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Modern power systems operate much closer to their stability limits than before. With the introduction of highly sensitive industrial and residential loads, the loss of system stability becomes increasingly costly. Reinforcing the power grid by installing additional transmission lines, creating more complicated meshed networks and increasing the voltage level are among the effective, yet expensive solutions. An alternative approach is to improve the performance of the existing power system components by incorporating more intelligent control techniques. This can be achieved in two ways: introducing intelligent local controllers for the existing components in the power network in order to employ their utmost capabilities, and implementing global intelligent schemes for optimizing the performance of multiple local controllers based on an objective function associated with the overall performance of the power system. Both these aspects are investigated in this thesis. In the first section, artificial neural networks are adopted for designing an optimal nonlinear controller for a static compensator (STATCOM) connected to a multimachine power system. The neurocontroller implementation is based on the adaptive critic designs (ACD) technique and provides an optimal control policy over the infinite horizon time of the problem. The ACD based neurocontroller outperforms a conventional controller both in terms of improving the power system dynamic stability and reducing the control effort required. The second section investigates the further improvement of the power system behavior by introducing an ACD based neurocontroller for hierarchical control of a multimachine power system. The proposed wide area controller improves the power system dynamic stability by generating optimal control signals as auxiliary reference signals for the synchronous generators automatic voltage regulators and the STATCOM line voltage controller. This multilevel hierarchical control scheme forces the different controllers throughout the power system to optimally respond to any fault or disturbance by reducing a predefined cost function associated with the power system performance.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-09-04T13:10:41Z No. of bitstreams: 1 Marcio Eduardo.pdf: 7939176 bytes, checksum: 3b90c4b32aeabafd3b87e4f3c36d2ed6 (MD5) / Made available in DSpace on 2017-09-04T13:10:41Z (GMT). No. of bitstreams: 1 Marcio Eduardo.pdf: 7939176 bytes, checksum: 3b90c4b32aeabafd3b87e4f3c36d2ed6 (MD5) Previous issue date: 2014-08-21 / The technique of linear control based on the minimization of a quadratic performance index using the second method of Lyapunov to guarantee the stability of the system, if this is controllable and observable. however, this technique is inevitably necessary to find the solution of the HJB or Riccati equation. The control system design online need, real time, to adjust your feedback gain to maintain a certain dynamic, it requires the calculation of the Riccati equation solution in each sampling generating a large computational load that can derail its implementation. This work shows an intelligent control system design that meets the optimal or suboptimal control action from the sensory data of process states and the instantaneous cost observed after each state transition. To find this optimal control action or policy, the approximate dynamic programming and adaptive critics are used, based on the parameterizations given by the problem of linear quadratic regulator (LQR), but without explicitly solving the associated Riccati equation. More specifically, the LQR problem is solved by four different methods which are the Dynamic Programming Heuristic, the Dual Heuristic Dynamic Programming, Action Dependent Dynamic Programming Heuristic and Action Dependent Dual Heuristic Dynamic Programming algorithms. However, these algorithms depend on knowledge of the value functions to derive the optimal control actions. These value functions with known structures have their parameters estimated using the least mean square family and Recursive Least Squares algorithms. Two processes that have the Markov property were used in the computational validation of the algorithms adaptive critics implemented, one corresponds to the longitudinal dynamics of an aircraft and the other to an electrical circuit. / A técnica de controle linear baseado na minimização de um índices de desempenho quadrático utilizando o segundo método de Liapunov garante a estabilidade do sistema, se este for controlável e observável. Por outro lado, nessa técnica inexoravelmente é necessário encontrar a solução da Equação Hamilton-Jacobi-Bellman (HJB) ou Riccati. Em projeto de sistema de controle online que necessita, em tempo real, alterar seus ganhos de retroação para manter uma certa dinâmica, impõe o cálculo da solução da equação de Riccati em cada instante de amostragem gerando uma grande carga computacional que pode inviabilizar sua implementação. Neste trabalho, mostra-se o projeto de um sistema de controle inteligente que encontra a ação de controle ótima ou subótima a partir de dados sensoriais dos estados do processo e do custo instantâneo observados após cada transição de estado. Para encontrar essa ação de controle ou política ótima, a programação dinâmica aproximada ou críticos adaptativos são utilizados, tendo como base as parametrizações dado pelo problema do regulador linear quadrático (LQR), mas sem resolver explicitamente a equação de Riccati associada. Mais especificamente, o problema do LQR é resolvido por quatro métodos distintos que são os algoritmos de Programação Dinâmica Heurística, a Programação Dinâmica Heurística Dual, a Programação Dinâmica Heurística Dependente de Ação e a Programação Dinâmica Heurística Dual Dependente de Ação. Entretanto, esses algoritmos dependem do conhecimento das funções valor para, assim, derivar as ações de controle ótimas. Essas funções valor com estruturas conhecidas tem seus parâmetros estimados utilizando os algoritmos da família dos mínimos quadrados médios e o algoritmo de Mínimos Quadrados Recursivo. Dois processos que obedecem à propriedade de Markov foram empregados na validação computacional dos algoritmos críticos adaptativos, um corresponde à dinâmica longitudinal de uma aeronave e o outro à de um circuito elétrico.

[Truncated abstract. Please see the pdf version for the complete text. Also, formulae and special characters can only be approximated here. Please see the pdf version of this abstract for an accurate reproduction.] This thesis treats two aspects of intelligent control: The first part is about long-term optimization by approximating dynamic programming and in the second part a specific class of a fast neural network, related to support vector machines (SVMs), is considered. The first part relates to approximate dynamic programming, especially in the framework of adaptive critic designs (ACDs). Dynamic programming can be used to find an optimal decision or control policy over a long-term period. However, in practice it is difficult, and often impossible, to calculate a dynamic programming solution, due to the 'curse of dimensionality'. The adaptive critic design framework addresses this issue and tries to find a good solution by approximating the dynamic programming process for a stationary environment. In an adaptive critic design there are three modules, the plant or environment to be controlled, a critic to estimate the long-term cost and an action or controller module to produce the decision or control strategy. Even though there have been many publications on the subject over the past two decades, there are some points that have had less attention. While most of the publications address the training of the critic, one of the points that has not received systematic attention is training of the action module.¹ Normally, training starts with an arbitrary, hopefully stable, decision policy and its long-term cost is then estimated by the critic. Often the critic is a neural network that has to be trained, using a temporal difference and Bellman's principle of optimality. Once the critic network has converged, a policy improvement step is carried out by gradient descent to adjust the parameters of the controller network. Then the critic is retrained again to give the new long-term cost estimate. However, it would be preferable to focus more on extremal policies earlier in the training. Therefore, the Calculus of Variations is investigated to discard the idea of using the Euler equations to train the actor. However, an adaptive critic formulation for a continuous plant with a short-term cost as an integral cost density is made and the chain rule is applied to calculate the total derivative of the short-term cost with respect to the actor weights. This is different from the discrete systems, usually used in adaptive critics, which are used in conjunction with total ordered derivatives. This idea is then extended to second order derivatives such that Newton's method can be applied to speed up convergence. Based on this, an almost concurrent actor and critic training was proposed. The equations are developed for any non-linear system and short-term cost density function and these were tested on a linear quadratic regulator (LQR) setup. With this approach the solution to the actor and critic weights can be achieved in only a few actor-critic training cycles. Some other, more minor issues, in the adaptive critic framework are investigated, such as the influence of the discounting factor in the Bellman equation on total ordered derivatives, the target interpretation in backpropagation through time as moving and fixed targets, the relation between simultaneous recurrent networks and dynamic programming is stated and a reinterpretation of the recurrent generalized multilayer perceptron (GMLP) as a recurrent generalized finite impulse MLP (GFIR-MLP) is made. Another subject in this area that is investigated, is that of a hybrid dynamical system, characterized as a continuous plant and a set of basic feedback controllers, which are used to control the plant by finding a switching sequence to select one basic controller at a time. The special but important case is considered when the plant is linear but with some uncertainty in the state space and in the observation vector, and a quadratic cost function. This is a form of robust control, where a dynamic programming solution has to be calculated. &sup1Werbos comments that most treatment of action nets or policies either assume enumerative maximization, which is good only for small problems, except for the games of Backgammon or Go [1], or, gradient-based training. The latter is prone to difficulties with local minima due to the non-convex nature of the cost-to-go function. With incremental methods, such as backpropagation through time, calculus of variations and model-predictive control, the dangers of non-convexity of the cost-to-go function with respect to the control is much less than the with respect to the critic parameters, when the sampling times are small. Therefore, getting the critic right has priority. But with larger sampling times, when the control represents a more complex plan, non-convexity becomes more serious.

High penetration of wind energy requires innovations in different areas of power engineering. Methods for improving wind energy and power system interconnection, control, and operation are proposed in this dissertation. A feed-forward transient compensation control scheme is proposed to enhance the low-voltage ride-through capability of wind turbines equipped with doubly fed induction generators. Stator-voltage transient compensation terms are introduced to suppress rotor-current overshoots and torque ripples during grid faults. A dynamic stochastic optimal power flow control scheme is proposed to optimally reroute real-time active and reactive power flow in the presence of high variability and uncertainty. The performance of the proposed power flow control scheme is demonstrated in test power systems with large wind plants. A combined energy-and-reserve wind market scheme is proposed to reduce wind production uncertainty. Variable wind reserve products are created to absorb part of the wind production variation. These fast wind reserve products can then be used to regulate system frequency and improve system security.

A variety of factors such as increasing electrical energy demand, slow expansion of transmission infrastructures, and electric energy market deregulation, are forcing utilities and system operators to operate power systems closer to their design limits. Operating under stressed regimes can have a detrimental effect on the rotor-angle stability of the system. This stability reduction is often reflected by the emergence or worsening of poorly damped low-frequency electromechanical oscillations. Without appropriate measures these can lead to costly blackouts. To guarantee system security, operators are sometimes forced to limit power transfers that are economically beneficial but that can result in poorly damped oscillations. Controllers that damp these oscillations can improve system reliability by preventing blackouts and provide long term economic gains by enabling more extensive utilization of the transmission infrastructure. Previous research in the use of artificial neural network-based intelligent controllers for power system damping control has shown promise when tested in small power system models. However, these controllers do not scale-up well enough to be deployed in realistically-sized power systems. The work in this dissertation focuses on improving the scalability of intelligent power system stabilizing controls so that they can significantly improve the rotor-angle stability of large-scale power systems. A framework for designing effective and robust intelligent controllers capable of scaling-up to large scale power systems is proposed. Extensive simulation results on a large-scale power system simulation model demonstrate the rotor-angle stability improvements attained by controllers designed using this framework.

Worldwide concern about the environmental problems and a possible energy crisis has led to increasing interest in clean and renewable energy generation. Among various renewable energy sources, wind power is the most rapidly growing one. Therefore, how to provide efficient, reliable, and high-performance wind power generation and distribution has become an important and practical issue in the power industry. In addition, because of the new constraints placed by the environmental and economical factors, the trend of power system planning and operation is toward maximum utilization of the existing infrastructure with tight system operating and stability margins. This trend, together with the increased penetration of renewable energy sources, will bring new challenges to power system operation, control, stability and reliability which require innovative solutions. Flexible ac transmission system (FACTS) devices, through their fast, flexible, and effective control capability, provide one possible solution to these challenges. To fully utilize the capability of individual power system components, e.g., wind turbine generators (WTGs) and FACTS devices, their control systems must be suitably designed with high reliability. Moreover, in order to optimize local as well as system-wide performance and stability of the power system, real-time local and wide-area coordinated control is becoming an important issue. Power systems containing conventional synchronous generators, WTGs, and FACTS devices are large-scale, nonlinear, nonstationary, stochastic and complex systems distributed over large geographic areas. Traditional mathematical tools and system control techniques have limitations to control such complex systems to achieve an optimal performance. Intelligent and bio-inspired techniques, such as swarm intelligence, neural networks, and adaptive critic designs, are emerging as promising alternative technologies for power system control and performance optimization. This work focuses on the development of advanced optimization and intelligent control algorithms to improve the stability, reliability and dynamic performance of WTGs, FACTS devices, and the associated power networks. The proposed optimization and control algorithms are validated by simulation studies in PSCAD/EMTDC, experimental studies, or real-time implementations using Real Time Digital Simulation (RTDS) and TMS320C6701 Digital Signal Processor (DSP) Platform. Results show that they significantly improve electrical energy security, reliability and sustainability.

Electrical Power System is one of the most complex real time operating systems. It is probably one of the best examples of a large interconnected nonlinear system of varying nature. The system needs to be operated and controlled with component or system problems, often with combinatorial complexity. In addition, time scales of operation and control can vary from milliseconds to minutes to hours. It is difficult to maintain such a system at constant operating condition due to both small and large disturbances such as sudden change in loads, change in network configuration, fluctuations in turbine output, and various types of faults etc. The system is therefore affected by a variety of instability problems. Among all these instability problems one of the important modes of instability is related to dynamic instability or more precisely the small perturbation oscillatory instability. Oscillations of small magnitude and low frequency (in the range of 0.1Hz to 2.5Hz) could persist for long periods, limiting the power transfer capability of the transmission lines. Power System Stabilizers (PSS) were developed as auxiliary controllers on the excitation system to improve the system damping performance by modulating the generator excitation voltage. However, the synthesis of an effective PSS for all operating conditions still remains a difficult and challenging task. The design and tuning of PSS for robust operation is a laborious process. The existing PSS design techniques require considerable expertise, the complete system information and extensive eigenvalue calculations which increases the computational burden as the system size increases. Conventional automatic voltage regulator (AVR) and PSS designs are based on linearized models of power systems which fail to stabilize the system over a wide range of operating conditions. In the last decade or so, a variety of nonlinear control techniques have become available. In this thesis, an attempt is made to explore the suitability of some of these design techniques for designing excitation controllers to enhance small perturbation stability of power systems over a wide range of operating and system conditions. This thesis first proposes a method of designing power system stabilizers based on local measurements alone, in multi-machine systems. Next, a method has been developed to analyze and quantify the small signal performance benefits of replacing the existing AVR+PSS structure with nonlinear voltage regulators. A number of new nonlinear controller designs have been proposed subsequently. These include, (a) a new decentralized nonlinear voltage regulator for multi machine power systems with a single tunable parameter that can achieve effective trade of between both the voltage regulation and small signal objectives, (b) a decentralized Interconnection and Damping Assignment Passivity Based Controller in addition to a proportional controller that can achieve all the requirements of an excitation system and (c) a Nonlinear Quadratic Regulator PSS using Single Network Adaptive Critic architecture in the frame work of approximate dynamic programming. Performance of all the proposed controllers has been analyzed using a number of multi machine test systems over a range of operating conditions.