Alireza Abbasi

A novel adaptive modified harmony search algorithm to solve multi-objective environmental/economic dispatch

This paper proposes a novel adaptive modification approach based on harmony search algorithm (HS) to solve the multi- objective environmental economic dispatch problem. The proposed algorithm makes use of adaptive formulations to update the adjusting parameters of HS including the pitch adjusting and bandwidth parameters and the harmony memory consideration rate during the optimization process. Meanwhile, a useful modification is proposed to improve the variety of the harmony population effectively. In order to handle both the cost and emission objective functions, the ideas of trapezoidal fuzzy membership function and weighting factor are employed. The satisfying performance of the proposed method is examined through the IEEE standard test system.

Mechanical properties and machinability of 6061 aluminum alloy produced by equal-channel angular pressing

Equal-channel angular pressing is a process used for the effective improvement of certain mechanical properties of various metallic alloys by producing an ultrafine-grained microstructure, although subsequent machining may be necessary in some cases for practical applications. The objective of this article was to investigate the mechanical properties and machinability of a 6061-T6 aluminum alloy produced by equal-channel angular pressing on different routes. Equal-channel angular pressing was found to improve not only hardness in certain specimens tested but also the strength of others as revealed by tensile tests. Moreover, the two most important machinability criteria, that is, cutting forces and surface roughness, were measured to find a significant reduction in cutting forces. The specimens produced by the process indeed exhibited better machinability properties as required by the cutting force criteria. Finally, a significant improvement was observed in the surface roughness of equal-channel angular pressing–produced specimens.

An smart stochastic approach to model plug-in hybrid electric vehicles charging effect in the optimal operation of micro-grids

This paper proposes a sufficient stochastic framework to assess the influence of charging demand of plug-in hybrid electric vehicles (PHEVs) on the operation of renewable micro-grids (MGs). In this regard, an intelligent charging approach is proposed to shift the charging demand of PHEVs to off-peak load hours. In order to reduce the total cost of the MG, battery as the storage device is incorporated in the MG. Since the problem investigated is a hard complicated optimization problem, a new optimization method called Modified Clonal Selection algorithm (MCSA) is utilized too. The proposed MCSA employs two modification techniques to improve the position of antibodies in the face of local optima. For modeling the uncertainty of parameters, 2m-point estimate method (2m-PEM) is utilized as the stochastic framework. The feasibility and appropriate performance of the proposed method are examined on a standard MG.

A new intelligent method for optimal allocation of D-STATCOM with uncertainty

According to the standards, optimal allocation and sizing of D-STATCOM should be implemented accurately to make the maximum efficiency. Thus, this paper suggests a new approach based on social spider optimization (SSO) algorithm to deal with the optimal allocation and sizing of D-STATCOM in the distribution systems. The problem is formulated in a multi-objective framework based on scenario production and SSO algorithm to capture the uncertainties of active and reactive loads suitably. The proposed scenario based method generates a number of possible scenarios with different probabilities to model the uncertainty effects. The feasibility and satisfying performance of the proposed method are examined using an IEEE standard distribution test system.

Optimal placement of distributed generation in radial networks considering reliability and cost indices

In the current paper, the optimal placement merits of distribution generations (DGs) are investigated in the economical version of a grid-parallel mode. The proposed method is constructed based on a probabilistic approach which is called Point Estimate Method (PEM) to consider the uncertainty associated with the load demand prediction error along with the variation of cost of electricity and power production of DGs. The objective functions are the total active power losses, the total emission produced and the total cost related to the grid & DGs and cost of reliability. These functions are set to be optimized that result in utilizing an interactive fuzzy approach based on improved particle swarm optimization (IPSO) to let the decision maker apply his/her preferences to the system. It is apt to mention that the application of the interactive fuzzy approach is based on the result of conflicting behaviors of the investigated objective functions. Through using the Tai-Power distribution test system the feasibility and the efficiency of the proposed method have been shown. 6 7 8 9 10 11 12 13 14

The Role of Tool Pin Profile and Temperature on Friction Stir Welding of High Zinc Brass

In this experimental study, high zinc brass was welded by friction stir welding (FSW). A threaded cylindrical tool was used for welding the brass plates in butt configuration. Mechanical tests i.e. hardness, tensile, bending, and erichsen tests were performed for evaluating the welding strength. In addition, optical microscopy (OM) and scanning electron microscopy (SEM) were used as microstructural tests for estimating the material morphology. Furthermore, temperature as a function of time was measured during the welding. The results indicated close correlation between temperature and microhardness distribution as well as the uniformity of microstructure. Moreover, the welded sample showed acceptable mechanical strength during the applied mechanical tests due to adequate primary welding parameters and tool which led to sufficient produced temperature and material bonding.

Consideration effect of wind farms on the network reconfiguration in the distribution systems in an uncertain environment

Abstract This article proposes a novel probabilistic Distribution Feeder Reconfiguration (DFR) based method to consider the uncertainty impacts into account with high accuracy. In order to achieve the set aim, different scenarios are generated to demonstrate the degree of uncertainty in the investigated elements which are known as the active and reactive load consumption and the active power generation of the wind power units. Notably, a normal Probability Density Function (PDF) based on the desired accuracy is divided into several class intervals for each uncertain parameter. Besides, the Weiball PDF is utilised for modelling wind generators and taking the variation impacts of the power production in wind generators. The proposed problem is solved based on Fuzzy Adaptive Modified Particle Swarm Optimisation to find the most optimal switching scheme during the Multi-objective DFR. Moreover, this paper holds two suggestions known as new mutation methods to adjust the inertia weight of PSO by the fuzzy rules to enhance its ability in global searching within the entire search space.

A sufficient stochastic framework to capture the uncertainty of load models in the management of distributed generations in power systems

With the high penetration of renewable power sources in the form of distributed generations (DGs), the amount of uncertainty in the power systems is increased greatly. This high uncertainty has affected most of the grid operation strategies including the optimal management of DGs. One significant source of uncertainty is the forecast error in the modeling of the future active and reactive load values. In order to deal with this problem, this paper suggests a new stochastic framework based on the scenario generation process and roulette wheel mechanism. This method converts the stochastic problem into a number of deterministic problems with different probabilities. Since the problem investigated is a complex nonlinear optimization problem, a sufficient optimization algorithm based on the bio-inspired krill herd algorithm is proposed to solve the problem effectively. The satisfying performance of the proposed method is examined on the IEEE standard test system.

Optimal probabilistic reconfiguration of smart distribution grids considering penetration of plug-in hybrid electric vehicles

The capable incorporation of Plug-in Hybrid Electric Vehicles (PHEVs) in the upcoming transportation area presents several technical challenges to electrical distribution networks for example voltage drop and loss increase. The energy demand of these movable loads is stochastic naturally owing to the uncertainties accompanied by their location and amount of required energy. Accordingly, a new optimal stochastic reconfiguration procedure based on Gravitational Search Algorithm (GSA) is suggested that diminishes resistive loss and costs of radial distribution grids. The proposed technique is equipped with mutative operators to surpass the optimization process. Furthermore, a novel local smart charging pattern is recommended which lessens the congestion influence of PHEVs on system load curve successfully. The uncertainties of PHEV loads are modeled with Monte Carlo Simulation (MCS) and the proposed methodology is examined on Tai-power distribution network to validate its performance and robustness.

Consideration effect of uncertainty in the reliability indices of power systems using a scenario-based approach

In power systems, reliability evaluations mainly depend on the precise forecasting of the reliability parameters of the internal components. Nonetheless, the procedure of calculating the reliability parameters such as failure rate and repair rate parameters is based on the failure statistics and measurements which can cause much inherent uncertainty and thus affecting the reliability indices of the system. Therefore, the main purpose of this work is to suggest a sufficient and easy-implemented approach based on scenario production to capture the uncertainty of the reliability parameters including the failure rate and repair rate variables. Here, the scenario generation process is constructed using the roulette wheel mechanism along with the probability density function of the random variables. Then, the proposed scenario based approach makes use of a scenario reduction mechanism to avoid high computational burden. This aspect of the proposed stochastic framework makes it useful for the optimization applications which require iterative analysis. In order to see the feasibility and satisfying performance of the proposed framework, an IEEE standard test system is used as the case study.