Volume 62, Issue 10 will be published on 02 December 2020
Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
With the fast growth in the switching of power electronics especially the high-power semiconductor switches with turn on/off Ability have become now available, Voltage Source Converter (VSC) subordinate control units for applications of power transmission system become a reality. In this paper, the archetypes of some of the second generation Flexible AC Transmission System (FACTS) controllers for series device types such as TSSC, TCSC, SSSC and TCSR, parallel device types such as TCR, TSC, SVC, STATCOM and SVG, and finally, compensators combined such as IPFC and UPFC have presented with analysis and effect when injected in power transmission system. This paper provides a comprehensive review of the progress made in the facts. as well as, the general illustration of the FACTS controllers is presented
A noise that has is a normal distribution is often added to autoregressive (AR) time series models. Generally, the methods for estimating the parameter of the AR model based on normality assumptions. One of the AR time series models that do not verify normality assumptions is the Laplace AR model. If the estimation method based on the normality assumption is used on the Laplace AR time series model, the estimation method will produce a very biased estimate. This study proposes the reversible jump Markov Chain Monte Carlo (MCMC) algorithm to estimate the parameters of the Laplace AR time series models. The parameters of the Laplace AR time series models are model order, model coefficient, and noise variance. The parameter estimation of the Laplace AR time series models is done in the Bayesian framework. A Binomial distribution is selected as a prior distribution for the model order. A uniform distribution is selected as a prior distribution for the model coefficient. An inverse-Gamma distribution is selected as a prior distribution for the noise variance. This prior distribution is combined with the likelihood function of the data to get the posterior distribution. Parameter estimation is based on the posterior distribution. The reversible MCMC algorithm allows simultaneously estimating the model order, model coefficients, and noise variance. The performance of the algorithm is tested by using some synthetic signals generated from the simulation. The simulation results show that the reversible jump MCMC algorithm can estimate the Laplace AR model parameters well. The advantage of the reversible jump MCMC algorithm is that this algorithm can estimate the parameters of the stationary Laplace AR time series models