Introduction: Usually osseointegration takes between three to six months after implant placement but patients are interested to have early loading. There are no definitive criteria for measuring bone mineral density (BMD), insertion torque (IT) (final torque force) and resonance frequency analysis (RFA) (primary implant stability) to determine exact loading time based on the relationship between the above-mentioned parameters. The aim of this study was to determine the relationship between IT, RFA and BMD in screw-type implants.Materials and Methods: This clinical trial was conducted on 18 patients who were candidates for ITI implant placement. Written consent was taken and jaw bone density was determined via a digital radiography technique before surgery. After implant placement, RFA and IT were measured. Fifty-five ITI implants of the total 62 implants placed were evaluated; the implants were 12 mm long with a diameter of 4.1 mm. Data was analyzed with Pearson’s test using SPSS.15 software (a=0.05).Results: There was a significant relationship between IT, RFA and BMD. Pearson’s test showed a correlation coefficient of 0.872 to 0.789 between the three parameters, indicating a strong relationship between them. The mean bone density was 1.468±0.042 g/cm2; the mean RFA was 66.01±2.2 ISQ and the mean IT was 34.62±3.33 N/cm2.Conclusion: Based on the results of the present study there is a significant relationship between, IT, RFA and BMD (p value=0.001).

In this paper, a hybrid of Neural Network (NN) and Fast Traversal Filter (FTF) based controller in each area is used to determine the optimal parameters of Load frequency control (LFC) of a realistic two area power system. The two area power system is modeled considering the various non -linearities like governor dead band, generation rate constraint (GRC) and boiler dynamics. Input to the controller i.e. the error signal is divided into two parts- linear and non- linear. The linear part of the input signal is minimized by the FTF algorithm, whereas the non- linear part is minimized by the NN algorithm. The output of the controller is the sum of the outputs of NN and FTF networks. The proposed hybrid controller requires less number of samples for training of weights, thus making the system fast. This is highly desirable in power quality problems. The various components of power system are reduced to transfer functions and the system performance is analyzed for 1% step load perturbation in area1 with different control lersproportional and integral (PI), neural network (NN) and NN+FTF based controllers. The simulations demonstrate the fast and smooth performance of the power system with the proposed controller. Simulated results evince the superiority of the proposed hybrid controller.

A common method for controlling a group of parallel converters in decentralized control strategy structure in an island microgrid, the use is the droop-down characteristics of frequency ω-P and voltage E-Q. However, the problem with using this method is that the reactive power is not properly distributed (in proportion to the capacity of the micronutrients) between the micronutrients, which may lead to overload in the converters. Microgrids may also suffer from dynamic stability problems such as power fluctuations, which can be increased by switching between active and reactive power control. To avoid this problem, the X / R ratio of transmission lines is an important parameter that should be carefully considered in the design of micronutrient controllers. By linearizing and simplifying conditions, the control system conversion function model becomes a single input-single output system, which is efficient enough to show the relationship between control parameters such as slope of droop characteristics and derivative sentences, virtual impedance, and voltage controllers. Using this model, stability conditions for different parameters are analyzed. Also, to improve power distribution stability, common droop strategies are modified by adjusting the slope as well as adding nonlinear sentence sections. This approach reduces the coupling between active and reactive power control and reduces the dependence of power distribution on grid parameters such as the X / R ratio. To evaluate the reliability of the proposed model, the simulation results in a sample island microgrid in MATLAB software are presented.

Microgrids (MG) are a branch of distributed energy sources, which often use renewable energies to produce electrical power and gives service to scattered loads in Island and Grid connected operation modes. Because of the uncertainties of power system and natural variations of the power produced by renewable energies, in this paper, fractional order PID (FOPID) is used to control the frequency of the microgrid. The output of a fuzzy system is the input of the FOPID controller which results in Fuzzy fractional order PID (FFOPID). Imperialist competitive algorithm (ICA) determines the optimal values of the controller parameters. Comparison of the proposed FFOPID with FOPID and classical PID controllers in several load changes scenarios shows a better performance of the proposed controller in terms of RMS, overshoot and undershoot, number of oscillations, and settling time of the frequency deviations. Simulations indicate the robust performance of the proposed controller against the changes of system parameters as well.

In this article, the sliding mode control of frequency load control of power systems is studied. The study area consists of a system of water and heat. First, a mathematical model of the proposed system disturbances is made and then sliding control mode for frequency load control is provided. By the system simulation and sliding mode control, it can be shown that the damping of oscillations is well led.

Concerns over the depletion of fossil fuel resources, rising global temperatures and environmental challenges have accelerated the integration of distributed generation units and renewable energy sources into power grids. Unlike traditional large power plants, which predominantly use synchronous generators, these distributed units exhibit significantly lower inertia and damping properties. The increasing penetration of renewable energy sources has led to reduced system inertia, posing challenges for grid stability and control. Consequently, analyzing and controlling power grids in the presence of distributed and renewable generation have become a critical area of research. This study proposes an optimal control method for virtual synchronous generators (VSGs), using power electronics and advanced mechanical control techniques to provide the necessary virtual inertia for grid stability. A nonlinear model of a power network, containing multiple parallel VSGs along with local loads, is developed and analyzed in both grid-connected and islanded operating modes. The nonlinear models are subsequently linearized, and a model predictive control (MPC) strategy is employed to enhance frequency regulation within the grid. To validate the effectiveness of the proposed approach, simulations are conducted in the MATLAB/SIMULINK environment under various disturbance scenarios.