardware platform experiments, proving its superior identification performance for complex PQDs.

Accurate assessment of the operation state for smart electricity meters (SEMs) is crucial for electrical metering and service. Nevertheless, the real operation state estimation often ignores the impacts of multiple environmental stresses. In this article, a novel model based on multiple fusion support vector regression (MFSVR) and improved simulated annealing (ISA) is proposed to assess the operation state of SEMs under multiple environmental stresses. First, the MFSVR is used to integrate different input information, in which different types of kernel functions are weighted for emphasizing different feature attributes including time, temperature, and humidity. Then, the ISA is further presented to optimize the model parameters in MFSVR, which can contribute to improving the assessment accuracy. In ISA, the adaptive temperature control strategy and modified Metropolis-based criteria are carried out to enhance the parameter search efficiency. The MFSVR model is set up using the libSVM platform in MATLAB, and the dataset of SEMs is collected from the actual high-cold region in China to test our model. Extensive experiments are conducted for verification analysis from the aspects of accuracy, robustness, and sensitivity. The result shows that the proposed model has the lowest average RMSE of 3.40$\times\; 10^{-2}$and MAE of 2.14$\times\; 10^{-2}$, respectively. Besides, the proposed MFSVR-ISA obtains an average R$^{2}$of 97.8% under the 20 dB noise level, which is more accurate and robust than some popular data-driven prediction methods.

Electrical impedance spectroscopy (EIS) is a noninvasive, inexpensive, and rapid detection technique that is widely used for industrial troubleshooting and medical disease diagnosis. Accurate impedance demodulation is essential to obtain reliable EIS information. This article proposes a general and accurate impedance demodulator based on the improved discrete Fourier transform (DFT), which overcomes the inherent drawbacks of the traditional DFT-based demodulator operating under noninteger period sampling condition. First, the sampled sequence is truncated by the user-selected window function. Second, the discrete spectral line with the largest amplitude is found and its spectral value is calculated by the DFT operation. Finally, a general formula for calculating impedance that is independent of sampling conditions is derived. The effects of the proposed method on the impedance demodulation accuracy with different sampling lengths, window functions, analog-to-digital converter quantization bits, and signal-to-noise ratios are investigated by simulations. Moreover, several passive two-resistors and one-capacitor circuits and fresh ex-vivo biological tissues, including carrot, pumpkin, and beef, are measured using a custom-built EIS meter based on the NI-PXI (PCI eXtensions for Instrumentation) platform. Simulation and experimental results demonstrate that the proposed method can not only achieve accurate impedance demodulation at arbitrary measurement frequencies but also has excellent noise immunity. Importantly, this method has the potential to be applied directly in numerous industrial and medical applications.

In recent years, owing to the penetration of renewable energy and the widespread use of power electronic equipment, power quality disturbances (PQDs) have become more complex and hazardous. As the premise of power quality control, complex PQDs require more accurate and efficient detection. To address this issue, this article proposes a novel automatic method for detecting complex PQDs based on integrated intrinsic variable time-scale decomposition (I-IVTD) and weighted recurrent layer aggregation (WRLA) network. The proposed I-IVTD method reduces aliasing and endpoint effects and improves antinoise performance by innovative use of variable time scales and multiple integrations. The improved WRLA network enhances learning ability and accelerates convergence by adding three weights to each unit. The proposed framework can effectively detect 27 complex disturbances automatically and does not require manual feature design. Finally, a large number of experiments are conducted, including simulation experiments and tests on a PQD analysis platform. The test results based on the analysis platform indicate that the accuracy for complex disturbances is higher than 98%, which demonstrates the superior performance of the proposed framework. Notably, it is effective for detecting nonlinear disturbances as well.

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.

The measurement error assessment for smart electricity meters consists of the measurement error prediction and the stress factors evaluation, which can be used for improving equipment quality and saving power grid costs, especially under extreme natural environmental stresses. However, actual measurement error assessment suffers from the environmental noise and insufficient feature information. To tackle this problem, in this article, an optimized kernel density estimation (OKDE) is first proposed to identify potential outliers, where a modified distance function and adaptive kernel bandwidth are used to obtain the outlier score. Next, a measurement error assessment method, namely the modified double-kernel support vector regression (MKSVR), is proposed to fuse measurement error and multiple stress features using the modified double-kernel function. Combining the OKDE and MKSVR, actual dataset from the high dry heat region shows that the proposed assessment framework has better evaluation performance. Compared with some classical prediction methods, the OKDE–MKSVR framework has profound outlier detection and measurement error assessment performance under the small sample conditions.

Harmonic estimation plays an important part in harmonic suppression of power system. Due to the normal fluctuation of power frequency, it is difficult to realize synchronous sampling. Consequently, the unavoidable productions, i.e., spectral leakage and picket fence effect, will affect the accuracy of harmonic analysis significantly when using the Fourier transform. To overcome this problem, a novel algorithm for power system harmonic estimation called frequency shifting and filtering (FSF) algorithm is proposed in this paper. A reference signal is at first generated to shift the frequency of the sampled signal. Then the iterative averaging filter is adopted to eliminate the spectral interferences. Finally, accurate estimation of harmonics can be achieved because only interested components are retained. Furthermore, a simplified FSF with much less computational burden is presented by using an equivalent weighting filter. The salient features of the proposed algorithm are validated by the simulations and practical experiments.

The S transform is widely used in time‐frequency analysis (TFA). However, the standard S transform suffers from the poor energy concentration in time‐frequency distribution (TFD). In this study, an adaptive Dolph–Chebyshev window instead of the Gaussian window‐based S transform and its fast realisation strategy are introduced for non‐stationary signal TFA. By controlling the shape of the Dolph–Chebyshev window adaptively to signal, the new TFA method is able to achieve a high energy concentration in TFD. In addition, the new method is superior to other classical methods for instantaneous frequency estimation. Several examples are presented to illustrate its behaviour on different signals and demonstrate its validity.

The method of calculating voltage unbalance factor (VUF) recommended by the IEC61000-4-27 has the disadvantage of requiring square root operation. This paper focuses on the fast and accurate calculation of VUF of three-phase power system. The approximate algorithm for calculating the magnitude of the zero, positive, and negative sequences by simple algebraic equation is presented. In the proposed method, the square root operation is first transformed to simple trigonometric equations based on the geometric figure, and then the trigonometric equations are approximated by the algebraic operations, which thus reduced the computation burden sufficiently. The simulation and practical experiment results show that the proposed method can achieve the same level of precision as the method recommended by the IEC61000-4-27, while much less clock cycles are required, which indicates its wide application in digital signal processor (DSP).