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Although the wide integration of advanced metering infrastructure on distribution network facilitates the application of volt/var optimisation (VVO) in real-time circumstance, the contradiction between heavy computation load and low solution efficiency is still a big challenge, thus the system scales investigated in the literature are limited. In this study, the full AC real-time VVO is formulated based on particle swarm optimisation (PSO) framework and direct approach (DA) power flow method, where all components, such as distributed generator and on-load tap changer transformer, are formulated and integrated into the iterative DA process. Since both PSO and DA are suitable for parallel implementation, the graphics processing unit (GPU) is introduced for acceleration in order to achieve the possibility for real-time application. All the solution process is executed by GPU with the well-established data structure and thread organisation pattern, resulting in high efficiency by guaranteeing coalesced access within each warp. Case studies are conducted on four systems with sizes ranging from 136-bus to 1760-bus. Solution accuracy and convergence property are validated by the popular open source package Matpower. Based on the results from solution efficiency comparison between CPU sequential, CPU parallel, and GPU parallel programs, the promise of the proposed parallel implementation scheme for practical application is established. [ABSTRACT FROM AUTHOR]

In this paper, we use 2 iterative learning control schemes (P‐type and PI‐type) with an initial learning rule to achieve the formation control of linear fractional‐order multiagent systems. To realize the finite‐time consensus, we assume repeatable operation environments as well as a fixed but directed communication topology for the fractional‐order multiagent systems. Both P‐type and PI‐type update laws are applied to generate the control commands for each agent. It is strictly proved that all agents are driven to achieve an asymptotical consensus as the iteration number increases. Two examples are simulated to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper deals with the high-precision consensus seeking problem of multi-agent systems when they are subject to switching topologies and varying communication time-delays. By combining the iterative learning control (ILC) approach, a distributed consensus seeking algorithm is presented based on only the relative information between every agent and its local (or nearest) neighbors. All agents can be enabled to achieve consensus exactly on a common output trajectory over a finite time interval. Furthermore, conditions are proposed to guarantee both exponential convergence and monotonic convergence for the resulting ILC processes of multi-agent consensus systems. In particular, the linear matrix inequality technique is employed to formulate the established convergence conditions, which can directly give formulas for the gain matrix design. An illustrative example is included to validate the effectiveness of the proposed ILC-motivated consensus seeking algorithm. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]