Basic goal of power system is to continuously provide electrical energy to the users. Like with any other system, failures in power system can occur. In those situations it is critical that correct remedial actions are applied as soon as possible after the accurate fault condition and location are detected. This thesis has been focusing on automated fault location procedure. Different fault location algorithms, classified according to the spatial placement of physical measurements on single ended, multiple ended and sparse system-wide, are investigated. As outcome of this review, methods are listed as function of different parameters that influence their accuracy. This comparison is than used for generating procedure for optimal fault location algorithm selection. According to available data, and position of the fault with respect to the data, proposed procedure decides between different algorithms and selects an optimal one. A new approach is developed by utilizing different data structures such as binary tree and serialization in order to efficiently implement algorithm decision engine. After accuracy of algorithms is strongly influenced by available input data, different data sources are recommended in proposed architecture such as the digital fault recorders, circuit breaker monitoring, SCADA, power system model and etc. Algorithm for determining faulted section is proposed based on the data from circuit breaker monitoring devices. This algorithm works in real time by recognizing to which sequence of events newly obtained recording belongs. Software prototype of the proposed automated fault location analysis is developed using Java programming language. Fault location analysis is automatically triggered by appearance of new event files in a specific folder. The tests were carried out using the real life transmission system as an example.

Basic goal of power system is to continuously provide electrical energy to the users. Like with any other system, failures in power system can occur. In those situations it is critical that correct remedial actions are applied as soon as possible after the accurate fault condition and location are detected. This thesis has been focusing on automated fault location procedure. Different fault location algorithms, classified according to the spatial placement of physical measurements on single ended, multiple ended and sparse system-wide, are investigated. As outcome of this review, methods are listed as function of different parameters that influence their accuracy. This comparison is than used for generating procedure for optimal fault location algorithm selection. According to available data, and position of the fault with respect to the data, proposed procedure decides between different algorithms and selects an optimal one. A new approach is developed by utilizing different data structures such as binary tree and serialization in order to efficiently implement algorithm decision engine. After accuracy of algorithms is strongly influenced by available input data, different data sources are recommended in proposed architecture such as the digital fault recorders, circuit breaker monitoring, SCADA, power system model and etc. Algorithm for determining faulted section is proposed based on the data from circuit breaker monitoring devices. This algorithm works in real time by recognizing to which sequence of events newly obtained recording belongs. Software prototype of the proposed automated fault location analysis is developed using Java programming language. Fault location analysis is automatically triggered by appearance of new event files in a specific folder. The tests were carried out using the real life transmission system as an example.

In a typical utility control center, there could be hundreds of applications running to take care of day-to-day functionality. In many cases, these applications are custom-built by different vendors. With the expectation for high reliability of the electric power grid, many utilities are increasingly moving towards sharing data with each other and with security coordinators. But this data exchange is hampered by incompatible electrical applications built on proprietary data formats and file systems. Electric Power Research Institute's (EPRI‟s) Common Information Model (CIM) was envisioned as a one-sizeits-all data model to remove incompatibility between applications. This research work utilizes the CIM models to exchange power system models and measurements between a state estimator application and sensor web application. The CIM was further extended to include few unique devices from the shipboard medium voltage DC power system. Finally, a wide-area monitoring test bed was set up at MSU to perform wide-area monitoring using phasor measurement units (PMU). The outputs from the Phasor Data Concentrator (PDC) were then converted into CIM/XML documents to make them compatible with the sensor web application. These applications have created advancements in power system monitoring and interoperability

The Estimation of Thevenin Equivalent Parameters is useful for System Monitoring and Protection. We studied a method for estimating the Thevenin equivalent circuits. We then studied two applications including voltage stability and fault location. A study of the concepts of Voltage Stability is done in the initial part of this thesis. A Six Bus Power System Model was simulated using MATLAB SIMULINK®. Subsequently, the Thevenin Parameters were calculated. The results were then used for two purposes, to calculate the Maximum Power that can be delivered and for Fault Location.

Real time monitoring of the shipboard power system is a complex task to address. Unlike the terrestrial power system, the shipboard power system is a comparatively smaller system but with more complexity in terms of its system operation. This requires the power system to be continuously monitored to detect any type of fluctuations or disturbances. Planning metering systems in the power system of a ship is a challenging task not only due to the dimensionality of the problem, but also due to the need for reducing redundancy while improving network observability and efficient data collection for a reliable state estimation process. This research is geared towards the use of a Genetic Algorithm for intelligent placement of meters in a shipboard system for real time power system monitoring taking into account different system topologies and critical parameters to be measured from the system. The algorithm predicts the type and location of meters for identification and collection of measurements from the system. The algorithm has been tested with several system topologies.

Wide-area measurement systems (WAMS) enable the monitoring of overall bulk power systems and provide critical information for understanding and responding to power system disturbances and cascading failures. The North American Frequency Monitoring Network (FNET) takes GPS-synchronized wide-area measurements in a low-cost, easily deployable manner at the 120 V distribution level, which presents more opportunities to study power system dynamics. This work explores the topics of power system disturbance analysis and detection by utilizing the wide-area measurements obtained in the distribution networks. In this work, statistical analysis is conducted based on the major disturbances in the North American Interconnections detected by the FNET situation awareness system between 2006 and 2008. Typical frequency patterns of the generation and load loss events are analyzed for the three North American power Interconnections: the Eastern Interconnection (EI), the Western Electricity Coordinating Council (WECC), and the Electric Reliability Council of Texas (ERCOT). The linear relationship between frequency deviation and frequency change rate during generation/loss mismatch events is verified by the measurements in the three Interconnections. The relationship between the generation/load mismatch and system frequency is also examined based on confirmed generation loss events in the EI system. And a power mismatch estimator is developed to improve the current disturbance detection program. Various types of power system disturbances are examined based on frequency, voltage and phase angle to obtain the event signatures in the measurements. To better understand the propagation of disturbances in the power system, an automated visualization tool is developed that can generate frequency and angle replays of disturbances, as well as image snapshots. This visualization tool correlates the wide-area measurements with geographical information by displaying the measurements over a geographical map. This work makes an attempt to investigate the visualization of the angle profile in the wide-area power system to improve situation awareness. This work explores the viability of relying primarily on distribution-level measurements to detect and identify line outages, a topic not yet addressed in previous works. Line outage sensitivity at different voltage levels in the Tennessee Valley Authority (TVA) system is examined to analyze the visibility of disturbances from the point of view of wide-area measurements. The sensor placement strategy is proposed for better observability of the line trip disturbances. The characteristics of line outages are studied extensively with simulations and real measurements. Line trip detection algorithms are proposed that employs the information in frequency and phase angle measurements. In spite of the limited FDR coverage and confirmed training cases, an identification algorithm is developed which uses the information in the real measurements as well as the simulation cases to determine the tripped line. / Ph. D.

The FDR (Frequency Disturbance Recorder) is a data acquisition device for the power system. The device is portable and can be used with any residential wall outlet for frequency data collection. Furthermore, the FDR transmits calculated frequency data to the web for access by authorized users via Ethernet connection. As a result, Virginia Tech implemented Frequency Monitoring Network (FNET) with these FDR devices. FNET is a collection of identical FDRs placed in different measurement sites to allow for data integration and comparison. Frequency is an important factor for power system control and stabilization. With funding and support provided by ABB, TVA and NSF the FDRs are placed strategically all over the United States for frequency analysis, power system protection and monitoring. The purpose of this study is to refine the current FDR hardware design and establish a new design that will physically fit all the components on one Printed Circuit Board (PCB). At the same time, the software that is to be implemented on the new board is to be kept similar if not the same as that of the current design. The current FDR uses the Axiom CME555 development board and it is interfaced to the external devices through its communication ports. Even through the CME555 board is able to meet the demands of the basic FDR operations, there are still several problems associated with this design. This paper will address some of those hardware problems, as well as propose a new board design that is specifically aimed for operations of FDR. / Master of Science

The ability to implement Wide Area Monitoring and Control in power systems is developing into a need in order to prevent wide scale cascading outages. Monitoring of events in the power system provides a great deal of insight into the behaviour of the system. The research work presented in this thesis focussed on two tools that aid in monitoring: State Estimation and Synchronised Phasors provided by Phasor Measurement Units (PMU). State Estimation is essentially an on-line data processing scheme used to estimate the best possible state (i.e. voltage phasors) from a monitored set of measurements (active and reactive powers/voltage phasor measurements). The ever growing complexity and developments in the state of art calls for robust state estimators that converge accurately and rapidly. Newton’s method forms the basis for most of the solution approaches. For real-time application in modern power systems, the existing Newton-based state estimation algorithms are too fragile numerically. It is known that Newton’s algorithm may fail to converge if the initial nominal point is far from the optimal point. Sometimes Newton’s algorithm can converge to a local minima. Also Newton’s step can fail to be a descent direction if the gain matrix is nearly singular or ill-conditioned. This thesis proposes a new and more robust method that is based on linear programming and trust region techniques. The proposed formulation is suitable for Upper Bound Linear Programming. The formulation is first introduced and its convergence characteristics with the use of Upper Bound Linear Programming is studied. In the subsequent part, the solution to the same formulation is obtained using trust region algorithms. Proposed algorithms have been tested and compared with well known methods. The trust region method-based state estimator is found to be more reliable. This enhanced reliability justifies the additional time and computational effort required for its execution. One of the key elements in the synchrophasor based wide area monitoring is the Phasor Measurement Unit. Synchronized, real time, voltage phasor angle, phasor measurements over a distributed power network presents an excellent opportunity for major improvements in power system control and protection. Two of the most significant applications include state estimation and instability prediction. In recent years, there has been a significant research activity on the problem of finding the suitable number of PMUs and their optimal locations. For State Estimation, such procedures, which basically ensure observability based on network topology, are sufficient. However for instability prediction, it is very essential that the PMUs are located such that important/vulnerable buses are also directly monitored. In this thesis a method for optimal placement of PMUs, considering the vulnerable buses is developed. This method serves two purposes viz., identifying optimal locations for PMU (planning stage), and identifying the set PMUs to be closely monitored for instability prediction. The major issue is to identify the key buses when the angular and voltage stability prediction is taken into account. Integer Linear Programming technique with equality and inequality constraints is used to find out the optimal placement set. Further, various aspects of including the Phasor Measurements in state estimation algorithms are addressed. Studies are carried out on various sample test systems, an IEEE 30-bus system and real life Indian southern grid equivalents of 24-bus system, 72-bus system and 205-bus system.

The research topics presented in this dissertation focus on validation of customer-level voltage synchrophasor data for transmission system analysis, detection and categorization of power system events as measured by phasor measurement units (PMUs), and identification of the influence of power system conditions (wind power, daily and seasonal load variation) on low-frequency oscillations. Synchrophasor data can provide information across entire power systems but obtaining the data, handling the large dataset and developing tools to extract useful information from it is a challenge. To overcome the challenge of obtaining data, an independent synchrophasor network was created by taking synchrophasor measurements at customer-level voltage. The first objective is to determine if synchrophasor data taken at customer-level voltage is an accurate representation of power system behavior. The validation process was started by installing a transmission level (69 kV) PMU. The customer-level voltage measurements were validated by comparison of long term trends and low-frequency oscillations estimates. The techniques best suited for synchrophasor data analysis were identified after a detailed study and comparison. The same techniques were also applied to detect power system events resulting in the creation of novel categories for numerous events based on shared characteristics. The numerical characteristics for each category and the ranges of each numerical characteristic for each event category are identified. The final objective is to identify trends in power system behavior related to wind power and daily and seasonal variations by utilizing signal processing and statistical techniques. / text

To view the electricity supply in our society as just sockets mountedin our walls with a constant voltage output is far from the truth. Inreality, the power system supplying the electricity or the grid, is themost complex man-made dynamical system there is. It demands severecontrol and safety measures to ensure a reliable supply of electric power.Throughout the world, incidents of widespread power grid failures havebeen continuously reported. The state where electricity delivery to customersis terminated by a disturbance is called a blackout. From a stateof seemingly stable operating conditions, the grid can fast derail intoan uncontrollable state due to cascading failures. Transmission linesbecome automatically disconnected due to power flow redirections andparts of the grid become isolated and islands are formed. An islandedsub-grid incapable of maintaining safe operation conditions experiencesa blackout. A widespread blackout is a rare, but an extremely costlyand hazardous event for society.During recent years, many methods to prevent these kinds of eventshave been suggested. Controlled islanding has been a commonly suggestedstrategy to save the entire grid or parts of the grid from a blackout.Controlled islanding is a strategy of emergency control of a powergrid, in which the grid is intentionally split into a set of islanded subgridsfor avoiding an entire collapse. The key point in the strategy is todetermine appropriate separation boundaries, i.e. the set of transmissionlines separating the grid into two or more isolated parts.The power grid exhibits highly nonlinear response in the case oflarge failures. Therefore, this thesis proposes a new controlled islandingmethod for power grids based on the nonlinear Koopman Mode Analysis(KMA). The KMA is a new analyzing technique of nonlinear dynamicsbased on the so-called Koopman operator. Based on sampled data followinga disturbance, KMA is used to identify suitable partitions of thegrid.The KMA-based islanding method is numerically investigated withtwo well-known test systems proposed by the Institute of Electrical andElectronics Engineers (IEEE). By simulations of controlled islanding inthe test system, it is demonstrated that the grid’s response following afault can be improved with the proposed method.The proposed method is compared to a method of partitioning powergrids based on spectral graph theory which captures the structural propertiesof a network. It is shown that the intrinsic structural propertiesof a grid characterized by spectral graph theory are also captured by theKMA. This is shown both by numerical simulations and a theoreticalanalysis.