257 research outputs found

    Cascading Outages Detection and Mitigation Tool to Prevent Major Blackouts

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    Due to a rise of deregulated electric market and deterioration of aged power system infrastructure, it become more difficult to deal with the grid operating contingencies. Several major blackouts in the last two decades has brought utilities to focus on development of Wide Area Monitoring, Protection and Control (WAMPAC) systems. Availability of common measurement time reference as the fundamental requirement of WAMPAC system is attained by introducing the Phasor Measurement Units, or PMUs that are taking synchronized measurements using the GPS clock signal. The PMUs can calculate time-synchronized phasor values of voltage and currents, frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can be utilized in several applications including disturbance and islanding detection, and control schemes. In this dissertation, an integrated synchrophasor-based scheme is proposed to detect, mitigate and prevent cascading outages and severe blackouts. This integrated scheme consists of several modules. First, a fault detector based on electromechanical wave oscillations at buses equipped with PMUs is proposed. Second, a system-wide vulnerability index analysis module based on voltage and current synchrophasor measurements is proposed. Third, an islanding prediction module which utilizes an offline islanding database and an online pattern recognition neural network is proposed. Finally, as the last resort to interrupt series of cascade outages, a controlled islanding module is developed which uses spectral clustering algorithm along with power system state variable and generator coherency information

    Monitoring of single and multiple line outages with synchrophasors in areas of the power system

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    When power grids are heavily stressed with a bulk power transfer, it is useful to have a fast indication of the increased stress when multiple line outages occur. Reducing the bulk power transfer when the outages are severe could forestall further cascading of the outages. Phasor measurement units (PMUs) are vital elements for monitoring and control of these heavily stressed power system. This work presents a new approach to implement and utilize PMU information to monitor operational transfer capability and limits based on voltage phasor angles with respect to thermal limits of transmission lines. This work demonstrates an algorithm to obtain thresholds based on the angle and then quickly deploy PMU data to monitor stress changes due to single and multiple outages in real time to send fast notification of emergency situations. Area angle uses the topology and the synchronized measurements of angles across an area of power system to measure stress caused by outages within the area. The proposed algorithm is easy, quick and computationally suitable for real systems to capture bulk stress caused by outages and also identify local stress. This work first illustrates the idea of area angle in a Japanese test system and then explores the choice of the border buses. It further investigates the relation between area angle to area susceptance and supports the findings in two areas of the Western North American power system. Finally, this work develops a procedure to define thresholds for the area angle that relate to the maximum power that can be transferred through the area until a line limit is reached. The algorithm finding the area angle thresholds offline and then in real time monitoring the area angle and comparing it to the thresholds after multiple outages determines the urgency (or not) of actions to reduce the bulk transfer of power through the area. The procedure also identifies exceptional cases in which separate actions to resolve local power distribution problems are needed. The findings are supported by testing on a 1553 bus reduced model of the Western interconnection power system

    Real-time Prediction of Cascading Failures in Power Systems

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    Blackouts in power systems cause major financial and societal losses, which necessitate devising better prediction techniques that are specifically tailored to detecting and preventing them. Since blackouts begin as a cascading failure (CF), an early detection of these CFs gives the operators ample time to stop the cascade from propagating into a large-scale blackout. In this thesis, a real-time load-based prediction model for CFs using phasor measurement units (PMUs) is proposed. The proposed model provides load-based predictions; therefore, it has the advantages of being applicable as a controller input and providing the operators with better information about the affected regions. In addition, it can aid in visualizing the effects of the CF on the grid. To extend the functionality and robustness of the proposed model, prediction intervals are incorporated based on the convergence width criterion (CWC) to allow the model to account for the uncertainties of the network, which was not available in previous works. Although this model addresses many issues in previous works, it has limitations in both scalability and capturing of transient behaviours. Hence, a second model based on recurrent neural network (RNN) long short-term memory (LSTM) ensemble is proposed. The RNN-LSTM is added to better capture the dynamics of the power system while also giving faster responses. To accommodate for the scalability of the model, a novel selection criterion for inputs is introduced to minimize the inputs while maintaining a high information entropy. The criteria include distance between buses as per graph theory, centrality of the buses with respect to fault location, and the information entropy of the bus. These criteria are merged using higher statistical moments to reflect the importance of each bus and generate indices that describe the grid with a smaller set of inputs. The results indicate that this model has the potential to provide more meaningful and accurate results than what is available in the previous literature and can be used as part of the integrated remedial action scheme (RAS) system either as a warning tool or a controller input as the accuracy of detecting affected regions reached 99.9% with a maximum delay of 400 ms. Finally, a validation loop extension is introduced to allow the model to self-update in real-time using importance sampling and case-based reasoning to extend the practicality of the model by allowing it to learn from historical data as time progresses
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