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Aiming at the problems of energy consumption optimization of park integrated energy source load storage system in power grid dispatching, a modeling method of park integrated energy consumption optimization dispatching considering user portrait is proposed. Firstly, an improved k-means portrait method is proposed for park users; Secondly, on the premise of considering the constraints of equipment operation in the park, cluster users in the park, and establish an energy consumption optimization scheduling model with the objective function of user satisfaction, net income of wind power consumption and equipment operation cost; Finally, SAPSO algorithm and fuzzy theory are used for decision-making. The example simulation shows that the proposed model is correct and effective, which is more precise and objective than the traditional model.

This study proposes a short-term load prediction method of a bidirectional long short-term memory network based on feature mining of the power consumption big data in combination with the attention mechanism (AT) of Bayesian optimization to address the problems that a considerable amount of feature factors exist and the feature relationship is obscured in the historical power consumption big data. The method comprehensively considers the global features of the power consumption data in space and the local features in time. First, the Cen-CK-means clustering method is used to cluster the electricity consumption data of users, and the statistical, combination, and time category characteristics are extracted according to the meteorological factors related to load over multiple time scales. Second, the Bayesian and bidirectional long and short memory networks are combined to extract the temporal and spatial characteristics of the load data itself. Meanwhile, the AT is introduced to automatically assign the corresponding weights to the hidden layer state of the bidirectional long and short memory. This task is carried out to distinguish the importance of the different time load series, which can effectively reduce the loss of historical information and highlight information about key historical time points. Finally, taking the first type of load as an example, compared with the SVP, RBPNN, BiLSTM, and BO-BiLSTM algorithms, the MAPE index is reduced by 1.05%, 1.75%, 0.52%, and 0.26%, respectively. RMSE decreased by 186.61, 154.93, 91.88, and 15.76 MW, respectively, while R2 increased by 0.04, 0.07, 0.03, and 0.03, respectively. In the one-week forecast time, MAPE index decreased by 1.97%, 2.44%, 1.21%, and 0.6%, respectively; RMSE decreased by 271.18, 305.7, 183.13, and 97.91 MW, respectively; and R2 increased by 0.12, 0.08, 0.04, and 0.03, respectively. [ABSTRACT FROM AUTHOR]

Smart Substation becomes more vulnerable to cyber attacks due to the high integration of information technologies, so it is essential to detect intrusion behaviour by abnormal traffic analysis in smart substations. Although there have been many detection methods for abnormal traffic, the existing ones all focus on the format check of a single field of the industrial transmission protocol, and ignore the deep coupling relationships among multiple protocol fields, which lead to more or less false detections and missed detections. To overcome this problem and further improve the detection accuracy, in this paper, we propose an abnormal traffic detection method based on the coupling field extraction and the density-based spatial clustering of applications with noise (DBSCAN). By using correlation analysis to extract the coupling fields of the protocol fields and using DBSCAN to remove the noise in the coupling fields, the deep coupling relationship between the coupling fields can be mined by the piecewise linear function fitting method, and used to detect abnormal traffic. The simulation results on 10,000 frames traffic prove that the proposed detection method can effectively identify the abnormal traffic.

The dynamic line rating (DLR) is recognized as an effective method to improve the integration of renewable energies, such as wind power by enhancing the flexibility of the system. The principle of DLR is to increase the transmission capacity of a transmission line according to the forecasted weather data. However, the uncertainties of DLR and wind power caused by the forecasting errors might bring in some severe consequences to the power system, such as line overloads. In this article, a tri-level preventive dispatch model is proposed to deal with this issue. To reduce the conservativeness of the solution, the density-based spatial clustering of application with noise method is employed to identify and remove the extreme scenarios of DLR. And a decomposition method is proposed to solve the tri-level model. In particular, a fast solution method by identifying the high-risk lines is also presented to obtain the optimal solution quickly. The numerical simulations on several IEEE bus systems demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Background/aims: Percutaneous transluminal angioplasty (PTA) is a significant intervention to deal with occlusion and stenosis of vascular access. The study aimed to explore the risk factors of repeated PTA (re-PTA) after the initial intervention in patients undergoing hemodialysis., Methods: This retrospective study included 180 patients who underwent successful PTA for the first time between March 2016 and December 2020. Information on demographic, clinical, anatomical, and medication variables was collected. Survival curves were plotted using Kaplan-Meier analysis and the risk factors associated with re-PTA were analyzed using univariate and multivariate Cox proportional hazards analyses., Results: The primary clinical patency rates at 6, 12, and 24 months after PTA were found to be 85.2%, 70.7%, and 58.6%, respectively. The univariate Cox proportion hazards analysis revealed the association of non-antiplatelet agents (HR 2.368 95% CI 1.351 to 4.150, p = .003) and arteriovenous graft (AVG) (HR 2.096 95% CI 1.147 to 3.831, p = .016) with re-PTA. However, only non-antiplatelet therapy showed statistical significance (HR 2.368 95% CI 1.351 to 4.150, p = .003) in multivariate Cox proportional hazards analysis., Conclusions: Among the patients undergoing hemodialysis, the use of antiplatelet agents was associated with a lower risk of re-PTA. Therefore, the use of antiplatelet drugs may reduce the rates of re-PTA and help in maintaining the patency of vascular access., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.