فصلنامه دریا فنون
سال ششم شماره 1 (پیاپی 15، بهار 1398)

Nowadays microinverter with power levels ranging from 150 to 300 W, has become the trend for some grid-connected PV systems specially for residential PV systems. Also, in marine industries, photovoltaic microinverters can be used to produce a part of the electrical energy required on ships. In single phase microinverters there is a double-frequency ripple on the DC bus voltage that causes harmonics and reduces power factor. In order to reduce this ripple, large electrolytic capacitors are often used at the DC bus that the drawback is short lifetime of these capacitors. To overcome this problem, it is necessary to replace electrolytic capacitors with thin film capacitors that have long life and low capacitance. By decreasing the capacitance the amplitude of double frequency ripple on the bus voltage increases so the common bus voltage controller (PI) can’t solve the tradeoff between the output current THD and bus voltage fluctuations (overshoot and undershoot). In this paper, by using the proposed control methods a microinverter is designed and simulated so that this tradeoff has been solved without using additional hardware. This microinverter is connected to the grid (220V, 50Hz). It has a very small 20µF bus capacitor. The bus voltage has negligible overshoot and undershoot in response to a 200W input power step. THD of the injected current to the grid is 0.44% at 250W output power. The effectiveness of the proposed method is validated by simulation results in Matlab-Simulink software.

In many practical applications, implementation of algorithms is required into low-cost and low-power hardware, proper processing power, simplicity in algorithm development and maximum flexibility. Proper implementation of these methods and real-time operations for defense systems has particular importance. Studies have shown that Raspberry Pi 2 has sufficient computational power to implement an infrared target detection algorithm. Therefore, in this paper, Raspberry Pi 2 is considered as low-cost, low-weight, and low-power hardware for optimum implementing infrared target detection methods and to optimize and reduce runtime, it with the overclocking technique is used. Finally, their performance is compared with other hardware with different software development environment. These comparisons include the Qt software development environment based on the OpenCV image processing library in the Raspberry Pi 2 hardware with Qt software development environment based on the OpenCV library functions in the PC hardware, as well as the high-level MATLAB software. The results show that implementation on the Raspberry Pi 2 in comparison with MATLAB speeds up implementation of the algorithm 6.5 times. As well as, implementation time of the infrared target detection algorithm (C ++) using the OpenCV library on the PC is approximately eight times that of Raspberry Pi 2. Also, comparing Raspberry Pi 2 and PC in terms of power consumption, weight and cost is observed that Raspberry Pi 2 has a much better performance in terms of power consumption, weight and cost than PCs. The results show that although the use of high-level software such as MATLAB has background suppression factor (SCR) and signal to clutter ratio (BSF) higher than use of the OpenCV library, the results of runtime indicate that the proposed hardware improves the runtime of high-level software like MATLAB. The results of optimization on the Raspberry Pi 2 show that speed of the algorithm is improved by more than 40%.

Coral reef communities face unprecedented pressures at local, regional and global scales as a consequence of climate change and anthropogenic disturbance. Remote sensing, from satellites or aircraft, is possibly the only means to measure the effects of such stresses at appropriately large spatial scales. Coral reefs are indicators of environmental, climate and sea surface change which shows the reefs damages. For this purpose, an algorithm that includes relation between those variables was used. Australia’s Heron reef island is considered to be studied. In order to modeling the coral reef health condition by support vector regression (SVR), water and coral important spectral indexes and features was found using landsat-8. From this method, the SVR is achieved the high performance for modeling the statistical problem .In this study we used genetic algorithm (GA) and sequential feature selection (SFS) for selecting suitable features in order to estimate model. The results for the testing data in this area of best model is [RMSE= 0.53 and R² =0.983] that show the high SVR performance.

Model predictive controllers rely on dynamic model of the process, most often linear models obtained by system identification. The advantage of MPC is the fact that it allows the current timeslot to be optimized by keeping future timeslots in account. In this paper, the transient performance of MPC method against DTC with non linear Hysteresis based controllers is presented. Then a comparative performance in terms of torque and flux ripple are presented for PTC and DTC of three phase induction motor drive. In predictive control the control objectives are defined as cost function, which is to be minimized to have a greater flexibility to include constraints which results in low computational complexity compared to DTC scheme. simulation results of PTC and DTC in MATLAB/ SIMULINK are shown and compared. This comparison between is carried out by validating the feasibility of PTC and highlighting some important implementation issues.

The main responsibility of an engine mount as a component which attaches the engine to chassis is control and absorption of different resources of vibration. Design process of marine engine mounts is one of main issues in the recent years in marine industries. Hence, a deep study in diverse design methodologies and performance analysis would be necessary to suggest an appropriate process for engine mount design. This study based on researches on design methods, suggest design and selection algorithm for marine engine mounts in four steps: primarily design, conceptual design, detail design, and finally performance assessment. Afterwards, the suggested process will imply for an internal marine engine mount with 200 hp. Based on achieved results, the final designed engine mount for this case in critical situation will have safety factor of 2.1 in static analysis and it also have a desired distance from excited frequency in frequency analysis. Finally, dynamic modeling results for this engine mount will be presented based on designed GUI by research team.

A theorical method is presented to analyse a way to increase generated power and energy efficiency in wind turbine by optimizing the Cut-in, Rated, Furling speeds by the ant colony algorithm. In this paper, we first discuss the wind turbine math modeling, which ultimately achieves the objective function. Then, by collecting the turbine statistical data, the computerized code of the neural network, wind turbine is modeled in MATLAB software and its results are presented as tables and diagrams. By examining the results of optimization by ant colony algorithm and comparing the data with the results of the research in the article on exergy analysis of wind turbines and Also the paper that describes the exergy analysis and optimization of wind turbines by genetic algorithm and searching algorithm optimizing the turbine, Finally, 65.63% increase in output power and 61.54% increase in energy efficiency was observed in the ant algorithm.

In this research, simulation of a marine propeller at 3-D geometry by using the commercial software of ANSYS-FLUENT was presented. In this study a fluid dynamic analysis for two models namely, B-Series and standard model 3686 was conducted.Numerical results with considered turbulence models were found to be in acceptable agreement with the experimental results. In this paper, performance of the propeller was investigated at different range of advance coefficient (J) by considering KT, Kq and η coefficients. Thrust and Torque coefficients were estimated and found to be in good agreement with the experimental results. In order to find the optimum domain, twenty computational domains were examined.

Many researchers have investigated seismic performance of pile supported wharfs, but few studies are conducted to examine the effect of loading and unloading cranes on seismic responses of this type of wharfs. This was due to an expired belief which considers the crane as a damper for the structure beneath and reduces the seismic responses of the wharf. Since, ignoring the crane would enhance the safety factor of the design. This research aims to find the impact of crane on seismic responses of a pile supported wharf. Hence, a common type of pile supported wharf by specified geotechnical and structural parameters used in Persian Gulf ports with and without crane is 3-D modeled in SAP2000. Assessment of the seismic responses of the wharf is done via an incremental dynamic analysis. Finally, by using the destruction criterions which are adapted from push-over analysis, fragility curves of the wharf are proposed in three different moods. Analyzing and observing the resulting values and curves show that a crane does not affect the seismic and vulnerability responses of the wharf in the direction parallel to the seashore however, in the direction perpendicular to the seashore, despite of the reduction of the wharf seismic responses the vulnerability responses increase.

Meta-heuristic Algorithms (MA) are widely accepted as excellent ways to solve a variety of optimization problems in recent decades. Grey Wolf Optimization (GWO) is a novel Meta-heuristic Algorithm (MA) that has been generated a great deal of research interest due to its advantages such as simple implementation and powerful exploitation. This study proposes a novel GWO-based MA and two extra features called Individual Best Memory (IBM) and Penalty Factor (PF) to train Feed-forward Neural Network (FNN) for the classification of Sonar and Radar datasets. Besides, FNN is accompanied by Feature Selection (FS) using GWO. Experiments were done on Sonar and Radar datasets obtained from the University of California, Irvin (UCI) to evaluate the performance of the proposed MA; the results demonstrated the proposed MA is markedly better than GWO in terms of classification accuracy, avoiding local optima stagnation, and convergence speed. This framework can be applied to naval navigation systems or atmospheric research.