Sharif Digital Repository

This paper proposes a novel formula to calculate sensitivities of electromechanical modes to generators active power changes. Quadratic eigenvalue problem is applied to construct the framework of the proposed formula. Sensitivity factors are calculated using power system model parameters and power flow variables, which can be either obtained via state estimation or measured directly by phasor measurement units. The 39-bus New England power system is used to verify performance of the proposed method  

This paper proposes a comprehensive analytic method for applying various optimal remedial actions to improve critical electromechanical modes damping without jeopardizing damping of non-critical modes and violating security constraints of power system. Generators and reactive power sources redisptach, demand side management and the generators voltage reference tuning are remedial actions that are considered here. Dynamic equations of the flux-decay dynamic model of generators, standard dynamic models of excitation system and power system stabilizer and algebraic equations of active and reactive powers balance are formulated in the quadratic eigenvalue problem framework. With simultaneous use... 

A surface to separate oil–water mixtures is a global concern and highly needed particularly in oil industries. The present study was conducted to create a novel superhydrophilic/superoleophobic nanocomposite coating on the stainless-steel mesh for the aim of oil/water separation. Different hydrophilic resins along with PFOA as oleophobic agent with 15 flours in its chemical structure and various oxide nanoparticles containing SiO2 and TiO2 at different concentrations were studied to achieve superhydrophilic/superoleophobic surface. The fabricated nanocomposites were fully characterized via field-emission scanning microscopy (FESEM), atomic force microscopy (AFM) and Fourier-transform... 

The first spoken dialogue system is developed for the Persian language is introduced. This is a ticket reservation system with Persian ASR and NLU modules. The focus of the paper is on learning the dialogue management module. In this work, real on-line training data are used during the learning process. For on-line learning, the effect of the variations of discount factor (γ) on the learning speed is investigated as the second contribution of the research. The optimal values for γ were found and the variation pattern of the action-value function (Q) in the learning process was obtained. A probabilistic policy for selecting actions is used in this work for the first time instead of greedy... 

This paper proposes a novel error-feedback lattice recursive least-squares (EF-LRLS) filter for online estimation of power system oscillatory modes. The EF-LRLS filter is applied to ambient data provided by phasor measurement units to identify the autoregressive (AR) model parameters. This filter has a modular structure; accordingly, if the length of the filter equals N, it identifies AR(1) to AR(N) models concurrently. In the proposed method, removing very low and high frequencies and re-sampling steps are fulfilled in an online fashion. This adaptive filter has less computational complexity than standard RLS filter, making it an appropriate choice for online system identification. The... 

Online monitoring of inter-area oscillations is a vital need for secure operation of large-scale power systems. This paper aims at online estimation of inter-area modes by a novel stabilized fast transversal recursive least squares (SFTRLS) filter running on ambient power system data received from phasor measurement units. The SFTRLS filter is a fast finite-impulse response adaptive filter that is widely used in communication applications, such as noise and echo cancelation. It has the lowest computational complexity among least-squares filters and directly calculates coefficients of the parametric model selected for identifying a system under study. In the proposed method, the SFTRLS filter... 

This paper proposes a novel error-feedback lattice recursive least-squares (EF-LRLS) filter for online estimation of power system oscillatory modes. The EF-LRLS filter is applied to ambient data provided by phasor measurement units to identify the autoregressive (AR) model parameters. This filter has a modular structure; accordingly, if the length of the filter equals $N$, it identifies AR(1) to ${m{AR(}}N)$ models concurrently. In the proposed method, removing very low and high frequencies and resampling steps are fulfilled in an online fashion. This adaptive filter has less computational complexity than standard RLS filter, making it an appropriate choice for online system identification.... 

This paper presents a model-based method for applying online proactive generators redispatch to improve damping of the critical electromechanical oscillations of power system. The proposed method comprises two stages: 1) monitoring modal characteristics of oscillatory modes in ambient condition, and 2) applying generators redispatch based on sensitivities of the critical mode to the generators active power changes using a new analytic method. An online identification method such as error feedback lattice recursive least square adaptive filter is applied for online estimation of the oscillatory modes. Then, whenever the damping ratio of an identified mode is less than a preset threshold, its... 

In this study, a noble robust superhydrophobic and highly oleophobic polyurethane (PU)–SiO2 nanoparticle (NP) coating is specially designed using sol-gel process. For this purpose the effective parameters on surface tension and durability of the synthesized coating investigated and optimized. This new superhydrophobic and highly oleophobic coating exhibits good pensile hardness as high as 6H with adhesive force grade of 5B and repels water and oil with contact angles (CAs) of 159° and 140° respectively. The synthesized PU-SiO2 composite also retains an excellent amphiphobicity after a 7 days immersion in water with water and oil with CAs of 150° and 130°. Facile fabrication of PU–SiO2... 

The separation of water-oil mixtures has attracted widespread attention because of the increasing amounts of oily wastewater produced from the daily activities of humans and different industrial processes. Therefore, the development of facile and efficient oil-water separation technologies is imperative. In this work, a new highly superhydrophilic-superoleophobic coated stainless steel mesh was fabricated using virtue of the surface modification of poly (BzVimBr-Vim)@PFOA@SiO2 nanoparticles (NPs) through a facile preparation process. The new fabricated superhydrophilic and highly oleophobic coating exhibits good adhesive properties. The oil contact angle (OCA) and water contact angle (WCA)... 

The design and development of efficient approaches for water–oil separation have had widespread interest. Most previously introduced techniques and materials used for development of the successful separation of oily wastewater could not answer all the desired demands, such as being efficient and environmentally and economically friendly. Therefore, in seeking a novel method capable of answering these expectations, surfaces with special wettability were introduced. A novel, reusable, and recyclable superhydrophilic and superoleophobic poly(Vsim-Vim)@PFOA@SiO2 nanocomposite-coated stainless steel mesh was synthesized through a facile preparation process. Since the most important factors of... 

The design and development of efficient approaches for water–oil separation have had widespread interest. Most previously introduced techniques and materials used for development of the successful separation of oily wastewater could not answer all the desired demands, such as being efficient and environmentally and economically friendly. Therefore, in seeking a novel method capable of answering these expectations, surfaces with special wettability were introduced. A novel, reusable, and recyclable superhydrophilic and superoleophobic poly(Vsim-Vim)@PFOA@SiO2 nanocomposite-coated stainless steel mesh was synthesized through a facile preparation process. Since the most important factors of... 

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies. Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission. Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest...