Doctor of Philosophy / Department of Electrical and Computer Engineering / Shelli K. Starrett / Monitoring and control of modern power systems have become very complex tasks due to the interconnection of power grids. These large-scale power grids confront system operators with a huge set of system inputs and control parameters. This work develops and compares intelligent systems-based algorithms which may be considered by power system operators or planners to help manage, process, and evaluate large amounts of data due to varying conditions within the system. The methods can be used to provide assistance in making operational control and planning decisions for the system in a timely manner. The effectiveness of the proposed algorithms is tested and validated on four different power systems. First, Artificial Neural Network (ANN) models are developed and compared for two different voltage collapse indices and utilizing two different-sized sets of inputs. The ANNs monitor and evaluate the voltage profile of a system and generate intelligent conclusions regarding the status of the system from a voltage stability perspective. A feature reduction technique, based on the analysis of generated data, is used to decrease the number of inputs fed to the ANN, decreasing the number of physical quantities that need to be measured. The major contribution of this work is the development of four different algorithms to control the VAR resources in a system. Four different objectives were also considered in this part of the work, namely: minimization of the number of control changes needed, minimization of the system power losses, minimization of the system's voltage deviations, and consideration of the computational time required. Each of the algorithms is iterative in nature and is designed to take advantage of a method of decoupling the load flow Jacobian matrix to decrease the time needed per iteration. The methods use sensitivity information derived from the load flow Jacobian and augmented with equations relating the desired control and dependent variables. The heuristic-sensitivity based method is compared to two GA-based methods using two different objective functions. In addition, a FL algorithm is added to the heuristic-sensitivity algorithm and compared to a PS-based algorithm. The last part of this dissertation presents the use of one of the GA-based algorithms to identify the size of shunt capacitor necessary to enhance the voltage profile of a system. A method is presented for utilizing contingency cases with this algorithm to determine required capacitor size.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Anil Pahwa / Shelli K. Starrett / Analysis of power system voltage stability has practical value in increasing wind penetration levels. As wind penetration levels increase in power systems, voltage stability challenges arise due to locating wind resources far away from load centers. This dissertation presents several different voltage stability methods for sizing new wind farms. Power system wind penetration levels depend on the available voltage stability margins (VSMs) of the existing power system and system load characteristics. Three new iterative methods have been developed to maximize wind penetration level in weak power systems based on systems’ VSMs. The first two methods use an iterative approach for increasing the size of each wind farm until reaching the collapse point. Wind farms with less negative impact on system VSMs are sized larger than others. A third wind farm sizing method has been developed using modal analysis in conjunction with the traditional voltage stability method (Q-V method). Wind farms are placed at buses in the power system which have the lowest negative impact on voltage instability modes (strong wind injection buses). By placing the wind farms at the strongest wind injection buses, higher amounts of wind power can be injected into the power system. To further increase wind penetration in weak power systems, two additional techniques are introduced and applied to the western Kansas power system. The first technique uses modes of voltage instability to place voltage support equipment like static var compensators at locations in the power system where they provide the needed reactive power support for increasing levels of wind penetration. The second technique uses the fact that wind patterns at a wind farm site may rarely allow the wind farm to produce its maximum capacity during the peak loading hours. Wind farm maximum sizes can be increased above their maximum voltage stable size limit without driving the power system into becoming voltage unstable. Preventing voltage collapse for the additional increases in wind farm sizes is accomplished by disconnecting some wind turbines inside the wind farm during critical times to reduce its power output to a voltage stable level.

The basic idea deals with detecting the voltage collapse ahead of time to provide the operators a lead time for remedial actions and for possible prevention of blackouts. To detect cases of voltage collapse, we shall create methods using pattern recognition in conjunction with real time simulation of case studies and shall develop heuristic methods for separating voltage stable cases from voltage unstable cases that result in response to system contingencies and faults. Using Real Time Simulator in Entergy-UNO Power & Energy Research Laboratory, we shall simulate several contingencies on IEEE 39-Bus Test System and compile the results in two categories of stable and unstable voltage cases. The second stage of the proposed work mainly deals with the study of different patterns of voltage using artificial neural networks. The final stage deals with the training of the controllers in order to detect stability of power system in advance.

In recent years, power demands around the world and particularly in North America increased rapidly due to increase in customer’s demand, while the development in transmission system is rather slow. This stresses the present transmission system and voltage stability becomes an important issue in this regard. Pattern recognition in conjunction with voltage stability analysis could be an effective tool to solve this problem In this thesis, a methodology to detect the voltage stability ahead of time is presented. Dynamic simulation software PSS/E is used to simulate voltage stable and unstable cases, these cases are used to train and test the pattern recognition algorithms. Statistical and algorithmic pattern recognition methods are used. The proposed method is tested on IEEE 39 bus system. Finally, the pattern recognition models to predict the voltage stability of the system are developed.

The purpose of this dissertation is to analyze dynamic behavior of a stressed power system and to correlate the dynamic responses to a near future system voltage abnormality. It is postulated that the dynamic response of a stressed power system in a short period of time-in seconds-contains sufficient information that will allow prediction of voltage abnormality in future time-in minutes. The PSSE dynamics simulator is used to study the dynamics of the IEEE 39 Bus equivalent test system. To correlate dynamic behavior to system voltage abnormality, this research utilizes two different pattern recognition methods one being algorithmic method known as Regularized Least Square Classification (RLSC) pattern recognition and the other being a statistical method known as Classification and Regression Tree (CART). Dynamics of a stressed test system is captured by introducing numerous contingencies, by driving the system to the point of abnormal operation, and by identifying those simulated contingencies that cause system voltage abnormality. Normal and abnormal voltage cases are simulated using the PSSE dynamics tool. The results of simulation from PSSE dynamics will be divided into two sets of training and testing set data. Each of the two sets of data includes both normal and abnormal voltage cases that are used for development and validation of a discriminator. This research uses stressed system simulation results to train two RLSC and CART pattern recognition models using the training set obtained from the dynamic simulation data. After the training phase, the trained pattern recognition algorithm will be validated using the remainder of data obtained from simulation of the stressed system. This process will determine the prominent features and parameters in the process of classification of normal and abnormal voltage cases from dynamic simulation data. Each of the algorithmic or statistical pattern recognition methods have their advantages and disadvantages and it is the intention of this dissertation to use them only to find correlations between the dynamic behavior of a stressed system in response to severe contingencies and the outcome of the system behavior in a few minutes into the future.

Installation of reactive compensators is widely used for improving power system voltage stability. Reactive compensation also improves the system loading margin resulting in more stable and reliable operation. The improvements in system performance are highly dependent on the location where the reactive compensation is placed in the system. This paper compares three load flow analysis methods - PV curve analysis, QV sensitivity analysis, and Continuation Load Flow - in identifying system weak buses for placing reactive compensation. The methods are applied to three IEEE test systems, including modified IEEE 14-bus system, IEEE 30-bus system, and IEEE 57-bus system. Locations of reactive compensation and corresponding improvements in loading margin and voltages in each test system obtained by the three methods are compared. The author also analyzes the test systems to locate the optimal placement of reactive compensation that yields the maximum loading margin. The results when compared with brute force placement of reactive compensation show the relationship between effectiveness of the three methods and topology of the test systems.

[pt] O crescente aumento da complexidade dos sistemas elétricos de potência gera a necessidade de desenvolvimento de ferramentas que melhorem as condições de análise. O objetivo deste trabalho é aprimorar a ferramenta computacional de avaliação das condições de segurança (ou estabilidade) de tensão. No que diz respeito às barras associadas ao controle remoto de tensão por geração de potência reativa, são propostos novos modelos que representam mais adequadamente as condições operativas no momento do cálculo dos índices de segurança de tensão. Em relação à barra associada ao controle local de tensão por geração de potência reativa é proposta nova modelagem, aplicável tanto no problema de fluxo de potência, utilizando o método de Newton- Raphson, quanto no cálculo dos índices de segurança de tensão. Este modelo,mais robusto e flexível, inclui o controle de tensão local da barra no problema geral de fluxo de potência, formando um sistema de equações de ordem (2*número de barras+número de barras controladas localmente). Para o tratamento de múltiplas barras swing, é proposto um novo modelo, de novo para representar mais adequadamente as condições operativas. É aplicável tanto no problema básico de fluxo de potência, como no cálculo dos índices de segurança de tensão. O modelo proposto considera que apenas o ângulo de uma barra swing é especificado, com os ângulos das demais barras swing livres para variar. Testes numéricos com sistemas-teste (5 e 6 barras) comprovam a aplicabilidade e adequação dos modelos propostos comparando-os aos modelos usados atualmente. / [en] The crescent increase of the complexity of the electric power systems generates the need of development of tools to improve the analysis conditions. The objective of this work is to improve the computational tool of voltage security (stability) conditions assessment. As regards to the buses associated to remote voltage control by reactive power generation, new models that represent more appropriately the operatives conditions at the moment of the calculations of the voltage security indexes, are proposed. As regards to the bus associated to local voltage control by reactive power generation, it is proposed a new modeling, applicable as much in the power flow problem, using the Newton-Raphson method, as in the calculation of the voltage security indexes. This model, more robust and flexible, includes the local voltage control of the bus in the general power flow problem, constituting an equations system of order (2*number of system buses + number of buses with local voltage control). As regard to the multiples swing buses, it is proposed a new model, again to represents more appropriately the operatives conditions. It is applicable as much in the basic power flow problem, as in the calculation of the voltage security indexes. The proposed model considers that just one swing bus has your voltage angle specified and the others swing buses of the power system have your voltage angles free to vary.

[pt] Com o uso extremo das linhas de transmissão surgiram os problemas de estabilidade, ou mais apropriadamente, de segurança de tensão. A avaliação das condições de segurança de tensão é realizada pelo programa computacional ESTABTEN. Como os índices calculados são baseados em um ponto de operação do sistema e em um modelo linearizado das equações de fluxo de carga, assim como a função Fluxo de Carga do pacote computacional ANAREDE também o é, é importante que os modelos matemáticos do sistema, de equipamentos, de controles e de limites sejam compatíveis nos dois programas. Assim como o programa de fluxo de carga é continuamente estendido, o programa ESTABTEN deve continuar a ter sua capacidade estendida para atender as necessidades dos estudos.Estuda-se neste trabalho a modelagem em regime permanente de alguns equipamentos especiais da rede de transmissão e sua incorporação à função de avaliação da segurança de tensão. Os equipamentos contemplados foram: elos de corrente contínua (elo CC), compensadores estáticos de potência reativa (CER), esquemas de HVDC/CCC e linhas com compensação série controlada a tiristores (CSC). São mostrados exemplos numéricos que ilustram a necessidade de uma modelagem realista, na medida do possível. / [en] With the extreme use of the transmission lines, the voltage stability problem, or more properly, the voltage security problem has appeared. The voltage security condition assessment is achieved using the computational program ESTABTEN. The calculated indexes are based on a system operation point and on a linear model of load flow equations, likewise the ANAREDE load flow function. It is important that the mathematical models of systems, equipments, control devices and limits are compatible in both programs. As the load flow program is continually extended, the ESTABTEN program is to have its capability enhanced in order to attend the study requirements. This work is concerned with the steady-state modelling of some special equipments of the transmission network and its incorporation to the voltage security assessment function. The equipments considered were: direct current link (DC link), static VAR compensators (SVC), HVDC/CCC and lines with series controlled compensation (TSSC).Numerical examples are presented to illustrate the necessity of realistic modelling.

The 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.

Present-day economic and environmental constraints push power systems to be operated closer to their limits. A common limiting factor for power transmission is the risk of voltage instability in recent years. As the ultimate countermeasure to voltage collapse, load shedding is normally considered the last resort, when there are no other alternatives to stop an approaching voltage collapse. The requirements of a practical load shedding scheme are to prevent a power system from voltage collapse and to maximize its reliability. In order to design such a scheme, the following tasks are equally important: 1. Recognizing the approaching voltage collapse. 2. Determining the best load shedding locations. 3. Minimizing the amount of load shedding. This thesis firstly investigates the widely used undervoltage load shedding schemes (UVLS) and the single-port impedance match (SPIM) based schemes. The findings explain the difficulties faced by them. An original load shedding oriented voltage stability monitoring scheme, which involves developing a new multi-port network equivalent, is then developed. With the help of the multi-port network equivalent, the monitoring scheme can not only recognize the approaching voltage collapse in time, but also can easily rank the load buses based on their weakness. The results of ranking are consistent with those obtained from modal analysis method. This thesis then proposes a practical event-driven load shedding scheme based on the experiences learned from the schemes implemented by various utilities. The scheme involves developing a multistage method, which is to optimize the amount of load shedding. A general design procedure for the scheme is presented in the thesis. Using a real 2038 bus system as an example, the design methodology is described in detail. The methodology is expected to help power system engineers develop their own load shedding schemes. A practical emergency demand response scheme is also developed and presented in the appendix. It is aimed at choosing the proper demand response participants and minimizing the total cost while achieving a certain level of operation reserves. / Power Engineering and Power Electronics