In this thesis catastrophe theory is applied to the voltage stability problem in power systems. A general model for predicting voltage stability from the system conditions is presented and then applied to both a simple 2-bus explanatory power system and to a larger more realistic power system. The model is based on the swallowtail catastrophe which with its three control variables is able to determine the voltage stability of the system. The model is derived directly from the systems equations. The voltage stability of the system at each specified system bus is determined by comparing the values of the swallowtail catastrophe control variables with those of the unique region of voltage stability. The control variables are calculated from the system operating conditions. If the control variables specify a point inside the stability region, the system is voltage stable; otherwise it is voltage unstable. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Renewable energy resources (RES) are becoming more popular for electricity generation due to their easy installation, flexibility, low cost, environmental compatibility, etc. However, their fluctuating nature is a major drawback, which decreases the power quality and makes them less trusty in the power system. To mitigate this problem, battery energy storage (BES) has been widely used with renewable energy sources. Because batteries are designed to handle “steady fluctuations” of power, the “sudden and peak” fluctuating power levels of renewable energy sources may cause shorter life spans for them, which may cause dramatic economic loss or negatively impact the power quality. Also, even though batteries have been used as a backup for RES, high power quality cannot be guaranteed when there is a rapid and peak fluctuations on source/load.

Recent events, such as the Northeast Blackout of 2003, have highlighted the need for accurate real-time stability assessment techniques to detect when an electric power system is on the brink of voltage collapse. While many techniques exist, most techniques are computationally demanding and cannot be used in an on-line application. A voltage stability index (VSI) can be designed to estimate the distance of the current operating point to the voltage marginally stable point during the system operation. In this research work, a new VSI was developed that not only can detect the system voltage marginally stable point but also is computationally efficient for on-line applications. Starting with deriving a method to predict three types of maximum transferable power of a single source power system, the new VSI is based on the three calculated load margins. In order to apply the VSI to large power systems, a method has been developed to simplify the large network behind a load bus into a single source and a single transmission line given the synchronized phasor measurements of the power system variables and network parameters. The simplified system model, to which the developed VSI can be applied, preserves the power flow and the voltage of the particular load bus. The proposed voltage stability assessment method, therefore, provides a VSI of each individual load bus and can identify the load bus that is the most vulnerable to voltage collapse. Finally, the new VSI was tested on three power systems. Results from these three test cases provided validation of the applicability and accuracy of the proposed VSI.

Various sensors distributed across different parts of the electric power grid provide measurements to the control center operator for situational awareness of the system. Voltage transformer, current transformer, relay and phasor measurement units (PMU) are types of sensors for power system monitoring. The utilities monitor the operating condition of their system by processing the measurements received from these various sensors using a state estimator. A state estimator refines these measurements, compensates for any lost data and provides a snapshot of the power system. The operator at the control center does further analysis using energy management system tools based on the most recent data and required state of the system. The electric power grid is vulnerable to blackouts caused by physical disturbances, human errors and external disasters. These disturbances can also cause loss of data, sensor failure or communication link failure. This research work focuses on comparing state estimation algorithms with loss of measurement data. The measurements are assumed to be lost as clustered and scattered data sets. Weighted Least Square (WLS), Least Absolute Value (LAV) and Iteratively Reweighted Least Squares (IRLS) implementation of Weighted Least Absolute Value (WLAV) algorithms are compared for state estimation with clustered and scattered loss of data. These algorithms are tested on a six bus, I 30 bus and 137 bus utility test cases. The test results indicate the best possible algorithm in several considered scenarios based on an error index. Additionally, phasor measurements data are included in two of the state estimation algorithms to study their ability to mitigate the loss of measurement data.

One of the major objectives at Mississippi State University’s Power and Energy Research Laboratory (PERL) is to develop an adaptive protective controller for Shipboard Power System (SPS) protection. This thesis work focuses on developing an overcurrent relay model in LabVIEW software and validating the developed model by conducting Hardware-in-the-Loop (HIL) tests with Real-Time Digital Simulator (RTDS) and commercial Schweitzer Engineering Laboratories (SEL)-351S directional over-current relay. Simulation results show that the developed relay model is quite flexible, efficient and can be used in real-time. Modeling efforts to establish a HIL platform using National Instruments devices have been presented here. This thesis work also proposes a high-performance and low-cost National Instruments-PXI platform for power system simulations. Two-bus, eight-bus and shipboard power system (SPS) test cases are developed using Matlab/Simulink.Software-in-the-Loop (SIL) tests are conducted for these test cases with Matlab/Simulink overcurrent relay model for several fault conditions. To determine the performance of the NI-PXI system, open loop tests are done between the NI-PXI and the SEL-351S relay and these results are compared with the results of open loop test conducted between the RTDS and SEL-351S relay. HIL tests are done between the NI-PXI system and the dSPACE relay model. HIL tests are also done between the NI-PXI and the commercial SEL-351S relay. These results show that the NI-PXI controller can be used as a power system simulator.

Accuracy of the measurement data used for the decision making process or for shipboard operations and control is very important to ensure the reliability and survivability. The uncertainties present in measurement data need to be minimized for reliable system operation. In this work, a fuzzy logic based model is developed to deal with uncertainty in the meter data. Operational and historical parameters of the meters were used to determine a ‘trust’ value of individual meter. A fuzzy correction system for measurement data was used to generate an input dataset for a genetic algorithm based reconfiguration system. Additionally, with the goal of optimizing the performance of power system operator, the effects of Decision Support System (DSS) on the quality of decisions taken by the operator were examined. Unaided and aided interface prototypes were developed and usability tests were carried out on interface prototypes with users having knowledge of power systems.

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This dissertation is a systematic study of artificial neural networks (ANN) applications in power system restoration (PSR). PSR is based on available generation and load to be restored analysis. A literature review showed that the conventional PSR methods, i.e. the pre-established guidelines, the expert systems method, the mathematical programming method and the petri-net method have limitations such as the necessary time to obtain the PSR plan. ANN may help to solve this problem presenting a reliable PSR plan in a smaller time. Based on actual and past experiences, a PSR engine based on ANN was proposed and developed. Data from the Iowa 162 bus power system was used in the implementation of the technique. Reactive and real power balance, fault location, phase angles across breakers and intentional islanding were taken into account in the implementation of the technique. Constraints in PSR as thermal limits of transmission lines (TL), stability issues, number of TL used in the restoration plan and lockout breakers were used to create feasible PSR plans. To compare the time necessary to achieve the PSR plan with another technique a PSR method based on a breadth-search algorithm was implemented. This algorithm was also used to create training and validation patterns for the ANN used in the scheme. An algorithm to determine the switching sequence of the breakers was also implemented. In order to determine the switching sequence of the breakers the algorithm takes into account the most priority loads and the final system configuration generated by the ANN. The PSR technique implemented is composed by several pairs of ANN, each one assigned to an individual island of the system. The restoration of the system is done in parallel in each island. After each island is restored the tie lines are closed. The results encountered shows that ANN based schemes can be used in PSR helping the operators restore the system under the stressful conditions following a blackout. / Ph. D.