The main objectives of this research are (a) to develop advanced simulation methods for voltage-recovery phenomena using improved, realistic system models and accurate solution techniques and (b) to develop methods for the mitigation of problems related to slow voltage recovery. Therefore, this work concentrates on the areas of voltage-recovery analysis in electric power systems, dynamic load modeling with emphasis on induction-motor models, dynamic simulation with emphasis on the numerical integration methods, and optimal allocation and operation of static and dynamic VAr resources. In the first part of this work, a general framework for power-system analysis is presented the main characteristics of which are (a) the utilization of full three-phase models and (b) the use of a "quadratized" mathematical formulation, which models the system under study as a set of mathematical equations of order no more than two. The modeling approach is essentially the same for steady-state, quasi-steady-state, and dynamic analysis. Furthermore, a new approach for time-domain transient simulation of electric power systems and dynamical systems, in general, is introduced in this research. The new methodology has been named quadratic integration method. The method is based on a numerical integration scheme that assumes that the system states vary quadraticaly within an integration time step. Accurate modeling and simulation of voltage-recovery phenomena allows the development of mitigation methodologies via the optimal allocation and operation of static and dynamic VAr resources over the planning horizon. This problem is solved with successive dynamic programming techniques with the following two innovations: (a) the states at each stage (candidate solutions) are obtained with static and dynamic (trajectory) sensitivity analysis and (b) each candidate solution is evaluated by considering the optimal operation of installed static and dynamic VAr sources utilizing concepts from the theory of applied optimal control and trajectory optimization.

This dissertation deals with the challenge of power utility in South Africa which is on proactive detection of fallen power line conductors and real time sagging measurement together with slipping of such conductors. Various methods which are currently used for sag detection were characterized and evaluated to the aim of the research. A mathematical reconstruction done to estimate the lowest point of the conductor in a span is presented. Practical simulations and application of radio frequency identification (RFID) for sag detection is attempted through matlab software. RFID radar system is then analyzed in different modes and found to give precision measurement for sag in real time as opposed to global positioning system (GPS) if one dimension of the tag assumed fixed on the power line. Lastly errors detected on the measurements are corrected using a trainable artificial neural network. A conclusion is made by making recommendations in the advancement of the research. / Electrical Engineering / M. Tech. (Electrical Engineering)

Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2009 / The purpose of this project is to develop methods for distribution network power quality' variations monitoring. Power quality (PO) has become a significant issue for both power suppliers and customers. There have been important changes in power system regarding to power quality requirements. "Power quality" is the combination at voltage quality and current quality. The main research problem of the project is to investigate the power quality of a distribution network by selection of proper measurement, applying and developing the existing classic and modern signal conditioning methods for power disturbance's parameters extracting and monitoring. The research objectives are: To study the standard lEC 61000-4-30 requirements. to investigate the common couplings in the distribution network. To identity the points for measurement, to develop MySQL database for the data from the measurement and to develop MATLAB software tor simulation of the network To develop methods based on Fourier transforms for estimation of the parameters of the disturbances. To develop software for the methods implementation, The influence of different loads on power quality disturbances are considered in the distribution network. Points on the network and meters according to the lEC power quality standards are investigated and applied for the CPUT Bellville campus distribution network. The implementation of the power quality monitoring for the CPUT Bellville campus helps the quality of power supply to be improved and the used power to be reduced. MATLAB programs to communicate with the database and calculate the disturbances and power quality parameters are developed.

CAPES / Este trabalho apresenta novas abordagens para duas das etapas fundamentais relacionadas com análise automática de oscilografias de redes de distribuição: a detecção dos instantes transitórios e a sua classificação. Para comparação e validação dos métodos são utilizadas duas bases de dados, sendo uma delas formada por dados simulados no aplicativo Alternative Transient Program e outra contendo dados reais de oscilógrafos instalados em uma rede de distribuição de energia elétrica. Os dados reais apresentam um conjunto de eventos relevante para as análises aqui propostas, principalmente por conter uma gama variada de eventos, incluindo transitórios decorrentes de descargas atmosféricas. Com relação à detecção de transitórios (segmentação de oscilografias), foram testados os métodos atualmente propostos na literatura, os quais contemplam Filtro de Kalman, Transformada Wavelet Discreta e Modelos Autorregressivos, além de serem propostas duas novas técnicas baseadas no Operador de Energia de Teager e Representação de Dados Utilizando Vetores Suporte. Demonstra-se que, tanto para dados simulados quanto para dados reais, o método de detecção baseado na Representação de Dados utilizando Vetores Suporte aponta para um melhor desempenho global no processo de detecção. Com relação à classificação automática de oscilografias, propõe-se uma nova abordagem incluindo um estágio dedicado à detecção de padrões não inseridos no aprendizado prévio do classificador, denominados de novidades, além da própria classificação multiclasse normalmente empregada para diferenciar múltiplas classes conhecidas a priori. São testadas abordagens utilizando a detecção de novidades e classificação multiclasse em estágios simultâneos e subsequentes, com base nos classificadores X-Médias, K-Vizinhos-Mais-Próximos e Representação de Dados Utilizando Vetores Suporte com diferentes formulações, além do próprio classificador multiclasse baseado em Máquinas de Vetor Suporte. Adicionalmente, é proposto um tratamento aos padrões considerados como novidades, com o intuito de fornecer informações ao especialista sobre as similaridades existentes entre os padrões desse conjunto. Para realizar esse processo, optou-se por utilizar modelos de agrupamento automático. Os resultados finais, principalmente para a base de dados incluindo eventos reais, mostram que é possível obter um desempenho de classificação relevante (acima de 80%) para cada um dos estágios do processo de classificação proposto, o qual inclui a detecção de novidades, a classificação multiclasse e o processamento de padrões classificados como novidades (agrupamento automático). / This work presents new approaches for two of the fundamental steps in automatic waveform analysis in electrical distribution systems: transient time detection and its classification. Two datasets were used to compare and validate the proposed methods. The first is composed by simulated waveforms, by using the Alternative Transient Program, while the second is formed by real data from a monitoring system developed for overhead distribution power lines. The real data present a set of relevant events for the analysis proposed here, mainly due to the variety of events, including lightning-related transients. Regarding transient detection (waveform segmentation), the experiments involve usual segmentation methods, such as Kalman filtering, standard Discrete Wavelet Transform, and autoregressive models, besides two new techniques based on the Teager Energy Operator and Support Vector Data Description. The results obtained on both simulated and real world data demonstrate that the method based on Support Vector Data Description outperforms other methods in the transient identification task. Regarding the automatic waveform classification, a new approach including the detection of classes not defined in the training stage (called novelties) is presented. Also, the classifier is able to discriminate among multiple known classes, normally defined as multi-class classification. Two different approaches are compared, by using multi-class classification and novelty detection in two subsequent stages and in a simultaneous way. The following classifiers were assessed: X-Means, K-Nearest-Neighbors, and Support Vector Data Description with different formulations, besides the Support Vector Machine for multi-class classification. Furthermore, a technique for the post-processing of the novelties is presented, in order to provide some useful information for the experts, regarding possible similarities in the novelty set. To accomplish this task, automatic clustering methods were used. The final results, especially for the dataset with real examples, show that it is possible to obtain a relevant classification performance (above 80%) in each one of the three stages of the classification process: multi-class classification, novelty detection, and the post-processing applied to the novelties (automatic clustering).

CAPES / Este trabalho apresenta novas abordagens para duas das etapas fundamentais relacionadas com análise automática de oscilografias de redes de distribuição: a detecção dos instantes transitórios e a sua classificação. Para comparação e validação dos métodos são utilizadas duas bases de dados, sendo uma delas formada por dados simulados no aplicativo Alternative Transient Program e outra contendo dados reais de oscilógrafos instalados em uma rede de distribuição de energia elétrica. Os dados reais apresentam um conjunto de eventos relevante para as análises aqui propostas, principalmente por conter uma gama variada de eventos, incluindo transitórios decorrentes de descargas atmosféricas. Com relação à detecção de transitórios (segmentação de oscilografias), foram testados os métodos atualmente propostos na literatura, os quais contemplam Filtro de Kalman, Transformada Wavelet Discreta e Modelos Autorregressivos, além de serem propostas duas novas técnicas baseadas no Operador de Energia de Teager e Representação de Dados Utilizando Vetores Suporte. Demonstra-se que, tanto para dados simulados quanto para dados reais, o método de detecção baseado na Representação de Dados utilizando Vetores Suporte aponta para um melhor desempenho global no processo de detecção. Com relação à classificação automática de oscilografias, propõe-se uma nova abordagem incluindo um estágio dedicado à detecção de padrões não inseridos no aprendizado prévio do classificador, denominados de novidades, além da própria classificação multiclasse normalmente empregada para diferenciar múltiplas classes conhecidas a priori. São testadas abordagens utilizando a detecção de novidades e classificação multiclasse em estágios simultâneos e subsequentes, com base nos classificadores X-Médias, K-Vizinhos-Mais-Próximos e Representação de Dados Utilizando Vetores Suporte com diferentes formulações, além do próprio classificador multiclasse baseado em Máquinas de Vetor Suporte. Adicionalmente, é proposto um tratamento aos padrões considerados como novidades, com o intuito de fornecer informações ao especialista sobre as similaridades existentes entre os padrões desse conjunto. Para realizar esse processo, optou-se por utilizar modelos de agrupamento automático. Os resultados finais, principalmente para a base de dados incluindo eventos reais, mostram que é possível obter um desempenho de classificação relevante (acima de 80%) para cada um dos estágios do processo de classificação proposto, o qual inclui a detecção de novidades, a classificação multiclasse e o processamento de padrões classificados como novidades (agrupamento automático). / This work presents new approaches for two of the fundamental steps in automatic waveform analysis in electrical distribution systems: transient time detection and its classification. Two datasets were used to compare and validate the proposed methods. The first is composed by simulated waveforms, by using the Alternative Transient Program, while the second is formed by real data from a monitoring system developed for overhead distribution power lines. The real data present a set of relevant events for the analysis proposed here, mainly due to the variety of events, including lightning-related transients. Regarding transient detection (waveform segmentation), the experiments involve usual segmentation methods, such as Kalman filtering, standard Discrete Wavelet Transform, and autoregressive models, besides two new techniques based on the Teager Energy Operator and Support Vector Data Description. The results obtained on both simulated and real world data demonstrate that the method based on Support Vector Data Description outperforms other methods in the transient identification task. Regarding the automatic waveform classification, a new approach including the detection of classes not defined in the training stage (called novelties) is presented. Also, the classifier is able to discriminate among multiple known classes, normally defined as multi-class classification. Two different approaches are compared, by using multi-class classification and novelty detection in two subsequent stages and in a simultaneous way. The following classifiers were assessed: X-Means, K-Nearest-Neighbors, and Support Vector Data Description with different formulations, besides the Support Vector Machine for multi-class classification. Furthermore, a technique for the post-processing of the novelties is presented, in order to provide some useful information for the experts, regarding possible similarities in the novelty set. To accomplish this task, automatic clustering methods were used. The final results, especially for the dataset with real examples, show that it is possible to obtain a relevant classification performance (above 80%) in each one of the three stages of the classification process: multi-class classification, novelty detection, and the post-processing applied to the novelties (automatic clustering).

Power systems must face new challenges in the current environment. The energy market liberalization and the increase in the loading level make the occurrence of instability phenomena leading to large blackouts more likely. Existing tools must be improved and new tools must be developed to avoid them.<p><p>The aim of this thesis is the improvement of the voltage stability assessment efficiency. Two orientations are studied: the monitoring methods and the load modeling.<p><p>The purpose of the monitoring methods is to evaluate the voltage stability using only measurements and without running simulations. <p><p>The first approach considered is local. The parameters of the Thevenin equivalent seen from a load bus are assessed thanks to a stream of local voltage and current measurements. Several issues are investigated using measurements coming from complete time-domain simulations. The applicability of this approach is questioned.<p><p>The second approach is global and uses measurements acquired by a Wide-Area Measurement System (WAMS). An original approach with a certain prediction capability is proposed, along with intuitive visualizations that allow to understand the deterioration process leading to the collapse.<p><p>The load modeling quality is certainly the weak point of the voltage security assessment tools which run simulations to predict the stability of the power system depending on different evolutions. Appropriate load models with accurate parameters lead to a direct improvement of the prediction precision.<p><p>An innovative procedure starting from data of long measurement campaigns is proposed to automatically evaluate the parameters of static and dynamic load models. Real measurements taken in the Belgian power system are used to validate this approach.<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Voltage stability is the ability of the power system to maintain steady acceptable voltages at all the buses in a system under normal operating conditions and after being subjected to a disturbance. The increased consumption of electricity without the augmentation of the necessary transmission infrastructure has resulted in the overloading of the transmission lines. As a result, the transmission lines operate near the steady state stability limit. The transmission of large amounts of power through the lines results in the large voltage drops in the lines. Sudden disturbances like line or generator outage and fault in the transmission lines may occur because of natural or man made causes. Under the above mentioned conditions, the transmission system may not be able to supply the load demand. This results in drops in the system bus voltages which may be sudden or progressive. If the necessary remedial measures are not taken, then this may lead to blackout or collapse of the whole system. As a result of a number of voltage stability incidents reported from various countries, there is a widespread interest in understanding, characterizing and preventing this phenomena. This thesis is essentially concerned with analyzing the existing methods and the development of new methods for the assessment of voltage stability of power systems. We examine four existing methods for assessing voltage stability with regard to the computational effort involved in their calculation, the useful information we get by using them, their relative effectiveness in assessing the voltage stability and their consistency in predicting the voltage stability of the system. We also study the impact of the system conditions on several of these indices. Further, we propose a set of new indices which provide information similar to the conventional indices but are slightly different. The generalized circle diagram approach proposed earlier to study the variation of the system variables with respect to the independent node parameters is shown to be adoptable for finding the voltage stability limit of a system. It has been shown that the well known continuation power flow method used for voltage stability analysis is identical to the generalized circle diagram approach. A computationally simple approach, based on the Thevenin equivalent of the power system is used to determine the loadability limit of a system. In the continuation power flow method, it is inherently assumed that only one generator responds to the real power load increase of the system. However, an alternate view is presented where all the generators respond to the real power increase in the system and an algorithm is proposed to realize this condition. Using this algorithm, the generation pattern of the system is modified so as to increase the loadability limit of the system considerably. The origin of the voltage instability in power systems can be traced to the load characteristics. Induction motors constitute a significant proportion of the total industrial and residential loads. Two algorithms that are useful to study the voltage stability of systems having induction machines have been presented and validated. These methods are based on the induction machine static equations. The first method is useful in assessing the impact of network disturbances on voltage stability and the second facilitates the computation of the loadability limit. A criterion has been proposed to find the stability limit, stable and unstable operating regions for a system considering various types of induction motor loads on the basis of which, a practical algorithm is proposed and validated to determine the stability of the induction motors driving different types of loads in a large power system. In addition, a method is developed to determine the stability aspects when the constant torque loads and the constant input power loads driven by induction motors operate in a power system, which contains other types of loads like the constant P - Q type of loads. Switching capacitors at the induction motor terminals is one of the ways by which voltage instability occurring due to the induction motor loads can be prevented. A new technique is proposed wherein knowing the capacitance and the slip at the instant of switching, the rotor dynamics following the switching and the existence of a steady state operating point following the switching can be predicted. This approach can be used to choose appropriate capacitances to be switched at the induction motor terminals to prevent its stalling following a sudden load disturbance.

Indiana University-Purdue University Indianapolis (IUPUI) / In a wide-area power system, detecting dynamic events is critical to maintaining system stability. Large events, such as the loss of a generator or fault on a transmission line, can compromise the stability of the system by causing the generator rotor angles to diverge and lose synchronism with the rest of the system. If these events can be detected as they happen, controls can be applied to the system to prevent it from losing synchronous stability. In order to detect these events, pattern recognition tools can be applied to system measurements. In this thesis, the pattern recognition tool decision trees (DTs) were used for event detection. A single DT produced rules distinguishing between and the event and no event cases by learning on a training set of simulations of a power system model. The rules were then applied to test cases to determine the accuracy of the event detection. To use a DT to detect events, the variables used to produce the rules must be chosen. These variables can be direct system measurements, such as the phase angle of bus voltages, or indices created by a combination of system measurements. One index used in this thesis was the integral square bus angle (ISBA) index, which provided a measure of the overall activity of the bus angles in the system. Other indices used were the variance and rate of change of the ISBA. Fitting a polynomial curve to a sliding window of these indices and then taking the difference between the polynomial and the actual index was found to produce a new index that was non-zero during the event and zero all other times for most simulations. After the index to detect events was chosen to be the error between the curve and the ISBA indices, a set of power system cases were created to be used as the training data set for the DT. All of these cases contained one event, either a small or large power injection at a load bus in the system model. The DT was then trained to detect the large power injection but not the small one. This was done so that the rules produced would detect large events on the system that could potentially cause the system to lose synchronous stability but ignore small events that have no effect on the overall system. This DT was then combined with a second DT that predicted instability such that the second DT made the decision whether or not to apply controls only for a short time after the end of every event, when controls would be most effective in stabilizing the system.

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium-ion battery is one kind of rechargeable battery, and also renewable, sustainable and portable. With the merits of high density, slow loss of charge when spare and no memory effect, lithium-ion battery is widely used in portable electronics and hybrid vehicles. Apart from its advantages, safety is a major concern for Lithium-ion batteries due to devastating incidents with laptop and cell phone batteries. Overcharge and over-discharge are two of the most common electrical abuses a lithium-ion battery suffers. In this thesis, a fuzzy-rule-based system is proposed to detect the over-charge and over-discharge failure in early time. The preliminary results for the failure signatures of overcharged and over-discharged lithium-ion are listed based on the experimental results under both room temperature and high temperature. A fuzzy-rule-based model utilizing these failure signatures is developed and validated. For over-charge case, the abnormal increase of the surface temperature and decrease of the voltage are captured. While for over discharge case, unusual temperature increase during overcharge phases and abnormal current decrease during overcharge phases are obtained. The inference engine for fuzzy-rule-based system is designed based on these failure signatures. An early warning signal will be given by this algorithm before the failure occurs. This failure detection and early warning system is verified to be effective through experimental validation. In the validation test, the proposed methods are successfully implemented in a real-time system for failure detection and early warning. The result of validation is compatible with the design expectation. Finally an accurate failure detection and early warning system is built and tested successfully.

Indiana University-Purdue University Indianapolis (IUPUI) / Reliability of the wide-area power system is becoming a greater concern as the power grid is growing. Delivering electric power from the most economical source through fewest and shortest transmission lines to customers frequently increases the stress on the system and prevents it from maintaining its stability. Events like loss of transmission equipment and phase to ground faults can force the system to cross its stability limits by causing the generators to lose their synchronism. Therefore, a helpful solution is detection of these dynamic events and prediction of instability. Decision Trees (DTs) were used as a pattern recognition tool in this thesis. Based on training data, DT generated rules for detecting event, predicting loss of synchronism, and selecting stabilizing control. To evaluate the accuracy of these rules, they were applied to testing data sets. To train DTs of this thesis, direct system measurements like generator rotor angles and bus voltage angles as well as calculated indices such as the rate of change of bus angles, the Integral Square Bus Angle (ISBA) and the gradient of ISBA were used. The initial method of this thesis included a response based DT only for instability prediction. In this method, time and location of the events were unknown and the one shot control was applied when the instability was predicted. The control applied was in the form of fast power changes on four different buses. Further, an event detection DT was combined with the instability prediction such that the data samples of each case was checked with event detection DT rules. In cases that an event was detected, control was applied upon prediction of instability. Later in the research, it was investigated that different control cases could behave differently in terms of the number of cases they stabilize. Therefore, a third DT was trained to select between two different control cases to improve the effectiveness of the methodology. It was learned through internship at Midwest Independent Transmission Operators (MISO) that post-event steady-state analysis is necessary for better understanding the effect of the faults on the power system. Hence, this study was included in this research.