Browsing by Author "Mehrizi-Sani, Ali"

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    5G Scheduling for Distributed Control in Microgrids
    Iyer, Rahul Rajan (Virginia Tech, 2021-11-12)
    There is an increasing integration of distributed energy resources (DER), controllable loads, and other technologies that are making the grid more robust, reliable, and decentralized. Communication is a major aspect that enables this decentralization and can improve control of important system parameters by allowing different grid components to communicate their states with each other. This information exchange requires a reliable and fast communication infrastructure. Different communication techniques can be used towards this objective, but with recent technological advancements, 5G communication is proving to be a very viable option. 5G is being widely deployed throughout the world due to its high data rates combined with increased reliability compared with its predecessor technologies. This thesis focuses on application and performance analysis of a 5G network for different power system test cases. These test cases are microgrids, and consist of DERs that use distributed control for efficient operation. Under distributed control, the DERs communicate with each other to achieve fast and improved dynamic response. This work develops a co-simulation platform to analyze the impact that a 5G network has in this distributed control objective. This offers key insights on 5G's capability to support critical functions. Different scenarios including set point changes and transients are evaluated. Since distributed control is a time-critical application and DERs rely on the availability of up-to-date information, the scheduling aspect of 5G becomes very important and is given more focus. Information freshness measured using age of information (AoI) is used in this work. Information freshness is a measure of how recent and updated the information communicated by DERs is. This thesis compares the performance of AoI-based schedulers against standard schedulers. These different schedulers are then used on test systems employing distributed control.
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    AI-based Detection Against Cyberattacks in Cyber-Physical Distribution Systems
    Sahani, Nitasha (Virginia Tech, 2024-06-05)
    Integration of a cyber system and communication systems with the traditional power grid has enabled better monitoring and control of the smart grid making it more reliable and resilient. This empowers the system operators to make informed decisions as a result of better system visibility. The grid has moved from a completely air-gapped structure to a well-connected network. However, this remote-control capability to control distributed physical components in a distribution system can be exploited by adversaries with malicious intent to disrupt the power supply to the customers. Therefore, while taking advantage of the cyber-physical posture in the smart grid for improved controllability, there is a critical need for cybersecurity research to protect the critical power infrastructure from cyberattacks. While the literature regarding cybersecurity in distribution systems has focused on detecting and mitigating the cyberattack impact on the physical system, there has been limited effort towards a preventive approach for detecting cyberattacks. With this in mind, this dissertation focuses on developing intelligent solutions to detect cyberattacks in the cyber layer of the distribution grid and prevent the attack from impacting the physical grid. There has been a particular emphasis on the impact of coordinated attacks and the design of proactive defense to detect the attacker's intent to predict the attack trajectory. The vulnerability assessment of the cyber-physical system in this work identifies the key areas in the system that are prone to cyberattacks and failure to detect attacks timely can lead to cascading outages. A comprehensive cyber-physical system is developed to deploy different intrusion detection solutions and quantify the effect of proactive detection in the cyber layer. The attack detection approach is driven by artificial intelligence to learn attack patterns for effective attack path prediction in both a fully observable and partially observable distribution system. The role of effective communication technology in attack detection is also realized through detailed modeling of 5G and latency requirements are validated.
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    Anomaly Detection for Control Centers
    Gyamfi, Cliff Oduro (Virginia Tech, 2024-06)
    The control center is a critical location in the power system infrastructure. Decisions regarding the power system’s operation and control are often made from the control center. These control actions are made possible through SCADA communication. This capability however makes the power system vulnerable to cyber attacks. Most of the decisions taken by the control center dwell on the measurement data received from substations. These measurements estimate the state of the power grid. Measurement-based cyber attacks have been well studied to be a major threat to control center operations. Stealthy false data injection attacks are known to evade bad data detection. Due to the limitations with bad data detection at the control center, a lot of approaches have been explored especially in the cyber layer to detect measurement-based attacks. Though helpful, these approaches do not look at the physical layer. This study proposes an anomaly detection system for the control center that operates on the laws of physics. The system also identifies the specific falsified measurement and proposes its estimated measurement value.
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    Application of Neural Networks to Inverter-Based Resources
    Venkatachari, Sidhaarth (Virginia Tech, 2021-05-18)
    With the deployment of sensors in hardware equipment and advanced metering infrastructure, system operators have access to unprecedented amounts of data. Simultaneously, grid-connected power electronics technology has had a large impact on the way electrical energy is generated, transmitted, and delivered to consumers. Artificial intelligence and machine learning can help address the new power grid challenges with enhanced computational abilities and access to large amounts of data. This thesis discusses the fundamentals of neural networks and their applications in power systems such as load forecasting, power system stability analysis, and fault diagnosis. It extends application of neural networks to inverter-based resources by studying the implementation and performance of a neural network controller emulator for voltage-sourced converters. It delves into how neural networks could enhance cybersecurity of a component through multiple hardware and software implementations of the same component. This ensures that vulnerabilities inherent in one form of implementation do not affect the system as a whole. The thesis also proposes a comprehensive support vector classifier (SVC)--based submodule open-circuit fault detection and localization method for modular multilevel converters. This method eliminates the need for extra hardware. Its efficacy is discussed through simulation studies in PSCAD/EMTDC software. To ensure efficient usage of neural networks in power system simulation softwares, this thesis entails the step by step implementation of a neural network custom component in PSCAD/EMTDC. The custom component simplifies the process of recreating a neural network in PSCAD/EMTDC by eliminating the manual assembly of predefined library components such as summers, multipliers, comparators, and other miscellaneous blocks.
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    Control of a Three-Phase Grid-Connected Voltage-Sourced Converter Using Long Short-Term Memory Networks
    Ghidewon-Abay, Sengal; Mehrizi-Sani, Ali (MDPI, 2022-12-31)
    With the rise of inverter-based resources (IBRs) within the power system, the control of grid-connected converters (GCCs) has become pertinent due to the fact they interface IBRs to the grid. The conventional method of control for a GCC such as the voltage-sourced converter (VSC) is through a decoupled control loop in the synchronous reference frame. However, this model-based control method is sensitive to parameter changes causing deterioration in controller performance. Data-driven approaches such as machine learning can be utilized to design controllers that are capable of operating GCCs in various system conditions. This work explores a deep learning-based control method for a three-phase grid-connected VSC, specifically utilizing a long short-term memory (LSTM) network for robust control. Simulations of a conventional controlled VSC are conducted using Simulink to collect data for training the LSTM-based controller. The LSTM model is built and trained using the Keras and TensorFlow libraries in Python and tested in Simulink. The performance of the LSTM-based controller is evaluated under different case studies and compared to the conventional method of control. Simulation results demonstrate the effectiveness of this approach by outperforming the conventional controller and maintaining stability under different system parameter changes.
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    Control of Grid-Connected Converters using Deep Learning
    Ghidewon-Abay, Sengal (Virginia Tech, 2023-01-12)
    With the rise of inverter-based resources (IBRs) within the power system, the control of grid-connected converters (GCC) has become pertinent due to the fact they interface IBRs to the grid. The conventional method of control for grid-connected converters (GCCs) such as the voltage-sourced converter (VSC) is through a decoupled control loop in the synchronous reference frame. However, this model-based control method is sensitive to parameter changes causing deterioration in controller performance. Data-driven approaches such as machine learning can be utilized to design controllers that are capable of operating GCCs in various system conditions. This work reviews different machine learning applications in power systems as well as the conventional method of controlling a VSC. It explores a deep learning-based control method for a three-phase grid-connected VSC, specifically utilizing a long short-term memory (LSTM) network for robust control. Simulations of a conventional controlled VSC are conducted using Simulink to collect data for training the LSTM-based controller. The LSTM model is built and trained using the Keras and TensorFlow libraries in Python and tested in Simulink. The performance of the LSTM-based controller is evaluated under different case studies and compared to the conventional method of control. Simulation results demonstrate the effectiveness of this approach by outperforming the conventional controller and maintaining stability under different system parameter changes.
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    Energy And Power Systems Simulated Attack Algorithm For Defense Testbed And Analysis
    Ruttle, Zachary Andrew (Virginia Tech, 2023-05-31)
    The power grid has evolved over the course of many decades with the usage of cyber systems and communications such as Supervisory Control And Data Acquisition (SCADA); however, due to their connectivity to the internet, the cyber-power system can be infiltrated by malicious attackers. Encryption is not a singular solution. Currently, there are several cyber security measures in development, including those based on artificial intelligence. However, there is a need for a varying but consistent attack algorithm to serve as a testbed for these AI or other practices to be trained and tested. This is important because in the event of a real attacker, it is not possible to know exactly where they will attack and in what order. Therefore, the proposed method in this thesis is to use criminology concepts and fuzzy logic inference to create this algorithm and determine its effectiveness in making decisions on a cyber-physical system model. The method takes various characteristics of the attacker as an input, builds their ideal target node, and then compares the nodes to the high-impact target and chooses one as the goal. Based on that target and their knowledge, the attackers will attack nodes if they have resources. The results show that the proposed method can be used to create a variety of attacks with varying damaging effects, and one other set of tests shows the possibility for multiple attacks, such as denial of service and false data injection. The proposed method has been validated using an extended cyber-physical IEEE 13-node distribution system and sensitivity tests to ensure that the ruleset created would take each of the inputs well.
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    Facilitating the Transition to an Inverter Dominated Power System: Experimental Evaluation of a Non-Intrusive Add-On Predictive Controller
    Syed, Mazheruddin H.; Guillo-Sansano, Efren; Mehrizi-Sani, Ali; Burt, Graeme M. (MDPI, 2020-08-16)
    The transition to an inverter-dominated power system is expected with the large-scale integration of distributed energy resources (DER). To improve the dynamic response of DERs already installed within such a system, a non-intrusive add-on controller referred to as SPAACE (set point automatic adjustment with correction enabled), has been proposed in the literature. Extensive simulation-based analysis and supporting mathematical foundations have helped establish its theoretical prevalence. This paper establishes the practical real-world relevance of SPAACE via a rigorous performance evaluation utilizing a high fidelity hardware-in-the-loop systems test bed. A comprehensive methodological approach to the evaluation with several practical measures has been undertaken and the performance of SPAACE subject to representative scenarios assessed. With the evaluation undertaken, the fundamental hypothesis of SPAACE for real-world applications has been proven, i.e., improvements in dynamic performance can be achieved without access to the internal controller. Furthermore, based on the quantitative analysis, observations, and recommendations are reported. These provide guidance for future potential users of the approach in their efforts to accelerate the transition to an inverter-dominated power system.
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    Frequency Response Improvement of PMSG Wind Turbines Using a Washout Filter
    Shabestari, Parisa M.; Mehrizi-Sani, Ali (MDPI, 2020-09-14)
    High integration of renewable energy resources, such as wind turbines, to the power grid decreases the power system inertia. To improve the frequency response of a low-inertia system, virtual inertia approach can be used. This letter proposes a control method to decrease the frequency transients and restore frequency to its nominal value. A wind turbine usually works based on maximum power point tracking (MPPT) curves to achieve the maximum power. In this letter, the proposed controller uses a non-MPPT method to leave power for frequency regulation during transients. Moreover, it uses a washout filter-based method to remove the steady-state error in the frequency. Simulation results in the PSCAD environment validate the improved performance of the proposed method during load changes by comparing it with the MPPT and non-MPPT methods.
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    Frequency Scan–Based Mitigation Approach of Subsynchronous Control Interaction in Type-3 Wind Turbines
    Alatar, Faris Muhanned Lutfi (Virginia Tech, 2021-08-16)
    Subsynchronous oscillations (SSO) were an issue that occurred in the past with conventional generators and were studied extensively throughout the years. However, with the rise of inverter-based resources, a new form of SSO emerged under the name subsynchronous control interaction (SSCI). More specifically, a resonance case occurs between Type-3 wind turbines and series compensation that can damage equipment within the wind farm and disrupt power generation. This work explores the types of SSCI and the various analysis methods as well as mitigation of SSCI. The work expands on the concept of frequency scan to be able to use it in an on-line setting with its output data used to mitigate SSCI through the modification of wind turbine parameters. Multiple frequency scans are conducted using PSCAD/EMTDC software to build a lookup table and harmonic injection is used in a parallel configuration to obtain the impedance of the system. Once the impedance of the system is obtained then the value of the parameters is adjusted using the look-up table. Harmonic injection is optimized through phase shifts to ensure minimal disruption of the steady-state operating point and is conducted using Python programming language with PSCAD Automation Library. Simulation results demonstrate the effectiveness of this approach by ensuring oscillations do not grow exponentially in comparison to the regular operation of the wind farm.
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    Frequency Scan–Based Mitigation Approach of Subsynchronous Control Interaction in Type-3 Wind Turbines
    Alatar, Faris; Mehrizi-Sani, Ali (MDPI, 2021-07-30)
    Integration of wind energy resources into the grid creates several challenges for power system dynamics. More specifically, Type-3 wind turbines are susceptible to subsynchronous control interactions (SSCIs) when they become radially connected to a series-compensated transmission line. SSCIs can cause disruptions in power generation and can result in significant damage to wind farm (WF) components and equipment. This paper proposes an approach to mitigate SSCIs using an online frequency scan, with optimized phase angles of voltage harmonic injection to maintain steady-state operation, to modify the controllers or the operating conditions of the wind turbine. The proposed strategy is simulated in PSCAD/EMTDC software on the IEEE second benchmark model for subsynchronous resonance. Simulation results demonstrate the effectiveness of this strategy by ensuring oscillations do not grow.
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    Hybrid Modular Multilevel Converter Family and Modular DC Circuit Breaker for Medium-voltage DC (MVDC) Applications
    Liu, Jian (Virginia Tech, 2023-09-12)
    With the increasing maturity and flexibility of power electronics-based voltage conversion techniques, DC grids, and distribution systems have gained significant interest. These systems offer advantages such as improved power quality, efficiency, and flexibility. Medium-voltage DC (MVDC) applications, including shipboard, railway systems, distribution networks, and microgrids, are emerging as critical areas of interest. To integrate MVDC systems with existing power grids, MV AC/DC conversion techniques are crucial. Moreover, the lack of mature protection strategies and equipment, particularly DC circuit breakers (DCCB), poses a significant challenge to the development of MVDC systems. Therefore, this thesis aims to address two primary challenges in the field: the improved topologies of MV AC/DC conversion techniques for interfacing MVDC systems with power grids and the development of high power density DCCB for MVDC systems. The traditional modular multilevel converter (MMC) is widely used for medium voltage (MV) AC/DC conversion due to its modularity, scalability, and reliability. However, the presence of numerous semiconductor devices and capacitors in MMCs results in challenges such as low power efficiency and density. To enhance the performance of MMCs, this thesis proposes several novel hybrid MMC (HMMC) topologies, including the three-level HMMC, flying capacitor HMMC, and hybrid-leg MMC. These topologies aim to leverage the advantages of both conventional multilevel converters and MMCs. By replacing the low-voltage (LV) submodule (SM) in MMCs with a simple high-voltage (HV) switch, higher efficiency, a smaller footprint, and lower cost can be achieved. The HV switch operates at line frequency, simplifying device-switching and addressing the challenges of series-connected devices. The introduction of additional HV switches enables alternative connections compared to traditional MMCs, reducing the number of required SMs. Consequently, there is a significant reduction in the number of semiconductor devices, capacitor energy storage, and power losses. Furthermore, an average model is developed for the three-level HMMC to illustrate the additional power flow path between the AC and DC sides, as well as the reduced SM capacitor energy storage requirement. As a result, the proposed HMMCs exhibit substantial potential to replace traditional MMCs, offering higher efficiency and power density. Unidirectional high-voltage (HV) and medium-voltage (MV) rectifiers are essential for applications where power flows exclusively from the AC to the DC side. Examples of such applications include HVDC transmission, front-end converters for electric vehicle (EV) charging stations, and data centers. Therefore, hybrid modular multilevel rectifiers (HMMRs) are proposed for these unidirectional AC/DC applications. Instead of utilizing active devices for HV switches, the HMMR employs HV diode to achieve step-up HMMR, step-down HMMR, and flying capacitor HMMR configurations. As diodes are passive devices that do not require gate driver units, the HMMR design becomes simpler, resulting in cost and volume savings. Additionally, voltage sharing among the HV diode stack becomes more manageable as concerns regarding gate signal mismatch are eliminated. However, it is important to note that diodes lack current interruption capability. This limitation requires further investigation, particularly in non-unity power factor (PF) operations, which may impose restrictions on the operational range of the rectifiers. In terms of medium voltage (MV) DC circuit breakers (DCCB), this paper introduces the concept and design procedure of a high-power-density, modular, and scalable power electronic interrupter (PEI) for MV hybrid circuit breakers (HCB). The analysis includes trade-offs and limiting factors of various components within a single PEI module. A prototype of a 12 kV, 1 kA breaking-capable PEI is constructed, and new staged turn-off strategies are proposed to ensure the balanced distribution of metal-oxide varistor (MOV) energy. The developed PEI achieves a peak power density of 7.4 kW/cm$^3$, much higher than the solution based on the IGBT modules. After integrating the developed PEI into a full-scale HCB, the breaking capability of the developed PEI and the effectiveness of the staged turn-off strategy are validated. Furthermore, the scalability of the HCB is evaluated, which can simplify the design process from a low-voltage HCB to a higher-voltage version. For series-connected devices in SSCB or HCB configurations, the conventional gate driver structure necessitates an individual gate driver unit, fiber-optic, and isolated power supplies for each device. This design increases cost and volume, particularly for this single-pulse application. To address this issue, two new single gate driver structures are proposed to reduce component count and system complexity. The first solution, namely the MOV-coupled structure, employs a metal-oxide varistor (MOV) for the turn-off path. On the other hand, the transformer-coupled structure combines the auxiliary power and gate signal, enabling both simultaneous and staged turn-off schemes. Moreover, the cascaded high- and lower-voltage transformer structure simplifies insulation design and demonstrates improved scalability. These proposed gate driver structures aim to streamline the system, reduce component numbers, and simplify control for series-connected devices, leading to cost savings and improved overall performance.
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    Islanding Detection and Cybersecurity in Inverter-Based Microgrids Under a High-Noise Environment
    Amini, Hossein (Virginia Tech, 2024-08-21)
    Islanding occurs when a connected load to the grid is disconnected from the grid and energized solely by local generators. Islanding can result in frequency and voltage instability, changes in current, and overall poor power quality. Poor power quality can interrupt industrial operations, damage sensitive electrical equipment, and induce outages upon the resynchronization of the island with the grid. This study proposes an islanding detection method employing Duffing oscillators to analyze fluctuations at the point of common coupling (PCC) under a high-noise environment, focusing on decreasing detection period, zero power mismatch nondetection zone, and power quality degradation. Unlike existing methods, which overlook the noise effect, this study mitigates noise impact on islanding detection. Power system noise in PCC measurements arises from switching transients, harmonics, grounding issues, voltage sags, voltage swells, electromagnetic interference, and power quality issues that affect islanding detection. Transient events, like lightning-induced traveling waves can also introduce noise levels exceeding the voltage amplitude, disturbing conventional detection techniques~cite{IEEE1313}. The noise interferes with measurements and increases the nondetection zone (NDZ), causing failed or delayed islanding detection. Duffing oscillator nonlinear dynamics enable detection capabilities at a high noise level. The proposed methods are designed to detect the PCC measurement fluctuations based on the IEEE standard 1547 through the Duffing oscillator. The basic idea is that the Duffing oscillator phase trajectory changes from periodic to chaotic mode and sends an islanded operation command to the inverter. The proposed islanding detection method can distinguish switching transients and faults from an islanded operation.
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    A Multi-Agent Defense Methodology with Machine Learning against Cyberattacks on Distribution Systems
    Appiah-Kubi, Jennifer (Virginia Tech, 2022-08-17)
    The introduction of communication technology into the electric power grid has made the grid more reliable. Power system operators gain visibility over the power system and are able to resolve operational issues remotely via Supervisory Control And Data Acquisition (SCADA) technology. This reduces outage periods. Nonetheless, the remote-control capability has rendered the power grid vulnerable to cyberattacks. In December 2015, over 200,000 people in Ukraine became victims of the first publicly reported cyberattack on the power grid. Consequently, cyber-physical security research for the power system as a critical infrastructure is in critical need. Research on cybersecurity for power grids has produced a diverse literature; the multi-faceted nature of the grid makes it vulnerable to different types of cyberattacks, such as direct power grid, supply chain and ransom attacks. The attacks may also target different levels of grid operation, such as the transmission system, distribution system, microgrids, and generation. As these levels are characterized by varying operational constraints, the literature may be categorized not only according to the type of attack it targets, but also according to the level of power system operation under consideration. It is noteworthy that cybersecurity research for the transmission system dominates the literature, although the distribution system is noted to have a larger attack surface. For the distribution system, a notable attack type is the so-called direct switching attack, in which an attacker aims to disrupt power supply by compromising switching devices that connect equipment such as generators, and power grid lines. To maximize the damage, this attack tends to be coordinated as the attacker optimally selects the nodes and switches to attack. This decision-making process is often a bi- or tri-level optimization problem which models the interaction between the attacker and the power system defender. It is necessary to detect attacks and establish coordination/correlation among them. Determining coordination is a necessary step to predict the targets of an attack before attack completion, and aids in the mitigation strategy that ensues. While the literature has addressed the direct switching attack on the distribution system in different ways, there are also shortcomings. These include: (i) techniques to establish coordination among attacks are centralized, making them prone to single-point failures; (ii) techniques to establish coordination among attacks leverage only power system models, ignoring the influence of communication network vulnerabilities and load criticality in the decisions of the attacker; (iii) attacker-defender optimization models assume specific knowledge of the attacker resources and constraints by the defender, a strong unrealistic assumption that reduces their usability; (iv) and, mitigation strategies tend to be static and one-sided, being implemented only at the physical level, or at the communication network level. In light of this, this dissertation culminates in major contributions concerning real-time decentralized correlation of detected direct switching attacks and hybrid mitigation for electric power distribution systems. Concerning this, four novel contributions are presented: (i) a framework for decentralized correlation of attacks and mitigation; (ii) an attacker-defender optimization model that accounts for power system laws, load criticality, and cyber vulnerabilities in the decision-making process of the attacker; (iii) a real-time learning-based mechanism for determining correlation among detected attacks and predicting attack targets, and which does not assume knowledge of the attacker's resources and constraints by the power system defender; (iv) a hybrid mitigation strategy optimized in real-time based on information learned from detected attacks, and which combines both physical level and communication network level mitigation. Since the execution of intrusion detection systems and mechanisms such as the ones proposed in this dissertation may deter attackers from directly attacking the power grid, attackers may perform a supply chain cyberattack to yield the same results. Although, supply chain cyberattacks have been acknowledged as potentially far-reaching, and compliance directives put forward for this, the detection of supply chain cyberattacks is in a nascent stage. Consequently, this dissertation also proposes a novel method for detecting supply chain cyberattacks. To the best of the knowledge of the author, this work is the first preliminary work on supply chain cyberattack detection.
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    New Differential Zone Protection Scheme Using Graph Partitioning for an Islanded Microgrid
    Alsaeidi, Fahad S. (Virginia Tech, 2022-05-19)
    Microgrid deployment in electric grids improves reliability, efficiency, and quality, as well as the overall sustainability and resiliency of the grid. Specifically, microgrids alleviate the effects of power outages. However, microgrid implementations impose additional challenges on power systems. Microgrid protection is one of the technical challenges implicit in the deployment of microgrids. These challenges occur as a result of the unique properties of microgrid networks in comparison to traditional electrical networks. Differential protection is a fast, selective, and sensitive technique. Additionally, it offers a viable solution to microgrid protection concerns. The differential zone protection scheme is a cost-effective variant of differential protection. To implement a differential zone protection scheme, the network must be split into different protection zones. The reliability of this protection scheme is dependent upon the number of protective zones developed. This thesis proposes a new differential zone protection scheme using a graph partitioning algorithm. A graph partitioning algorithm is used to partition the microgrid into multiple protective zones. The IEEE 13-node microgrid is used to demonstrate the proposed protection scheme. The protection scheme is validated with MATLAB Simulink, and its impact is simulated with DIgSILENT PowerFactory software. Additionally, a comprehensive comparison was made to a comparable differential zone protection scheme.
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    Optimization of Distribution Systems: Transactive Energy and Resilience Enhancement
    Qi, Chensen (Virginia Tech, 2024-05-21)
    The increasing penetration of electric vehicles (EVs) and other distributed energy resources (DERs) offers enhanced flexibility and resilience. During extreme conditions, grid-connected EVs and DERs can provide electricity service and restore critical loads when the utility system is unavailable. On the other hand, during normal operation, these proactive devices can provide ancillary services to alleviate voltage fluctuations and support frequency regulation. In comparison with other DERs, EVs are more flexible in providing ancillary services due to their mobile nature. However, the proliferation of EVs and DERs also introduces operational challenges to the distribution grid. For instance, EVs primarily fulfill their transportation needs. Uncoordinated charging of a large number of EVs can increase the burden on the distribution system. Due to the limited charging rate and battery size, it is generally impractical for a single EV to directly participate in the ancillary service market. A conventional distribution system is designed for unidirectional flow of electric energy. With the growing installation of DERs on the distribution system, the flow of electric energy is bi-directional and, therefore, there is a higher risk of protection miscoordination due to the fault currents resulting from DERs. With limited communication capability, these undetected protective device (PD) actuations can cause uncertainties and delay the service restoration process. This dissertation makes contributions to the coordination of EVs and DERs. It introduces four innovative models for EV coordination: 1) A transactive energy (TE) trading mechanism is proposed to coordinate EVs and aggregators. 2) Optimal tools are provided to assist EVs and aggregators in optimal decision making while participating in TE. 3) A charging station model is developed to allow EVs to provide ancillary service aligned with their mobile nature. 4) A utility function model is presented to capture the EV owners' behaviors for providing ancillary services and charging vehicles. Charging stations can estimate the electric energy demand and optimize ancillary service provision to meet their goals. Simulation cases validated that the proposed optimization tools can align EV owners' preferences in providing ancillary service to enhance distribution system operation flexibility. To enhance the resilience of distribution systems, two novel optimization strategies are presented: 1) An advanced outage management (AOM) is proposed to utilize smart meters and fault indicators (FIs) to identify the most credible outage scenario and fault locations. 2) An advanced feeder restoration (AFR) is developed to provide an optimal restoration strategy to enhance system resilience. The proposed optimization models have been validated with realistic simulation cases.
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    Passive Islanding Detection of Inverter-Based Resources in a Noisy Environment
    Amini, Hossein; Mehrizi-Sani, Ali; Noroozian, Reza (MDPI, 2024-09-03)
    Islanding occurs when a load is energized solely by local generators and can result in frequency and voltage instability, changes in current, and poor power quality. Poor power quality can interrupt industrial operations, damage sensitive electrical equipment, and induce outages upon the resynchronization of the island with the grid. This study proposes an islanding detection method employing a Duffing oscillator to analyze voltage fluctuations at the point of common coupling (PCC) under a high-noise environment. Unlike existing methods, which overlook the noise effect, this paper mitigates noise impact on islanding detection. Power system noise in PCC measurements arises from switching transients, harmonics, grounding issues, voltage sags and swells, electromagnetic interference, and power quality issues that affect islanding detection. Transient events like lightning-induced traveling waves to the PCC can also introduce noise levels exceeding the voltage amplitude by more than seven times, thus disturbing conventional detection techniques. The noise interferes with measurements and increases the nondetection zone (NDZ), causing failed or delayed islanding detection. The Duffing oscillator nonlinear dynamics enable detection capabilities at a high noise level. The proposed method is designed to detect the PCC voltage fluctuations based on the IEEE standard 1547 through the Duffing oscillator. For the voltages beyond the threshold, the Duffing oscillator phase trajectory changes from periodic to chaotic mode and sends an islanded operation command to the inverter. The proposed islanding detection method distinguishes switching transients and faults from an islanded operation. Experimental validation of the method is conducted using a 3.6 kW PV setup.
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    Protection and Cybersecurity in Inverter-Based Microgrids
    Mohammadhassani, Ardavan (Virginia Tech, 2023-07-06)
    Developing microgrids is an attractive solution for integrating inverter-based resources (IBR) in the power system. Distributed control is a potential strategy for controlling such microgrids. However, a major challenge toward the proliferation of distributed control is cybersecurity. A false data injection (FDI) attack on a microgrid using distributed control can have severe impacts on the operation of the microgrid. Simultaneously, a microgrid needs to be protected from system faults to ensure the safe and reliable delivery of power to loads. However, the irregular response of IBRs to faults makes microgrid protection very challenging. A microgrid is also susceptible to faults inside IBR converters. These faults can remain undetected for a long time and shutdown an IBR. This dissertation first proposes a method that reconstructs communicated signals using their autocorrelation and crosscorrelation measurements to make distributed control more resilient against FDI attacks. Next, this dissertation proposes a protection scheme that works by classifying measured harmonic currents using support vector machines. Finally, this dissertation proposes a protection and fault-tolerant control strategy to diagnose and clear faults that are internal to IBRs. The proposed strategies are verified using time-domain simulation case studies using the PSCAD/EMTDC software package.
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    Protection and Cybersecurity of Inverter-Based Resources
    Alexander, Brady Steven (Virginia Tech, 2024-05-14)
    Traditionally, power system protection describes detecting, clearing, and locating faults in the power system. Traditional methods for detecting and locating faults may not be sufficient for inverter-based resources (IBR) as the fault response of an IBR differs from the response of a synchronous generator. As the composition of the power grid continues to evolve to integrate more IBRs that employ communication-based control algorithms; the power system is also exposed to cyberattacks. Undetected cyberattacks can disrupt normal system operation causing local outages. Therefore, power system protection must evolve with the changes in the grid to not only detect, locate, and clear faults with IBR generation but also detect and mitigate cyberattacks on IBR controllers. This thesis proposes methods for protecting an IBR-based transmission system from: (i) GPS spoofing cyberattacks on a power sharing controller; (ii) open-circuit faults. The GPS spoofing detection algorithm is a decision tree that enables either the proposed state observer--based mitigation technique or the proposed long short-term memory (LSTM)-based mitigation algorithm. The proposed logic for detecting open-circuit faults addresses each subcategory of open-circuit faults: breaker malfunctions, broken conductors, and series arc faults. PSCAD/EMTDC simulations are performed to test the effectiveness of the proposed methods.
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    Reinforcement Learning for the Cybersecurity of Grid-Forming and Grid-Following Inverters
    Kwiatkowski, Brian Michael (Virginia Tech, 2024-12-06)
    The U.S. movement toward clean energy generation has increased the number of installed inverter-based resources (IBR) in the grid, introducing new challenges in IBR control and cybersecurity. IBRs receive their set point through the communication link, which may expose them to cyber threats. Previous work has developed various techniques to detect and mitigate cyberattacks on IBRs, developing schemes for new inverters being installed in the grid. This work focuses on developing model-free control techniques for already installed IBR in the grid without the need to access IBR internal control parameters. The proposed method is tested for both the grid-forming and grid-following inverter control. Separate detection and mitigation algorithms are used to enhance the accuracy of the proposed method. The proposed method is tested using the modified CIGRE 14-bus North American grid with 7 IBRs in PSCAD/EMTDC. Finally, the performance of the detection algorithm is tested under grid normal transients, such as set point change, load change, and short-circuit fault, to make sure the proposed detection method does not provide false positives.