Data-driven Protection of Transformers, Phase Angle Regulators, and Transmission Lines in Interconnected Power Systems

This dissertation highlights the growing interest in and adoption of machine learning approaches for fault detection in modern electric power grids. Once a fault has occurred, it must be identified quickly and a variety of preventative steps must be taken to remove or insulate it. As a result, detecting, locating, and classifying faults early and accurately can improve safety and dependability while reducing downtime and hardware damage. Machine learning-based solutions and tools to carry out effective data processing and analysis to aid power system operations and decision-making are becoming preeminent with better system condition awareness and data availability. Power transformers, Phase Shift Transformers or Phase Angle Regulators, and transmission lines are critical components in power systems, and ensuring their safety is a primary issue. Differential relays are commonly employed to protect transformers, whereas distance relays are utilized to protect transmission lines. Magnetizing inrush, overexcitation, and current transformer saturation make transformer protection a challenge. Furthermore, non-standard phase shift, series core saturation, low turn-to-turn, and turn-to-ground fault currents are non-traditional problems associated with Phase Angle Regulators. Faults during symmetrical power swings and unstable power swings may cause mal-operation of distance relays, and unintentional and uncontrolled islanding. The distance relays also mal-operate for transmission lines connected to type-3 wind farms. The conventional protection techniques would no longer be adequate to address the above-mentioned challenges due to their limitations in handling and analyzing the massive amount of data, limited generalizability of conventional models, incapability to model non-linear systems, etc. These limitations of conventional differential and distance protection methods bring forward the motivation of using machine learning techniques in addressing various protection challenges. The power transformers and Phase Angle Regulators are modeled to simulate and analyze the transients accurately. Appropriate time and frequency domain features are selected using different selection algorithms to train the machine learning algorithms. The boosting algorithms outperformed the other classifiers for detection of faults with balanced accuracies of above 99% and computational time of about one and a half cycles. The case studies on transmission lines show that the developed methods distinguish power swings and faults, and determine the correct fault zone. The proposed data-driven protection algorithms can work together with conventional differential and distance relays and offer supervisory control over their operation and thus improve the dependability and security of protection systems

Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform

In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape

Detection of High Impedance Faults in Microgrids using Machine Learning

This article presents differential protection of the distribution line connecting a wind farm in a microgrid. Machine Learning (ML) based models are built using differential features extracted from currents at both ends of the line to assist in relaying decisions. Wavelet coefficients obtained after feature selection from an extensive list of features are used to train the classifiers. Internal faults are distinguished from external faults with CT saturation. The internal faults include the high impedance faults (HIFs) which have very low currents and test the dependability of the conventional relays. The faults are simulated in a 5-bus system in PSCAD/EMTDC. The results show that ML-based models can effectively distinguish faults and other transients and help maintain security and dependability of the microgrid operation

Spring warming of the eastern Arabian Sea and Bay of Bengal from buoy data

Observations from moored buoys during spring of 1998-2000 suggest that the warming of the mixed layer (~20 m deep) of the north Indian Ocean warm pool is a response to net surface heat flux Qnet (~100 W m-2) minus penetrative solar radiation Qpen (~45 W m-2). A residual cooling due to vertical mixing and advection is indirectly estimated to be about 25 W m-2. The rate of warming due to typical values of Qnet minus Qpen is not very sensitive to the depth of the mixed layer if it lies between 10 m and 30 m

Implementation of a MSP430-based digital thermometer using the slope ADC of the timer port module

This report describes the slope A/D measurement of a resistance and the ease with which it can be applied to MSP430 microcontrollers. It describes a digital thermometer design that uses the slope ADC capabilities of the Timer Port module on the MSP430x3xx microcontrollers. It is used more generally as a reference on how to connect resistive sensors and reference resistors to the Timer Port module. All MSP430x3xx devices include the Timer Port module. The module allows several resistive sensors and reference resistors to be connected in an application. Unused module pins can be used as independent outputs. Slope A/D conversion is an analog-to-digital conversion technique that can be implemented with a comparator rather than a standalone ADC module or device. The technique is based on the charging/discharging of a capacitor with a known value. The number of clock cycles necessary to discharge the capacitor is then counted. Longer discharge times indicate larger voltages. The voltage is derived from the discharge time using the standard equation for capacitor discharge. In addition to digitizing voltages, a variation of the technique can be used to measure resistance. This is valuable in measuring any component that can have varying resistance, such as potentiometers and various types of transducers. Unlike voltage measurement, where the key relationship is between voltage and time while the resistance is constant, the key relationship in resistance measurement is between resistance and time, while the initial voltage remains constant. The R-relationship is linear, which means the calculation is easier and less- costly to implement in a microcontroller than for the exponential V-t relationship. The thermometer has been simulated by using a variable resistance instead of a thyristor. In addition care has been taken to optimize the power consumption by forcing the microcontroller to several low-power modes during the operation. The combination of the Timer Port module, the 16-bit CPU, and the ultra low power design provide unmatched MIPS per watt performance. The set up can be extended to provide a low power thermostat

Standard Gravity and Wind Load Analysis on 103-years old Unreinforced Masonry Building

Finite element modelling and analysis has been performed on 103-years old unreinforced masonry Senate hall building (SHB), Allahabad University, India. It is an Indo-Saracenic style of architecture which was built in 1915. An in-situ survey is conducted to know the present condition of the SHB. The major and minor cracks are visible, and construction material has deteriorated at various part of the SHB. The old documents, reports, on-site measurement, and photographs are used to gather the historical data and prepared the accurate model of the SHB on Ansys workbench (ANSYS 14.0) tool. Macro and homogenisation approach has used in the modelling of the SHB. The standard gravity and wind load analysis is performed with a fixed boundary condition on its based of SHB. In gravity analysis, maximum stress (5.69MPa) has been observed at the connections of the ground floor and maximum deformation (7.8mm) on the crown of the arch of the first-floor. The maximum stress and deformation 14.286MPa and 12.491mm have been observed under live load analysis. Further, the maximum stress and deformation obtained under wind load analysis are 4.10MPa and 8.07mm, respectively. The finite element simulation and visual inspection of the SHB are in good agreement with the present condition of the structure

Hypertrophic pachymeningitis: a rare manifestation of IgG4 related disease

Hypertrophic pachymeningitis (HP) is a rare form of diffuse inflammatory disease that causes thickening of the dura mater. It can involve the cranial or the spinal dura or both. An increasingly well-known symptom of IgG4-related illness, a fibroinflammatory syndrome that may affect almost any organ, is IgG4-related hypertrophic pachymeningitis (IgG4-RHP). It is estimated that IgG4-RHP may account for a high proportion of cases of hypertrophic pachymeningitis once considered idiopathic. Contrast magnetic resonance imaging (MRI) shows pachymeningeal enhancement. Serum IgG4 levels may be elevated but are normal in most patients. However, most patients have elevated cerebrospinal fluid (CSF) IgG4 index. Hence, CSF IgG4 index could serve as a less invasive diagnostic marker of IgG4-RHP. Confirmation of diagnosis is by meningeal biopsy that shows swirling “storiform” fibrosis with lymphocytic infiltrates, obliterate phlebitis and IgG4 positive plasma cells. This case highlights the diagnostic dilemma of IgG4-RHP as gold standard of diagnosis is meningeal biopsy which has many of its own limitations. CSF IgG4 index could be an alternate option for meningeal biopsy when the procedure is contraindicated or uninformative