A distributed active and reactive power control (DARPC) strategy based on the alternating direction method of multipliers (ADMM) is proposed for regional AC transmission system (TS) with wind farms (WFs). The proposed DARPC strategy optimizes the power distribution among the WFs to minimize the power losses of the AC TS while tracking the active power reference from the transmission system operator (TSO), and minimizes the voltage deviation of the buses inside the WF from the rated voltage as well as the power losses of the WF collection system. The optimal power flow (OPF) of the TS is relaxed by using the semidefinite programming (SDP) relaxation while the branch flow model is used to model the WF collection system. In the DARPC strategy, the large-scale strongly-coupled optimization problem is decomposed by using the ADMM, which is solved in the regional TS controller and WF controllers in parallel without loss of the global optimality. The boundary information is exchanged between the regional TS controller and WF controllers. Compared with the conventional OPF method of the TS with WFs, the optimality and accuracy of the system operation can be improved. Moreover, the proposed strategy efficiently reduces the computation burden of the TS controller and eliminates the need of a central controller. The protection of the information privacy can be enhanced. A modified IEEE 9-bus system with two WFs consisting of 64 wind turbines (WTs) is used to validate the proposed DARPC strategy.Index Terms--Alternating direction method of multipliers (ADMM), distributed active and reactive power control (DAR-PC), optimal power flow (OPF), semidefinite programming (SDP), wind farm.
Recommendation algorithms are reshaping the ecology of digital video-sharing platforms and users' media usage behaviors. TikTok's recommender system is widely considered to be an outstanding representative among them. Although a large amount of research has been conducted in relation to TikTok, most of these studies pay attention to content analysis, platform features study, user behavior examination and technical aspects of platform algorithm. However, there is markedly less research into TikTok’s recommendation algorithm as well as relevant theoretical and empirical support for this. Based on a slightly simplified variant of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines (Page et al., 2021), this paper reviews the literature on the use of recommendation algorithm in TikTok, aiming to serve as a brief primer to answer the strengths and dilemmas of the adoption of recommendation algorithm on the TikTok platform, and to propose possible directions for short-form mobile video platforms.
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