This article presents a novel synchronization technique that utilizes cellular networks to deliver microsecond-level timing signals to grid-edge devices, offering a reliable timing solution in areas where Global Navigation Satellite System is challenged, such as indoors or urban canyons. First, the primary synchronization signal (PSS) from the cellular network is periodically sampled. The frequency of the timing signal is then precisely adjusted using a proportional–integral controller, based on the position of PSS within the sampling window. This adjustment process is continued until the position of PSS aligns consistently with its designated position in the radio frame, ensuring that the timing signal is synchronized with the cellular network. A test bench is developed using the B210 model of the Universal Software Radio Peripheral platform, and extensive tests are conducted in various environments. Experimental results demonstrate that the proposed method delivers stable timing signals with a timing error below$\pm 5$$\mu$s to grid situational awareness devices in indoor scenarios.

Operational status evaluation (OSE) is essential for the health management of smart electricity meters (SEM). This article develops a machine learning-enabled OSE method for SEM. Specifically, the Gaussian process regression (GPR) is developed for modeling analysis, where an optimized automatic relevance determination (OARD) kernel is first used. Though the conventional ARD kernel structure can capture the potential mapping relationship between running time, temperature, humidity, and measurement error (ME), it cannot extract the influence degree of different stresses on the ME. To address this problem, an OARD kernel structure is proposed to identify the contribution of each part in ARD structure to the target data. Furthermore, the quartiles line instead of 95% confidence interval is exploited to enhance the performance of GPR for long-term prediction. Combining the two above improvements, a novel OSE model is established for the future-oriented long-term operation of the SEM. It is the first-known data-driven application that utilizes the GPR with OARD kernel to perform OSE for SEM. Actual SEM datasets collected from both dry and hot region are used for model validation and prediction. The results demonstrate that the proposed GPR model with OARD Matern52 kernel outperforms other conventional kernel approaches in the aspect of interpretability. More importantly, the operational status of the SEM in future can be assessed via the proposed OSE framework.

Developing efficient defensive strategies against cyber-attacks is a major concern of modern power system research. With the goal of minimizing the expected load loss, this article proposes a robust probabilistic substation-based defender-attacker-defender (DAD) model to allocate the substation's defensive resources. The proposed model aims to minimize the risk of cyber-attacks on intelligent electronic devices (IEDs). The game between the defender and the attacker concerning multiple substations, IEDs, and their connected lines is particularly modeled. In addition, we extend the proposed probabilistic DAD model to an observability-ensured DAD model where the existing phasor measurement units in the power grids are considered in the defensive resource allocation. Integrated with the logarithmic transformation and piecewise linearization techniques, a customized column-and-constraint generation algorithm is developed to solve the proposed model. Finally, the numerical results on IEEE RTS 24-bus and 118-bus systems validate the proposed model.

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.

This article presents a novel approach to physical-displacement-based power grid measuring via an intensity-modulated fiber-optic sensor (IMFOS). An IMFOS utilizes one fiber to transmit the intensity modulated light from its electro-optic controller to a fiber-optic probe. The power grid voltage and current can induce physical displacements in transducers via the piezoelectric effect and the Lorentz law, respectively, which then result in a distance change between the optical probe and the reflective surface of the transducers. In parallel, multiple fibers are used to collect the reflective light for electro-optic conversion. A National-Instruments-based characterization platform is set up for performance evaluation. The testing result demonstrates that the IMFOS is immune to the inherent dc and low-frequency saturation issues prevalent in conventional potential and current transformers. Finally, the IMFOS is implemented in a universal grid analyzer to illustrate its applicability for phasor estimation in actual power grids.

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 high voltage direct current (HVdc) intertie has been applied to provide ancillary-services for ac grids, utilizing the real-time feedback from phasor measurement units (PMUs). However, PMU data communication is vulnerable to false data injection attacks (FDIA) due to protocol defects, thus the HVdc ancillary control and system stability will be threatened. To address this issue, this article proposes a novel HVdc control strategy based on a hybrid data-driven (HDD) methodology. The HDD methodology is first proposed to detect the types and duration time of multiple frequency attacks. Specifically, the Hilbert Huang transform (HHT) is used to decompose the frequency data, using variational mode decomposition instead of the traditional empirical mode decomposition, to extract data features. Second, a multikernel support vector machine is proposed to classify the attacked data based on the designed distinctive features from HHT. Meanwhile, the attacking duration time is decided using an unsupervised technique. Third, an HDD-based HVdc ancillary control strategy is established to eliminate the effect of FDIAs on the HVdc frequency response. Comprehensive experiments of HDD-based HVdc ancillary controls under different FDIAs suggest that the proposed HDD could fast and accurately classify the FDIAs, and the HDD-based HVdc ancillary control strategy could significantly suppress the impact of the FDIAs.

The failure evaluation of electric energy metering equipment is essential for the equipment design and accurate measurement of electric energy, especially in extreme environmental stress. However, actual failure assessment is often affected by the environmental noise and insufficient interpretability. To address this problem, this article first proposes an improved k-nearest neighbor (IkNN) to identify potential outliers. In addition, an optimized distance function is used to obtain the score for each outlier. Next, a probability analysis method, namely, the weighted fusion Bayesian (WFB), is proposed to fuse multiple extreme environmental stresses and failure rate using the proposed nonlinear fusion function. Combining the WFB and the IkNN, examples from three extreme environmental regions show that the proposed evaluation framework has a higher assessment performance and less uncertainty. Compared with the classical prediction methods, our framework has profound outlier detection and failure prediction performance ever under the condition of small samples. More importantly, the parameters of this model are interpretable compared to some conventional approaches.

The increasing deployment of phasor measurement units and the advances of their reporting rates are challenging the present data centers in terms of storing and analyzing large-volume data. Under power system disturbance conditions, it is difficult to retain critical information while compressing the synchrophasor data effectively. This article combines the cross entropy and the singular value decomposition, proposing a novel model to compress the synchrophasor data to an extremely small size yet keep superior accuracy. The proposed model is extensively tested and compared with the state-of-the-art algorithms using the simulated and the FNET/GridEye field-collected data. The result indicates that the proposed algorithm has superior performance in compressing the data while retaining critical information under disturbance conditions.

Intelligent identification of multiple power quality (PQ) disturbances is very useful for pollution control of power systems. In this paper, we propose a novel detection framework for complex PQ disturbances based on multifusion convolutional neural network (MFCNN). Our contributions focus on automatic extraction and fusion of features from multiple sources. First, an information fusion structure is introduced in which the time domain and frequency domain information of the PQ disturbance signal are used as inputs. Additionally, the one-dimensional composite convolution is proposed to improve the diversity of network features based on the standard convolution and dilated convolution. Then, to speed up the training and prevent overfitting, batch normalization is used to adjust the distribution of features. Second, we use several visualization methods to resolve the internal mode of MFCNN, and demonstrate the working mechanism of the proposed method. Finally, we conduct various experiments to verify the effectiveness of the MFCNN. Compared with the handcrafted feature design methods and the general convolutional neural network models, the simulation under different noises and hardware platform-based experiments verify the effectiveness of noise immunity, higher training speed, and better accuracy of the method.

This paper estimates amplitude of a power distribution systems signal corrupted with white noise and harmonics by using the windowed symmetrical interpolation fast Fourier transform. The polynomial coefficients of amplitude estimation are derived. The influence of harmonic and the spectral interference (leakage) from image parts is analyzed. The analytical expression of the amplitude estimation variance is derived and compared with the unbiased Cramer-Rao lower bound. The proposed methods are validated through computer simulations and 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.