Enhancement of Breakdown Voltage in AlGaN/GaN High Electron Mobility Transistors Using Double Buried p-Type Layers

A novel A1GaN/GaN high electron mobility transistor （HEMT） with double buried p-type layers （DBPLs） in the GaN buffer layer and its mechanism are studied. The DBPL A1GaN/GaN HEMT is characterized by two equi-long p-type GaN layers which are buried in the GaN buffer layer under the source side. Under the condition of high-voltage blocking state, two reverse p-n junctions introduced by the buried p-type layers will effectively modulate the surface and bulk electric fields. Meanwhile, the buffer leakage is well suppressed in this structure and both lead to a high breakdown voltage. The simulations show that the breakdown voltage of the DBPL structure can reach above 2000 V from 467 V of the conventional structure with the same gate-drain length of 8μm.

Optimal Placement and Sizing of Distributed Generation Using Metaheuristic Algorithm

Power loss and voltage uncertainty are the major issues prevalently faced in the design of distribution systems.But such issues can be resolved through effective usage of networking reconfiguration that has a combination of Distributed Generation(DG)units from distribution networks.In this point of view,optimal placement and sizing of DGs are effective ways to boost the performance of power systems.The optimum allocation of DGs resolves various problems namely,power loss,voltage profile improvement,enhanced reliability,system stability,and performance.Several research works have been conducted to address the distribution system problems in terms of power loss,energy loss,voltage profile,and voltage stability depending upon optimal DG distribution.With this motivation,the current study designs a Chaotic Artificial Flora Optimization based on Optimal Placement and Sizing of DGs(CAFO-OPSDG)to enhance the voltage profiles and mitigate the power loss.Besides,the CAFO algorithm is derived from the incorporation of chaos theory concept into conventional artificial flora optimization AFO algorithm with an aim to enhance the global optimization abilities.The fitness function of CAFO-OPSDG algorithm involves voltage regulation,power loss minimization,and penalty cost.To consider the actual power system scenario,the penalty factor acts as an important element not only to minimize the total power loss but to increase the voltage profiles as well.The experimental validation of the CAFO-OPSDG algorithm was conducted against IEEE 33 Bus system and IEEE 69 Bus system.The outcomes were examined under various test scenarios.The results of the experiment established that the presented CAFO-OPSDG model is effective in terms of reducing the power loss and voltage deviation and boost-up the voltage profile for the specified system.

Probabilistic total transfer capability analysis based on static voltage stability region integrated with a modified distributed-level nodal-loading model

In this paper, a new evaluation method of probabilistic TTC based on SVSR calculation is developed through a hierarchical simulation. A smooth technology based on the non-parametric kernel estimator is adapted to obtain the time-dependent probabilistic density function of the feeder-head load data. In order to describe possible operating change directions of the operating point, the original hyper-cone-like(HCL) model is constructed to consider the probabilistic distribution function(PDF) extracted from feeder-head load data to replace the simple Normal Distribution model and the uncertain generator outputs. To realize the fast TTC calculation of the current operating point in random conditions, a sub-hyper-cone-like(SHCL) model in full power injections space is proposed, which is a similarity transformation of the original one.

Simultaneous allocation of renewable energy sources and custom power quality devices in electrical distribution networks using artificial rabbits optimization

This study suggests an optimal renewable energy source(RES)allocation and distribution-static synchronous compensator(D-STATCOM)and passive power filters(PPFs)for an electrical distribution network(EDN)to improve its performance and power quality(PQ).First,the latest metaheuristic artificial rabbits optimization(ARO)is used to locate and size solar photovoltaic(PV),wind turbine(WT)and D-STATCOM units.In the second stage,ratings of single-tuned PPFs and D-STATCOMs at the RESs are determined,considering non-linear loads in the network.The multi-objective function reduces power loss,improves the voltage stability index(VSI)and limits total harmonic distortion.Simulations using the IEEE 33-bus EDN compared the ARO results with those of previous studies.In the first scenario,ideally integrated D-STATCOMs,PVs and WTs reduced losses by 34.79%,64.74%and 94.15%,respectively.VSI increases from 0.6965 to 0.7749,0.8804 and 0.967.The optimal WT integration of the first scenario outperformed the PVs and D-STATCOMs.The second step optimizes the WTs and PQ devices for non-linear loads.WTs and D-STATCOMs reduce the maximum total harmonic distortion of the voltage waveform by 5.21%with non-linear loads to 3.23%,while WTs and PPFs reduce it to 4.39%.These scenarios demonstrate how WTs and D-STATCOMs can improve network performance and PQ.The computational efficiency of ARO is compared to that of the pathfinder algorithm,future search algorithm,butterfly optimization algorithm and coyote optimization algorithm.ARO speeds up convergence and improves solution quality and comprehension.