Appliance-level data is a prerequisite for establishing friendly two-way interactions between customers and the power company, and this data is now mainly obtained by non-intrusive load monitoring. However, as the number of loads increases, the number of possible appliances state combinations tends to grow exponentially, leading to a significant increase in the time of load identification. In order to reduce the search range of the load state combinations and shorten the algorithm response time, a non-intrusive load monitoring method based on the time-segmented state probability is proposed in this paper. Firstly, the affinity propagation (AP) clustering algorithm is introduced to obtain the power templates of the load, and then the power templates are used to count the time-segmented state probabilities. Secondly, a number of appliance state matrices are generated using the probabilities, and the optimal matrix is selected by the function as the identification result of the appliance state. Finally, the performance of the algorithm is tested on the public NILM dataset and compared to several state-of-the-art techniques. The results illustrate that the proposed method achieves an accuracy of 96% for load state identification and 89% for power decomposition of the load, and is able to meet the real-time application requirements.
Mobile social networks are dominating in our society’s daily life because of fast advancements of information technologies. To further exploit benefits from the ubiquitous service, studying the influence of information dissemination in this kind of social network becomes a necessity. This paper proposes a mobile social network influence model with regard to multiple roles. In the model, the concept of group is adopted to analyze a user’s role in different contexts. Through the introduction of role’s level and group’s relativity, information dissemination can be investigated deeply, and then, with the Floyd-Warshall algorithm, information strength matrix is constructed to study each node’s influence and under-influence indexes in the network, in addition, the comprehensive influence under multi-role view is also expressed distinctly in the fuzzy form. The result of this research will help find out preferable information disseminators as a new business strategy in e-commerce. Furthermore, it is also useful for detecting gossips and controlling its dissemination in social management.
Because of the crosstalk of neutral line signals in adjacent distribution transformers, the phase attribution relationship between users and transformer is not clear, which brings difficulties to the collection of users' electricity consumption information, file management, and power systems business development. Accordingly, this paper proposes a phase identification method for distribution network users based on dynamic phasor measurement, which can effectively identify all phase meter devices under the same station area. Firstly, considering that the signal on the power line contains complex frequency components and interference, the time‐varying phasor is approximated by the Taylor series. Secondly, by combining the discrete Fourier transform results of two adjacent data windows and calling the offline calculation matrix, accurate phasor measurements can be obtained. Phase shift processing is to obtain the phase angle of the bus line on the low‐voltage side of the transformer and the user at the same reporting time. In addition, the phase angle distortion caused by the impedance of the transmission line under different currents is also considered, so we make phase angle compensation to decrease the influence of the impedance of the line on the voltage phase angle. Simulation results and field experiments show that the proposed method can achieve a recognition success rate of 100% under frequency deviation conditions. Under the dynamic modulation and harmonic interference conditions, the recognition success rate could achieve 94.83% and 96.55%, respectively, it is much higher than that of the voltage correlation analysis method. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
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