For most capacitive power transfer applications, a reliable signal transfer link between the primary side and the secondary side plays an essential role. The parameters design of the system has a key effect on the ope...
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For most capacitive power transfer applications, a reliable signal transfer link between the primary side and the secondary side plays an essential role. The parameters design of the system has a key effect on the operating performance, however, the existing parameter design method can only get an acceptable solution, instead of a globally optimal solution. This study has proposed a general and global optimal parameter design method based on the constraint multi-objective algorithm. The mathematical models of the power and signal channels as long as the power noise is established and all the objective functions are given according to the models. Besides, the constraints considering the reasonable region of the parameter values and the limitation of the voltage and current on the elements are given. Then the multi-objective optimisation problem with the objective functions and constraints are solved by the non-dominated sorting genetic algorithm II, and the proper global optimal parameters are selected from the Pareto set. Finally, a simulation model and an experimental setup with 180 W power and 200 kbps signal transfer capability are constructed, and the results verified the effectiveness and correctness of this method.
A new multi-objective optimisationalgorithm called non-dominated sorting and local search (NSLS) algorithm is proposed for uniformly excited aperiodic array synthesis here. Two design cases of uniformly excited linea...
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A new multi-objective optimisationalgorithm called non-dominated sorting and local search (NSLS) algorithm is proposed for uniformly excited aperiodic array synthesis here. Two design cases of uniformly excited linear and planar array synthesis are conducted to verify the outperformance of NSLS. Synthesis results indicate that NSLS is able to obtain the lowest maximum sidelobe level (MSLL) and the deepest null depth level in linear array design case, along with the lowest MSLLs in two planes of radiation pattern in planar array design case compared with the other latest algorithms. In addition, the convergence performance and computational costs of NSLS in the design cases are investigated. The synthesis results combined with excellent convergence performance and low computational costs of NSLS make it a reliable and promising optimisationalgorithm for uniformly excited aperiodic array synthesis.
Circuit clustering algorithms fit synthesised circuits into field programmable gate array (FPGA) configurable logic blocks (CLBs) efficiently. This fundamental process in FPGA CAD flow directly impacts both effort req...
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Circuit clustering algorithms fit synthesised circuits into field programmable gate array (FPGA) configurable logic blocks (CLBs) efficiently. This fundamental process in FPGA CAD flow directly impacts both effort required and performance achievable in subsequent place-and-route processes. Circuit clustering is limited by hardware constraints of specific target architectures. Hence, better circuit clustering approaches are essential for improving device utilisation whilst at the same time optimising circuit performance parameters such as, e.g. power and delay. In this study, the authors present a method based on multi-objective genetic algorithm (MOGA) to facilitate circuit clustering. They address a number of challenges including CLB input bandwidth constraints, improvement of CLB utilisation, minimisation of interconnects between CLBs. The authors' new approach has been validated using the 'Golden 20' MCNC benchmark circuits that are regularly used in FPGA-related literature. The results show that the method proposed in this study achieves improvements of up to 50% in clustering, routability and timing when compared to state-of-the-art approaches including VPack, T-VPack, RPack, DPack, HDPack, MOPack and iRAC. The key contribution of this work is a flexible EDA flow that can incorporate numerous objectives required to successfully tackle real-world circuit design on FPGA, providing device utilisation at increased design performance.
In this study, using the concept of setting groups (SGs), an adaptive protection scheme is proposed to increase the reliability of the system. Connection and disconnection of switches and distributed generators result...
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In this study, using the concept of setting groups (SGs), an adaptive protection scheme is proposed to increase the reliability of the system. Connection and disconnection of switches and distributed generators result in various scenarios for network topology changes. A hybrid genetic algorithm (GA) and linear programming (LP) method is utilised to solve the problem, where the GA, in a near-optimal manner, classifies the scenarios of the network topology changes into a limited number of SGs and the LP algorithm optimally coordinates the overcurrent relays within the SGs. Simulations are performed on a radial distribution network and a meshed distribution network. Although by increasing the number of SGs the average operating time of the relays is decreased, the number of changes in the relay settings is increased. Therefore, the multi-objective optimisationalgorithm is used to determine, the desired number of SGs. The results show the efficiency of the proposed adaptive protection scheme.
Reliability has become a key design aspect in modern energy system's planning. Owing to the higher fault rate in power distribution systems (PDSs), comparing with generation and transmission systems, considering r...
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Reliability has become a key design aspect in modern energy system's planning. Owing to the higher fault rate in power distribution systems (PDSs), comparing with generation and transmission systems, considering reliability in PDSs' planning is very crucial. This study presents a novel robust approach to cluster the existing PDSs with intermittent distributed generators (DGs) into a set of reliable microgrids (MGs). For this purpose, first, a new reliability index is defined to evaluate the reliability of MGs in terms of real and reactive power adequacy as well as frequency and duration of interruptions. Then, the k-means algorithm, based on weighted graph partitioning method, is proposed for changing the system into a multi-MG system. Furthermore, a modified version of particle swarm optimisation approach is proposed and the Silhouette technique is used to determine the optimal location and sizes of DGs as well as the number of MGs. The design and sensitivity analysis performed by the proposed multi-objective optimisationalgorithm on the well known IEEE 69-bus distribution system show the effectiveness and robustness of the proposed algorithms for constructing reliable MGs in modern PDSs.
Here, the multi-objective optimisationalgorithm speed-constrained multi-objective particle swarm optimisation is applied to the synthesis of uniformly excited sparse-rectangular planar array with low sidelobe level. ...
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Here, the multi-objective optimisationalgorithm speed-constrained multi-objective particle swarm optimisation is applied to the synthesis of uniformly excited sparse-rectangular planar array with low sidelobe level. Numerical simulations have been conducted to test the performance of multi-objective optimisationalgorithm when used in this synthesis problem, and resulting radiation patterns are compared with those from other existing synthesis algorithms in literatures. Comparative results illustrate that the proposed algorithm has obtained the lowest maximum sidelobe level (MSLL) in phi=0 plane (phi is the elevation angle) of radiation pattern combined with the lowest maximum MSLL in phi=pi/2 plane. In addition, convergence performance of the multi-objective optimisationalgorithm when used in this synthesis problem has also been investigated here.
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