The measurement error evaluation of power metering equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially under extreme environmental stresses. However, actual measurement error assessment is often disturbed by the environmental noise and insufficient input information. To address this problem, an improved local outlier factor (ILOF) method is first presented to detect potential outliers. And an optimized distance function and adaptive threshold constraint method based on box plot are used to improve the outlier detection performance of ILOF. Next, an error prediction method, namely kernel support vector regression (KSVR), is presented to fuse measurement error and multiple extreme environmental stresses by using the proposed kernel approach. Integrating the ILOF and KSVR, examples from the extreme environmental region demonstrate that the proposed evaluation framework has a higher assessment performance. Compared with several state-of-art prediction methods, our framework has profound outlier identification and error prediction performance under small sample conditions.

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.

The failure evaluation of Electric Energy Metering Equipment (EEME) 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 paper first proposes an improved k-Nearest Neighbor (IkNN) to identify potential outliers. And 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 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.

Accurate identification of complex power quality disturbances (PQDs) is highly important for the pollution control of modern power systems. However, the occurrence of nonlinear loads causes the grid signals distorted and unstable, resulting in the difficulty of efficient classification. This article aims to develop an effective detection method for complex PQD automatic classification. First, a modified Kaiser window-based S-Transform (MKS) is proposed for converting the PQD time-series signals to time–frequency feature matrix, where the time–frequency performance of MKS can be improved through setting different window control functions in low-frequency and high-frequency parts, respectively. Next, a multiscale parallel attention residual network (MPARN) is presented to extract and classify disturbance information based on the optimized residual structure. Integrating MKS and MPARN, an automatic classification framework is further proposed to identify various PQDs. Simulation and hardware platform experiments demonstrate that our classification strategy can obtain superior performance than several state-of-the-art methods for complex even nonlinear PQD identification under different noise levels.

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.

RF-stress effects on MOS power-amplifier performance are studied. The class-E power-amplifier power efficiency as a function of breakdown (BD) location has been evaluated. The power efficiency degrades with gate-oxide RF stress due to increased gate leakage current and reduced output voltage. The degradation is enhanced when the BD location is closer to the drain side than the source side.