Synchrophasor Measurement Using Substation Intelligent Electronic Devices: Algorithms and Test Methodology

This dissertation studies the performance of synchrophasor measurement obtained using substation Intelligent Electronic Devices (IEDs) and proposes new algorithms and test methodology to improve and verify their performance when used in power system applications. To improve the dynamic performance when exposed to sinusoidal waveform distortions, such as modulation, frequency drift, abrupt change in magnitude, etc, an adaptive approach for accurately estimating phasors while eliminating the effect of various transient disturbances on voltages and currents is proposed. The algorithm pre-analyzes the waveform spanning the window of observation to identify and localize the discontinuities which affect the accuracy of phasor computation. A quadratic polynomial signal model is used to improve the accuracy of phasor estimates during power oscillations. Extensive experimental results demonstrate the advantages. This algorithm can also be used as reference algorithm for testing the performance of the devices extracting synchronized phasor measurements. A novel approach for estimating the phasor parameters, namely frequency, magnitude and angle in real time based on a newly constructed recursive wavelet transform is developed. This algorithm is capable of estimating the phasor parameters in a quarter cycle of an input signal. It features fast response and achieves high accuracy over a wide range of frequency deviations. The signal sampling rate and data window size can be selected to meet desirable application requirements, such as fast response, high accuracy and low computational burden. In addition, an approach for eliminating a decaying DC component, which has significant impact on estimating phasors, is proposed using recursive wavelet transform. This dissertation develops test methodology and tools for evaluating the conformance to standard-define performance for synchrophasor measurements. An interleaving technique applied on output phasors can equivalently increase the reporting rate and can precisely depict the transient behavior of a synchrophasor unit under the step input. A reference phasor estimator is developed and implemented. Various types of Phasor Measurement Units (PMUs) and PMU-enabled IEDs (Intelligent Electronic Devices) and time synchronization options have been tested against the standards using the proposed algorithm. Test results demonstrate the effectiveness and advantages

Increasing the robustness of autonomous systems to hardware degradation using machine learning

Autonomous systems perform predetermined tasks (missions) with minimum supervision. In most applications, the state of the world changes with time. Sensors are employed to measure part or whole of the world’s state. However, sensors often fail amidst operation; feeding as such decision-making with wrong information about the world. Moreover, hardware degradation may alter dynamic behaviour, and subsequently the capabilities, of an autonomous system; rendering the original mission infeasible. This thesis applies machine learning to yield powerful and robust tools that can facilitate autonomy in modern systems. Incremental kernel regression is used for dynamic modelling. Algorithms of this sort are easy to train and are highly adaptive. Adaptivity allows for model adjustments, whenever the environment of operation changes. Bayesian reasoning provides a rigorous framework for addressing uncertainty. Moreover, using Bayesian Networks, complex inference regarding hardware degradation can be answered. Specifically, adaptive modelling is combined with Bayesian reasoning to yield recursive estimation algorithms that are robust to sensor failures. Two solutions are presented by extending existing recursive estimation algorithms from the robotics literature. The algorithms are deployed on an underwater vehicle and the performance is assessed in real-world experiments. A comparison against standard filters is also provided. Next, the previous algorithms are extended to consider sensor and actuator failures jointly. An algorithm that can detect thruster failures in an Autonomous Underwater Vehicle has been developed. Moreover, the algorithm adapts the dynamic model online to compensate for the detected fault. The performance of this algorithm was also tested in a real-world application. One step further than hardware fault detection, prognostics predict how much longer can a particular hardware component operate normally. Ubiquitous sensors in modern systems render data-driven prognostics a viable solution. However, training is based on skewed datasets; datasets where the samples from the faulty region of operation are much fewer than the ones from the healthy region of operation. This thesis presents a prognostic algorithm that tackles the problem of imbalanced (skewed) datasets

Measurements, Models, Systems and Design

531 s.

A nonlinear adaptive filter for improved operation and protection of power systems

Includes abstract.Includes bibliographical references (leaves 161-171).This thesis presents the application of a nonlinear adaptive filter to selected areas in power systems. The filter has demonstrated excellent performance against con-ventional methods in biomedical applications. The algorithm is robust in structure and highly immune to noise. Applications in this thesis include (1) sag detection, (2) symmetrical component estimation, (3) phase and frequency estimation, (4) sag analysis and (5) distributed generation synchronisation and protection. The appli-cations were chosen such that the amplitude, phase and frequency tracking ability are thoroughly tested

Open research issues on multi-models for complex technological systems

Abstract -We are going to report here about state of the art works on multi-models for complex technological systems both from the theoretical and practical point of view. A variety of algorithmic approaches (k-mean, dss, etc.) and applicative domains (wind farms, neurological diseases, etc.) are reported to illustrate the extension of the research area

Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise