Rasoul Rahmani

Simulation and Hardware Implementation of New Maximum Power Point Tracking Technique for Partially Shaded PV System Using Hybrid DEPSO Method

In photovoltaic (PV) power generation, partial shading is an unavoidable complication that significantly reduces the efficiency of the overall system. Under this condition, the PV system produces a multiple-peak function in its output power characteristic. Thus, a reliable technique is required to track the global maximum power point (GMPP) within an appropriate time. This study aims to employ a hybrid evolutionary algorithm called the DEPSO technique, a combination of the differential evolutionary (DE) algorithm and particle swarm optimization (PSO), to detect the maximum power point under partial shading conditions. The paper starts with a brief description about the behavior of PV systems under partial shading conditions. Then, the DEPSO technique along with its implementation in maximum power point tracking (MPPT) is explained in detail. Finally, Simulation and experimental results are presented to verify the performance of the proposed technique under different partial shading conditions. Results prove the advantages of the proposed method, such as its reliability, system-independence, and accuracy in tracking the GMPP under partial shading conditions.

Maximum power point tracking of partial shaded photovoltaic array using an evolutionary algorithm: a particle swarm optimization technique

Partial shading is one of the unavoidable complications in the field of solar power generation. Although the most common approach in increasing a photovoltaic (PV) arrays efficiency has always been to introduce a bypass diode to the said array, this poses another problem in the form of multi-peaks curves whenever the modules are partially shaded. To further complicate matters, most conventional Maximum Power Point Tracking methods develop errors under certain circumstances (for example, they detect the local Maximum Power Point (MPP) instead of the global MPP) and reduce the efficiency of PV systems even further. Presently, much research has been undertaken to improve upon them. This study aims to employ an evolutionary algorithm technique, also known as particle swarm optimization, in MPP detection.

A novel method for optimal placing wind turbines in a wind farm using particle swarm optimization (PSO)

In this paper, for the first time a particle swarm optimization (PSO) method is utilized to optimize placing of wind turbines in a wind park. The location of each wind turbine could be freely adjusted within a predefined cell in order to maximize the generated energy. Simulated results and graphs are carried out to prove that the present study is improved wind farm efficiency and extract more electrical power in respect of the total costs.

A hybrid method consisting of GA and SVM for intrusion detection system

In this paper, a hybrid method of support vector machine and genetic algorithm (GA) is proposed and its implementation in intrusion detection problem is explained. The proposed hybrid algorithm is employed in reducing the number of features from 45 to 10. The features are categorized into three priorities using GA algorithm as the highest important is the first priority and the lowest important is placed in the third priority. The feature distribution is done in a way that 4 features are placed in the first priority, 4 features in the second, and 2 features in the third priority. The results reveal that the proposed hybrid algorithm is capable of achieving a true-positive value of 0.973, while the false-positive value is 0.017.

Development of solution-gated graphene transistor model for biosensors

AbstractThe distinctive properties of graphene, characterized by its high carrier mobility and biocompatibility, have stimulated extreme scientific interest as a promising nanomaterial for future nanoelectronic applications. In particular, graphene-based transistors have been developed rapidly and are considered as an option for DNA sensing applications. Recent findings in the field of DNA biosensors have led to a renewed interest in the identification of genetic risk factors associated with complex human diseases for diagnosis of cancers or hereditary diseases. In this paper, an analytical model of graphene-based solution gated field effect transistors (SGFET) is proposed to constitute an important step towards development of DNA biosensors with high sensitivity and selectivity. Inspired by this fact, a novel strategy for a DNA sensor model with capability of single-nucleotide polymorphism detection is proposed and extensively explained. First of all, graphene-based DNA sensor model is optimized using particle swarm optimization algorithm. Based on the sensing mechanism of DNA sensors, detective parameters (Ids and Vgmin) are suggested to facilitate the decision making process. Finally, the behaviour of graphene-based SGFET is predicted in the presence of single-nucleotide polymorphism with an accuracy of more than 98% which guarantees the reliability of the optimized model for any application of the graphene-based DNA sensor. It is expected to achieve the rapid, quick and economical detection of DNA hybridization which could speed up the realization of the next generation of the homecare sensor system.

A new simple, fast and efficient algorithm for global optimization over continuous search-space problems

We propose a new stochastic global optimization method for continuous search-space.We chose ten benchmark functions to evaluate the ability of the proposed algorithm.The results are compared with two other methods which are PSO and DE.The proposed method obtains proper and fast solution and escapes from local optima.Being robust, fast and needing less memory are the main features of the method. Optimization of non-linear and non-differentiable problems has been considered as an important issue for mathematicians and engineers. A new stochastic global optimization method for non-linear and non-differentiable problems is proposed and extensively explained, in this article. It is a swarm-based method which uses spherical boundaries in a vector search-space to explore for the optimal solution. Having a few numbers of parameter to be adjusted, being robust and fast, needing small memory storage size and capability of escaping from local optima, are the main features of this new algorithm. To analyze and evaluate the capability of this novel method, ten benchmark functions are chosen and the results are compared with two existing optimization algorithms which are Differential Evolution and Particle Swarm Optimization. Comparisons are made based on the consistency in obtaining optimal solutions, computation time and convergence profile. Results show the capability of the proposed method in finding a proper solution in a very short time and also escaping from local optima of the solution-space.

Analytical prediction of liquid-gated graphene nanoscroll biosensor performance

The latest discovery of the graphene nanoscroll has provided enormous new stimuli to carbon nanoresearch. Due to its one-dimensional structure and tunable core size, the graphene nanoscroll is suitable for nanoscale applications such as in nanotransistors, and biosensor devices. DNA sensing is critical in the identification of the genetic risk factors associated with complex human diseases, and continues to have an emerging role in therapeutics and personalized medicine. This paper presents the analytical model of liquid-gated field effect transistors (LGFETs) for zig-zag graphene nanoscrolls (ZGNSs) inspired by carbon nanotube behavior when exposed to DNA molecules. First of all, in order to gain physical insight into GNS-based devices, the conductance of GNSs is analytically modelled. Based on the sensing mechanism of the DNA sensor, GNS controlling elements (ηGNS and eGNS) are proposed and the behavior of LGFETs-based GNS nanomaterial in the presence of DNA molecules is predicted to get a greater insight into the rapid development of DNA sensors and their application. Because of the channel-doping effect due to the adsorption of the DNA molecules, the conductance of the channel is altered. On the other hand, the applied voltage effect in the form of tilted electron energy levels is utilized in the form of normalized Fermi energy variation which is used in the sensor modelling. This study emphasizes the promising nature of carbon nanoscrolls for a number of electronic device applications.

Optimization of DNA sensor model based nanostructured graphene using particle swarm optimization technique

It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.

Analytical development and optimization of a graphene-solution interface capacitance model

Summary Graphene, which as a new carbon material shows great potential for a range of applications because of its exceptional electronic and mechanical properties, becomes a matter of attention in these years. The use of graphene in nanoscale devices plays an important role in achieving more accurate and faster devices. Although there are lots of experimental studies in this area, there is a lack of analytical models. Quantum capacitance as one of the important properties of field effect transistors (FETs) is in our focus. The quantum capacitance of electrolyte-gated transistors (EGFETs) along with a relevant equivalent circuit is suggested in terms of Fermi velocity, carrier density, and fundamental physical quantities. The analytical model is compared with the experimental data and the mean absolute percentage error (MAPE) is calculated to be 11.82. In order to decrease the error, a new function of E composed of α and β parameters is suggested. In another attempt, the ant colony optimization (ACO) algorithm is implemented for optimization and development of an analytical model to obtain a more accurate capacitance model. To further confirm this viewpoint, based on the given results, the accuracy of the optimized model is more than 97% which is in an acceptable range of accuracy.

Structure and Thickness Optimization of Active Layer in Nanoscale Organic Solar Cells

This paper presents the development of a two-dimensional model of multilayer bulk heterojunction organic nanoscale solar cells, consisting of the thickness of active layer and morphology of the device. The proposed model is utilized to optimize the device parameters in order to achieve the best performance using particle swarm optimization algorithm. The organic solar cells under research are from poly (3-hexylthiophene) and [6,6]-phenyl C61-butyric acid methyl ester type which are modelled to be investigated for performance enhancement. A three-dimensional fitness function is proposed involving domain size and active layer thickness as variables. The best results out of 20 runs of optimization show that the optimized value for domain size is 17 nm, while the short-circuit current vs. voltage characteristic shows a very good agreement with the experimental results obtained by previous researchers.