Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the power grid and enhancing the energy efficiency of users. NILM mainly includes data collection and preprocessing, event detection, feature extraction, and appliance identification. One of the most critical steps in NILM is signature extraction, which is the basis for all algorithms to achieve good state detection and energy disaggregation. With the generalization of machine learning algorithms, different algorithms have also been used to extract unique signatures of appliances. Recently, the development and deployment of the voltage–current (V-I) trajectory signatures applied for appliance identification motivated us to present a comprehensive review in this domain. The V-I trajectory signatures have the potential to be an intermediate domain between computer vision and NILM. By identifying the V-I trajectory, we can detect the operating state of the appliance. We also summarize existing papers based on V-I trajectories and look forward to future research directions that help to promote the field’s development.

Federated learning (FL) is a machine-learning framework that effectively addresses privacy concerns. It harnesses fragmented data from devices across the globe for model training and optimization while strictly adhering to user privacy protection and regulatory compliance. This framework holds immense potential for widespread applications in the smart-grid domain. Through FL, power companies can collaborate to train smart-grid models without revealing users’ electricity consumption data, thus safeguarding their privacy. However, the data collected by clients often exhibits heterogeneity, which can lead to biases towards certain data features during the model-training process, therefore affecting the fairness and performance of the model. To tackle the fairness challenges that emerge during the federated-learning process in smart grids, this paper introduces FedCSGP, a novel federated-learning approach that incorporates client sampling and gradient projection. The main idea of FedCSGP is to categorize the causes of unfairness in federated learning into two parts: internal conflicts and external conflicts. Among them, the client-sampling strategy is used to resolve external conflicts, while the gradient-projection strategy is employed to address internal conflicts. By tackling both aspects, FederCSGP aims to enhance the fairness of the federated-learning model while ensuring the accuracy of the global model. The experimental results demonstrate that the proposed method significantly improves the accuracy of poorly performing clients in smart-grid scenarios with lower communication costs, therefore enhancing the fairness of the federated-learning algorithm.

With the advancement of source-load interaction in the new power systems, data-driven approaches have provided a foundational support for aggregating and interacting between sources and loads. However, with the widespread integration of distributed energy resources, fine-grained perception of intelligent sensing devices, and the inherent stochasticity of source-load dynamics, a massive amount of raw data is being recorded and accumulated in the data center. Valuable information is often dispersed across different paragraphs of the raw data, making it challenging to extract effectively. Distribution substation inspection plays a crucial role in ensuring the safe operation of the power system. Traditional methods for inspection report text classification typically rely on manual judgment and accumulated experience, resulting in low efficiency and a significant misjudgment rate. Therefore, this paper proposes a text classification method for inspection reports based on the pre-trained BERT-TextRCNN model. By utilizing the dense connection between the BERT embedding layer and the neural network, the proposed method improves the accuracy of matching long texts. This article collected 2,831 maintenance data for the first quarter of 2023 from the distribution room, including approximately 58 environmental testing data, 738 environmental box testing data, approximately 672 distribution room testing data, and approximately 1,363 box type substation testing data. A text corpus was constructed for experiments. Experimental results demonstrate that the proposed model automatically classifies a large volume of manually recorded inspection report data based on time, location, and faults, achieving a classification accuracy of 94.7%, precision of 92%, recall of 92%, and F1 score of 90.3%.

The placement of micro phasor measurement units in an active distribution network can improve the monitoring performance of the system. However, the price and placement cost of micro phasor measurement units are high, so existing research mostly focuses on solving the problem of achieving global observability with the minimum number of micro phasor measurement units. However, the problems of frequent topology change in the distribution network and the increase in the number of faults that will be caused by the high proportion of distributed energy resources connected to the system may lead to the above placement scheme losing the observability of the system. The balance of maintaining the number of micro phasor measurement units placed while ensuring the monitoring stability of the system remains a crucial challenge. To address this issue, a micro phasor measurement unit placement method that considers the observability of multiple monitoring devices and node importance is proposed in this paper, which, on the one hand, fully considers the impact of the observability of existing monitoring devices on the micro phasor measurement units placement scheme and reduces the number of micro phasor measurement units that are used to achieve global observability, and, on the other hand, gives priority to placing the micro phasor measurement units at important nodes in the distribution network that are closely related to observation stability, improving the observation performance of the system. The superiority of the proposed μPMU method is verified in standard systems such as IEEE-33, IEEE-34, and P&G69, in which the number of required μPMUs is reduced by 20%, and the observation performance of the system is improved by 30% in special cases.

SE is critical in ADNs integrating renewable DGs. Traditional SE methods face the challenges of increasing SE errors and decreased robustness due to the adverse impact of bad data in measurements and topology parameters. To address these issues, this paper proposes a robust SE method that considers bad data in measurements and topology parameters. First, a bad measurement data processing model is proposed to improve measurement and SE accuracy by generating high-precision pseudo-measurements through adaptive learning from historical data sequences to replace the bad measurement data in measurements. Second, a robust SE model combining network estimation and linear estimation is proposed, which enhances SE accuracy and robustness under bad data generated in measurements and topology parameters in ADNs. In a simulation, the proposed method’s effectiveness is verified on the modified IEEE 33-node system.

Beidou-3 (BD3) uses the pseudo range transmission method to calculate the clock difference through the pseudo range. However, due to the influence of electromagnetic interference, the clock difference measurement error is too large, which affects the timing accuracy of the power system. This paper first introduces the principle of Beidou timing service; Secondly, it introduces the clock error calculation method; Finally, combined with the clock difference calculation method, this paper presents an anti-interference clock difference correction method based on Least Mean Square (LMS) filter. According to the principle of LMS filter, the influence of electromagnetic interference on pseudo distance can be eliminated, and the clock difference can be calculated. Then the clock difference can be corrected. The simulation results show that the method is feasible and effective.