Dynamic analysis of synchronous machine using neural network based characterization clustering and pattern recognition

Synchronous generators form the principal source of electric energy in power systems. Dynamic analysis for transient condition of a synchronous machine is done under different fault conditions. Synchronous machine models are simulated numerically based on mathematical models where saturation on main flux was ignored in one model and taken into account in another. The developed models were compared and scrutinized for transient conditions under different kind of faults – loss of field (LOF), disturbance in torque (DIT) & short circuit (SC). The simulation was done for LOF and DIT for different levels of fault and time durations, whereas, for SC simulation was done for different time durations. The model is also scrutinized for stability stipulations. Based on the synchronous machine model, a neural network model of synchronous machine is developed using neural network based characterization. The model is trained to approximate different transient conditions; such as – loss of field, disturbance in torque and short circuit conditions. In the case of multiple or mixture of different kinds of faults, neural network based clustering is used to distinguish and identify specific fault conditions by looking at the behaviour of the load angle. By observing the weight distribution pattern of the Self Organizing Map (SOM) space, specific kinds of faults is recognized. Neural network patter identification is used to identify and specify unknown fault patterns. Once the faults are identified neural network pattern identification is used to recognize and indicate the level or time duration of the fault

Fast Recall for Complex-Valued Hopfield Neural Networks with Projection Rules

Many models of neural networks have been extended to complex-valued neural networks. A complex-valued Hopfield neural network (CHNN) is a complex-valued version of a Hopfield neural network. Complex-valued neurons can represent multistates, and CHNNs are available for the storage of multilevel data, such as gray-scale images. The CHNNs are often trapped into the local minima, and their noise tolerance is low. Lee improved the noise tolerance of the CHNNs by detecting and exiting the local minima. In the present work, we propose a new recall algorithm that eliminates the local minima. We show that our proposed recall algorithm not only accelerated the recall but also improved the noise tolerance through computer simulations

Computational analysis of a permanent magnet synchronous machine using numerical techniques

Ph.DDOCTOR OF PHILOSOPH

Transient stability assessment of hybrid distributed generation using computational intelligence approaches

Includes bibliographical references.Due to increasing integration of new technologies into the grid such as hybrid electric vehicles, distributed generations, power electronic interface circuits, advanced controllers etc., the present power system network is now more complex than in the past. Consequently, the recent rate of blackouts recorded in some parts of the world indicates that the power system is stressed. The real time/online monitoring and prediction of stability limit is needed to prevent future blackouts. In the last decade, Distributed Generators (DGs) among other technologies have received increasing attention. This is because DGs have the capability to meet peak demand, reduce losses, due to proximity to consumers and produce clean energy and thus reduce the production of CO₂. More benefits can be obtained when two or more DGs are combined together to form what is known as Hybrid Distributed Generation (HDG). The challenge with hybrid distributed generation (HDG) powered by intermittent renewable energy sources such as solar PV, wind turbine and small hydro power is that the system is more vulnerable to instabilities compared to single renewable energy source DG. This is because of the intermittent nature of the renewable energy sources and the complex interaction between the DGs and the distribution network. Due to the complexity and the stress level of the present power system network, real time/online monitoring and prediction of stability limits is becoming an essential and important part of present day control centres. Up to now, research on the impact of HDG on the transient stability is very limited. Generally, to perform transient stability assessment, an analytical approach is often used. The analytical approach requires a large volume of data, detailed mathematical equations and the understanding of the dynamics of the system. Due to the unavailability of accurate mathematical equations for most dynamic systems, and given the large volume of data required, the analytical method is inadequate and time consuming. Moreover, it requires long simulation time to assess the stability limits of the system. Therefore, the analytical approach is inadequate to handle real time operation of power system. In order to carry out real time transient stability assessment under an increasing nonlinear and time varying dynamics, fast scalable and dynamic algorithms are required. Transient Stability Assessment Of Hybrid Distributed Generation Using Computational Intelligence Approaches These algorithms must be able to perform advanced monitoring, decision making, forecasting, control and optimization. Computational Intelligence (CI) based algorithm such as neural networks coupled with Wide Area Monitoring System (WAMS) such as Phasor Measurement Unit (PMUs) have been shown to successfully model non-linear dynamics and predict stability limits in real time. To cope with the shortcoming of the analytical approach, a computational intelligence method based on Artificial Neural Networks (ANNs) was developed in this thesis to assess transient stability in real time. Appropriate data related to the hybrid generation (i.e., Solar PV, wind generator, small hydropower) were generated using the analytical approach for the training and testing of the ANN models. In addition, PMUs integrated in Real Time Digital Simulator (RTDS) were used to gather data for the real time training of the ANNs and the prediction of the Critical Clearing Time (CCT)

A New Chaotic System with Line of Equilibria: Dynamics, Passive Control and Circuit Design

A new chaotic system with line equilibrium is introduced in this paper. This system consists of five terms with two transcendental nonlinearities and two quadratic nonlinearities. Various tools of dynamical system such as phase portraits, Lyapunov exponents, Kaplan-Yorke dimension, bifurcation diagram and Poincarè map are used. It is interesting that this system has a line of fixed points and can display chaotic attractors. Next, this paper discusses control using passive control method. One example is given to insure the theoretical analysis. Finally, for the  new chaotic system, An electronic circuit for realizing the chaotic system has been implemented. The numerical simulation by using MATLAB 2010 and implementation of circuit simulations by using MultiSIM 10.0 have been performed in this study

ANALYSIS OF THE VOLTAGE STABILITY PROBLEM IN ELECTRIC POWER SYSTEMS USING ARTIFICIAL NEURAL NETWORKS

PhDThe voltage stability problem in electric power systems is concerned with the analysis of events and mechanisms that can lead a system into inadmissible operating conditions from the voltage viewpoint. In the worst case, total collapse of the system may result, with disastrous consequences for both electricity utilities and customers. The analysis of this problem has become an important area of research over the past decade due to some instances of voltage collapse that have occurred in electric systems throughout the world. This work addresses the voltage stability problem within the framework of artificial neural networks. Although the field of neural networks was established during the late 1940s, only in the past few years has it experienced rapid development. The neural network approach offers some potential advantages to the solution of problems for which an analytical solution is difficult. Also, efficient and accurate computation may be achieved through neural networks. The first contribution of this work refers to the development of an artificial neural network capable of computing a static voltage stability index, which provides information on the stability of a given operating state in the power system. This analytical tool was implemented as a self-contained computational system which exhibited good accuracy and extremely low processing times when applied to some study cases. Dynamic characteristics of the electrical system in the voltage stability problem are very important. Therefore, in a second stage of the present work, the scope of the research was extended so as to take into account these new aspects. Another neural network-based computational system was developed and implemented with the purpose of providing some information on the behaviour of the electrical system in the immediate future. Examples and case studies are presented throughout the thesis in order to illustrate the most relevant aspects of both artificial neural networks and the computational models developed. A general discussion summarises the main contributions of the present work and topics for further research are outlined.CNPq -Conselho Nacional de Desenvolvimento Cientffico e Tecnoldgico EPUSP -Escola Politecnica da Universidade de Sao Paul