Now showing 1 - 7 of 7
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    Gradient Boosting Trees for Fault Identification in Water Distribution Networks
    (01-01-2022)
    Mankad, Jaivik
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    Events such as pipe burst, pipe clogging, excess demand often disrupt the water distribution network operation. As a result, water utilities have to bear an extra financial burden. The knowledge of such events could help the water supply board formulate and operate the network with minimal loss. This work aims to identify the nature of such uncertain events with limited operational information using simple measures designed to monitor the network locally at nodes. Instead of acquiring information from all the nodes, a few nodes are identified for sensor placement by solving a Mixed Integer Linear Programming (MILP) problem. The objectives for solving MILP are to minimise total sensor cost and maximise the sensitivity of measurements to any given fault. Sensitivity is calculated from operational data generated by performing Monte Carlo simulations. These simulations generate data for each fault of different magnitude at different locations. Gradient boosting trees are trained using the limited operational information from the network. Accuracy of identification of faults of 78% on the passive network and over 81% on active networks was obtained using limited features with extreme gradient boosting (XGBoost) model.
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    Data driven approach for performance assessment of linear and nonlinear Kalman filters
    A new technique is developed for assessing the performance of linear and nonlinear Kalman filter based state estimators. The proposed metric will indicate the performance of these state estimators which will be primarily influenced by: (i) difference between the model dynamics and process dynamics and, (ii) various approximations of the nonlinear plant dynamics used in nonlinear Kalman filters. Currently, there exists no such quantification method to analyze the performance of linear and nonlinear Kalman filters, a key requirement for improvement and a practical benchmark for comparison of these state estimation algorithms. The proposed technique uses the generalized Hurst exponent of the prediction errors (difference in measured output and a posteriori estimates) obtained from the state estimators to quantify the performance. This technique could be implemented on-line as it requires only plant operating data and the predicted outputs (from the linear and nonlinear Kalman filters) to assess the performance. Several simulation studies demonstrate the applicability of the proposed performance metric to both linear and non-linear Kalman filters. © 2014 American Automatic Control Council.
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    Addressing Uncertainties within Active Learning for Industrial IoT
    (14-06-2021)
    Agarwal, Deepesh
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    Srivastava, Pravesh
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    Martin-Del-Campo, Sergio
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    Natarajan, Balasubramaniam
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    Internet of Things (IoT) is a key enabler of Industry 4.0 with networked devices providing sensor data to help manage, automate, streamline and optimize assets, operations and processes. In such industrial IoT settings, reliability and process experts spend a considerable amount of time in creating accurate ground-truth data to assist with the inferencing capabilities of Artificial Intelligence (AI) engines. This process can be time-consuming and sometimes inaccurate, depending on the complexity of data. Accurate expert annotated data is the foundation for many AI applications because data needs to be classified on several bases, for instance into 'normal', 'abnormal' or 'pre-abnormal' states. Such problem formulations can be appropriately addressed using Active Learning (AL) techniques. We propose an AL framework capable of handling two practical challenges: oracle uncertainty and quantification of model performance in the absence of ground truth. Consequently, the proposed approach addresses uncertainties within AL techniques by fusing information pertaining to expertise levels of the human annotators and their confidence levels corresponding to the annotation provided.
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    Enhancing Performance of Compressive Sensing-based State Estimators using Dictionary Learning
    (01-01-2022)
    Madbhavi, Rahul
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    Smart grids integrate computing and communication infrastructure with conventional power grids to improve situational awareness, control, and safety. Several technologies such as automatic fault detection, automated reconfiguration, and outage management require close network monitoring. Therefore, utilities utilize sensing equipment such as PMUs (phasor measurement units), smart meters, and bellwether meters to obtain grid measurements. However, the expansion in sensing equipment results in an increased strain on existing communication infrastructure. Prior works overcome this problem by exploiting the sparsity of power consumption data in the Haar, Hankel, and Toeplitz transformation bases to achieve sub-Nyquist compression. However, data-driven dictionaries enable superior compression ratios and reconstruction accuracy by learning the sparsifying basis. Therefore, this work proposes using dictionary learning to learn the sparsifying basis of smart meter data. The smart meter data sent to the data centers are compressed using a random projection matrix prior to transmission. These measurements are aggregated to obtain the compressed measurements at the primary nodes. Compressive sensing-based estimators are then utilized to estimate the system states. This approach was validated on the IEEE 33-node distribution system and showed superior reconstruction accuracy over conventional transformation bases and over-complete dictionaries. Voltage magnitude and angle estimation error less than 0.3% mean absolute percentage error and 0.04 degree mean absolute error, respectively, were achieved at compression ratios as high as eight.
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    Analysis of Control Room Operators' Competence using Cognitive Engineering Approaches to Improve Process Safety
    (01-01-2021)
    Shahab, Mohammed Aatif
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    Operator competence is critical to ensure safe and profitable operation in hazard intensive chemical process industries. Human errors account for the majority of the accidents in these industries. Therefore, it is imperative to develop methodologies to assess the competence of operators to minimize human errors. Traditional approaches to elicit operator competence are based on subjective measures and/or measures derived primarily from the process and operator actions. These approaches ignore the cognitive aspects of operators such as perception, decision-making strategy, and workload, which are crucial for improving performance. Recent development in sensor technology has enabled the researchers to measure human cognitive behavior objectively. Sensors such as eye-Tracking, electroencephalography (EEG) and galvanic skin resistance (GSR) are found to provide intrinsic human characteristics that cannot be measured otherwise. In this paper, we discuss how eye-Tracking can be used to capture control room operators' cognition and help infer their competence. Eye-Tracking provides information about the location of a person's gaze, which serves as a trace of their attention allocation. Eye gaze based metrics show a strong correlation with operator cognitive behavior such as situation awareness and cognitive workload during process monitoring tasks.
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    On-line performance monitoring of PEM fuel cell using a fast EIS approach
    The Polymer Electrolyte Membrane Fuel Cell is a widely researched fuel cell, and a highly potential candidate for alternate power generation. However, technical issues such as membrane flooding and drying prevent its deployment in many applications. Electrochemical Impedance Spectroscopy (EIS) is a very powerful technique that is used to isolate flooding and drying of the fuel cell from operating data. Such information about the state of operation of the cell is critical to deciding necessary control actions to maintain the health and performance of the cell. However, the time taken in obtaining measurements in EIS can be large enough to allow the cell to flood or dry beyond irreparable damage, rendering it a mere postmortem technique. Moreover, after long durations of perturbation, the cell takes a considerable amount of time to return to its regular operation. A new technique is proposed that uses the concept of EIS, but is computationally faster and gives results comparable with those of traditional EIS. This technique is based on perturbing the cell with a small chirp signal containing large number of frequencies instead of series of small sinusoids at different frequencies. Simulation results on isolation of flooding and drying based on Fast EIS are illustrated and future work directions are indicated.
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    Phasor data correction and transmission system state estimation under Man-in-the-Middle attack
    (01-01-2023)
    Tharzeen, Aabila
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    Natarajan, Balasubramaniam
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    Cyberinfrastructure (e.g., sensors, actuators and the associated communication network) has become an integral part of our modern power grid. While these cyber technologies enhance situational awareness and operational efficiency, they also expose the physical system to cyber-attacks. In this paper, we consider the problem of transmission system state estimation based on measurements from a number of PMUs. In this context, a PMU data integrity attack, Man-in-the-Middle (MitM) attack that can potentially cause a severe impact on the grid is analyzed. Specifically, we propose a novel method based on an alternate expectation-maximization framework to mitigate the effects of these attacks on the state estimation process. Numerical tests are conducted on IEEE-14, 30 and 118 bus systems with different attack scenarios to validate the developed method. Unlike existing works, the proposed algorithm provides accurate state estimates without any prior knowledge of the location of the attack or the number of meters being attacked.