Ali Ghasemi

A new metaheuristic algorithm based on shark smell optimization

In this article, a new metaheuristic optimization algorithm is introduced. This algorithm is based on the ability of shark, as a superior hunter in the nature, for finding prey, which is taken from the smell sense of shark and its movement to the odor source. Various behaviors of shark within the search environment, that is, sea water, are mathematically modeled within the proposed optimization approach. The effectiveness of the suggested approach is compared with many other heuristic optimization methods based on standard benchmark functions. Also, to illustrate the efficiency of the proposed optimization method for solving real-world engineering problems, it is applied for the solution of load frequency control problem in electrical power systems. The obtained results confirm the validity of the proposed metaheuristic optimization algorithm.

Modeling of Wind/Environment/Economic Dispatch in power system and solving via an online learning meta-heuristic method

Load dispatch is developed by thermal and wind units with its uncertainties.Consider many objective functions into a new multi objective method.Propose a new decision making method to select best Pareto front.Propose some new operators for local and global searches.Propose a new online training mechanism for self adaptive goals. This paper addresses the effect of the wind power units into the classical Environment/Economic Dispatch (EED) model which called hereafter as Wind/Environment/Economic Dispatch (WEED) problem. The optimal dispatch between thermal and wind units so that minimized the total generating costs are considered as multi objective model. Normally, the nature of the wind energy as a renewable energy sources has uncertainty in generation. Therefore, in this paper, use a practical model known as 2m-point to estimate the uncertainty of wind power. To solve the WEED problem, this paper proposed a new meta-heuristic optimization algorithm that uses online learning mechanism. Honey Bee Mating Optimization (HBMO), a moderately new population-based intelligence algorithm, shows fine performance on optimization problems. Unfortunately, it is usually convergence to local optima. Therefore, in the proposed Online Learning HBMO (OLHBMO), two neural networks are trained when reached to the predefined threshold by current and previous position of solutions and their fitness values. Moreover, Chaotic Local Search (CLS) operator is use to develop the local search ability and a new data sharing model determine the set of non-dominated optimal solutions and the set of non-dominated solutions to kept in the external memory. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as a decision-making technique is employed to find the best solution from the set of Pareto solutions. The proposed model has been individually examined and applied on the IEEE 30-bus 6-unit, the IEEE 118-bus 14-unit, and 40-unit with valve point effect test systems. The robustness and effectiveness of this algorithm is shows by these test systems compared to other available algorithms.

Improved time varying inertia weight PSO for solved economic load dispatch with subsidies and wind power effects

Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, IranReceived 8 May 2014; revised 30 August 2014; accepted 5 October 2014This article presents a new approach to economic load dispatch (ELD) problems by the considering the cost func-tions, impact renewable energy as wind turbin and subsidies.Economicdispatch is the short-term determination ofthe optimal output of a number of electricity generation facilities, to meet the system load, at the lowest possiblecost, subject to transmission and operational constraints. The main goal in the deregulated system is subsidies andanalysis performance on government to minimize the total fuel cost while satisfying the load demand and opera-tional constraints. The practical ELD problems have nonsmooth cost functions with equality and inequality con-straints, which make the problem of finding the global optimum difficult when using any mathematicalapproaches. Accordingly, particle swarm optimization with time-varying inertia weight (PSO-TVIW) used for solv-ing this problem. The effectiveness of the proposed strategy is applied over real-world engineering problem andhighly constrained. Obtained results indicate that PSO-TVIW can successfully solve this problem.

A safe stable directional vehicular platoon

This paper addresses the problem of platoon control. The stability and the string stability of a platoon of vehicles in the simultaneous presence of the time delay and the lag are investigated. The contribution of this paper is twofold. First, the paradigm of the cluster treatment of characteristic roots is utilized in order to reveal the stabilizing parametric regions in the domain of the delays to render the stability of a closed-loop system. Second, the restrictions which need to be imposed on the control parameters are considered in order to make the vehicle behave monotonically in the forward direction without collision as well as the requirement for the string stability. Finally, an example of multiple-vehicle platoon control is presented, which demonstrates the effectiveness and the robustness of the proposed method.

Reactive power planning using a new hybrid technique

Voltage deviation and stability constrained VAr planning or reactive power planning (RPP) is an important challenging issue in power systems. This paper presents a new hybrid technique for modeling and solving RPP problem taking into account the static voltage stability constraint. First, the uncertain fuzzy clustering theory is employed to select new candidate VAr source locations. Then, modified gray code is applied and used to represent a series of non-uniform VAr capacity intervals at different candidate buses. Based on the new ordering of the VAr capacity intervals, a simplified piecewise linear function between the total transfer capability and new VAr capacity is derived and applied as static voltage stability constraint in RPP problem. Last, the RPP optimization problem is solved by a self adaptive fuzzy chaotic interactive honey bee mating optimization (FCIHBMO) technique taking advantage of the modified gray code. In the FCIHBMO algorithm, a modified definition of the updating factors on generation solution is proposed. In the case study, uncertain fuzzy clustering mechanism, the modified gray code, and the modified HBMO are applied to the IEEE 118-bus and IEEE 300-bus systems. Test results conclude that the proposed hybrid technique is a simplified and effective approach for voltage stability constrained VAr planning with contingency considered.

Control design and stability analysis of homogeneous traffic flow under time delay: A new spacing policy

This paper details control design and stability analysis of homogeneous traffic flow by considering it as the interaction between inter-connected vehicular platoons. A third-order linear differential model is used to describe the longitudinal motion of each vehicle. As the lead vehicle of the whole traffic flow may be not available, the inter-platoon communication structure is assumed to be a bidirectional virtual leader following (BDVLF) topology. Both communication and parasitic delays are considered in the system modeling and control design. By employing an appropriate state transformation, the 3N-order closed-loop dynamics are decoupled to N third-order dynamical equations. The cluster treatment characteristic root (CTCR) method is employed to perform the inter-platoon stability analysis. The intra-platoon communication structure is assumed to be general. Therefore, the eigenvalues of matrix H maybe complex which makes the stability analysis more intricate. By introducing a new decoupling approach and applying a centralized control for each vehicle in the platoon, the necessary conditions on control parameters satisfying intra-platoon stability are presented. The most important merit of this method compared to previous works, is that control parameters are independent of eigenvalues of matrix H. Several simulation studies are provided to show the effectiveness of the proposed approaches.

Formation control of longitudinal vehicular platoons under generic network topology with heterogeneous time delays

The problem of third-order consensus of homogeneous vehicular platoons in the presence of communication and parasitic delays is investigated. The communication topology of vehicular networks is assumed to be directed and generic. Therefore, a number of eigenvalues of the network’s matrix are complex, entangling the stability analysis of the closed-loop dynamics. By considering both communication and parasitic delays, a new linear centralized consensus protocol is designed for each vehicle. It will be shown that the closed-loop dynamics of vehicular networks with generic topology is in the form of linear systems with multiple delays. By presenting a new approach, the resultant linear time delay system is decoupled to individual third- and sixth-order dynamical equations. By performing the stability analysis of the new equations, it will be proven that the control parameters are independent of the network’s topology. Therefore, the control design and stability analysis will be significantly simplified compared with previous studies. To find the stable regions of time delay, the cluster treatment characteristic root method is employed. Simulation results are provided to show the effectiveness of the proposed approach.