Matching Items (36)
Description
The high uncertainty of renewables introduces more dynamics to power systems. The conventional way of monitoring and controlling power systems is no longer reliable. New strategies are needed to ensure the stability and reliability of power systems. This work aims to assess the use of machine learning methods in analyzing data from renewable integrated power systems to aid the decisionmaking of electricity market participants. Specifically, the work studies the cases of electricity price forecast, solar panel detection, and how to constrain the machine learning methods to obey domain knowledge.Chapter 2 proposes to diversify the data source to ensure a more accurate electricity price forecast. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules: the mapping between the historical wind power and the historical price and the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the learned mapping between power and price. The massive numerical comparison gives guidance for choosing proper machine learning methods and proves the effectiveness of the proposed method.
Chapter 3 proposes to integrate advanced data compression techniques into machine learning algorithms to either improve the predicting accuracy or accelerate the computation speed. New semi-supervised learning and one-class classification methods are proposed based on autoencoders to compress the data while refining the nonlinear data representation of human behavior and solar behavior. The numerical results show robust detection accuracy, laying down the foundation for managing distributed energy resources in distribution grids. Guidance is also provided to determine the proper machine learning methods for the solar detection problem.
Chapter 4 proposes to integrate different types of domain knowledge-based constraints into basic neural networks to guide the model selection and enhance interpretability. A hybrid model is proposed to penalize derivatives and alter the structure to improve the performance of a neural network. We verify the performance improvement of introducing prior knowledge-based constraints on both synthetic and real data sets.
ContributorsLuo, Shuman (Author) / Weng, Yang (Thesis advisor) / Lei, Qin (Committee member) / Fricks, John (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2022
Description
Distributed energy resources have experienced dramatic growth and are beginning to support a significant amount of customer loads. Power electronic converters are the primary interface between the grid and the distributed energy resources/storage and offer several advantages including fast control, flexibility and high efficiency. The efficiency and the power density by volume are important performance metrics of a power converter. Compact and high efficiency power converter is beneficial to the cost-effectiveness of the converter interfaced generations. In this thesis, a soft-switching technique is proposed to reduce the size of passive components in a grid-connected converter while maintaining a high power conversion efficiency. The dynamic impact of the grid-connected converters on the power system is causing concerns as the penetration level of the converter interfaced generation increases, necessitating a detailed dynamic analysis. The unbalanced nature of distribution systems makes the conventional transient stability simulation based on positive sequence components unsuitable for this purpose. Methods suitable for the dynamic simulation of grid-connected converters in large scale unbalanced and single-phase systems are presented in this thesis to provide an effective way to study the dynamic interactions between the grid and the converters. Dynamic-link library (DLL) of converter dynamic models are developed by which converter dynamic simulations can be easily conducted in OpenDSS. To extend the converter controls testing beyond pure simulation, real-time simulation can be utilized where partial realistic scenarios can be created by including realistic components in the simulation loop. In this work, a multi-platform, real-time simulation testbed including actual digital controller platforms, communication networks and inverters has been developed for validating the microgrid concepts and implementations. A hierarchical converted based microgrid control scheme is proposed which enables the islanded microgrid operation with 100% penetration level of converter interfaced generation. Impact of the load side dynamic modeling on the converter response is also discussed in this thesis.
ContributorsYu, Ziwei (Author) / Ayyanar, Raja (Thesis advisor) / Vittal, Vijay (Committee member) / Qin, Jiangchao (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2018
Description
The students of Arizona State University, under the mentorship of Dr George Karady, have been collaborating with Salt River Project (SRP), a major power utility in the state of Arizona, trying to study and optimize a battery-supported grid-tied rooftop Photovoltaic (PV) system, sold by a commercial vendor. SRP believes this system has the potential to satisfy the needs of its customers, who opt for utilizing solar power to partially satisfy their power needs.
An important part of this elaborate project is the development of a new load forecasting algorithm and a better control strategy for the optimized utilization of the storage system. The built-in algorithm of this commercial unit uses simple forecasting and battery control strategies. With the recent improvement in Machine Learning (ML) techniques, development of a more sophisticated model of the problem in hand was possible. This research is aimed at achieving the goal by utilizing the appropriate ML techniques to better model the problem, which will essentially result in a better solution. In this research, a set of six unique features are used to model the load forecasting problem and different ML algorithms are simulated on the developed model. A similar approach is taken to solve the PV prediction problem. Finally, a very effective battery control strategy is built (utilizing the results of the load and PV forecasting), with the aim of ensuring a reduction in the amount of energy consumed from the grid during the “on-peak” hours. Apart from the reduction in the energy consumption, this battery control algorithm decelerates the “cycling aging” or the aging of the battery owing to the charge/dis-charges cycles endured by selectively charging/dis-charging the battery based on need.
ii
The results of this proposed strategy are verified using a hardware implementation (the PV system was coupled with a custom-built load bank and this setup was used to simulate a house). The results pertaining to the performances of the built-in algorithm and the ML algorithm are compared and the economic analysis is performed. The findings of this research have in the process of being published in a reputed journal.
An important part of this elaborate project is the development of a new load forecasting algorithm and a better control strategy for the optimized utilization of the storage system. The built-in algorithm of this commercial unit uses simple forecasting and battery control strategies. With the recent improvement in Machine Learning (ML) techniques, development of a more sophisticated model of the problem in hand was possible. This research is aimed at achieving the goal by utilizing the appropriate ML techniques to better model the problem, which will essentially result in a better solution. In this research, a set of six unique features are used to model the load forecasting problem and different ML algorithms are simulated on the developed model. A similar approach is taken to solve the PV prediction problem. Finally, a very effective battery control strategy is built (utilizing the results of the load and PV forecasting), with the aim of ensuring a reduction in the amount of energy consumed from the grid during the “on-peak” hours. Apart from the reduction in the energy consumption, this battery control algorithm decelerates the “cycling aging” or the aging of the battery owing to the charge/dis-charges cycles endured by selectively charging/dis-charging the battery based on need.
ii
The results of this proposed strategy are verified using a hardware implementation (the PV system was coupled with a custom-built load bank and this setup was used to simulate a house). The results pertaining to the performances of the built-in algorithm and the ML algorithm are compared and the economic analysis is performed. The findings of this research have in the process of being published in a reputed journal.
ContributorsHariharan, Aashiek (Author) / Karady, George G. (Thesis advisor) / Heydt, Gerald Thomas (Committee member) / Qin, Jiangchao (Committee member) / Allee, David R. (Committee member) / Arizona State University (Publisher)
Created2018
Description
The most important metrics considered for electric vehicles are power density, efficiency, and reliability of the powertrain modules. The powertrain comprises of an Electric Machine (EM), power electronic converters, an Energy Management System (EMS), and an Energy Storage System (ESS). The power electronic converters are used to couple the motor with the battery stack. Including a DC/DC converter in the powertrain module is favored as it adds an additional degree of freedom to achieve flexibility in optimizing the battery module and inverter independently. However, it is essential that the converter is rated for high peak power and can maintain high efficiency while operating over a wide range of load conditions to not compromise on system efficiency. Additionally, the converter must strictly adhere to all automotive standards.
Currently, several hard-switching topologies have been employed such as conventional boost DC/DC, interleaved step-up DC/DC, and full-bridge DC/DC converter. These converters face respective limitations in achieving high step-up conversion ratio, size and weight issues, or high component count. In this work, a bi-directional synchronous boost DC/DC converter with easy interleaving capability is proposed with a novel ZVT mechanism. This converter steps up the EV battery voltage of 200V-300V to a wide range of variable output voltages ranging from 310V-800V. High power density and efficiency are achieved through high switching frequency of 250kHz for each phase with effective frequency doubling through interleaving. Also, use of wide bandgap high voltage SiC switches allows high efficiency operation even at high temperatures.
Comprehensive analysis, design details and extensive simulation results are presented. Incorporating ZVT branch with adaptive time delay results in converter efficiency close to 98%. Experimental results from a 2.5kW hardware prototype validate the performance of the proposed approach. A peak efficiency of 98.17% has been observed in hardware in the boost or motoring mode.
Currently, several hard-switching topologies have been employed such as conventional boost DC/DC, interleaved step-up DC/DC, and full-bridge DC/DC converter. These converters face respective limitations in achieving high step-up conversion ratio, size and weight issues, or high component count. In this work, a bi-directional synchronous boost DC/DC converter with easy interleaving capability is proposed with a novel ZVT mechanism. This converter steps up the EV battery voltage of 200V-300V to a wide range of variable output voltages ranging from 310V-800V. High power density and efficiency are achieved through high switching frequency of 250kHz for each phase with effective frequency doubling through interleaving. Also, use of wide bandgap high voltage SiC switches allows high efficiency operation even at high temperatures.
Comprehensive analysis, design details and extensive simulation results are presented. Incorporating ZVT branch with adaptive time delay results in converter efficiency close to 98%. Experimental results from a 2.5kW hardware prototype validate the performance of the proposed approach. A peak efficiency of 98.17% has been observed in hardware in the boost or motoring mode.
ContributorsMullangi Chenchu, Hemanth (Author) / Ayyanar, Raja (Thesis advisor) / Qin, Jiangchao (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Switching surges are a common type of phenomenon that occur on any sort of power system network. These are more pronounced on long transmission lines and in high voltage substations. The problem with switching surges is encountered when a lot of power is transmitted across a transmission line
etwork, typically from a concentrated generation node to a concentrated load. The problem becomes significantly worse when the transmission line is long and when the voltage levels are high, typically above 400 kV. These overvoltage transients occur following any type of switching action such as breaker operation, fault occurrence/clearance and energization, and they pose a very real danger to weakly interconnected systems. At EHV levels, the insulation coordination of such lines is mainly dictated by the peak level of switching surges, the most dangerous of which include three phase line energization and single-phase reclosing. Switching surges can depend on a number of independent and inter-dependent factors like voltage level, line length, tower construction, location along the line, and presence of other equipment like shunt/series reactors and capacitors.
This project discusses the approaches taken and methods applied to observe and tackle the problems associated with switching surges on a long transmission line. A detailed discussion pertaining to different aspects of switching surges and their effects is presented with results from various studies published in IEEE journals and conference papers. Then a series of simulations are presented to determine an arrangement of substation equipment with respect to incoming transmission lines; that correspond to the lowest surge levels at that substation.
etwork, typically from a concentrated generation node to a concentrated load. The problem becomes significantly worse when the transmission line is long and when the voltage levels are high, typically above 400 kV. These overvoltage transients occur following any type of switching action such as breaker operation, fault occurrence/clearance and energization, and they pose a very real danger to weakly interconnected systems. At EHV levels, the insulation coordination of such lines is mainly dictated by the peak level of switching surges, the most dangerous of which include three phase line energization and single-phase reclosing. Switching surges can depend on a number of independent and inter-dependent factors like voltage level, line length, tower construction, location along the line, and presence of other equipment like shunt/series reactors and capacitors.
This project discusses the approaches taken and methods applied to observe and tackle the problems associated with switching surges on a long transmission line. A detailed discussion pertaining to different aspects of switching surges and their effects is presented with results from various studies published in IEEE journals and conference papers. Then a series of simulations are presented to determine an arrangement of substation equipment with respect to incoming transmission lines; that correspond to the lowest surge levels at that substation.
ContributorsShaikh, Mohammed Mubashir (Author) / Qin, Jiangchao (Thesis advisor) / Heydt, Gerald T (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Over the years, the growing penetration of renewable energy into the electricity market has resulted in a significant change in the electricity market price. This change makes the existing forecasting method prone to error, decreasing the economic benefits. Hence, more precise forecasting methods need to be developed. This paper starts with a survey and benchmark of existing machine learning approaches for forecasting the real-time market (RTM) price. While these methods provide sufficient modeling capability via supervised learning, their accuracy is still limited due to the single data source, e.g., historical price information only. In this paper, a novel two-stage supervised learning approach is proposed by diversifying the data sources such as highly correlated power data. This idea is inspired by the recent load forecasting methods that have shown extremely well performances. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules. The first one is the mapping between the historical wind power and the historical price. The second is the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the first learned mapping between power and price. Additionally, we observed that it is not the more training data the better, leading to our validation steps to quantify the best training intervals for different datasets. We conduct comparisons of numerical results between existing methods and the proposed methods based on datasets from the Electric Reliability Council of Texas (ERCOT). For each machine learning step, we examine different learning methods, such as polynomial regression, support vector regression, neural network, and deep neural network. The results show that the proposed method is significantly better than existing approaches when renewables are involved.
ContributorsLuo, Shuman (Author) / Weng, Yang (Thesis advisor) / Lei, Qin (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
The following report details the motivation, design, analysis, simulation and hardware implementation of a DC/DC converter in EV drivetrain architectures. The primary objective of the project was to improve overall system efficiency in an EV drivetrain. The methodology employed to this end required a variable or flexible DC-Link voltage at the input of the inverter stage. Amongst the several advantages associated with such a system are the independent optimization of the battery stack and the inverter over a wide range of motor operating conditions. The incorporation of a DC/DC converter into the drivetrain helps lower system losses but since it is an additional component, a number of considerations need to be made during its design. These include stringent requirements on power density, converter efficiency and reliability.
These targets for the converter are met through a number of different ways. The switches used are Silicon Carbide FETs. These are wide band gap (WBG) devices that can operate at high frequencies and temperatures. Since they allow for high frequency operation, a switching frequency of 250 khz is proposed and implemented. This helps with power density by reducing the size of passive components. High efficiencies are made possible by using a simple soft switching technique by augmenting the DC/DC converter with an auxiliary branch to enable zero voltage transition.
The efficacy of the approach is tested through simulation and hardware implementation of two different prototypes. The Gen-I prototype was a single soft switched synchronous boost converter rated at 2.5kw. Both the motoring mode and regenerative modes of operation (Boost and Buck) were hardware tested for over 2kw and efficiency results of over 98.15% were achieved. The Gen-II prototype and the main focus of this work is an interleaved soft switched synchronous boost converter. This converter has been implemented in hardware as well and has been tested at 6.7kw and an efficiency of over 98% has been achieved in the boost mode of operation.
These targets for the converter are met through a number of different ways. The switches used are Silicon Carbide FETs. These are wide band gap (WBG) devices that can operate at high frequencies and temperatures. Since they allow for high frequency operation, a switching frequency of 250 khz is proposed and implemented. This helps with power density by reducing the size of passive components. High efficiencies are made possible by using a simple soft switching technique by augmenting the DC/DC converter with an auxiliary branch to enable zero voltage transition.
The efficacy of the approach is tested through simulation and hardware implementation of two different prototypes. The Gen-I prototype was a single soft switched synchronous boost converter rated at 2.5kw. Both the motoring mode and regenerative modes of operation (Boost and Buck) were hardware tested for over 2kw and efficiency results of over 98.15% were achieved. The Gen-II prototype and the main focus of this work is an interleaved soft switched synchronous boost converter. This converter has been implemented in hardware as well and has been tested at 6.7kw and an efficiency of over 98% has been achieved in the boost mode of operation.
ContributorsRaza, Bassam (Author) / Ayyanar, Raja (Thesis advisor) / Qin, Jiangchao (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2019
Description
The demand for cleaner energy technology is increasing very rapidly. Hence it is
important to increase the eciency and reliability of this emerging clean energy technologies.
This thesis focuses on modeling and reliability of solar micro inverters. In
order to make photovoltaics (PV) cost competitive with traditional energy sources,
the economies of scale have been guiding inverter design in two directions: large,
centralized, utility-scale (500 kW) inverters vs. small, modular, module level (300
W) power electronics (MLPE). MLPE, such as microinverters and DC power optimizers,
oer advantages in safety, system operations and maintenance, energy yield,
and component lifetime due to their smaller size, lower power handling requirements,
and module-level power point tracking and monitoring capability [1]. However, they
suer from two main disadvantages: rst, depending on array topology (especially
the proximity to the PV module), they can be subjected to more extreme environments
(i.e. temperature cycling) during the day, resulting in a negative impact to
reliability; second, since solar installations can have tens of thousands to millions of
modules (and as many MLPE units), it may be dicult or impossible to track and
repair units as they go out of service. Therefore identifying the weak links in this
system is of critical importance to develop more reliable micro inverters.
While an overwhelming majority of time and research has focused on PV module
eciency and reliability, these issues have been largely ignored for the balance
of system components. As a relatively nascent industry, the PV power electronics
industry does not have the extensive, standardized reliability design and testing procedures
that exist in the module industry or other more mature power electronics
industries (e.g. automotive). To do so, the critical components which are at risk and
their impact on the system performance has to be studied. This thesis identies and
addresses some of the issues related to reliability of solar micro inverters.
This thesis presents detailed discussions on various components of solar micro inverter
and their design. A micro inverter with very similar electrical specications in
comparison with commercial micro inverter is modeled in detail and veried. Components
in various stages of micro inverter are listed and their typical failure mechanisms
are reviewed. A detailed FMEA is conducted for a typical micro inverter to identify
the weak links of the system. Based on the S, O and D metrics, risk priority number
(RPN) is calculated to list the critical at-risk components. Degradation of DC bus
capacitor is identied as one the failure mechanism and the degradation model is built
to study its eect on the system performance. The system is tested for surge immunity
using standard ring and combinational surge waveforms as per IEEE 62.41 and
IEC 61000-4-5 standards. All the simulation presented in this thesis is performed
using PLECS simulation software.
important to increase the eciency and reliability of this emerging clean energy technologies.
This thesis focuses on modeling and reliability of solar micro inverters. In
order to make photovoltaics (PV) cost competitive with traditional energy sources,
the economies of scale have been guiding inverter design in two directions: large,
centralized, utility-scale (500 kW) inverters vs. small, modular, module level (300
W) power electronics (MLPE). MLPE, such as microinverters and DC power optimizers,
oer advantages in safety, system operations and maintenance, energy yield,
and component lifetime due to their smaller size, lower power handling requirements,
and module-level power point tracking and monitoring capability [1]. However, they
suer from two main disadvantages: rst, depending on array topology (especially
the proximity to the PV module), they can be subjected to more extreme environments
(i.e. temperature cycling) during the day, resulting in a negative impact to
reliability; second, since solar installations can have tens of thousands to millions of
modules (and as many MLPE units), it may be dicult or impossible to track and
repair units as they go out of service. Therefore identifying the weak links in this
system is of critical importance to develop more reliable micro inverters.
While an overwhelming majority of time and research has focused on PV module
eciency and reliability, these issues have been largely ignored for the balance
of system components. As a relatively nascent industry, the PV power electronics
industry does not have the extensive, standardized reliability design and testing procedures
that exist in the module industry or other more mature power electronics
industries (e.g. automotive). To do so, the critical components which are at risk and
their impact on the system performance has to be studied. This thesis identies and
addresses some of the issues related to reliability of solar micro inverters.
This thesis presents detailed discussions on various components of solar micro inverter
and their design. A micro inverter with very similar electrical specications in
comparison with commercial micro inverter is modeled in detail and veried. Components
in various stages of micro inverter are listed and their typical failure mechanisms
are reviewed. A detailed FMEA is conducted for a typical micro inverter to identify
the weak links of the system. Based on the S, O and D metrics, risk priority number
(RPN) is calculated to list the critical at-risk components. Degradation of DC bus
capacitor is identied as one the failure mechanism and the degradation model is built
to study its eect on the system performance. The system is tested for surge immunity
using standard ring and combinational surge waveforms as per IEEE 62.41 and
IEC 61000-4-5 standards. All the simulation presented in this thesis is performed
using PLECS simulation software.
ContributorsManchanahalli Ranganatha, Arkanatha Sastry (Author) / Ayyanar, Raja (Thesis advisor) / Karady, George G. (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
This dissertation presents innovative techniques to develop performance-based models and complete transient models of induction motor drive systems with vector controls in electro-magnetic transient (EMT) and positive sequence transient stability (PSTS) simulation programs. The performance-based model is implemented by obtaining the characteristic transfer functions of perturbed active and reactive power consumptions with respect to frequency and voltage perturbations. This level of linearized performance-based model is suitable for the investigation of the damping of small-magnitude low-frequency oscillations. The complete transient model is proposed by decomposing the motor, converter and control models into d-q axes components and developing a compatible electrical interface to the positive-sequence network in the PSTS simulators. The complete transient drive model is primarily used to examine the system response subject to transient voltage depression considering increasing penetration of converter-driven motor loads.
For developing the performance-based model, modulations are performed on the supply side of the full drive system to procure magnitude and phase responses of active and reactive powers with respect to the supply voltage and frequency for a range of discrete frequency points. The prediction error minimization (PEM) technique is utilized to generate the curve-fitted transfer functions and corresponding bode plots. For developing the complete drive model in the PSTS simulation program, a positive-sequence voltage source is defined properly as the interface of the model to the external system. The dc-link of the drive converter is implemented by employing the average model of the PWM converter, and is utilized to integrate the line-side rectifier and machine-side inverter.
Numerical simulation is then conducted on sample test systems, synthesized with suitable characteristics to examine performance of the developed models. The simulation results reveal that with growing amount of drive loads being distributed in the system, the small-signal stability of the system is improved in terms of the desirable damping effects on the low-frequency system oscillations of voltage and frequency. The transient stability of the system is also enhanced with regard to the stable active power and reactive power controls of the loads, and the appropriate VAr support capability provided by the drive loads during a contingency.
For developing the performance-based model, modulations are performed on the supply side of the full drive system to procure magnitude and phase responses of active and reactive powers with respect to the supply voltage and frequency for a range of discrete frequency points. The prediction error minimization (PEM) technique is utilized to generate the curve-fitted transfer functions and corresponding bode plots. For developing the complete drive model in the PSTS simulation program, a positive-sequence voltage source is defined properly as the interface of the model to the external system. The dc-link of the drive converter is implemented by employing the average model of the PWM converter, and is utilized to integrate the line-side rectifier and machine-side inverter.
Numerical simulation is then conducted on sample test systems, synthesized with suitable characteristics to examine performance of the developed models. The simulation results reveal that with growing amount of drive loads being distributed in the system, the small-signal stability of the system is improved in terms of the desirable damping effects on the low-frequency system oscillations of voltage and frequency. The transient stability of the system is also enhanced with regard to the stable active power and reactive power controls of the loads, and the appropriate VAr support capability provided by the drive loads during a contingency.
ContributorsLiu, Yuan (Author) / Vittal, Vijay (Thesis advisor) / Undrill, John (Committee member) / Ayyanar, Raja (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2016
Description
The past decades have seen a significant shift in the expectations and requirements re-lated to power system analysis tools. Investigations into major power grid disturbances have suggested the need for more comprehensive assessment methods. Accordingly, sig-nificant research in recent years has focused on the development of better power system models and efficient techniques for analyzing power system operability. The work done in this report focusses on two such topics
1. Analysis of load model parameter uncertainty and sensitivity based pa-rameter estimation for power system studies
2. A systematic approach to n-1-1 analysis for power system security as-sessment
To assess the effect of load model parameter uncertainty, a trajectory sensitivity based approach is proposed in this work. Trajectory sensitivity analysis provides a sys-tematic approach to study the impact of parameter uncertainty on power system re-sponse to disturbances. Furthermore, the non-smooth nature of the composite load model presents some additional challenges to sensitivity analysis in a realistic power system. Accordingly, the impact of the non-smooth nature of load models on the sensitivity analysis is addressed in this work. The study was performed using the Western Electrici-ty Coordinating Council (WECC) system model. To address the issue of load model pa-rameter estimation, a sensitivity based load model parameter estimation technique is presented in this work. A detailed discussion on utilizing sensitivities to improve the ac-curacy and efficiency of the parameter estimation process is also presented in this work.
Cascading outages can have a catastrophic impact on power systems. As such, the NERC transmission planning (TPL) standards requires utilities to plan for n¬-1-1 out-ages. However, such analyses can be computationally burdensome for any realistic pow-er system owing to the staggering number of possible n-1-1 contingencies. To address this problem, the report proposes a systematic approach to analyze n-1-1 contingencies in a computationally tractable manner for power system security assessment. The pro-posed approach addresses both static and dynamic security assessment. The proposed methods have been tested on the WECC system.
1. Analysis of load model parameter uncertainty and sensitivity based pa-rameter estimation for power system studies
2. A systematic approach to n-1-1 analysis for power system security as-sessment
To assess the effect of load model parameter uncertainty, a trajectory sensitivity based approach is proposed in this work. Trajectory sensitivity analysis provides a sys-tematic approach to study the impact of parameter uncertainty on power system re-sponse to disturbances. Furthermore, the non-smooth nature of the composite load model presents some additional challenges to sensitivity analysis in a realistic power system. Accordingly, the impact of the non-smooth nature of load models on the sensitivity analysis is addressed in this work. The study was performed using the Western Electrici-ty Coordinating Council (WECC) system model. To address the issue of load model pa-rameter estimation, a sensitivity based load model parameter estimation technique is presented in this work. A detailed discussion on utilizing sensitivities to improve the ac-curacy and efficiency of the parameter estimation process is also presented in this work.
Cascading outages can have a catastrophic impact on power systems. As such, the NERC transmission planning (TPL) standards requires utilities to plan for n¬-1-1 out-ages. However, such analyses can be computationally burdensome for any realistic pow-er system owing to the staggering number of possible n-1-1 contingencies. To address this problem, the report proposes a systematic approach to analyze n-1-1 contingencies in a computationally tractable manner for power system security assessment. The pro-posed approach addresses both static and dynamic security assessment. The proposed methods have been tested on the WECC system.
ContributorsMitra, Parag (Author) / Vittal, Vijay (Thesis advisor) / Heydt, Gerald T (Committee member) / Ayyanar, Raja (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2016