Modular multilevel converter based LCL DC/DC converter for high power DC transmission grids

This paper presents a modular multilevel converter (MMC) based DC/DC converter with LCL inner circuit for HVDC transmission and DC grids. Three main design challenges are addressed. The first challenge is the use of MMCs with higher operating frequency compared to common transformer-based DC/DC converters where MMC operating frequency is limited to a few hundred hertz due to core losses. The second issue is the DC fault response. With the LCL circuit, the steady state fault current is limited to a low magnitude which is tolerable by MMC semiconductors. Mechanical DC circuit breakers can therefore be used to interrupt fault current for permanent faults and extra sub-module bypass thyristors are not necessary to protect antiparallel diodes. Thirdly, a novel controller structure is introduced with multiple coordinate frames ensuring zero local reactive power at both bridges in the whole load range. The proposed controller structure is also expandable to a DC hub with multiple ports. Detailed simulations using PSCAD/EMTDC are performed to verify the aforementioned design solutions in normal and fault conditions

ANN for Predicting Medical Expenses

Abstract: In this research, the Artificial Neural Network (ANN) model was developed and tested to predict the rate of treatment expenditure on an individual or family in a country. A number of factors have been identified that may affect treatment expenses. Factors such as age, grade level such as primary, preparatory, secondary or college, sex, size of disability, social status, and annual medical expenses in fixed dollars excluding dental and outpatient clinics among others, as input variables for the ANN model. A model based on the multi-layer Perceptron topology was developed and trained using data on 5574 cases. The evaluation of the test data shows that the ANN model is capable of predicting correctly Medical Expenses

Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network

Reactive power flow increases dual active bridge (DAB) converter RMS current leading to an increase in conduction losses especially in high power applications. This paper proposes a new optimized triple phase shift (TPS) switching algorithm that minimizes the total reactive power of the converter. The algorithm iteratively searches for TPS control variables that satisfy the desired active power flow while selecting the operating mode with minimum reactive power consumption. This is valid for the whole range of converter operation. The iterative algorithm is run offline for the entire active power range (-1pu to 1pu) and the resulting data is used to train an open loop artificial neural network controller to reduce computational time and memory allocation necessary to store the data generated. To validate the accuracy of the proposed controller, a 500-MW 300kV/100kV DAB model is simulated in Matlab/Simulink, as a potential application for DAB in DC grids

Analysis of AC link topologies in non-isolated DC/DC triple active bridge converter for current stress minimization

This paper presents analysis of the non-isolated DC/DC triple active bridge (TAB) converter under various purely inductor-based AC link topologies. The objective of the analysis is to find the topology that incorporates the least value of the AC link inductors which leads to reduced converter footprint in addition to minimum internal current stresses. Modelling of the TAB under each of the different topologies is presented in per unit expressions of power transfer and reactive power assuming fundamental harmonic analysis. The power expressions are used to calculate the inductor values necessary to achieve same rated power transfer of Dual Active Bridge (DAB) converter for the sake of standardizing comparison. On this basis, the topology requiring the least value of interface inductors, hence lowest footprint, is identified. Furthermore, based on phase shift control, particle swarm optimization (PSO) is used to calculate optimal phase shift ratios in each of the proposed topologies to minimize reactive power loss (hence current stress). The topology with minimum stresses is therefore identified and the results are substantiated using a Matlab-Simulink model to verify the theoretical analysis