Real-time inertia monitoring is essential for maintaining stable power system operations, particularly in grids with high levels of renewable energy penetration. Existing inertia estimation methods often overlook the contributions of loads or inverter-based resources synthetic inertia. They also do not verify individual inertia values through response measurements. To address this challenge, a real-time, low-cost, and accurate inertia monitoring and estimation technology is developed to quantify the effective inertia for the actual grid response. First, the mechanism of the synchrophasor response during pumped storage hydro (PSH) is analyzed. Then, a comprehensive synchrophasor traces-based trigger is designed to detect the operation from PSH plants, utilizing its instantaneous step change in active power at the plant’s connection point during the pump turn-offoperation. Next, an improved adaptive-window prior-event rate of change of frequency tolerant inertia calculation method is proposed, with the advantage of real-time, noise immunity, and high accuracy. Furthermore, an inertia monitoring system based on PSH operation traces is established, integrating measurements from frequency disturbance recorders. By partnering with NERC, Dominion Energy, and Tennessee Valley Authority, this initiative addresses one of the primary challenges of operating a high-penetration renewable, thereby paving the way for a carbon-free power sector with significantly improved operational performance in the U.S.

The measurement accuracy of electric energy meters (EEMs) is crucial for respecting the fairness of the electricity market and the justice of electric energy settlement. However, the basic error (BE) of the measurement suffers from external stress, especially under extreme environments. It is challenging to evaluate the degradation trend of EEMs under multi-stress. To address this issue, this paper proposes modeling and evaluating methods for precise degradation analysis of EEMs under multi-stress. First, an optimized k-nearest neighbor (OKNN) is proposed to correct the outliers, where the stress-related weight and weighting factor are designed. Then, a varied-bias wiener process with hierarchical Bayesian (VWHB) model is introduced to evaluate and explore the impact of stress on the BE. The multi-stress, as well as the BE, are fused to parameterize this impact. Integrating the OKNN and VWHB, a degradation analysis framework is further proposed to model the data and conduct the degradation analysis. Extensive experiments on field data collected from the typical operating environment lab demonstrated that the proposed degradation analysis framework reveals superior performance than some state-of-art approaches. The root of the mean of the square of errors and the mean of absolute value of errors of OKNN-VWHB are the lowest with 0.0356 and 0.0281, respectively, for all the EEMs from three different companies.

The increasing integration of renewable energy and electronic power devices into power systems is expanding the data volume and seriously challenging data interaction. The improvement of data reliability contributes to ensuring power supply reliability through the accurate assessment of the power grid's status and the correct response of equipment. To quantify the impact of data reliability on power supply reliability, a data-driven analysis method is proposed. First, based on low-voltage telemetry data, a set of evaluation indicators for data reliability and high-quality power supply reliability is proposed. Then, a reliability scoring method considering combined weight is developed based on the improved technique for order preference by similarity to an ideal solution and grey relational analysis. Additionally, the analysis method based on convolutional neural networks and support vector machines nonlinear fitting is proposed to indicate the impact of data on power supply reliability and find a reasonable range of data reliability indicators. Finally, the effectiveness of the proposed method is verified by the experiments based on actual distribution network data from the eastern province of China.

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.

False data injection attacks (FDIAs) represent a significant threat to power grid cybersecurity, designed to manipulate crucial measurement data and thereby compromise the operation of power grids. This article proposes an ac FDIA method based on tensor principle component analysis (TPCA), requiring no prior knowledge of system parameters. The goal of the proposed approach is to produce false data that can break through the bad data detection (BDD) of realistic ac state estimation. Specifically, ac state estimation model is transformed into a tensor representation, encapsulating measurement variables, state variables, and system parameters as a combination of multiple tensor products. Following this, by formulating multiple measurement data into a tensor, TPCA is used to decompose the measurement data tensor to obtain a space of matrices. Subsequently, the vector of false data ensuring the stealthiness is produced by finding a rank-1 approximation of matrices in this space. Notably, the proposed method distinguishes itself from existing parameter-free FDIA methods by eschewing any simplification or approximation of ac state estimation model. Numerous cases in IEEE 5, 14, 57, 118, 300-bus, European 1354-bus, and Polish 3120-bus testing systems provide substantial evidence that the proposed approach can obtain higher attack successful rate. It achieves 99.3% attack successful rate on average against the common$\chi ^{2}$BDD with 0.9 confidence level. And compared with existing methods, the attack successful rate improves 4%, 2%, 13%, 11%, 10%, and 27% in these six systems, respectively.

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 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.

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.