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- Genre: Doctoral Dissertation
Description
The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation.
ContributorsHaghnevis, Moeed (Author) / Askin, Ronald G. (Thesis advisor) / Armbruster, Dieter (Thesis advisor) / Mirchandani, Pitu (Committee member) / Wu, Tong (Committee member) / Hedman, Kory (Committee member) / Arizona State University (Publisher)
Created2013
Description
Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance the reliability of power systems. Specifically, this study is broadly organized into the following two parts: I) spatio-temporal wind power analysis for wind generation forecast and integration, and II) data mining and information fusion of synchrophasor measurements toward secure power grids. Part I is centered around wind power generation forecast and integration. First, a spatio-temporal analysis approach for short-term wind farm generation forecasting is proposed. Specifically, using extensive measurement data from an actual wind farm, the probability distribution and the level crossing rate of wind farm generation are characterized using tools from graphical learning and time-series analysis. Built on these spatial and temporal characterizations, finite state Markov chain models are developed, and a point forecast of wind farm generation is derived using the Markov chains. Then, multi-timescale scheduling and dispatch with stochastic wind generation and opportunistic demand response is investigated. Part II focuses on incorporating the emerging synchrophasor technology into the security assessment and the post-disturbance fault diagnosis of power systems. First, a data-mining framework is developed for on-line dynamic security assessment by using adaptive ensemble decision tree learning of real-time synchrophasor measurements. Under this framework, novel on-line dynamic security assessment schemes are devised, aiming to handle various factors (including variations of operating conditions, forced system topology change, and loss of critical synchrophasor measurements) that can have significant impact on the performance of conventional data-mining based on-line DSA schemes. Then, in the context of post-disturbance analysis, fault detection and localization of line outage is investigated using a dependency graph approach. It is shown that a dependency graph for voltage phase angles can be built according to the interconnection structure of power system, and line outage events can be detected and localized through networked data fusion of the synchrophasor measurements collected from multiple locations of power grids. Along a more practical avenue, a decentralized networked data fusion scheme is proposed for efficient fault detection and localization.
ContributorsHe, Miao (Author) / Zhang, Junshan (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Hedman, Kory (Committee member) / Si, Jennie (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation presents a new hybrid fault current limiter (FCL) topology that is primarily intended to protect single-phase power equipment. It can however be extended to protect three phase systems but would need three devices to protect each individual phase. In comparison against the existing fault current limiter technology, the salient fea-tures of the proposed topology are: a) provides variable impedance that provides a 50% reduction in prospective fault current; b) near instantaneous response time which is with-in the first half cycle (1-4 ms); c) the use of semiconductor switches as the commutating switch which produces reduced leakage current, reduced losses, improved reliability, and a faster switch time (ns-µs); d) zero losses in steady-state operation; e) use of a Neodym-ium (NdFeB) permanent magnet as the limiting impedance which reduces size, cost, weight, eliminates DC biasing and cooling costs; f) use of Pulse Width Modulation (PWM) to control the magnitude of the fault current to a user's desired level. g) experi-mental test system is developed and tested to prove the concepts of the proposed FCL. This dissertation presents the proposed topology and its working principle backed up with numerical verifications, simulation results, and hardware implementation results. Conclu-sions and future work are also presented.
ContributorsPrigmore, Jay (Author) / Karady, George G. (Thesis advisor) / Ayyanar, Raja (Committee member) / Holbert, Keith E. (Committee member) / Hedman, Kory (Committee member) / Arizona State University (Publisher)
Created2013
Description
Electric power systems are facing great challenges from environmental regulations, changes in demand due to new technologies like electric vehicle, as well as the integration of various renewable energy sources. These factors taken together require the development of new tools to help make policy and investment decisions for the future power grid. The requirements of a network equivalent to be used in such planning tools are very different from those assumed in the development of traditional equivalencing procedures. This dissertation is focused on the development, implementation and verification of two network equivalencing approaches on large power systems, such as the Eastern Interconnection. Traditional Ward-type equivalences are a class of equivalencing approaches but this class has some significant drawbacks. It is well known that Ward-type equivalents "smear" the injections of external generators over a large number of boundary buses. For newer long-term investment applications that take into account such things as greenhouse gas (GHG) regulations and generator availability, it is computationally impractical to model fractions of generators located at many buses. A modified-Ward equivalent is proposed to address this limitation such that the external generators are moved wholesale to some internal buses based on electrical distance. This proposed equivalencing procedure is designed so that the retained-line power flows in the equivalent match those in the unreduced (full) model exactly. During the reduction process, accommodations for special system elements are addressed, including static VAr compensators (SVCs), high voltage dc (HVDC) transmission lines, and phase angle regulators. Another network equivalencing approach based on the dc power flow assumptions and the power transfer distribution factors (PTDFs) is proposed. This method, rather than eliminate buses via Gauss-reduction, aggregates buses on a zonal basis. The bus aggregation approach proposed here is superior to the existing bus aggregation methods in that a) under the base case, the equivalent-system inter-zonal power flows exactly match those calculated using the full-network-model b) as the operating conditions change, errors in line flows are reduced using the proposed bus clustering algorithm c) this method is computationally more efficient than other bus aggregation methods proposed heretofore. A critical step in achieving accuracy with a bus aggregation approach is selecting which buses to cluster together and how many clusters are needed. Clustering in this context refers to the process of partitioning a network into subsets of buses. An efficient network clustering method is proposed based on the PTDFs and the data mining techniques. This method is applied to the EI topology using the "Saguaro" supercomputer at ASU, a resource with sufficient memory and computational capability for handling this 60,000-bus and 80,000-branch system. The network equivalents generated by the proposed approaches are verified and tested for different operating conditions and promising results have been observed.
ContributorsShi, Di (Author) / Tylavsky, Daniel J (Thesis advisor) / Vittal, Vijay (Committee member) / Hedman, Kory (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2012
Description
ABSTRACT
This dissertation introduces a real-time topology monitoring scheme for power systems intended to provide enhanced situational awareness during major system disturbances. The topology monitoring scheme requires accurate real-time topology information to be effective. This scheme is supported by advances in transmission line outage detection based on data-mining phasor measurement unit (PMU) measurements.
A network flow analysis scheme is proposed to track changes in user defined minimal cut sets within the system. This work introduces a new algorithm used to update a previous network flow solution after the loss of a single system branch. The proposed new algorithm provides a significantly decreased solution time that is desired in a real- time environment. This method of topology monitoring can provide system operators with visual indications of potential problems in the system caused by changes in topology.
This work also presents a method of determining all singleton cut sets within a given network topology called the one line remaining (OLR) algorithm. During operation, if a singleton cut set exists, then the system cannot withstand the loss of any one line and still remain connected. The OLR algorithm activates after the loss of a transmission line and determines if any singleton cut sets were created. These cut sets are found using properties of power transfer distribution factors and minimal cut sets.
The topology analysis algorithms proposed in this work are supported by line outage detection using PMU measurements aimed at providing accurate real-time topology information. This process uses a decision tree (DT) based data-mining approach to characterize a lost tie line in simulation. The trained DT is then used to analyze PMU measurements to detect line outages. The trained decision tree was applied to real PMU measurements to detect the loss of a 500 kV line and had no misclassifications.
The work presented has the objective of enhancing situational awareness during significant system disturbances in real time. This dissertation presents all parts of the proposed topology monitoring scheme and justifies and validates the methodology using a real system event.
This dissertation introduces a real-time topology monitoring scheme for power systems intended to provide enhanced situational awareness during major system disturbances. The topology monitoring scheme requires accurate real-time topology information to be effective. This scheme is supported by advances in transmission line outage detection based on data-mining phasor measurement unit (PMU) measurements.
A network flow analysis scheme is proposed to track changes in user defined minimal cut sets within the system. This work introduces a new algorithm used to update a previous network flow solution after the loss of a single system branch. The proposed new algorithm provides a significantly decreased solution time that is desired in a real- time environment. This method of topology monitoring can provide system operators with visual indications of potential problems in the system caused by changes in topology.
This work also presents a method of determining all singleton cut sets within a given network topology called the one line remaining (OLR) algorithm. During operation, if a singleton cut set exists, then the system cannot withstand the loss of any one line and still remain connected. The OLR algorithm activates after the loss of a transmission line and determines if any singleton cut sets were created. These cut sets are found using properties of power transfer distribution factors and minimal cut sets.
The topology analysis algorithms proposed in this work are supported by line outage detection using PMU measurements aimed at providing accurate real-time topology information. This process uses a decision tree (DT) based data-mining approach to characterize a lost tie line in simulation. The trained DT is then used to analyze PMU measurements to detect line outages. The trained decision tree was applied to real PMU measurements to detect the loss of a 500 kV line and had no misclassifications.
The work presented has the objective of enhancing situational awareness during significant system disturbances in real time. This dissertation presents all parts of the proposed topology monitoring scheme and justifies and validates the methodology using a real system event.
ContributorsWerho, Trevor Nelson (Author) / Vittal, Vijay (Thesis advisor) / Heydt, Gerald (Committee member) / Hedman, Kory (Committee member) / Karady, George G. (Committee member) / Arizona State University (Publisher)
Created2015
Description
The electric power system is monitored via an extensive network of sensors in tandem with data processing algorithms, i.e., an intelligent cyber layer, that enables continual observation and control of the physical system to ensure reliable operations. This data collection and processing system is vulnerable to cyber-attacks that impact the system operation status and lead to serious physical consequences, including systematic problems and failures.
This dissertation studies the physical consequences of unobservable false data injection (FDI) attacks wherein the attacker maliciously changes supervisory control and data acquisition (SCADA) or phasor measurement unit (PMU) measurements, on the electric power system. In this context, the dissertation is divided into three parts, in which the first two parts focus on FDI attacks on SCADA and the last part focuses on FDI attacks on PMUs.
The first part studies the physical consequences of FDI attacks on SCADA measurements designed with limited system information. The attacker is assumed to have perfect knowledge inside a sub-network of the entire system. Two classes of attacks with different assumptions on the attacker's knowledge outside of the sub-network are introduced. In particular, for the second class of attacks, the attacker is assumed to have no information outside of the attack sub-network, but can perform multiple linear regression to learn the relationship between the external network and the attack sub-network with historical data. To determine the worst possible consequences of both classes of attacks, a bi-level optimization problem wherein the first level models the attacker's goal and the second level models the system response is introduced.
The second part of the dissertation concentrates on analyzing the vulnerability of systems to FDI attacks from the perspective of the system. To this end, an off-line vulnerability analysis framework is proposed to identify the subsets of the test system that are more prone to FDI attacks.
The third part studies the vulnerability of PMUs to FDI attacks. Two classes of more sophisticated FDI attacks that capture the temporal correlation of PMU data are introduced. Such attacks are designed with a convex optimization problem and can always bypass both the bad data detector and the low-rank decomposition (LD) detector.
This dissertation studies the physical consequences of unobservable false data injection (FDI) attacks wherein the attacker maliciously changes supervisory control and data acquisition (SCADA) or phasor measurement unit (PMU) measurements, on the electric power system. In this context, the dissertation is divided into three parts, in which the first two parts focus on FDI attacks on SCADA and the last part focuses on FDI attacks on PMUs.
The first part studies the physical consequences of FDI attacks on SCADA measurements designed with limited system information. The attacker is assumed to have perfect knowledge inside a sub-network of the entire system. Two classes of attacks with different assumptions on the attacker's knowledge outside of the sub-network are introduced. In particular, for the second class of attacks, the attacker is assumed to have no information outside of the attack sub-network, but can perform multiple linear regression to learn the relationship between the external network and the attack sub-network with historical data. To determine the worst possible consequences of both classes of attacks, a bi-level optimization problem wherein the first level models the attacker's goal and the second level models the system response is introduced.
The second part of the dissertation concentrates on analyzing the vulnerability of systems to FDI attacks from the perspective of the system. To this end, an off-line vulnerability analysis framework is proposed to identify the subsets of the test system that are more prone to FDI attacks.
The third part studies the vulnerability of PMUs to FDI attacks. Two classes of more sophisticated FDI attacks that capture the temporal correlation of PMU data are introduced. Such attacks are designed with a convex optimization problem and can always bypass both the bad data detector and the low-rank decomposition (LD) detector.
ContributorsZhang, Jiazi (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Committee member) / Hedman, Kory (Committee member) / Vittal, Vijay (Committee member) / Arizona State University (Publisher)
Created2017
Description
The flexibility in power system networks is not fully modeled in existing real-time contingency analysis (RTCA) and real-time security-constrained economic dispatch (RT SCED) applications. Thus, corrective transmission switching (CTS) is proposed in this dissertation to enable RTCA and RT SCED to take advantage of the flexibility in the transmission system in a practical way.
RTCA is first conducted to identify critical contingencies that may cause violations. Then, for each critical contingency, CTS is performed to determine the beneficial switching actions that can reduce post-contingency violations. To reduce computational burden, fast heuristic algorithms are proposed to generate candidate switching lists. Numerical simulations performed on three large-scale realistic power systems (TVA, ERCOT, and PJM) demonstrate that CTS can significantly reduce post-contingency violations. Parallel computing can further reduce the solution time.
RT SCED is to eliminate the actual overloads and potential post-contingency overloads identified by RTCA. Procedure-A, which is consistent with existing industry practices, is proposed to connect RTCA and RT SCED. As CTS can reduce post-contingency violations, higher branch limits, referred to as pseudo limits, may be available for some contingency-case network constraints. Thus, Procedure-B is proposed to take advantage of the reliability benefits provided by CTS. With the proposed Procedure-B, CTS can be modeled in RT SCED implicitly through the proposed pseudo limits for contingency-case network constraints, which requires no change to existing RT SCED tools. Numerical simulations demonstrate that the proposed Procedure-A can effectively eliminate the flow violations reported by RTCA and that the proposed Procedure-B can reduce most of the congestion cost with consideration of CTS.
The system status may be inaccurately estimated due to false data injection (FDI) cyber-attacks, which may mislead operators to adjust the system improperly and cause network violations. Thus, a two-stage FDI detection (FDID) approach, along with several metrics and an alert system, is proposed in this dissertation to detect FDI attacks. The first stage is to determine whether the system is under attack and the second stage would identify the target branch. Numerical simulations demonstrate the effectiveness of the proposed two-stage FDID approach.
RTCA is first conducted to identify critical contingencies that may cause violations. Then, for each critical contingency, CTS is performed to determine the beneficial switching actions that can reduce post-contingency violations. To reduce computational burden, fast heuristic algorithms are proposed to generate candidate switching lists. Numerical simulations performed on three large-scale realistic power systems (TVA, ERCOT, and PJM) demonstrate that CTS can significantly reduce post-contingency violations. Parallel computing can further reduce the solution time.
RT SCED is to eliminate the actual overloads and potential post-contingency overloads identified by RTCA. Procedure-A, which is consistent with existing industry practices, is proposed to connect RTCA and RT SCED. As CTS can reduce post-contingency violations, higher branch limits, referred to as pseudo limits, may be available for some contingency-case network constraints. Thus, Procedure-B is proposed to take advantage of the reliability benefits provided by CTS. With the proposed Procedure-B, CTS can be modeled in RT SCED implicitly through the proposed pseudo limits for contingency-case network constraints, which requires no change to existing RT SCED tools. Numerical simulations demonstrate that the proposed Procedure-A can effectively eliminate the flow violations reported by RTCA and that the proposed Procedure-B can reduce most of the congestion cost with consideration of CTS.
The system status may be inaccurately estimated due to false data injection (FDI) cyber-attacks, which may mislead operators to adjust the system improperly and cause network violations. Thus, a two-stage FDI detection (FDID) approach, along with several metrics and an alert system, is proposed in this dissertation to detect FDI attacks. The first stage is to determine whether the system is under attack and the second stage would identify the target branch. Numerical simulations demonstrate the effectiveness of the proposed two-stage FDID approach.
ContributorsLi, Xingpeng (Author) / Hedman, Kory (Thesis advisor) / Heydt, Gerald (Committee member) / Vittal, Vijay (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2017
Description
As the world embraces a sustainable energy future, alternative energy resources, such as wind power, are increasingly being seen as an integral part of the future electric energy grid. Ultimately, integrating such a dynamic and variable mix of generation requires a better understanding of renewable generation output, in addition to power grid systems that improve power system operational performance in the presence of anticipated events such as wind power ramps. Because of the stochastic, uncontrollable nature of renewable resources, a thorough and accurate characterization of wind activity is necessary to maintain grid stability and reliability. Wind power ramps from an existing wind farm are studied to characterize persistence forecasting errors using extreme value analysis techniques. In addition, a novel metric that quantifies the amount of non-stationarity in time series wind power data was proposed and used in a real-time algorithm to provide a rigorous method that adaptively determines training data for forecasts. Lastly, large swings in generation or load can cause system frequency and tie-line flows to deviate from nominal, so an anticipatory MPC-based secondary control scheme was designed and integrated into an automatic generation control loop to improve the ability of an interconnection to respond to anticipated large events and fluctuations in the power system.
ContributorsGanger, David (Author) / Vittal, Vijay (Thesis advisor) / Zhang, Junshan (Thesis advisor) / Hedman, Kory (Committee member) / Undrill, John (Committee member) / Arizona State University (Publisher)
Created2016
Description
Large-scale integration of wind generation introduces planning and operational difficulties due to the intermittent and highly variable nature of wind. In particular, the generation from non-hydro renewable resources is inherently variable and often times difficult to predict. Integrating significant amounts of renewable generation, thus, presents a challenge to the power systems operators, requiring additional flexibility, which may incur a decrease of conventional generation capacity.
This research investigates the algorithms employing emerging computational advances in system operation policies that can improve the flexibility of the electricity industry. The focus of this study is on flexible operation policies for renewable generation, particularly wind generation. Specifically, distributional forecasts of windfarm generation are used to dispatch a “discounted” amount of the wind generation, leaving a reserve margin that can be used for reserve if needed. This study presents systematic mathematic formulations that allow the operator incorporate this flexibility into the operation optimization model to increase the benefits in the energy and reserve scheduling procedure. Incorporating this formulation into the dispatch optimization problem provides the operator with the ability of using forecasted probability distributions as well as the off-line generated policies to choose proper approaches for operating the system in real-time. Methods to generate such policies are discussed and a forecast-based approach for developing wind margin policies is presented. The impacts of incorporating such policies in the electricity market models are also investigated.
This research investigates the algorithms employing emerging computational advances in system operation policies that can improve the flexibility of the electricity industry. The focus of this study is on flexible operation policies for renewable generation, particularly wind generation. Specifically, distributional forecasts of windfarm generation are used to dispatch a “discounted” amount of the wind generation, leaving a reserve margin that can be used for reserve if needed. This study presents systematic mathematic formulations that allow the operator incorporate this flexibility into the operation optimization model to increase the benefits in the energy and reserve scheduling procedure. Incorporating this formulation into the dispatch optimization problem provides the operator with the ability of using forecasted probability distributions as well as the off-line generated policies to choose proper approaches for operating the system in real-time. Methods to generate such policies are discussed and a forecast-based approach for developing wind margin policies is presented. The impacts of incorporating such policies in the electricity market models are also investigated.
ContributorsHedayati Mehdiabadi, Mojgan (Author) / Zhang, Junshan (Thesis advisor) / Hedman, Kory (Thesis advisor) / Heydt, Gerald (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2017
Description
The power system is the largest man-made physical network in the world. Performing analysis of a large bulk system is computationally complex, especially when the study involves engineering, economic and environmental considerations. For instance, running a unit-commitment (UC) over a large system involves a huge number of constraints and integer variables. One way to reduce the computational expense is to perform the analysis on a small equivalent (reduced) model instead on the original (full) model.
The research reported here focuses on improving the network reduction methods so that the calculated results obtained from the reduced model better approximate the performance of the original model. An optimization-based Ward reduction (OP-Ward) and two new generator placement methods in network reduction are introduced and numerical test results on large systems provide proof of concept.
In addition to dc-type reductions (ignoring reactive power, resistance elements in the network, etc.), the new methods applicable to ac domain are introduced. For conventional reduction methods (Ward-type methods, REI-type methods), eliminating external generator buses (PV buses) is a tough problem, because it is difficult to accurately approximate the external reactive support in the reduced model. Recently, the holomorphic embedding (HE) based load-flow method (HELM) was proposed, which theoretically guarantees convergence given that the power flow equations are structure in accordance with Stahl’s theory requirements. In this work, a holomorphic embedding based network reduction (HE reduction) method is proposed which takes advantage of the HELM technique. Test results shows that the HE reduction method can approximate the original system performance very accurately even when the operating condition changes.
The research reported here focuses on improving the network reduction methods so that the calculated results obtained from the reduced model better approximate the performance of the original model. An optimization-based Ward reduction (OP-Ward) and two new generator placement methods in network reduction are introduced and numerical test results on large systems provide proof of concept.
In addition to dc-type reductions (ignoring reactive power, resistance elements in the network, etc.), the new methods applicable to ac domain are introduced. For conventional reduction methods (Ward-type methods, REI-type methods), eliminating external generator buses (PV buses) is a tough problem, because it is difficult to accurately approximate the external reactive support in the reduced model. Recently, the holomorphic embedding (HE) based load-flow method (HELM) was proposed, which theoretically guarantees convergence given that the power flow equations are structure in accordance with Stahl’s theory requirements. In this work, a holomorphic embedding based network reduction (HE reduction) method is proposed which takes advantage of the HELM technique. Test results shows that the HE reduction method can approximate the original system performance very accurately even when the operating condition changes.
ContributorsZhu, Yujia (Author) / Tylavsky, Daniel John (Thesis advisor) / Vittal, Vijay (Committee member) / Hedman, Kory (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2017