Adaptive Fuzzy Synergetic PSS Design to Damp Power System Oscillations

This paper presents a novel indirect adaptive Power System Stabilizer (PSS) via a developed synergetic control methodology and fuzzy systems. Fuzzy system is utilized in an adaptive scheme to estimate the system using a nonlinear model. The synergetic control guarantees robustness of the controller and makes the controller easy to implement because of using a chatter free continuous control law. Additionally, the parameters of the controller are optimized by Imperialist Competitive Algorithm (ICA). The effectiveness of the proposed scheme is confirmed on a single machine power system while the stability is guaranteed through Lyapunov synthesis

Power System Stability Improvement via TCSC Controller Employing a Multi-objective Strength Pareto Evolutionary Algorithm Approach

This paper focuses on multi-objective designing of multi-machine Thyristor Controlled Series Compensator (TCSC) using Strength Pareto Evolutionary Algorithm (SPEA). The TCSC parameters designing problem is converted to an optimization problem with the multi-objective function including the desired damping factor and the desired damping ratio of the power system modes, which is solved by a SPEA algorithm. The effectiveness of the proposed controller validates on a multi-machine power system over a wide range of loading conditions. The results of the proposed controller (SPEATCSC) are compared with the Genetic Algorithm (GA) based tuned TCSC through some operating conditions to demonstrate its superior efficiency

Combined Use of Sensitivity Analysis and Hybrid Wavelet-PSO- ANFIS to Improve Dynamic Performance of DFIG-Based Wind Generation

In the past few decades, increasing growth of wind power plants causes different problems for the power quality in the grid. Normal and transient impacts of these units on the power grid clearly indicate the need to improve the quality of the electricity generated by them in the design of such systems. Improving the efficiency of the large-scale wind system is dependent on the control parameters. The main contribution of this study is to propose a sensitivity analysis approach integrated with a novel hybrid approach combining wavelet transform, particle swarm optimization and an Adaptive-Network-based Fuzzy Inference System (ANFIS) known as Wavelet-ANFIS-PSO to acquire the optimal control of Doubly-Fed Induction Generators (DFIG) based wind generation. In order to mitigate the optimization complexity, sensitivity analysis is offered to identify the Unified Dominate Control Parameters (UDCP) rather than optimization of all parameters. The robustness of the proposed approach in finding optimal parameters, and consequently achieve a high dynamic performance is confirmed on two area power system under different operating conditions

Improving the performance of MFCC for Persian robust speech recognition

The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to the noisy original speech signal. The pre-emphasized original  speech segmented into overlapping time frames, then it is windowed by a modified hamming window .Higher order autocorrelation coefficients are extracted. The next step is to eliminate the lower order of the autocorrelation coefficients. The consequence pass from FFT block and then power spectrum of output is calculated. A Gaussian shape filter bank is applied to the results. Logarithm and two compensator blocks form which one is mean subtraction and the other one are root block applied to the results and DCT transformation is the last step. We use MLP neural network to evaluate the performance of proposed MFCC method and to classify the results. Some speech recognition experiments for various tasks indicate that the proposed algorithm is more robust than traditional ones in noisy condition

Significance of Epicardial and Intrathoracic Adipose Tissue Volume among Type 1 Diabetes Patients in the DCCT/EDIC: A Pilot Study.

Introduction Type 1 diabetes (T1DM) patients are at increased risk of coronary artery disease (CAD). This pilot study sought to evaluate the relationship between epicardial adipose tissue (EAT) and intra-thoracic adipose tissue (IAT) volumes and cardio-metabolic risk factors in T1DM. Method EAT/IAT volumes in 100 patients, underwent non-contrast cardiac computed tomography in the Diabetes Control and Complications Trial /Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study were measured by a certified reader. Fat was defined as pixels’ density of -30 to -190 Hounsfield Unit. The associations were assessed using–Pearson partial correlation and linear regression models adjusted for gender and age with inverse probability sample weighting. Results The weighted mean age was 43 years (range 32–57) and 53% were male. Adjusted for gender, Pearson correlation analysis showed a significant correlation between age and EAT/IAT volumes (both p Conclusion T1DM patients with greater BMI, WTH ratio, weighted HbA1c level, triglyceride level and AER≥300/ESRD had significantly larger EAT/IAT volumes. Larger sample size studies are recommended to evaluate independency

Adaptive Fuzzy Synergetic PSS Design to Damp Power System Oscillations

ABSTRAC

Thermal and nonlinear optical properties of Tm3+-doped tellurite glasses

CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORFAPEMIG - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAISIn the present work, thermal and nonlinear properties of tellurite glasses doped with Tm2O3 were investigated by means of thermal lens, thermal relaxation and z-scan measurements. The composition of the samples was (78 - x) TeO2 + 4.5 Bi2O3 + 5.5 ZnO + 10.5 Li2O + 1.5 Nb2O5 + xTm(2)O(3) (x = 0.05, 0.1, 0.5, 1.0 and 1.5 mol%). Thermal diffusivity (D), specific heat (rho c), thermal conductivity (K) and optical path change with temperature (ds/dT) were determined as a function of Tm3+ concentration. Concerning thermal-optical properties, our results show that the 0.5 mol% Tm doped is the most suitable sample for laser applications as it presented the lowest ds/dT and highest thermal diffusivity (conductivity). Third-order nonlinearities were observed and discussed in 1.0 and 1.5 mol% samples. The nonlinear indices (n(2)) found were 1.16 x 10(-13) and 3.89 x 10(-13) cm(2) W-1, respectively. The data obtained show that the Tm3+-doped TBZLN glasses have potential for future opto-electronic devices.138529712978CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORFAPEMIG - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAISCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORFAPEMIG - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAISSem informaçãoSem informaçãoSem informaçã

A comprehensive review of identification methods for pathogenic yeasts: Challenges and approaches

Given the increasing incidence of yeast infections and the presence of drug-resistant isolates, accurate identification of the pathogenic yeasts is essential for the management of yeast infections. In this review, we tried to introduce the routine and novel techniques applied for yeast identification. Laboratory identification methods of pathogenic yeast are classified into three categories; I. conventional methods, including microscopical and culture-base methods II. biochemical/physiological-processes methods III. molecular methods. While conventional and biochemical methods require more precautions and are not specific in some cases, molecular diagnostic methods are the optimum tools for diagnosing pathogenic yeasts in a short time with high accuracy and specificity, and having various methods that cover different purposes, and affordable costs for researchers. Nucleotide sequencing is a reference or gold standard for identifying pathogenic yeasts. Since it is an expensive method, it is not widely used in developing countries. However, novel identification techniques are constantly updated, and we recommend further studies in this field. The results of this study will guide researchers in finding more accurate diagnostic method(s) for their studies in a short period of time