Thang Trung Nguyen

The application of one rank cuckoo search algorithm for solving economic load dispatch problems

A new improved meta-heuristic algorithm, one rank cuckoo search algorithm (ORCSA), is applied for solving economic load dispatch problem.The advantages of the proposed ORCSA are few control parameters, high solution quality and fast computational time.The proposed ORCSA is very efficient for the problem via the result comparison with many other methods.The proposed ORCSA can be a very favorable method for the problems with complex objective and constraints. In this paper, a one rank cuckoo search algorithm (ORCSA) is proposed for solving economic load dispatch (ELD) problems. The main objective of the ELD problem is to minimize total cost of thermal generators while satisfying power balance constraint, prohibited operating zones, ramp rate constraints and operating limits of generators. Moreover, the generating units considered in this paper have different characteristics such as quadratic fuel cost function, nonconvex fuel cost function and multiple fuel options. The proposed ORCSA method has been developed by performing two modifications on the original cuckoo search algorithm (CSA) to improve optimal solution quality and computational time. The first modification is to merge new solution generated from both Levy flights and replacement a fraction of egg together and to evaluate and rank the solutions at once only. A bound by best solution mechanism has been used in the second modification for properly handling the inequality constraints. The proposed ORCSA method has been tested on different systems with different characteristics of thermal units and constraints. The results obtained by ORCSA have been compared to those from other methods available in the literature and the result comparison has indicated that the ORCSA method can obtain better solution quality than many other methods. Therefore, the proposed ORCSA can be a very effective and efficient method for solving ELD problems.

Robust Pole Placement With Moore's Algorithm

We consider the classic problem of pole placement by linear state feedback. We adapt the Moore eigenstructure assignment algorithm to obtain a novel parametric form for the pole-placing gain matrix, and introduce an unconstrained nonlinear optimization algorithm to obtain a gain matrix that will deliver robust pole placement. Numerical experiments indicate the algorithms performance compares favorably against several other notable robust pole placement methods from the literature.

Performance survey of robust pole placement methods

The classic problem of robust pole placement for linear time invariant systems via state feedback has been studied for several decades, and involves obtaining a gain matrix that will assign a certain desired set of closed-loop poles, while also providing a robust eigenstructure that is insensitive to uncertainties in the system matrices. There are several ways of measuring the robustness of the eigenstructure, and numerous methodologies have appeared in the literature to address the problem. In this paper, results from extensive experiments comparing the performance of a number of methods-including variations on two methods previously proposed by the present authors-against a variety of robustness measures are reported. The size of the matrix gain, runtime and accuracy of the pole placement achieved by each method are also compared. The results show some notable differences between the methods surveyed.

Estimation of the shear force in Transverse Dynamic Force Microscopy using a sliding mode observer

This paper concerns the application of a sliding mode observer to the problem of estimation of the shear force affecting the cantilever dynamics of a Transverse Dynamic Force Microscope (TDFM). The oscillated cantilever in proximity to a specimen permits the investigation of the specimen topography at nano-metre precision. The oscillation amplitude, but also in particular the shear forces, are a measure of distance to the specimen, and therefore the estimation of the shear force is of significance when attempting to construct TDFM images at submolecular accuracy. For estimation of the shear forces, an approximate model of the cantilever is derived using the method of lines. Model order reduction and sliding mode techniques are employed to reconstruct the unknown shear force affecting the cantilever dynamics based on only tip position measurements. Simulations are presented to illustrate the proposed scheme, which is to be implemented on the TDFM set up at the Centre for NSQI at Bristol.

Cuckoo search algorithm using different distributions for short-term hydrothermal scheduling with cascaded hydropower plants

This paper proposes a cuckoo search algorithm (CSA) using different distributions for solving short-term hydrothermal scheduling (ST-HTS) problem with cascaded hydropower plants. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species for solving optimization problems. The advantages of the CSA method are few control parameters and effective for optimization problems with complicated constraints. In the proposed CSA, three distributions have been used including Lévy distribution, Gaussian distribution and Cauchy distribution. The proposed methods have been tested on one system having one thermal plant and four cascaded hydropower plants scheduled in twenty-four subintervals and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is a very favorable method for solving the short term hydrothermal scheduling problems with cascaded hydropower plants.

Multi-objective electric distribution network reconfiguration solution using runner-root algorithm

Display Omitted The runner-root algorithm (RRA) is adapted to solve the network reconfiguration problem.Five objectives namely power loss, load balancing among the branches, load balancing among the feeders, number of switching operations and node voltage deviation are considered.The proposed RRA method is applied to the 33-bus and 70-bus test networks for evaluation.The proposed RRA method has better performance in comparison to other methods. This paper presents a runner-root algorithm (RRA) for electric distribution network reconfiguration (NR) problem. The considered NR problem in this paper is to minimize real power loss, load balancing among the branches, load balancing among the feeders as well as number of switching operations and node voltage deviation using max-min method for selection of the final compromised solution. RRA is equipped with two explorative tools, which are random jumps with large steps and re-initialization strategy to escape from local optimal. Moreover, RRA is also equipped with an exploitative tool to search around the current best solution with large and small steps to ensure the obtained result of global optimization. The effectiveness of the applied RRA in both single- and multi-objective has been tested on 33-node and 70-node distribution network systems and the obtained test results have been compared to those from other methods in the literature. The simulation results show that the applied RRA can be an efficient method for network reconfiguration problems with single- and multi-objective.

Arbitrary pole placement with the extended Kautsky-Nichols-van Dooren parametric form with minimum gain

We consider the classic problem of pole placement by state feedback. We revisit the well-known eigenstructure assignment algorithm of Kautsky, Nichols and van Dooren [1] and extend it to obtain a novel parametric form for the pole-placing feedback matrix that can deliver any set of desired closed-loop eigenvalues, with any desired multiplicities. This parametric formula is then employed to introduce an unconstrained nonlinear optimisation algorithm to obtain a feedback matrix that delivers the desired pole placement with minimum gain.

An effective cuckoo search algorithm for large-scale combined heat and power economic dispatch problem

This paper develops an effective cuckoo search algorithm (ECSA) for searching optimal solutions for the problem of combined heat and power economic dispatch. The main task of the problem is to determine the optimal value of power of the pure power generators, of the heat of the pure heat generators and of both power and heat of cogenerators so that fuel cost is minimized while exactly meeting power and heat demands and power and heat limits as well as the complicated feasible operating zone of cogenerators. The proposed ECSA is a newly improved version of conventional cuckoo search algorithm to improve the quality of solutions and reduce the maximum number of iterations based on two modified techniques. The first technique is based on the ratio of the difference between the fitness function value of each solution and the lowest fitness function value of the best current solution to the lowest one to determine an effective operation for producing the second new solution generation. The second technique aims to integrate both previous and current solutions into one group and sort them in the descending order of fitness value. The effectiveness of ECSA has been validated via six cases corresponding to six test systems where the scale of the systems is ranged from the smallest system with four units to the largest one with forty-eight units with valve point loading effects. The comparisons of obtained results with other existing methods have indicated that the proposed ECSA is very effective and robust for finding optimal solutions for the CHPED problem.

Modified Bat Algorithm for Combined Economic and Emission Dispatch Problem

The paper proposes a new modified Bat algorithm (MBA) for solving combined economic and emission load dispatch (CEED) problems where transmission power losses are considered. The MBA is first developed in the paper by modifying the several modifications on the conventional Bat algorithm (BA) in aim to improve the performance of the BA. The MBA is tested on two different systems with the transmission power losses. The performance of the MBA is evaluated by comparing obtained results with BA and other existing algorithms available in the study. As a result, it can be concluded that the MBA outperforms the BA and is very strong for solving the CEED problem.

Estimation of the shear force in transverse dynamic force microscopy using a sliding mode observer

This paper concerns the application of a sliding mode observer to the problem of estimation of the shear force affecting the cantilever dynamics of a Transverse Dynamic Force Microscope (TDFM). The oscillated cantilever in proximity to a specimen permits the investigation of the specimen topography at nano-metre precision. The oscillation amplitude, but also in particular the shear forces, are a measure of distance to the specimen, and therefore the estimation of the shear force is of significance when attempting to construct TDFM images at submolecular accuracy. For estimation of the shear forces, an approximate model of the cantilever is derived using the method of lines. Model order reduction and sliding mode techniques are employed to reconstruct the unknown shear force affecting the cantilever dynamics based on only tip position measurements. Simulations are presented to illustrate the proposed scheme, which is to be implemented on the TDFM set up at the Centre for NSQI at Bristol.