فهرست مطالب m. nafar

For providing required load in n coastal and island regions, tidal barrage can be integrated in microgrids. To produce electricity from tides, in tidal barrage, water is moved between sea and reservoir through sluices containing turbines to generate electricity. In operation phase, produced power of tidal barrages depends on number of turbines, sluices and hydro-pumps. Thus, to maximize generated energy of tidal barrage, optimum number of turbines, sluices and hydro-pumps can be obtained through heuristic optimization techniques. Because of tidal level variation, generated power of tidal barrages changes over time. Thus, for load supplying, other renewable resources such as photovoltaic units, batteries, fuel-based generation units and grid-connected mode of microgrid are utilized. In this research, two-stage optimal operation of microgrids composed of tidal barrage, photovoltaic units, batteries and fuel-based generation units is done. In first stage, optimum number of turbines, sluices and hydro-pumps related to tidal barrage is determined for maximizing produced energy of tidal unit during time horizon of the study. In second stage, remaining load of microgrid is provided by photovoltaic units, batteries, fuel-based generation units and main network. To this end, generated power of fuel-based plants and power exchanged between microgrid and main grid are determined for minimizing operating cost of microgrid. The operating cost including operating cost of fuel-based generation units, cost of exchanged power between main grid and microgrid and penalties of load curtailment is optimized using particle swarm optimization method. Numerical results presents among different optimization algorithms, particle swarm method has performed best in operation studies of tidal barrage. For understudied microgrid, maximum generated energy of tidal barrage is 25.052 MWh, and minimum operating cost of the microgrid is 39868 $.

Due to the growing demand in the electricity sector and the shift to the operation of renewable sources, the use of solar arrays has been at the forefront of consumers' interests. In the meantime, since the production capacity of each solar cell is limited, in order to increase the production capacity of photovoltaic (PV) arrays, several cells are arranged in parallel or in series to form a panel in order to obtain the expected power. Short circuit (SC) and open circuit (OC) faults in the solar PV systems are the main factors that reduce the amount of solar power generation, which has different types. Partial shadow, cable rot, un-achieved maximum power point tracking (MPPT) and ground faults are some of these malfunctions that should be detected and located as soon as possible. Therefore, effective fault detection strategy is very essential to maintain the proper performance of PV systems to minimize network interruptions. The detection method must also be able to detect, locate and differentiate between SC and OC modules in irradiated PV arrays and non-uniform temperature distributions. In this paper, based on artificial intelligence (AI) and neural networks (NN), neutrons can be utilized, as they have been trained in machine learning process, to detect various types of faults in PV networks. The proposed technique is faster than other artificial neural networks (ANN) methods, since it uses an additional hidden layer that can also increase processing accuracy. The output results prove the superiority of this claim.

Distribution Static Synchronous Compensator (D-STATCOM) is a shunt compensator in the distribution systems that one of the most important tasks is to improve the imbalance, reduce and eliminate the nonlinear loads harmonics. Distributed generation resources such as photovoltaic (PV) array can be used as the DC input of D-STATCOM. In this paper, PV array and DC/DC boost convertor are used to stabilize the DC link voltage of D-STATCOM. The main advantage of the proposed method is that for all time provides continuous compensation. Another power quality issues are neutral current in four-wire systems that are created due to the harmonics and system imbalance. Zig-Zag transformer is one of the ways for compensating neutral current and providing isolation between the convertor and flow of the fundamental zero sequence component which contains harmonic neutral current. Role and performance of the distribution shunt compensator in the improvement of power quality indices depends on the performance of its control system. In this paper, the control scheme of synchronous reference frame theory based on fuzzy controller is used for D-STATCOM based on PV and Zig-Zag transformer. Performance and behaviour of the proposed system examined using Matlab/Simulink software and the results will be presented.

Ocean thermal energy conversion system uses from water in surface of the ocean as high temperature source and water in the depth of the ocean as low temperature source. Three types of ocean thermal energy conversion systems including close cycle, open cycle and hybrid systems are available. In a close cycle system, working fluid through a thermodynamic cycle based on the Rankine cycle can rotate the turbine and generate electricity. Due to the variation in the ocean surface temperature, the output power of the ocean thermal energy conversion system is not fixed and controllable and so this uncertainty nature results in the numerous states in the generated power of this plant. Thus, in integrating ocean thermal energy conversion systems to the power system, many aspects of power system such as reliability may be affected and so new approaches must be developed for investigation these effects. In this regard, in this paper for the first time, the reliability of power system containing ocean thermal energy conversion system is evaluated and the valuable indices such as loss of load expectation, expected energy not supplied and peak load carrying capability that can be used for generation expansion planning of power system, are calculated.

IgA nephropathy is the most common cause of primary glomerulonep-hritis throughout the most of developed countries. Since the biopsy is the only way for diagnosis of IgA nephropathy، finding an easy and non-invasive method seems to be necessary for prognosis، diagnosis and treatment of this disease. In this study، it was attempted to find some urine candidate biomarkers that represent the progression of disease in patients with IgA nephropathy.

Urine samples from 13 patients were collected and their prote-ome were extracted and analyzed with na-no-LC-MS/MS. The protein profile was obtained and those differential proteins bet-ween patients with advanced and mild disease states (based on the renal function; eGFR) were determined using orthogonal projection to latent structure discriminant analysis (OPLS-DA) and the acquired data underwent bioinformatics analysis.

A panel composed of 50 signi-ficant proteins was obtained in which 10 top candidate biomarkers were introdu-ced. Dermcidin and Osteopontin had highe-st variation amongst the proteins، so that they overrepresented and underrepresented، res-pectively. Complement system and inna-te immune response were introduced as the significantly important different processes between two groups of patients. Discussion &

The introduced panel of urinary biomarkers can open a new insight to the mechanism of disease progr-ession and may be helpful as a non-invasive diagnosis method.