Mahdiyeh Eslami

Locating the general failure surface of earth slope using particle swarm optimisation

In this study, a new particle swarm optimisation (PSO) approach is proposed for evaluating the factor of safety (FS) in a slope stability analysis based on the limit equilibrium method. The safety factors of the general slip surfaces are calculated using a concise algorithm of the Morgenstern–Price method, which satisfies both the force and the moment equilibriums. Each new slip surface is randomly generated by a straight-line technique. The performance of the proposed algorithm is evaluated using a set of three benchmark functions and three slope stability problems from the literature. The results indicate that the new method can provide high-quality, accurate and efficient solutions for computing the FS. Moreover, this method can predict a more critical failure mechanism of earth slope and outperform both the other methods in the literature and the standard PSO.

Optimal location of PSS using improved PSO with chaotic sequence

This paper proposed a novel method for the optimal location of the power system stabilizer (PSS) by integrating the improved particle swarm optimization (IPSO) with the chaotic. The modification in the particle swarm optimization (PSO) is made by introducing passive congregation which is an important biological force which preserves swarm integrity. It helps each swarm member in receiving a multitude of information from other members, and thus decreases the possibility of a failed attempt at detection or a meaningless search. Then, the IPSO and chaotic are hybridized (IPSOC), to improve the global searching capacity and prevent the premature convergence due to local minima. Location of PSS over a wide range of system configurations is formulated as a multi-objective function that is the aggregation of the three objectives related to the damping ratio and damping factor, and the number of PSS. The performance of the proposed IPSOC is compared to PSO through nonlinear time-domain simulation. Finally, the proposed method applies to find out the best candidate machines to be equipped with PSSs. The obtained results show that the new method can find the best locations and the optimum PSSs parameters simultaneously with an excellent global damping performance.

Coordinated design of PSS and SVC damping controller using CPSO

This paper proposes a novel optimization technique for simultaneous coordinated designing of power system stabilizer (PSS) and static VAR compensator (SVC) as a damping controller in the multi-machine power system. PSO and chaos theory is hybridized to form a chaotic PSO (CPSO), which reasonably combines the population-based evolutionary searching ability of PSO and chaotic searching behavior. The coordinated design problem of PSS and SVC controllers over a wide range of loading conditions are formulated as a multi-objective optimization problem which is the aggregation of the two objectives related to the damping ratio and damping factor. The proposed damping controllers are tested on a weakly connected power system. The effectiveness of the proposed controllers is demonstrated through the eigenvalue analysis and nonlinear time-domain simulation. The results of these studies show that the proposed coordinated controllers have an excellent capability in damping power system inter-area oscillations and enhance greatly the dynamic stability of the power system. Moreover, it is superior to both the manually coordinated stabilizers of the PSS and the SVC damping controller.

Coordinated design of PSS and TCSC controller for power system stability improvement

In this study, the problem of simultaneous and coordinated tuning method for thyristor-controlled series capacitor (TCSC) power oscillation damping controllers and conventional power system stabilizer (PSS) controllers in multi-machine power systems is considered. For a multi-machine power system, the tuning of the PSS and TCSC controllers is generally formulated as an objective function with constraints consisting of the damping ratio and damping factor. Using the linearized system model and the parameter-constrained nonlinear optimization algorithm, interactions among TCSC controller and PSS controllers are minimized. It is done by simultaneously optimizing, the parameters of the damping controllers using sequential quadratic programming technique. Simulation results of a two area multi-machine power system validate the effectiveness of the approach and its capacity in tuning multiple controllers in power systems simultaneously.

Power system stabilizer design based on optimization techniques

Power system stabilizers (PSSs) are the most well known and efficient devices to damp the power system oscillations caused by interruptions. Low frequency oscillation problems are very difficult to solve because power systems are very large, complex and geographically distributed. Therefore, it is necessary to utilize most efficient optimization methods to take full advantages in simplifying the problem and its implementation. From this perspective, many successful and powerful optimization methods and algorithms have been employed in formulating and solving this problem. These optimization methodologies and techniques are widely diverse and have been the subject of ongoing enhancements over the years. This paper presents a survey of literature on the various optimization methods applied to solve the PSS problems. A review of most of the publications on the topic is presented.

Stability assessment of earth slope using modified particle swarm optimization

This paper has proposed an effective method to determine the minimum factor of safety (FS) and associated critical failure surface in slope stability analysis. The search for the minimum FS based on limit equilibrium methods is a complex optimization problem as the objective function is non-smooth and non-convex. Recently, particle swarm optimization (PSO) as a meta-heuristic optimization algorithm has been developed with success in treating various types of problems. In the current study, a new approach of PSO is proposed to calculate the safety factor of earth slopes. The safety factors of the general slip surfaces are calculated using Spencer method of slices, and each new slip surface is randomly generated by straight line technique. The reliability and efficiency of the proposed algorithm are examined by considering a number of published cases. The results indicate that the new method can predict a more critical failure mechanism with a lower FS and can outperform the other methods in the literature as well as standard PSO. Finally, the proposed method will be validated by considering an existing slope failure in Ulu Klang, Malaysia.

Multi-objective optimisation of retaining walls using hybrid adaptive gravitational search algorithm

This paper presents an effective hybrid evolutionary approach for multi-objective optimisation of reinforced concrete (RC) retaining walls. The proposed algorithm combines an adaptive gravitational search algorithm (AGSA) with pattern search (PS) called AGSA–PS. In the resulting hybrid approach, the PS algorithm is employed as a local search algorithm around the global solution found by AGSA. The proposed algorithm was tested on a set of five well-known benchmark functions and simulation results demonstrate the superiority of the new method compared with the standard algorithm. Thereafter, the proposed AGSA–PS is applied for multi-objective optimisation of RC retaining walls. Two objective functions include total cost and embedded CO2 emissions of retaining wall are considered. The reliability and efficiency of the AGSA–PS for multi-objective optimisation of retaining structures are investigated by considering two design examples of retaining walls. Experimental results demonstrate that the resulting algorithm has high viability, accuracy and significantly outperforms the original algorithm and some other methods in the literature.

Firefly algorithm and pattern search hybridized for global optimization

Firefly optimization algorithm is one of the latest swarm intelligence based optimization algorithm. A new hybrid optimization algorithm, which combines pattern search with firefly algorithm, namely FAPS, is proposed for numerical global optimization. There are two alternative phases of the proposed algorithm: the global exploration phase realized by firefly algorithm and the exploitation phase completed by pattern search. The performance of the proposed FAPS algorithm was tested on a comprehensive set of benchmark functions. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and the performance of firefly algorithm is much improved by introducing a pattern search method.

Artificial bee colony algorithm for optimal design of power system stabilizer

A newly developed heuristic global optimization algorithm, artificial bee colony (ABC) algorithm, is applied for simultaneous coordinated tuning of the power system stabilizers (PSSs) in the multi-machine power system. ABC is a population based optimization algorithm inspired by the foraging behavior of bee colony and proven its superior capabilities, such as faster convergence and better global minimum achievement. To investigate the robustness and ability of the proposed algorithm in damping stabilizers design, a 2-area 4-machine system under different operating conditions is considered. The performance of the proposed ABC is compared with the genetic algorithm (GA) through eigenvalue analysis and nonlinear time-domain simulation. The simulation studies show that the stabilizers designed by ABC perform better than those by GA in damping the power system low-frequency oscillations and enhance greatly the dynamic stability of the power system.

Gradient-based artificial bee colony for damping controllers design

A novel optimization algorithm by combining the artificial bee colony (ABC) algorithm and the sequential quadratic programming (SQP), that is the gradient based ABC algorithm, is presented to resolve the problems of global optimization and inter-area oscillations damping in power system. The proposed algorithm merges the global exploration ability of the artificial bee colony to converge quickly to a near optimum resolution, and the correct local exploitation capacity of the sequential quadratic programming to accelerate the search process and discover a correct solution. To show the feasibility and efficiency of the new method, numerical result is investigated on the New England system by tuning a power system stabilizer and a controller for the static VAR compensator. The proposed gradient based ABC algorithm is compared with ABC. The simulations studies demonstrate that the proposed algorithm based designed damping controllers perform better than controller designed by ABC.