Active Learning for Incipient Fault Detection

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Description

Fault detection is an integral part for power systems as without its proper study, analysis and mitigation, people will not be able to power the various appliances and equipment required in all aspects of life. One such type of fault

Fault detection is an integral part for power systems as without its proper study, analysis and mitigation, people will not be able to power the various appliances and equipment required in all aspects of life. One such type of fault which is very criticalin an electrical cable but very difficult to spot is incipient fault. These momentary faults are observed for very short periods however, if it persists, this would lead to consequences like insulation wear-off and even, power outages. With the advent of
machine learning in the power systems fraternity, this paper also uses a new and updated Active Learning algorithm to detect incipient fault data from a simulated test case. The objective of the paper is to detect the fault data accurately using this new and precise method. For purposes of data collection and training of the model, MATLAB Simulink and Python programming has been used respectively.

Date Created
2023
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Optimal Utilization of Third-Party Demand Response Resources in Vertically Integrated Utilities: A Game Theoretic Approach

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Description

This report studies the optimal mechanisms for the vertically integrated utility to dispatch and incentivize the third-party demand response (DR) providers in its territory. A framework is proposed, with three-layer coupled Stackelberg and simultaneous games, to study the interactions and

This report studies the optimal mechanisms for the vertically integrated utility to dispatch and incentivize the third-party demand response (DR) providers in its territory. A framework is proposed, with three-layer coupled Stackelberg and simultaneous games, to study the interactions and competitions among the pro t-seeking process of the utility, the third-party DR providers, and the individual end users (EUs) in the DR programs. Two coupled single-leader-multiple-followers Stackelberg games with a three-layer structure are proposed to capture the interactions among the utility (modeled in the upper layer), the third-party DR providers (modeled in the middle layer), and the EUs in each DR program (modeled in the lower layer). The competitions among the EUs in each DR program is captured through a non-cooperative simultaneous game. An inconvenience cost function is proposed to model the DR provision willingness and capacity of different EUs. The Stackelberg game between the middle-layer DR provider and the lower-layer EUs is solved by converting the original bi-level programming to a single level programming. This converted single level programming is embedded in an iterative algorithm toward solving the entire coupled games framework. Case studies are performed on IEEE 34-bus and IEEE69-bus test systems to illustrate the application of the proposed framework.

Date Created
2023
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Correlated Scenario Generation Using Generative Models

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Description

With the continued increase in the amount of renewable generation in the formof distributed energy resources, reliability planning has progressively become a more
challenging task for the modern power system. This is because with higher penetration
of renewable generation, the

With the continued increase in the amount of renewable generation in the formof distributed energy resources, reliability planning has progressively become a more
challenging task for the modern power system. This is because with higher penetration
of renewable generation, the system has to bear a higher degree of variability
and uncertainty. One way to address this problem is by generating realistic scenarios
that complement and supplement actual system conditions. This thesis presents
a methodology to create such correlated synthetic scenarios for load and renewable
generation using machine learning.
Machine learning algorithms need to have ample amounts of data available to
them for training purposes. However, real-world datasets are often skewed in the
distribution of the different events in the sample space. Data augmentation and
scenario generation techniques are often utilized to complement the datasets with
additional samples or by filling in missing data points. Datasets pertaining to the
electric power system are especially prone to having very few samples for certain
events, such as abnormal operating conditions, as they are not very common in an
actual power system. A recurrent generative adversarial network (GAN) model is
presented in this thesis to generate solar and load scenarios in a correlated manner
using an actual dataset obtained from a power utility located in the U.S. Southwest.
The generated solar and load profiles are verified both statistically and by implementation
on a simulated test system, and the performance of correlated scenario
generation vs. uncorrelated scenario generation is investigated. Given the interconnected
relationships between the variables of the dataset, it is observed that correlated
scenario generation results in more realistic synthetic scenarios, particularly for abnormal
system conditions. When combined with actual but scarce abnormal conditions,
the augmented dataset of system conditions provides a better platform for performing contingency studies for a more thorough reliability planning.
The proposed scenario generation method is scalable and can be modified to work
with different time-series datasets. Moreover, when the model is trained in a conditional
manner, it can be used to synthesise any number of scenarios for the different
events present in a given dataset. In summary, this thesis explores scenario generation
using a recurrent conditional GAN and investigates the benefits of correlated
generation compared to uncorrelated synthesis of profiles for the reliability planning
problem of power systems.

Date Created
2022
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Detailed Primary and Secondary Distribution System Modeling and Validation of Feeders, Loads and Distributed Energy Resources Using Field Measurements

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Description

The past few years have witnessed a significant growth of distributed energy resources (DERs) in power systems at the customer level. Such growth challenges the traditional centralized model of conventional synchronous generation, making a transition to a decentralized network with

The past few years have witnessed a significant growth of distributed energy resources (DERs) in power systems at the customer level. Such growth challenges the traditional centralized model of conventional synchronous generation, making a transition to a decentralized network with a significant increase of DERs. This decentralized network requires a paradigm change in modeling distribution systems in more detail to maintain the reliability and efficiency while accommodating a high level of DERs. Accurate models of distribution feeders, including the secondary network, loads, and DER components must be developed and validated for system planning and operation and to examine the distribution system performance. In this work, a detailed model of an actual feeder with high penetration of DERs from an electrical utility in Arizona is developed. For the primary circuit, distribution transformers, and cables are modeled. For the secondary circuit, actual conductors to each house, as well as loads and photovoltaic (PV) units at each premise are represented. An automated tool for secondary network topology construction for load feeder topology assignation is developed. The automated tool provides a more accurate feeder topology for power flow calculation purposes. The input data for this tool consists of parcel geographic information system (GIS) delimitation data, and utility secondary feeder topology database. Additionally, a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements is presented. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. The method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. The accuracy of the method developed by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results and with the field measurements available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification.

Date Created
2022
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Multistep Multivariate Scenario Generation and Forecasting for Power Systems using Machine Learning

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Description

The penetration of renewable energy in the power system has grown considerably in the past few years. While this use may come with an abundance of advantages, it also introduces new challenges in operating the 100+ years old electrical network.

The penetration of renewable energy in the power system has grown considerably in the past few years. While this use may come with an abundance of advantages, it also introduces new challenges in operating the 100+ years old electrical network. Fundamentally, the power system relies on a real-time balance of generation and demand. However, renewable resources such as solar and wind farms are not available throughout the day. Furthermore, they introduce temporal variability to the generation process due to metrological factors, making the balance of generation and demand precarious. Utilities use standby units with reserve power and high ramp-up, ramp-down capabilities to ensure balance. However, such solutions can be very costly. An accurate scenario generation and forecasting of the stochastic variables (load and renewable resources) can help reduce the cost of these solutions. The goal of this research is to solve the scenario generation and forecasting problems using state-of-the-art machine learning techniques and algorithms. The training database is created using publicly available data obtained from NREL and the Texas-2000 bus system. The IEEE-30 bus system is used as the test system for the analysis conducted here. The conventional generators of this system are replaced with solar farms and wind farms. The ability of four machine learning algorithms in addressing the scenario generation and forecasting problems are investigated using appropriate metrics.
The first machine learning algorithm is the convolutional neural network (CNN). It is found to be well-suited for the scenario generation problem. However, its inability to capture certain intricate details about the different variables was identified as a possible drawback. The second algorithm is the long-short term memory-variational auto-encoder (LSTM-VAE). It generated scenarios that are very similar to the actual scenarios indicating that it is suitable for solving the forecasting problem. The third algorithm is the conditional generative adversarial network (C-GAN). It was extremely effective in generating scenarios when the number of variables were small. However, its scalability was found to be a concern. The fourth algorithm is the spatio-temporal graph convolutional network (STGCN). It was found to generate representative correlated scenarios effectively.

Date Created
2021
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Improvements in Saliency Tracking for use in Brushless DC Motors

Description

Brushless DC (BLDC) motors are becoming increasingly common in various industrial and commercial applications such as micromobility and robotics due to their high torque density and efficiency. A BLDC Motor is a three-phase synchronous motor that is very similar to

Brushless DC (BLDC) motors are becoming increasingly common in various industrial and commercial applications such as micromobility and robotics due to their high torque density and efficiency. A BLDC Motor is a three-phase synchronous motor that is very similar to a non-salient Permanent Magnet Synchronous Motor (PMSM) with key differences lying in the non-ideal characteristics of the motor; the most prominent of these is BLDC motors have trapezoidal-shaped Back-Electromotive Force (BEMF). Despite their advantages, a present weakness of BLDC motors is the difficulty controlling these motors at standstill and low-speed conditions that require high torque. These operating conditions are common in the target applications and almost always necessitate the use of external sensors which introduce additional costs and points of failure. As such, sensorless based methods of position estimation would serve to improve system reliability, cost, and efficiency. High Frequency (HF) pulsating voltage injection in the direct axis is a popular method of sensorless control of salient-pole Interior-mount Permanent Magnet Synchronous Motors (IPMSM); however, existing methods are not sufficiently robust for use in BLDC and small Surface-mount Permanent Magnet Synchronous Motors (SPMSM) and are accompanied by other issues, such as acoustic noise. This thesis proposes novel improvements to the method of High Frequency Voltage Injection to allow for practical use in BLDC Motors and small SPMSM. Proposed improvements include 1) a hybrid frequency generator which allows for dynamic frequency scaling to improve tracking and eliminate acoustic noise, 2) robust error calculation that is stable despite the non-ideal characteristics of BLDC Motors, 3) gain engineering of Proportional-Integral (PI) type Phase-Locked-Loop (PLL) trackers that further lend stability, 4) observer decoupling mechanism to allow for seamless transition into state-of-the-art BEMF sensing methods at high speed, and 5) saliency boosting that allows for continuous tracking of saliency under high torque load. Experimental tests with a quadrature encoder and torque efficiency calculations on a dynamometer verify the practicality of the proposed algorithm and improvements.

Date Created
2021
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GPS Spoofing attacks on PMUs: Practical Feasibility and Counter Measures

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Description

In order to meet the world’s growing energy need, it is necessary to create a reliable, robust, and resilient electric power grid. One way to ensure the creation of such a grid is through the extensive use of synchrophasor technology

In order to meet the world’s growing energy need, it is necessary to create a reliable, robust, and resilient electric power grid. One way to ensure the creation of such a grid is through the extensive use of synchrophasor technology that is based on devices called phasor measurement units (PMUs), and their derivatives, such as μPMUs. Global positioning system (GPS) time-synchronized wide-area monitoring, protection, and control enabled by PMUs has opened up new ways in which the power grid can tackle the problems it faces today. However, with implementation of new technologies comes new challenges, and one of those challenges when it comes to PMUs is the misuse of GPS as a method to obtain a time reference.The use of GPS in PMUs is very intuitive as it is a convenient method to time stamp electrical signals, which in turn helps provide an accurate snapshot of the performance of the PMU-monitored section of the grid. However, GPS is susceptible to different types of signal interruptions due to natural (such as weather) or unnatural (jamming, spoofing) causes. The focus of this thesis is on demonstrating the practical feasibility of GPS spoofing attacks on PMUs, as well as developing novel countermeasures for them.
Prior research has demonstrated that GPS spoofing attacks on PMUs can cripple power system operation. The research conducted here first provides an experimental evidence of the feasibility of such an attack using commonly available digital radios known as software defined radio (SDR). Next, it introduces a new countermeasure against such attacks using GPS signal redundancy and low power long range (LoRa) spread spectrum modulation technique. The proposed approach checks the integrity of the GPS signal at remote locations and compares the data with the PMU’s current output. This countermeasure is a steppingstone towards developing a ready-to-deploy system that can provide an instant solution to the GPS spoofing detection problem for PMUs already placed in the power grid.

Date Created
2021
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Power System Security Enhancement for Real-Time Operations During Multiple Outages using Network Science

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Description

Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional methods of steady-state security assessment in power systems involve contingency analysis based on AC or DC

Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional methods of steady-state security assessment in power systems involve contingency analysis based on AC or DC power flows. However, power flow based contingency analysis is not fast enough to evaluate all contingencies for real-time operations. Therefore, real-time contingency analysis (RTCA) only evaluates a subset of the contingencies (called the contingency list), and hence might miss critical contingencies that lead to cascading failures.This dissertation proposes a new graph-theoretic approach, called the feasibility test (FT) algorithm, for analyzing whether a contingency will create a saturated or over-loaded cut-set in a meshed power network; a cut-set denotes a set of lines which if tripped separates the network into two disjoint islands. A novel feature of the proposed approach is that it lowers the solution time significantly making the approach viable for an exhaustive real-time evaluation of the system. Detecting saturated cut-sets in the power system is important because they represent the vulnerable bottlenecks in the network. The robustness of the FT algorithm is demonstrated on a 17,000+ bus model of the Western Interconnection (WI).
Following the detection of post-contingency cut-set saturation, a two-component methodology is proposed to enhance the reliability of large power systems during a series of outages. The first component combines the proposed FT algorithm with RTCA to create an integrated corrective action (iCA), whose goal is to secure the power system against post-contingency cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) that quickly secures the system against saturated cut-sets.
The first component is more comprehensive than the second, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. Analysis of different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology enhances the scope and speed of power system security assessment during multiple outages.

Date Created
2021
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Coordinated Wide-Area Control of Multiple Controllers in a Modern Power System

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Description

Low frequency oscillations (LFOs) are recognized as one of the most challenging problems in electric grids as they limit power transfer capability and can result in system instability. In recent years, the deployment of phasor measurement units (PMUs) has increased

Low frequency oscillations (LFOs) are recognized as one of the most challenging problems in electric grids as they limit power transfer capability and can result in system instability. In recent years, the deployment of phasor measurement units (PMUs) has increased the accessibility to time-synchronized wide-area measurements, which has, in turn, enabledthe effective detection and control of the oscillatory modes of the power system. This work assesses the stability improvements that can be achieved through the coordinated wide-area control of power system stabilizers (PSSs), static VAr compensators (SVCs), and supplementary damping controllers (SDCs) of high voltage DC (HVDC) lines, for damping electromechanical oscillations in a modern power system. The improved damping is achieved by designing different types of coordinated wide-area damping controllers (CWADC) that employ partial state-feedback. The first design methodology uses a linear matrix inequality (LMI)-based mixed H2/Hinfty control that is robust for multiple operating scenarios. To counteract the negative impact of communication failure or missing PMU measurements on the designed control, a scheme to identify the alternate set of feedback signals is proposed. Additionally, the impact of delays on the performance of the control design is investigated.
The second approach is motivated by the increasing popularity of artificial intelligence (AI) in enhancing the performance of interconnected power systems. Two different wide-area coordinated control schemes are developed using deep neural networks (DNNs) and deep reinforcement learning (DRL), while accounting for the uncertainties present in the power system. The DNN-CWADC learns to make control decisions using supervised learning; the training dataset consisting of polytopic controllers designed with the help of LMI-based mixed H2/Hinfty optimization. The DRL-CWADC learns to adapt to the system uncertainties based on its continuous interaction with the power system environment by employing
an advanced version of the state-of-the-art deep deterministic policy gradient (DDPG) algorithm referred to as bounded exploratory control-based DDPG (BEC-DDPG). The studies performed on a 29 machine, 127 bus equivalent model of theWestern Electricity Coordinating Council (WECC) system-embedded with different types of damping controls have demonstrated the effectiveness and robustness of the proposed CWADCs.

Date Created
2021
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Machine Learning for the Analysis of Power System Loads: Cyber-Attack Detection and Generation of Synthetic Datasets

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Description

As the field of machine learning increasingly provides real value to power system operations, the availability of rich measurement datasets has become crucial for the development of new applications and technologies. This dissertation focuses on the use of time-series load

As the field of machine learning increasingly provides real value to power system operations, the availability of rich measurement datasets has become crucial for the development of new applications and technologies. This dissertation focuses on the use of time-series load data for the design of novel data-driven algorithms. Loads are one of the main factors driving the behavior of a power system and they depend on external phenomena which are not captured by traditional simulation tools. Thus, accurate models that capture the fundamental characteristics of time-series load dataare necessary.
In the first part of this dissertation, an example of successful application of machine learning algorithms that leverage load data is presented. Prior work has shown that power systems energy management systems are vulnerable to false data injection attacks against state estimation. Here, a data-driven approach for the detection and localization of such attacks is proposed. The detector uses historical data to learn the normal behavior of the loads in a system and subsequently identify if any of the real-time observed measurements are being manipulated by an attacker.
The second part of this work focuses on the design of generative models for time-series load data. Two separate techniques are used to learn load behaviors from real datasets and exploiting them to generate realistic synthetic data. The first approach is based on principal component analysis (PCA), which is used to extract common temporal patterns from real data. The second method leverages conditional generative adversarial networks (cGANs) and it overcomes the limitations of the PCA-based model while providing greater and more nuanced control on the generation of specific
types of load profiles. Finally, these two classes of models are combined in a multi-resolution generative scheme which is capable of producing any amount of time-series load data at any sampling resolution, for lengths ranging from a few seconds to years.

Date Created
2021
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