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Power System Transient Diagnostics Based on Novel Traveling Wave Detection
AuthorJalilzadeh Hamidi, Reza
Electrical and Biomedical Engineering
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Modern electrical power systems demand novel diagnostic approaches to enhancing the system resiliency by improving the state-of-the-art algorithms. The proliferation of high-voltage optical transducers and high time-resolution measurements provide opportunities to develop novel diagnostic methods of very fast transients in power systems. At the same time, emerging complex configuration, such as multi-terminal hybrid transmission systems, limits the applications of the traditional diagnostic methods, especially in fault location and health monitoring. The impedance-based fault-location methods are inefficient for cross-bounded cables, which are widely used for connection of offshore wind farms to the main grid. Thus, this dissertation first presents a novel traveling wave-based fault-location method for hybrid multi-terminal transmission systems. The proposed method utilizes time-synchronized high-sampling voltage measurements. The traveling wave arrival times (ATs) are detected by observation of the squares of wavelet transformation coefficients. Using the ATs, an over-determined set of linear equations are developed for noise reduction, and consequently, the faulty segment is determined based on the characteristics of the provided equation set. Then, the fault location is estimated. The accuracy and capabilities of the proposed fault location method are evaluated and also compared to the existing traveling-wave-based method for a wide range of fault parameters. In order to improve power systems stability, auto-reclosing (AR), single-phase auto-reclosing (SPAR), and adaptive single-phase auto-reclosing (ASPAR) methods have been developed with the final objectives of distinguishing between the transient and permanent faults to clear the transient faults without de-energization of the solid phases. However, the features of the electrical arcs (transient faults) are severely influenced by a number of random parameters, including the convection of the air and plasma, wind speed, air pressure, and humidity. Therefore, the dead-time (the de-energization duration of the faulty phase) is unpredictable. Accordingly, conservatively long dead-times are usually considered by protection engineers. However, if the exact arc distinction time is determined, the power system stability and quality will enhance. Therefore, a new method for detection of arc extinction times leading to a new ASPAR method utilizing power line carrier (PLC) signals is presented. The efficiency of the proposed ASPAR method is verified through simulations and compared with the existing ASPAR methods. High-sampling measurements are prone to be skewed by the environmental noises and analog-to-digital (A/D) converters quantization errors. Therefore noise-contaminated measurements are the major source of uncertainties and errors in the outcomes of traveling wave-based diagnostic applications. The existing AT-detection methods do not provide enough sensitivity and selectivity at the same time. Therefore, a new AT-detection method based on short-time matrix pencil (STMPM) is developed to accurately detect ATs of the traveling waves with low signal-to-noise (SNR) ratios. As STMPM is based on matrix algebra, it is a challenging to implement this new technique in microprocessor-based fault locators. Hence, a fully recursive and computationally efficient method based on adaptive discrete Kalman filter (ADKF) is introduced for AT-detection, which is proper for microprocessors and able to accomplish accurate AT-detection for online applications such as ultra-high-speed protection. Both proposed AT-detection methods are evaluated based on extensive simulation studies, and the superior outcomes are compared to the existing methods.