Academic literature on the topic 'Generator parameter estimation'

The simplified models of synchronous generators, widely used in stability studies of large electric power systems, are not completely suitable for the stability analysis and the design of controllers of distributed synchronous generators, generally connected to typically unbalanced branches. To more accurately analyze the systems with distributed generation, it is necessary to utilize synchronous generator models that consider frequency variation in their electrical equations. Furthermore, this model must represent possible unbalanced three-phase voltages at the generator terminals as well. Nonetheless, to provide reliable responses, the parameters of this more detailed model should be known. Thus, this work assesses the influence of the parameters on the responses of a detailed synchronous generator model, suitable to depict unbalanced operating conditions, and proposes an approach for the estimation of its most important parameters. In the proposed structure, we first employ Trajectory Sensitivity Functions to evaluate the dependency of the responses of this model with respect to its parameters and, from that, we rank them according to their importance. Subsequently, we apply an estimation process that utilizes the Unscented Kalman Filter with the aid of a genetic algorithm to estimate the main parameters of this synchronous generator model under unbalanced operating conditions. To obtain the results and, therefore, assess the proposed estimation approach, we make use of a system which comprises a synchronous generator connected to a three-phase unbalanced load. In addition to the unbalanced operation of the test system, we also consider noises due to the constant load switching, typical of distribution systems. The estimations performed for three operating conditions of the generator were very satisfactory, which demonstrates the efficiency of the proposed approach to obtain adequate models for the description of synchronous generator operation under unbalanced operating conditions.<br>Os modelos simplificados de geradores síncronos, amplamente utilizados em estudos de estabilidade de grandes sistemas elétricos de potência, não são completamente adequados para a análise de estabilidade e projetos de controladores dos geradores síncronos distribuídos, geralmente conectados a sistemas tipicamente desequilibrados. Para que os sistemas com geração distribuída possam ser analisados mais fidedignamente, é necessária a utilização de um modelo de gerador síncrono que considere a variação de frequência em suas equações elétricas. Além disso, esse modelo também deve ser capaz de representar possíveis tensões trifásicas desequilibradas nos terminais do gerador. Entretanto, para que esse modelo mais detalhado possa fornecer respostas coerentes com a realidade, deve-se conhecer seus parâmetros. Dessa forma, este trabalho avalia a influência dos parâmetros nas respostas de um modelo de gerador síncrono mais detalhado, adequado para representar operações desbalanceadas, e propõe uma abordagem para a estimação de seus parâmetros mais importantes. Nessa estrutura, inicialmente empregam-se as Funções de Sensibilidade de Trajetória para avaliar a dependência das respostas desse modelo em relação aos seus parâmetros e, a partir disso, ordená-los conforme sua importância. Em seguida, aplica-se um processo de estimação que utiliza o Filtro de Kalman Unscented com o auxílio de um algoritmo genético para estimar os principais parâmetros desse modelo de gerador síncrono em condições de desbalanço. Para a obtenção dos resultados e consequente avaliação da abordagem de estimação proposta, utiliza-se um sistema composto por um gerador síncrono conectado a uma carga trifásica desbalanceada. Além da operação desbalanceada desse sistema teste, também são considerados ruídos devidos ao constante chaveamento de cargas, típicos de sistemas de distribuição. As estimações realizadas para três condições de operação do gerador foram bem satisfatórias, indicando a eficiência da abordagem proposta na obtenção de modelos adequados para descrever a operação de geradores síncronos em condições de desbalanço.

Este trabalho apresenta uma nova metodologia para estimar parâmetros de geradores síncronos baseada na análise de sensibilidade de trajetória. Esta nova metodologia foi concebida com o objetivo de suplantar dificuldades de convergência que a metodologia de sensibilidade de trajetória tradicional apresenta devido a: (i) baixa robustez com relação aos valores iniciais dos parâmetros e ruído nas medidas, (ii) impossibilidade de lidar com singularidades que podem se apresentar nas equações algébricas do modelo de EAD (equações algébrica-diferenciais) que levam a inexistência de soluções, especialmente quando os parâmetros estão distantes dos valores verdadeiros. Apesar de ter sido desenvolvida para resolver o problema de estimação de parâmetros do gerador síncrono, a metodologia é geral e pode ser aplicada para uma classe grande de sistemas dinâmicos não-lineares. Neste sentido, a principal contribuição desta tese é a proposição de uma nova metodologia baseada na sensibilidade de trajetória para estimar parâmetros de sistemas dinâmicos não-lineares restritos, ou seja, modelados por EADs. Mais precisamente, relaxa-se a restrição de igualdade do sistema dinâmico, substituindo-a por uma formulação alternativa baseada na minimização da função algébrica do modelo de EAD. Uma segunda contribuição desta tese está relacionada à modelagem do gerador. Neste sentido, a escolha de variáveis de estado, das entradas e saídas, é fundamental para o sucesso da metodologia de estimação de parâmetros. Nesta tese, estas escolhas permitem que os parâmetros mecânicos e elétricos possam ser estimados independentemente. Para estimar os parâmetros elétricos, o gerador é modelado por um conjunto de EADs para que os seguintes requisitos práticos sejam atendidos: (i) estimar os parâmetros a partir de medidas de perturbações obtidas com o gerador em operação, (ii) usar apenas medidas de fácil acesso, (iii) não depender dos parâmetros da rede. Como resultado final, propõe-se um algoritmo que combina a nova metodologia de sensibilidade de trajetória para sistemas restritos com um algoritmo de estimação em duas fases para estimar os parâmetros do gerador síncrono. A metodologia proposta é robusta aos valores iniciais dos parâmetros e atende aos requisitos práticos mencionados anteriormente. Além disso, a estimação do ângulo de potência é um subproduto da metodologia proposta.<br>This research proposes a new methodology to estimate parameters of synchronous generators based on trajectory sensitivity analysis. This new methodology was created to overcome convergence difficulties presented by the traditional trajectory sensitivity methodology due to: (i) low robustness with relation to initial parameter values and noisy measurements; and (ii) singularities in the algebraic equation of the model of differential-algebraic equations (DAEs) that lead to the nonexistence of solutions, especially when the parameters are far from of the true values. Although the methodology has been developed to solve the synchronous generator problem, it is general and can be used for many types of nonlinear dynamic systems. Therefore, the main contribution of this thesis is the proposal of a new methodology based on trajectory sensitivity to estimate parameters of nonlinear dynamic systems with constraints, i.e., systems modeled by DAEs. More precisely, the equality constraint of the dynamic system is relaxed by an alternative formulation based on the minimization of the algebraic function of the model of DAEs. A second contribution of the thesis is related to the model of the generator. For this intention, the selection of the state variables, inputs and outputs is fundamental for the success of the parameter estimation methodology. In this thesis, this selection allows the generators mechanical and electrical parameters be estimated independently. In order to estimate the electrical parameters, the generator is modeled by a set of convenient DAEs to fulfill the following practical requirements: (i) estimation of the generator parameters from the disturbance measurements obtained with the machine in operation; (ii) use of easily accessible measurements; and (iii) independence of the network parameter. As a final result, an algorithm which combines the new methodology of trajectory sensitivity to constrained systems with the two-stage estimation algorithm is proposed to estimate the generator parameters. This proposed methodology is robust for parameters initial values and fulfills the practical requirements above mentioned. In addition, the estimation of the power angle is a byproduct of the proposed methodology.

The design of electrical power systems on offshore platforms involves ensuring the safe and reliable operation of the systems. These criteria prompted the need for performing short-circuit studies and fault level calculations. Using the results obtained from these studies together with the load data, the switchgear rating and fault rating of the switchboard can be determined. This study will aid the designer in determining both transient and substransient reactances of the generators, whereby the transient reactance is used in studying the effect of starting a large motor such as a compressor or a pump and the substransient reactance is used in short circuit studies. The generators parameters are therefore essential in the determination of circuit breaker setting, switchboard ratings and the starting capability of motors. These factors have prompted research into methods for determining the parameters of synchronous generators. The tests approved by the British Standards which are usually employed by manufacturers, however suffer from a number of disadvantages such as the possibility of damage to the generator windings as a result of the sudden three phase short circuit test. On the other hand some of these tests such as the d.c. standstill decay test are quoted as 'unconfirmed testing methods'. This study is therefore aimed at developing a method whereby the parameters of synchronous machines can be estimated directly from the on-line voltage and current waveforms. The study starts by determining the parameters of two salient pole generators using the conventional methods. This is used later to establish the accuracy of the proposed method. The effect of varying the generators parameters on voltage and current waveforms is then analysed, leading to the introduction of a method using only three simple tests. The accuracy and reliability of the method is demonstrated using the software simulator SABER. Finally the new parameter estimating technique is used to determine saturated parameters of the two laboratory generators for different field excitations.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2005.<br>Power system stability simulations are of growing importance for studying the operational integrity of modern power systems, especially in developing economies where generating and transmission capacity lead the demand by relatively small margins. The relevant model topologies, i.e. for synchronous generators, automatic voltage regulators (AVR) and governor control systems, and the simulation software tools are well established. The MATLAB® Power System Blockset provides engineers with a versatile power system stability simulation environment, particularly where the focus is on individual units or small systems. In comparison with dedicated power system simulation tools such as DIgSILENT®, the MATLAB® environment features a superior set of advanced data processing and data analysis features. This includes features such as optimisation and parameter estimation functions. The main aim of this project is to make use of the MATLAB® package in a bid to test an alternative platform with which to estimate the synchronous machine parameters. Conditioning of field data can delay the process considerably, thus the secondary task of this thesis is to solve this issue by ensuring that only one platform is needed for the entire process starting in the field and ending in the modelling and parameter estimation environment within MATLAB®. In closing, the following points summarise the essential aims of this project: • An application using MATLAB® Script must be created that is responsible for importing and processing the data, so it is suitable for analysis purposes. The processing could include cropping, scaling and filtering of data. • Once the data has been imported it must be used with appropriate models to estimate for machine parameters. This will require the use of the Power Systems Blockset. The actual estimation process also requires the creation of an effective cost function, thus a number of different scenarios will have to be investigated before a solution can be found.

The Queensland University of Technology (QUT) allows the presentation of a thesis for the Degree of Doctor of Philosophy in the format of published or submitted papers, where such papers have been published, accepted or submitted during the period of candidature. This thesis is composed of seven published/submitted papers, of which one has been published, three accepted for publication and the other three are under review. This project is financially supported by an Australian Research Council (ARC) Discovery Grant with the aim of proposing strategies for the performance control of Distributed Generation (DG) system with digital estimation of power system signal parameters. Distributed Generation (DG) has been recently introduced as a new concept for the generation of power and the enhancement of conventionally produced electricity. Global warming issue calls for renewable energy resources in electricity production. Distributed generation based on solar energy (photovoltaic and solar thermal), wind, biomass, mini-hydro along with use of fuel cell and micro turbine will gain substantial momentum in the near future. Technically, DG can be a viable solution for the issue of the integration of renewable or non-conventional energy resources. Basically, DG sources can be connected to local power system through power electronic devices, i.e. inverters or ac-ac converters. The interconnection of DG systems to power system as a compensator or a power source with high quality performance is the main aim of this study. Source and load unbalance, load non-linearity, interharmonic distortion, supply voltage distortion, distortion at the point of common coupling in weak source cases, source current power factor, and synchronism of generated currents or voltages are the issues of concern. The interconnection of DG sources shall be carried out by using power electronics switching devices that inject high frequency components rather than the desired current. Also, noise and harmonic distortions can impact the performance of the control strategies. To be able to mitigate the negative effect of high frequency and harmonic as well as noise distortion to achieve satisfactory performance of DG systems, new methods of signal parameter estimation have been proposed in this thesis. These methods are based on processing the digital samples of power system signals. Thus, proposing advanced techniques for the digital estimation of signal parameters and methods for the generation of DG reference currents using the estimates provided is the targeted scope of this thesis. An introduction to this research – including a description of the research problem, the literature review and an account of the research progress linking the research papers – is presented in Chapter 1. One of the main parameters of a power system signal is its frequency. Phasor Measurement (PM) technique is one of the renowned and advanced techniques used for the estimation of power system frequency. Chapter 2 focuses on an in-depth analysis conducted on the PM technique to reveal its strengths and drawbacks. The analysis will be followed by a new technique proposed to enhance the speed of the PM technique while the input signal is free of even-order harmonics. The other techniques proposed in this thesis as the novel ones will be compared with the PM technique comprehensively studied in Chapter 2. An algorithm based on the concept of Kalman filtering is proposed in Chapter 3. The algorithm is intended to estimate signal parameters like amplitude, frequency and phase angle in the online mode. The Kalman filter is modified to operate on the output signal of a Finite Impulse Response (FIR) filter designed by a plain summation. The frequency estimation unit is independent from the Kalman filter and uses the samples refined by the FIR filter. The frequency estimated is given to the Kalman filter to be used in building the transition matrices. The initial settings for the modified Kalman filter are obtained through a trial and error exercise. Another algorithm again based on the concept of Kalman filtering is proposed in Chapter 4 for the estimation of signal parameters. The Kalman filter is also modified to operate on the output signal of the same FIR filter explained above. Nevertheless, the frequency estimation unit, unlike the one proposed in Chapter 3, is not segregated and it interacts with the Kalman filter. The frequency estimated is given to the Kalman filter and other parameters such as the amplitudes and phase angles estimated by the Kalman filter is taken to the frequency estimation unit. Chapter 5 proposes another algorithm based on the concept of Kalman filtering. This time, the state parameters are obtained through matrix arrangements where the noise level is reduced on the sample vector. The purified state vector is used to obtain a new measurement vector for a basic Kalman filter applied. The Kalman filter used has similar structure to a basic Kalman filter except the initial settings are computed through an extensive math-work with regards to the matrix arrangement utilized. Chapter 6 proposes another algorithm based on the concept of Kalman filtering similar to that of Chapter 3. However, this time the initial settings required for the better performance of the modified Kalman filter are calculated instead of being guessed by trial and error exercises. The simulations results for the parameters of signal estimated are enhanced due to the correct settings applied. Moreover, an enhanced Least Error Square (LES) technique is proposed to take on the estimation when a critical transient is detected in the input signal. In fact, some large, sudden changes in the parameters of the signal at these critical transients are not very well tracked by Kalman filtering. However, the proposed LES technique is found to be much faster in tracking these changes. Therefore, an appropriate combination of the LES and modified Kalman filtering is proposed in Chapter 6. Also, this time the ability of the proposed algorithm is verified on the real data obtained from a prototype test object. Chapter 7 proposes the other algorithm based on the concept of Kalman filtering similar to those of Chapter 3 and 6. However, this time an optimal digital filter is designed instead of the simple summation FIR filter. New initial settings for the modified Kalman filter are calculated based on the coefficients of the digital filter applied. Also, the ability of the proposed algorithm is verified on the real data obtained from a prototype test object. Chapter 8 uses the estimation algorithm proposed in Chapter 7 for the interconnection scheme of a DG to power network. Robust estimates of the signal amplitudes and phase angles obtained by the estimation approach are used in the reference generation of the compensation scheme. Several simulation tests provided in this chapter show that the proposed scheme can very well handle the source and load unbalance, load non-linearity, interharmonic distortion, supply voltage distortion, and synchronism of generated currents or voltages. The purposed compensation scheme also prevents distortion in voltage at the point of common coupling in weak source cases, balances the source currents, and makes the supply side power factor a desired value.

Fault detection and diagnosis have gained central importance in the chemical process industries over the past decade. This is due to several reasons, one of them being that copious amount of data is available from a large number of sensors in process plants. Moreover, since industrial processes operate in closed loop with appropriate output feedback to attain certain performance objectives, instrument faults have a direct effect on the overall performance of the automation system. Extracting essential information about the state of the system and processing the measurements for detecting, discriminating, and identifying abnormal readings are important tasks of a fault diagnosis system. The goal of this dissertation is to develop such fault diagnosis systems, which use limited information about the process model to robustly detect, discriminate, and reconstruct instrumentation faults. Broadly, the proposed method consists of a novel nonlinear state and parameter estimator coupled with a fault detection, discrimination, and reconstruction system. The first part of this dissertation focuses on designing fault diagnosis systems that not only perform fault detection and isolation but also estimate the shape and size of the unknown instrument faults. This notion is extended to nonlinear processes whose structure is known but the parameters of the process are a priori uncertain and bounded. Since the uncertainty in the process model and instrument fault detection interact with each other, a novel two-time scale procedure is adopted to render overall fault diagnosis. Further, some techniques to enhance the convergence properties of the proposed state and parameter estimator are presented. The remaining part of the dissertation extends the proposed model-based fault diagnosis methodology to processes for which first principles modeling is either expensive or infeasible. This is achieved by using an empirical model identification technique called subspace identification for state-space characterization of the process. Finally the proposed methodology for fault diagnosis has been applied in numerical simulations to a non-isothermal CSTR (continuous stirred tank reactor), an industrial melter process, and a debutanizer plant.

This dissertation addresses motion planning, modeling, and feedback control for autonomous vehicle systems. A hierarchical approach for motion planning and control of nonlinear systems operating in obstacle environments is presented. To reduce computation time during the motion planning process, dynamically feasible trajectories are generated in real-time through concatenation of pre-specified motion primitives. The motion planning task is posed as a search over a directed graph, and the applicability of informed graph search techniques is investigated. Specifically, a locally greedy algorithm with effective backtracking ability is developed and compared to weighted A* search. The greedy algorithm shows an advantage with respect to solution cost and computation time when larger motion primitive libraries that do not operate on a regular state lattice are utilized. Linearization of the nonlinear system equations about the motion primitive library results in a hybrid linear time-varying model, and an optimal control algorithm using the L2-induced norm as the performance measure is applied to ensure that the system tracks the desired trajectory. The ability of the resulting controller to closely track the trajectory obtained from the motion planner, despite various disturbances and uncertainties, is demonstrated through simulation. Additionally, an approach for obtaining dynamically feasible reference trajectories and feedback controllers for a small unmanned aerial vehicle (UAV) based on an aerodynamic model derived from flight tests is presented. The modeling approach utilizes the two step method (TSM) with stepwise multiple regression to determine relevant explanatory terms for the aerodynamic models. Dynamically feasible trajectories are then obtained through the solution of an optimal control problem using pseudospectral optimal control software. Discrete-time feedback controllers are then obtained to regulate the vehicle along the desired reference trajectory. Simulations in a realistic operational environment as well as flight testing with the feedback controller demonstrate the capabilities of the approach. The TSM is also applied for system identification of an aircraft using motion capture data. In this application, time domain system identification techniques are used to identify both linear and nonlinear aerodynamic models of large-amplitude pitching motions driven by control surface deflections. The resulting models are assessed based on both their predictive capabilities as well as simulation results.<br>Ph. D.