Analyzing voltage sag direction using protective relays and deep-learning methods

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Patha, Lekhaj

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As the electricity demand continues to grow, power systems are becoming more complex and interconnected, making the need for reliable protection systems more important than ever. Protection systems are designed to detect and isolate faults and other abnormal conditions, preventing them from cascading through the power grid and causing widespread outages. The primary challenge in protection is to detect the fault, the type of fault, and the location of the fault. Traditional relays effectively locate, detect, and isolate faults. Circuit breakers, fuses, and relays, these devices work together to ensure the power system remains stable and reliable, under various conditions including system failures. Smart Intelligent relays (SIRs) are designed to perform a broader range of functions, such as fault location, and power quality monitoring. Machine learning techniques are increasingly applied in power systems protection to enhance fault detection and classification accuracy and speed. ML algorithms can be used to analyze real-time data from sensors and other devices to detect and classify faults, including those that may be too small or subtle to be detected by traditional protection systems. The thesis aims to study methods of identifying the direction of voltage sags in the distribution circuits. Voltage sags arise from the presence of short-circuit faults involving single, double, and three phase-to-ground conditions. The direction of the fault is based on the direction of power flow before the occurrence of the event. A fault can be classified as a downstream fault from a monitoring location if the direction of power flow is towards the fault location before the occurrence of the event. Similarly, a fault can be classified as an upstream fault if the direction of power flow is against the fault location before the occurrence of the fault. The terms upstream and downstream are relative to the monitor location. A downstream fault for one monitor can be an upstream fault for a different monitor. This thesis studies the applications of protective relaying and deep learning techniques in identifying the direction of voltage sags using the real-time waveforms of voltage and current to estimate whether the fault is upstream or downstream from the monitored location(s). The fault data was generated using a time-domain power system modeling tool with variable fault impedances and multiple fault locations. Relay-based approaches have been studied, and a deep-learning technique has been developed with the data generated. The relay-based techniques were capable of identifying the fault direction in all the cases irrespective of the fault location and fault duration. ML algorithms can help analyze large amounts of data and detect patterns that may be difficult or impossible for traditional protection systems to identify.


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