Matching Items (29)
Description
The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages) introduce new challenges to power systems. Contemporary approaches to model reserve requirements within the conventional security-constrained unit commitment (SCUC) models may not be satisfactory with increasing penetration levels of stochastic resources; such conventional models pro-cure reserves in accordance with deterministic criteria whose deliverability, in the event of an uncertain realization, is not guaranteed. Smart, well-designed reserve policies are needed to assist system operators in maintaining reliability at least cost.
Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements.
This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.
Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements.
This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.
ContributorsSinghal, Nikita Ghanshyam (Author) / Hedman, Kory W (Thesis advisor) / Vittal, Vijay (Committee member) / Sankar, Lalitha (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2018
Description
Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the grid. With the aid of advanced technology such as phasor measurement units (PMUs), considerable work has been done involving voltage stability analysis and power system dynamic behavior analysis to ensure security and reliability of the grid. Online dynamic security assessment (DSA) analysis has been developed and applied in several power system control centers. Existing applications of DSA are limited by the assumption of simplistic load profiles, which often considers a normative day to represent an entire year. To overcome these aforementioned challenges, this research developed a novel DSA scheme to provide security prediction in real-time for load profiles corresponding to different seasons. The major contributions of this research are to (1) develop a DSA scheme incorporated with PMU data, (2) consider a comprehensive seasonal load profile, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA. The ML algorithms that will be the focus of this study include decision trees (DTs), support vector machines (SVMs), random forests (RFs), and multilayer neural networks (MLNNs).
This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.
This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.
ContributorsNATH, ANUBHAV (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2019
Description
Alternative sources of power generation interconnected at the transmission level have witnessed an increase in investment in the last few years. On the other hand, when the power systems are being operated close to their limits, power system operators and engineers face the challenge of ensuring a safe and reliable supply of electricity. In such a scenario, the reliability of the transmission system is crucial as it ensures secure transfer of uninterrupted power from the generating sources to the load centers. This thesis is aimed at ensuring the reliability of the transmission system from two perspectives. First, this work monitors power system disturbances such as unintentional islanding to ensure prompt detection and implementation of restorative actions and thus, minimizes the extent of damage. Secondly, it investigates power system disturbances such as transmission line outages through reliability evaluation and outage analysis in order to prevent reoccurrence of similar failures.
In this thesis, a passive Wide Area Measurement System (WAMS) based islanding detection scheme called Cumulative Sum of Change in Voltage Phase Angle Difference (CUSPAD) is proposed and tested on a modified 18 bus test system and a modified IEEE 118 bus system with various wind energy penetration levels. Comparative analysis between accuracies of the proposed approach and the conventional relative angle difference approach in presence of measurement errors indicate a superior performance of the former. Results obtained from the proposed approach also reveal that power system disturbances such as unintentional island formations are accurately detected in wind integrated transmission systems.
Quantitative evaluation of the transmission system reliability aids in the assessment of the existing system performance. Further, post-mortem analysis of failures is an important step in minimizing recurrent failures. Reliability evaluation and outage analysis of transmission line outages carried out in this thesis have revealed chronological trends in the system performance. A new index called Outage Impact Index (OII) is also been proposed which can identify and prioritize outages based on their severity. This would serve as a baselining index for assessing and monitoring future transmission system performances and will facilitate implementation of reliability improvement measures if found necessary.
In this thesis, a passive Wide Area Measurement System (WAMS) based islanding detection scheme called Cumulative Sum of Change in Voltage Phase Angle Difference (CUSPAD) is proposed and tested on a modified 18 bus test system and a modified IEEE 118 bus system with various wind energy penetration levels. Comparative analysis between accuracies of the proposed approach and the conventional relative angle difference approach in presence of measurement errors indicate a superior performance of the former. Results obtained from the proposed approach also reveal that power system disturbances such as unintentional island formations are accurately detected in wind integrated transmission systems.
Quantitative evaluation of the transmission system reliability aids in the assessment of the existing system performance. Further, post-mortem analysis of failures is an important step in minimizing recurrent failures. Reliability evaluation and outage analysis of transmission line outages carried out in this thesis have revealed chronological trends in the system performance. A new index called Outage Impact Index (OII) is also been proposed which can identify and prioritize outages based on their severity. This would serve as a baselining index for assessing and monitoring future transmission system performances and will facilitate implementation of reliability improvement measures if found necessary.
ContributorsBarkakati, Meghna (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith E. (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2018
Description
The lifetime of a transformer is essentially determined by the life of its insulation
system which is a time function of the temperature defined by its thermal class. A large
quantity of studies and international standards have been published indicating the
possibility of increasing the thermal class of cellulose based materials when immersed
in natural esters which are superior to traditional mineral oils. Thus, a transformer
having thermally upgraded Kraft paper and natural ester dielectric fluid can be
classified as a high temperature insulation system. Such a transformer can also
operate at temperatures 20C higher than its mineral oil equivalent, holding additional
loading capability without losing life expectancy. This thesis focuses on evaluating
the use of this feature as an additional capability for enhancing the loadability and/or
extending the life of the distribution transformers for the Phoenix based utility - SRP
using FR3 brand natural ester dielectric fluid.
Initially, different transformer design options to use this additional loadability
are compared allowing utilities to select an optimal FR3 filled transformer design
for their application. Yearlong load profiles for SRP distribution transformers, sized
conventionally on peak load demands, are analyzed for their oil temperatures, winding
temperatures and loss of insulation life. It is observed that these load profiles can be
classified into two types: 1) Type-1 profiles with high peak and high average loads,
and 2) Type-2 profiles with comparatively low peak and low average load.
For the Type 1 load profiles, use of FR3 natural ester fluid with the same nominal
rating showed 7.4 times longer life expectation. For the Type 2 load profiles, a new
way of sizing ester filled transformers based on both average and peak load, instead of
only peak load, called “Sustainable Peak Loading” showed smaller size transformers
can handle the same yearly peak loads while maintaining superior insulation lifespan.
It is additionally possible to have reduction in the total energy dissipation over the
year. A net present value cost savings up to US$1200 per transformer quantifying
benefits of the life extension and the total ownership cost savings up to 30% for
sustainable peak loading showed SRP distribution transformers can gain substantial
economic savings when the distribution transformer fleet is replaced with FR3 ester
filled units.
system which is a time function of the temperature defined by its thermal class. A large
quantity of studies and international standards have been published indicating the
possibility of increasing the thermal class of cellulose based materials when immersed
in natural esters which are superior to traditional mineral oils. Thus, a transformer
having thermally upgraded Kraft paper and natural ester dielectric fluid can be
classified as a high temperature insulation system. Such a transformer can also
operate at temperatures 20C higher than its mineral oil equivalent, holding additional
loading capability without losing life expectancy. This thesis focuses on evaluating
the use of this feature as an additional capability for enhancing the loadability and/or
extending the life of the distribution transformers for the Phoenix based utility - SRP
using FR3 brand natural ester dielectric fluid.
Initially, different transformer design options to use this additional loadability
are compared allowing utilities to select an optimal FR3 filled transformer design
for their application. Yearlong load profiles for SRP distribution transformers, sized
conventionally on peak load demands, are analyzed for their oil temperatures, winding
temperatures and loss of insulation life. It is observed that these load profiles can be
classified into two types: 1) Type-1 profiles with high peak and high average loads,
and 2) Type-2 profiles with comparatively low peak and low average load.
For the Type 1 load profiles, use of FR3 natural ester fluid with the same nominal
rating showed 7.4 times longer life expectation. For the Type 2 load profiles, a new
way of sizing ester filled transformers based on both average and peak load, instead of
only peak load, called “Sustainable Peak Loading” showed smaller size transformers
can handle the same yearly peak loads while maintaining superior insulation lifespan.
It is additionally possible to have reduction in the total energy dissipation over the
year. A net present value cost savings up to US$1200 per transformer quantifying
benefits of the life extension and the total ownership cost savings up to 30% for
sustainable peak loading showed SRP distribution transformers can gain substantial
economic savings when the distribution transformer fleet is replaced with FR3 ester
filled units.
ContributorsVaidya, Chinmay Vishwas (Author) / Holbert, Keith E. (Thesis advisor) / Ayyanar, Raja (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2018
Description
Transmission line parameters play an important role in state estimation, dynamic line rating, and fault analysis. Because of this, several methods have been proposed in the literature for line parameter estimation, especially using synchrophasor data. However, success of most prior research has been demonstrated using purely synthetic data. A synthetic dataset does not have the problems encountered with real data, such as invariance of measurements and realistic field noise. Therefore, the algorithms developed using synthetic datasets may not be as effective when used in practice. On the other hand, the true values of the line parameters are unknown and therefore the algorithms cannot be directly implemented on real data. A multi-stage test procedure is developed in this work to circumvent this problem.
In this thesis, two popular algorithms, namely, moving-window total least squares (MWTLS) and recursive Kalman filter (RKF) are applied on real data in multiple stages. In the first stage, the algorithms are tested on a purely synthetic dataset. This is followed by testing done on pseudo-synthetic datasets generated using real PMU data. In the final stage, the algorithms are implemented on the real PMU data obtained from a local utility. The results show that in the context of the given problem, RKF has better performance than MWTLS. Furthermore, to improve the performance of RKF on real data, ASPEN data are used to calculate the initial estimates. The estimation results show that the RKF algorithm can reliably estimate the sequence impedances, using ASPEN data as a starting condition. The estimation procedure is repeated over different time periods and the corresponding results are presented.
Finally, the significance of data drop-outs and its impact on the use of parameter estimates for real-time power system applications, such as state estimation and dynamic line rating, is discussed. To address the problem (of data drop-outs), an auto regressive integrated moving average (ARIMA) model is implemented. The ability of this model to predict the variations in sequence impedances is demonstrated.
In this thesis, two popular algorithms, namely, moving-window total least squares (MWTLS) and recursive Kalman filter (RKF) are applied on real data in multiple stages. In the first stage, the algorithms are tested on a purely synthetic dataset. This is followed by testing done on pseudo-synthetic datasets generated using real PMU data. In the final stage, the algorithms are implemented on the real PMU data obtained from a local utility. The results show that in the context of the given problem, RKF has better performance than MWTLS. Furthermore, to improve the performance of RKF on real data, ASPEN data are used to calculate the initial estimates. The estimation results show that the RKF algorithm can reliably estimate the sequence impedances, using ASPEN data as a starting condition. The estimation procedure is repeated over different time periods and the corresponding results are presented.
Finally, the significance of data drop-outs and its impact on the use of parameter estimates for real-time power system applications, such as state estimation and dynamic line rating, is discussed. To address the problem (of data drop-outs), an auto regressive integrated moving average (ARIMA) model is implemented. The ability of this model to predict the variations in sequence impedances is demonstrated.
ContributorsMansani, Prashanth Kumar (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith E. (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2018
Description
On-line dynamic security assessment (DSA) analysis has been developed and applied in several power dispatching control centers. Existing applications of DSA systems are limited by the assumption of the present system operating conditions and computational speeds. To overcome these obstacles, this research developed a novel two-stage DSA system to provide periodic security prediction in real time. The major contribution of this research is to develop an open source on-line DSA system incorporated with Phasor Measurement Unit (PMU) data and forecast load. The pre-fault prediction of the system can provide more accurate assessment of the system and minimize the disadvantage of a low computational speed of time domain simulation.
This Thesis describes the development of the novel two-stage on-line DSA scheme using phasor measurement and load forecasting data. The computational scheme of the new system determines the steady state stability and identifies endangerments in a small time frame near real time. The new on-line DSA system will periodically examine system status and predict system endangerments in the near future every 30 minutes. System real-time operating conditions will be determined by state estimation using phasor measurement data. The assessment of transient stability is carried out by running the time-domain simulation using a forecast working point as the initial condition. The forecast operating point is calculated by DC optimal power flow based on forecast load.
This Thesis describes the development of the novel two-stage on-line DSA scheme using phasor measurement and load forecasting data. The computational scheme of the new system determines the steady state stability and identifies endangerments in a small time frame near real time. The new on-line DSA system will periodically examine system status and predict system endangerments in the near future every 30 minutes. System real-time operating conditions will be determined by state estimation using phasor measurement data. The assessment of transient stability is carried out by running the time-domain simulation using a forecast working point as the initial condition. The forecast operating point is calculated by DC optimal power flow based on forecast load.
ContributorsWang, Qiushi (Author) / Karady, George G. (Thesis advisor) / Pal, Anamitra (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2017
Description
After a major disturbance, the power system response is highly dependent on protection schemes and system dynamics. Improving power systems situational awareness requires proper and simultaneous modeling of both protection schemes and dynamic characteristics in power systems analysis tools. Historical information and ex-post analysis of blackouts reaffirm the critical role of protective devices in cascading events, thereby confirming the necessity to represent protective functions in transient stability studies. This dissertation is aimed at studying the importance of representing protective relays in power system dynamic studies. Although modeling all of the protective relays within transient stability studies may result in a better estimation of system behavior, representing, updating, and maintaining the protection system data becomes an insurmountable task. Inappropriate or outdated representation of the relays may result in incorrect assessment of the system behavior. This dissertation presents a systematic method to determine essential relays to be modeled in transient stability studies. The desired approach should identify protective relays that are critical for various operating conditions and contingencies. The results of the transient stability studies confirm that modeling only the identified critical protective relays is sufficient to capture system behavior for various operating conditions and precludes the need to model all of the protective relays. Moreover, this dissertation proposes a method that can be implemented to determine the appropriate location of out-of-step blocking relays. During unstable power swings, a generator or group of generators may accelerate or decelerate leading to voltage depression at the electrical center along with generator tripping. This voltage depression may cause protective relay mis-operation and unintentional separation of the system. In order to avoid unintentional islanding, the potentially mis-operating relays should be blocked from tripping with the use of out-of-step blocking schemes. Blocking these mis-operating relays, combined with an appropriate islanding scheme, help avoid a system wide collapse. The proposed method is tested on data from the Western Electricity Coordinating Council. A triple line outage of the California-Oregon Intertie is studied. The results show that the proposed method is able to successfully identify proper locations of out-of-step blocking scheme.
ContributorsHedman, Mojdeh Khorsand (Author) / Vittal, Vijay (Thesis advisor) / Ayyanar, Raja (Committee member) / Pal, Anamitra (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2017
Description
The holomorphic embedding method (HEM) applied to the power-flow problem (HEPF) has been used in the past to obtain the voltages and flows for power systems. The incentives for using this method over the traditional Newton-Raphson based nu-merical methods lie in the claim that the method is theoretically guaranteed to converge to the operable solution, if one exists.
In this report, HEPF will be used for two power system analysis purposes:
a. Estimating the saddle-node bifurcation point (SNBP) of a system
b. Developing reduced-order network equivalents for distribution systems.
Typically, the continuation power flow (CPF) is used to estimate the SNBP of a system, which involves solving multiple power-flow problems. One of the advantages of HEPF is that the solution is obtained as an analytical expression of the embedding parameter, and using this property, three of the proposed HEPF-based methods can es-timate the SNBP of a given power system without solving multiple power-flow prob-lems (if generator VAr limits are ignored). If VAr limits are considered, the mathemat-ical representation of the power-flow problem changes and thus an iterative process would have to be performed in order to estimate the SNBP of the system. This would typically still require fewer power-flow problems to be solved than CPF in order to estimate the SNBP.
Another proposed application is to develop reduced order network equivalents for radial distribution networks that retain the nonlinearities of the eliminated portion of the network and hence remain more accurate than traditional Ward-type reductions (which linearize about the given operating point) when the operating condition changes.
Different ways of accelerating the convergence of the power series obtained as a part of HEPF, are explored and it is shown that the eta method is the most efficient of all methods tested.
The local-measurement-based methods of estimating the SNBP are studied. Non-linear Thévenin-like networks as well as multi-bus networks are built using model data to estimate the SNBP and it is shown that the structure of these networks can be made arbitrary by appropriately modifying the nonlinear current injections, which can sim-plify the process of building such networks from measurements.
In this report, HEPF will be used for two power system analysis purposes:
a. Estimating the saddle-node bifurcation point (SNBP) of a system
b. Developing reduced-order network equivalents for distribution systems.
Typically, the continuation power flow (CPF) is used to estimate the SNBP of a system, which involves solving multiple power-flow problems. One of the advantages of HEPF is that the solution is obtained as an analytical expression of the embedding parameter, and using this property, three of the proposed HEPF-based methods can es-timate the SNBP of a given power system without solving multiple power-flow prob-lems (if generator VAr limits are ignored). If VAr limits are considered, the mathemat-ical representation of the power-flow problem changes and thus an iterative process would have to be performed in order to estimate the SNBP of the system. This would typically still require fewer power-flow problems to be solved than CPF in order to estimate the SNBP.
Another proposed application is to develop reduced order network equivalents for radial distribution networks that retain the nonlinearities of the eliminated portion of the network and hence remain more accurate than traditional Ward-type reductions (which linearize about the given operating point) when the operating condition changes.
Different ways of accelerating the convergence of the power series obtained as a part of HEPF, are explored and it is shown that the eta method is the most efficient of all methods tested.
The local-measurement-based methods of estimating the SNBP are studied. Non-linear Thévenin-like networks as well as multi-bus networks are built using model data to estimate the SNBP and it is shown that the structure of these networks can be made arbitrary by appropriately modifying the nonlinear current injections, which can sim-plify the process of building such networks from measurements.
ContributorsRao, Shruti Dwarkanath (Author) / Tylavsky, Daniel J (Thesis advisor) / Undrill, John (Committee member) / Vittal, Vijay (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2017
Description
The penetration of renewable energy in the power system has grown considerably in the past few years. While this use may come with an abundance of advantages, it also introduces new challenges in operating the 100+ years old electrical network. Fundamentally, the power system relies on a real-time balance of generation and demand. However, renewable resources such as solar and wind farms are not available throughout the day. Furthermore, they introduce temporal variability to the generation process due to metrological factors, making the balance of generation and demand precarious. Utilities use standby units with reserve power and high ramp-up, ramp-down capabilities to ensure balance. However, such solutions can be very costly. An accurate scenario generation and forecasting of the stochastic variables (load and renewable resources) can help reduce the cost of these solutions. The goal of this research is to solve the scenario generation and forecasting problems using state-of-the-art machine learning techniques and algorithms. The training database is created using publicly available data obtained from NREL and the Texas-2000 bus system. The IEEE-30 bus system is used as the test system for the analysis conducted here. The conventional generators of this system are replaced with solar farms and wind farms. The ability of four machine learning algorithms in addressing the scenario generation and forecasting problems are investigated using appropriate metrics.
The first machine learning algorithm is the convolutional neural network (CNN). It is found to be well-suited for the scenario generation problem. However, its inability to capture certain intricate details about the different variables was identified as a possible drawback. The second algorithm is the long-short term memory-variational auto-encoder (LSTM-VAE). It generated scenarios that are very similar to the actual scenarios indicating that it is suitable for solving the forecasting problem. The third algorithm is the conditional generative adversarial network (C-GAN). It was extremely effective in generating scenarios when the number of variables were small. However, its scalability was found to be a concern. The fourth algorithm is the spatio-temporal graph convolutional network (STGCN). It was found to generate representative correlated scenarios effectively.
ContributorsAlhazmi, Mohammed Ahmed (Author) / Pal, Anamitra (Thesis advisor) / Ayyanar, Raja (Committee member) / Holbert, Keith (Committee member) / Arizona State University (Publisher)
Created2021
Description
Brushless DC (BLDC) motors are becoming increasingly common in various industrial and commercial applications such as micromobility and robotics due to their high torque density and efficiency. A BLDC Motor is a three-phase synchronous motor that is very similar to a non-salient Permanent Magnet Synchronous Motor (PMSM) with key differences lying in the non-ideal characteristics of the motor; the most prominent of these is BLDC motors have trapezoidal-shaped Back-Electromotive Force (BEMF). Despite their advantages, a present weakness of BLDC motors is the difficulty controlling these motors at standstill and low-speed conditions that require high torque. These operating conditions are common in the target applications and almost always necessitate the use of external sensors which introduce additional costs and points of failure. As such, sensorless based methods of position estimation would serve to improve system reliability, cost, and efficiency. High Frequency (HF) pulsating voltage injection in the direct axis is a popular method of sensorless control of salient-pole Interior-mount Permanent Magnet Synchronous Motors (IPMSM); however, existing methods are not sufficiently robust for use in BLDC and small Surface-mount Permanent Magnet Synchronous Motors (SPMSM) and are accompanied by other issues, such as acoustic noise. This thesis proposes novel improvements to the method of High Frequency Voltage Injection to allow for practical use in BLDC Motors and small SPMSM. Proposed improvements include 1) a hybrid frequency generator which allows for dynamic frequency scaling to improve tracking and eliminate acoustic noise, 2) robust error calculation that is stable despite the non-ideal characteristics of BLDC Motors, 3) gain engineering of Proportional-Integral (PI) type Phase-Locked-Loop (PLL) trackers that further lend stability, 4) observer decoupling mechanism to allow for seamless transition into state-of-the-art BEMF sensing methods at high speed, and 5) saliency boosting that allows for continuous tracking of saliency under high torque load. Experimental tests with a quadrature encoder and torque efficiency calculations on a dynamometer verify the practicality of the proposed algorithm and improvements.
ContributorsYin, Kai (Author) / Vrudhula, Sarma (Thesis advisor) / Chickamenahalli, Shamala (Thesis advisor) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2021