Optimal Linear Control of Modular Multi-Level Converters with a Prescribed Degree of Stability

Abstract

In this paper, a new control approach using an optimal linear control with prescribed degree of stability for modular multi-level converters (MMC) is presented and analyzed. The proposed controller relies on a linear quadratic regulator with integral action which brings the ability of state variable reference tracking for modular multi-level converters. Since MMC is a complex system with several state variables, a unified control system design for this system is vital. The proposed controller of this study is designed to obtain wider stability margin thanks to the implementation of prescribed degree of stability concept to minimize the quadratic performance index of the control structure. By means of this method, the poles of the closed-loop system will be shifted to the desired places in the left half side of the S-plane. The main advantages of this control strategy compared to previous methods are that it will be possible to control the state of energy for each phase separately, while there will be superior tolerance to nonlinearities and the enhanced stability margin with less sensitivity to plant-parameter variations. The performance of the designed controller is verified through MATLAB^TM simulations (The MathWorks, Natick, MA, USA) with the nonlinear model of MMC.     

Novel polymorphous aluminosilicate nano minerals: Preparation,characterization and dyes wastewater treatment

Polymorphous aluminosilicate, such as Andalusite, Kyanite and Sillimanite, were prepared and characterized by XRF, XRD, FT-IR and SEM analysis. These cheap and accessible nanoparticles were used for removal of Disperse Red 177 and Disperse Blue 60 dyes. The adsorption process was held in a batch system considering the effects of major parameters consist of pH, adsorbent dosage, dye initial concentration and temperature. The obtained results show that both Freundlich and Temkin isotherms suitably fit with experimental data of adsorption of dyes in equilibrium mode. Also, the adsorption of dyes follows and matches pseudo-second-order kinetic model for Andalusite, Kyanite and Sillimanite nanoadsorbents. Thermodynamic study of dye adsorption process proves low randomness, exothermicity and spontaneous reactions. The comparison of three adsorbent efficiencies for adsorption of DR-177 and D-B-60 dyes was as: Andalusite>Sillimanite>Kyanite and Sillimanite>Kyanite>Andalusite, respectively.     

Proposing a new local density estimation outlier detection algorithm: an empirical case study on flow pattern experiments

Pattern Analysis and Applications - Outlier or anomaly detection is an important branch of data analysis that becomes a crucial task in many application domains. Data objects which significantly...     

A Comparative Assessment of Artificial Neural Network,Generalized Regression Neural Network,Least-Square Support Vector Regression,and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions

Monthly forecasting of streamflow is of particular importance in water resources management especially in the provision of rule curves for dams. In this paper, the performance of four data-driven models with different structures including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN), Least Square-Support Vector Regression (LS-SVR), and K-Nearest Neighbor Regression (KNN) are evaluated in order to forecast monthly inflow to Karkheh dam, Iran, in linear and non-linear conditions while the optimized values of the model parameters are determined in the same condition via the Leave-One-Out Cross Validation (LOOCV) method. Results show that the performance of the models is different in linear and nonlinear conditions; the cumulative ranking of the models according to the three assessment criteria including NSE, RMSE and R² indicates that ANN performs best in linear conditions while LS-SVR, GRNN and KNN are in the next ranks, respectively. But in nonlinear conditions, the best performance belongs to LS-SVR, followed by KNN, ANN, and GRNN models.     

Heuristic Optimization of Supplementary Controller for VSC-HVDC/AC Interconnected Grids Considering PLL

This paper presents a new approach to enhance the dynamic responses of AGC in power systems by means of heuristic optimization of VSC-HVDC supplementary controllers. The upcoming power electronics-based VSC-HVDC transmission systems offer new features that would be advantageous for improving the frequency control and thus for enhancing the stability of the transmission grids. In this paper, the parameters of the proposed control modulation are tuned using Genetic Algorithm and Simulated Annealing methods. The performance of the proposed intelligent based tuning approach is assessed through MATLAB simulations for an AC/DC interconnected system. For the sake of detailed analysis, the effects of PLL and frequency measurements are also included in the VSC-HVDC system modeling. Furthermore, to show merits of the proposed strategy, a comparison between AC and DC transmissions is presented.     

Comparison of different geostatistical methods to estimate groundwater level at different climatic periods

The main aim of this study has been to assess the spatially variability of groundwater level at different climatic periods. The study area is Saveh‐Nobaran aquifer in Iran. First, using two drought indices, different climatic periods in the region including wet, normal and dry during 1993–2003 were recognized. Second, with the use of measured elevations of the water table, experimental semivariograms (spherical, exponential and Gaussian) were constructed that characterize the spatial variability of the measured groundwater levels. Groundwater levels were interpolated by four different geostatistical methods. RMSE, MAE and R ² were calculated to determine the best method. The R ² coefficient obtained from kriging, cokriging, TPSS and IDW methods were 0.95, 0.97, 0.89 and 0.83, respectively. Cokriging method was used to create groundwater‐level maps, including maximum, average and minimum water levels for each climatic period. The results obtained from geostatistical analysis showed that groundwater depth varies spatially in different climatic conditions.