https://www.jstage.jst.go.jp/article/jsprs1975/37/6/37_6_39/_pdf

1998 IEEE International Conference on SMCに参加して

Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study.

In this paper a new meta-heuristic search method, called Cat Swarm Optimization (CSO) algorithm is applied to determine the best optimal impulse response coefficients of FIR low pass, high pass, band pass and band stop filters, trying to meet the respective ideal frequency response characteristics CSO is generated by observing the behaviour of cats and composed of two sub-models In CSO, one can decide how many cats are used in the iteration Every cat has its' own position composed of M dimensions, velocities for each dimension, a fitness value which represents the accommodation of the cat to the fitness function, and a flag to identify whether the cat is in seeking mode or tracing mode The final solution would be the best position of one of the cats CSO keeps the best solution until it reaches the end of the iteration The results of the proposed CSO based approach have been compared to those of other well-known optimization methods such as Real Coded Genetic Algorithm (RGA), standard Particle Swarm Optimization (PSO) and Differential Evolution (DE) The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems The performances of the CSO based designed FIR filters have proven to be superior as compared to those obtained by RGA, conventional PSO and DE The simulation results also demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters

Cat Swarm Optimization algorithm for optimal linear phase FIR filter design.

Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models

Optimal power flow (OPF) is one of the most important tools in power system operation and control, which determines the minimum operating cost and retains the control variables in their secure boundaries. This paper takes into account several unbridled practical constraints in the OPF problem, three of which – that is – valve-point effect, multi-fuel option, and, above all, prohibited operating zone are the most conspicuous ones. Further, the flexible alternating current transmission systems (FACTS) devices are considered, as well, which have several merits such as decreasing the active power transmission loss, controlling the power flow, and improving the voltage stability/profile, to name but a few. Accordingly, thyristor controlled series capacitor (TCSC) – the most popular and common component of the FACTS equipment’s category – is utilized in this study. As a result, the OPF problem integrated with such practical constraints referred to above as well as FACTS devices becomes a highly nonlinear-nonconvex optimization problem and to solve it, a reliable and efficient evolutionary algorithm such as fuzzy-based improved comprehensive-learning particle swarm optimization (FBICLPSO) algorithm is introduced. The proposed approach is scrutinized on IEEE 30-bus test system, which is a commonly used test system for solving the non-smooth and non-convex versions of the OPF problem. Comparing the obtained results by the proposed algorithm with the available alternatives in the literature corroborate the potential and effectiveness of the proposed approach.

An efficient particle swarm optimization algorithm to solve optimal power flow problem integrated with FACTS devices

https://cyberleninka.org/article/n/720021.pdf

A review on fault classification methodologies in power transmission systems: Part—I

A hybrid algorithm based optimal placement of DG units for loss reduction in the distribution system

Obtaining optimal power flow solution is a strenuous task for any power system engineer. The inclusion of FACTS devices in the power system network adds to its complexity. The dual objective of OPF with fuel cost minimization along with FACTS device location for IEEE 30 bus is considered and solved using proposed Enhanced Bacterial Foraging algorithm (EBFA). The conventional Bacterial Foraging Algorithm (BFA) has the difficulty of optimal parameter selection. Hence, in this paper, BFA is enhanced by including Nelder-Mead (NM) algorithm for better performance. A MATLAB code for EBFA is developed and the problem of optimal power flow with inclusion of FACTS devices is solved. After several run with different initial values, it is found that the inclusion of FACTS devices such as SVC and TCSC in the network reduces the generation cost along with increased voltage stability limits. It is also observed that, the proposed algorithm requires lesser computational time compared to earlier proposed algorithms.

J. Belwin Edward

Papers

A review on fault classification methodologies in power transmission systems: Part—I

A hybrid algorithm based optimal placement of DG units for loss reduction in the distribution system

An enhanced bacterial foraging algorithm approach for optimal power flow problem including FACTS devices considering system loadability.

Application of Wavelet Technique for Fault Classification in Transmission Systems

Voltage Profile Improvement of Solar PV Grid – Connected Inverter with Micro Grid Operation using PI Controller