Generalised additive multiscale wavelet models constructed using particle swarm optimisation and mutual information for spatio-temporal evolutionary system representation

A new class of generalised additive multiscale wavelet models (GAMWMs) is introduced for high dimensional spatio-temporal evolutionary (STE) system identification. A novel two-stage hybrid learning scheme is developed for constructing such an additive wavelet model. In the first stage, a new orthogonal projection pursuit (OPP) method, implemented using a particle swarm optimisation(PSO) algorithm, is proposed for successively augmenting an initial coarse wavelet model, where relevant parameters of the associated wavelets are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be a redundant model. In the second stage, a forward orthogonal regression (FOR) algorithm, implemented using a mutual information method, is then applied to refine and improve the initially constructed wavelet model. The proposed two-stage hybrid method can generally produce a parsimonious wavelet model, where a ranked list of wavelet functions, according to the capability of each wavelet to represent the total variance in the desired system output signal is produced. The proposed new modelling framework is applied to real observed images, relative to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, and the associated identification results show that the new modelling framework is applicable and effective for handling high dimensional identification problems of spatio-temporal evolution sytems

Communication Subsystems for Emerging Wireless Technologies

The paper describes a multi-disciplinary design of modern communication systems. The design starts with the analysis of a system in order to define requirements on its individual components. The design exploits proper models of communication channels to adapt the systems to expected transmission conditions. Input filtering of signals both in the frequency domain and in the spatial domain is ensured by a properly designed antenna. Further signal processing (amplification and further filtering) is done by electronics circuits. Finally, signal processing techniques are applied to yield information about current properties of frequency spectrum and to distribute the transmission over free subcarrier channels

Performance Comparison of No-preference and Weighted Sum Objective Methods in Multi-Objective Optimization of AVR-PSS Tuning in Multi-machine Power System

Simultaneous optimization of controllers in power systems is a challenging research due to the inherent nonlinearity of such a system. Multi-objective optimization is a useful tool for tuning excitation controllers and minimizing oscillations that are described through definition of transient and small-signal stability in power systems. In this paper, a Two-Area-Four-Machine (TAFM) power system model is tested on multiple short circuit and load disturbances. A multi-objective performance analysis is investigated by observing the system\u27s behaviour in different cases involving the no-preference method and a priori method called weighted sum objective. The analysis is done through observation of two different objective functions. First objective function includes the sum of the integral of time-weighted absolute errors of rotor speed differences, generator voltage, and tie-line power transfer. Second objective function observes time domain elements: overshoot, undershoot, and settling time of machines\u27 rotor speeds. Results are compared for two methods combined with four different algorithms to provide better insight into the computational performance of each algorithm and objective search method. Algorithms used for controllers\u27 parametrization include two novel algorithms: multi-objective ant lion optimizer (MOALO) and salp swarm algorithm (MOSSA), and two classic algorithms: multi-objective particle swarm optimization with velocity relaxation (MOVRPSO) and simulated annealing (MOSA)

Design of Hybrid Intelligent Power System Stabilizer for a Multi-Machine System

In this project a coordinated design of Fuzzy Power System Stabilizer (FPSS) and TCSC based power oscillation damping (POD) controller to improve power system small-signal stability need to be designed. Two controllers are used for optimizing the system for a better result. Conventional power system stabilizer is replaced by a Fuzzy PSS and the Particle Swarm Optimization (PSO) algorithm tries to minimize an eigenvalue-based multi-objective function by optimizing the parameters of the POD controller. Time domain simulations in MATLAB/SIMULINK performed on a two area four machine (2A4M) power system reveals that superior enhancement in damping of oscillations is achieved by employing coordinated control of FPSS-POD controller in comparison with conventional PSS-POD controller

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems