Open Theses & Dissertations

Permanent URI for this collection

https://unbscholar.lib.unb.ca/handle/1882/12778

Browse

Now showing 1 - 13 of 13

A battery storage system to support the frequency stability of grid-connected PMG-based wind energy conversion systems
(University of New Brunswick, 2019) St-Onge, Xavier F.; Saleh, Saleh
Over the past few decades, the generation of electric power has become concerning for the environment. As a result, several changes to power systems have been introduced. Among these changes, is the significant integration of distributed generation units (DGUs). For, DGUs strive to offset conventional generation by utilizing renewable energy. Largely, this approach to generation has trended toward wind systems. Modern wind-energy-conversion-systems (WECSs) have favored variable speed permanent magnet generator (PMG)-based topologies. The relative novelty of PMG-based WECSs has emphasized challenges that have limited their applicability. Of these challenges, PMG electromechanical torque pulsations and point-of-common coupling (PCC) frequency instability are regularly regarded as the most troublesome. On one hand, PCC frequency variations are dependent on the stable delivery of power by the interconnected WECS. On the other hand, generator torque pulsations are a consequence of the extensive use of power electronic converters (PECs) in PMG-based WECSs. As of late, energy storage systems (ESSs) and novel PEC technologies are being recommended to overcome these challenges. This thesis aims to develop and evaluate a split-bus PMG-based WECS. The developed WECS includes a generator-charged and PCC-discharged battery storage system (BSS) to support PCC frequency stability, as well as a modified cascaded H-bridge (MCHB) generator-side PEC to reduces PMG torque pulsations. The developed system is modeled in simulation and constructed in laboratory. Several operating conditions of wind speed, power command, and BSS charging are investigated. Experimental performance highlights the developed WECS’s ability in suppressing PMG torque pulsations while minimizing PCC frequency variation.
Automation of the Timed Up and Go test using an instrumented walking cane
(University of New Brunswick, 2021-10) Valsangkar, Ameya; Scheme, Erik
The Timed Up and Go (TUG) test is used to test a person’s mobility and static and dynamic balance. It measures the time a person takes to stand up from a chair, walk three meters, turn around, walk back to the chair, and sit down. Typically, the TUG test is assessed by a physiotherapist with a stopwatch, limiting its effectiveness and making it prone to user error. This has motivated research into automated approaches capable of assessing the various segments of the TUG test using a range of sensing modalities. This study extends upon this body of work by evaluating the feasibility of segmenting the TUG test using an instrumented walking cane. More general contributions are made by introducing the use of error in transition time, as opposed to accuracy, as the cost function during the design of the machine learning framework, and a time-series inspired binary segmentation approach that facilitates the comparison of only two segments at a time. Data was collected using an instrumented cane that measures loading and movement information from 16 participants with musculoskeletal injuries. As a group, the participants yielded TUG times ranging from 11.12s to 28.57s, and a mean of 17.8s. Results of segmenting the TUG test into six segments - Sitting to standing, Walking, Turning, Walking back, Turning back, Standing to sitting - were validated using a leave-one-trial-out and a leave-one-person-out approach, to test both within- and across-participant performance. Various approaches were explored, including conventional classifiers Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM), and extended time series and deep learning methods such as Hidden Markov Models (HMM), CNN LSTM (CLSTM) and Encoder-Decoder Temporal Convolutional Networks (EDTCN). A binary segmentation approach leveraging the temporal nature of the TUG test was adopted with a Dynamic Time Warping (DTW)-based postprocessing alignment. The calculated segmentation error for every case was recorded as both the performance measurement and the optimization parameter as opposed to the traditional use of accuracy of prediction. The results promisingly suggest that the segments or subtasks of a TUG test can be extracted using data collected from a smart cane, laying the groundwork for its automation.
Compact, low-cost pulse generator and embedded computing platform for distributed sensing
(University of New Brunswick, 2021-12) Selby, Quinn; Colpitts, Bruce
This thesis presents a number of improvements to the Brillouin optical time-domain analysis temperature and strain sensing system. A compact, low-cost ultrawideband pulse generator with variable pulse width was designed, simulated, built and tested in a series of BOTDA measurements, demonstrating sufficient specifications to drive any electro-optic modulator typically used in this application. Additionally, hardware and software was developed for a compact embedded computing platform capable of acquiring and processing BOTDA measurements. The complete design was then evaluated according to a number of performance metrics, including spatial resolution, frequency resolution, measurement time, and more.
Current magnitude based compensation of dead time in DCM for grid-connected bridge inverters
(University of New Brunswick, 2018) Spence, Katelin; Chang, Liuchen
Grid-connected inverters are becoming very common with the rise of distributed generation and residential photovoltaic systems. The most common type of inverter used in these applications is a full bridge voltage source inverter (VSI); which can be either single-phase or three-phase. One overlooked way to improve their performance is by reducing harmonics caused by dead time, by compensating discontinuous conduction mode (DCM) caused by dead time. While compensation methods have been developed for dead time during continuous conduction, limitations of these methods mean DCM is not addressed by most. This thesis work details a method to modify the command signals sent to inverter switching elements in the zero-crossing regions where DCM is likely to occur. The modified signals are able to incrementally change the duty cycle of these switching elements so that there is no effect from DCM; while maintaining the dead time that is necessary to prevent a switching overlap. This method eliminates the need for current polarity dependent compensation which allows DCM to be addressed. The method requires input measurements of current and voltage that are easily measured and fed to a digital signal processor (DSP), where the command signal will be calculated. The method was successfully simulated in PSim 11.0 for several switching schemes and tested experimentally on a 10kW single-phase wind and solar inverter for two switching schemes. Experimental results show that the proposed method works to reduce the output current total harmonic distortion (THD) in multiple different switching schemes by eliminating the discontinuity in the output current. Distortions that appear on the bridge voltage were also compensated, which is shown by simulated results.
Data-driven approaches to reducing the training burden in pattern recognition based myoelectric control
(University of New Brunswick, 2020) Campbell, Evan David; Scheme, Erik
Advancements in EMG pattern recognition have enabled intuitive gesture recognition of upper limb motions for use in human computer interfaces. Whether it be to control a prosthesis, a drone, or a virtual reality environment, all current implementations rely on the acquisition of representative training data from the end-user before use. This requirement has been reported as frustrating for users and a limitation to the broader adoption of EMG-based controllers. Consequently, this thesis aims to address the burden of the training protocol for EMG pattern recognition using data-driven approaches and cross-user models. Two exploratory studies were conducted to assess the impact and degree of inter-subject variability and difference in EMG information content between user groups. Based on observed subject differences, an adaptive domain adversarial neural network (ADANN) was subsequently developed to adapt a previously trained model to a new user using minimal training data. The proposed ADANN cross-subject model significantly outperformed the current state-of-the-art canonical correlation analysis (CCA) cross-subject model for both intact-limb and amputee populations (with 9.4% and 22.4% absolute improvement, respectively). Finally, a generative adversarial network architecture, SinGAN, was adopted as a novel alternative for reducing the amount of EMG data needed for training. SinGAN was able to generate synthetic EMG signals based on a single subject-supplied motion repetition, significantly improving accuracy compared to training with the single motion alone.
Evaluation of the real-time usability of force myography as a human-computer interface
(University of New Brunswick, 2018) Belyea, Alexander; Scheme, Erik; Englehart, Kevin
Force myography (FMG) is an alternative to electromyography (EMG) for the control of powered upper limb prostheses. FMG signals originate from deformations of muscles and surrounding tissue applying pressure to a force sensor. FMG-based pattern recognition classifiers have been shown to yield high classification rates. High classification accuracy, however, does not ensure great device usability. Instead, these control systems for prostheses should be evaluated based on real-time usability metrics. In the first section of this work a proportional control algorithm, critical to the completion of the second phase of work, was derived and compared to a mean signal amplitude-based approach. In the second, the real-time usability of high-density force myography (HDFMG) was compared to that of EMG in a Fitts’ Law virtual target acquisition task. FMG was found to significantly outperform EMG in throughput for both classification (0.901±0.357 bits/s versus 0.751±0.309 bits/s) and regression (0.871±0.325 bits/s versus 0.689±0.269 bits/s) control types. The evaluated regression-based proportional control algorithm also performed significantly better (ρ<0.001) than a standard mean signal amplitude-based approach. Subsequent data collection from an amputee subject achieved comparable classification accuracy to the able-bodied participants, but an R2 correlation coefficient of only 0.375 for regression based proportional control, significantly (ρ<0.001) lower than the able-bodied results. This work provides a comparison between the real-time usability of HD-FMG and EMG-based control in both a traditional classification-based pattern recognition scheme, with an additional proportional controller dictating device velocity and a regression-based control scheme. HD-FMG was shown to outperform EMG in both control schemes in both throughput and efficiency.
Flexible control strategies using FACTS schemes for motor drives and smart grid applications
(University of New Brunswick, 2015) Elbakush, Emhemed; Sharaf, Adel; Diduch, Chris
This research investigates the use of flexible alternating current transmission systems (FACTS) with new renewable energy interface schemes and control strategies, in order to explore renewable energy and energy effcient utilization and voltage stabilization. It explores mechanisms to stabilize integrated renewable energy schemes using new FACTS power filter compensators, which ensure stabilized voltage, limited inrush current conditions and transient voltages, and damped load excursions. DC and AC motors are used in electric vehicle (EV) drives fed from alternative/renewable sources, including tidal, wind, wave, photovoltaic (PV), fuel cell (FC), micro gas turbine as well as hybrid renewable energy source combinations. This research investigates new interface topologies and dynamic fast-acting control strategies for effcient utilization of renewable energy sources such as wind, PV, FC, biogas, and hybrid energy sources. It explores the use of FACTS-based power electronic interface converters including a family of switched power filters/capacitive compensation schemes with error-driven multi-loop, time-descaled control strategies for effcient utilization of DC-AC interface systems with flexible and robust control strategies to ensure maximum energy utilization, voltage stabilization, and improved power factor and power quality for electric vehicles, battery charging stations, and hybrid renewable energy utilization. The main objectives of the thesis are to develop and validate a number of new power electronic switching filter compensator topologies for power generation, electric vehicles, and battery charging to improve power quality, reduce total harmonic distortion, decrease AC and DC side inrush currents, and provide AC and DC side voltage stabilization. The new extended family of FACTS-based switched filter compensators (SFC) and flexible control schemes includes the DC-side green power filter compensators (GPFC), SFC, neutral point switched filter compensators (NP-SFC), and hybrid switched filter compensators (HSFC), which can effectively stabilize DC and AC Bus voltages, by reducing inrush current conditions, transient voltages under hybrid source changes, load excursions, and hence ensuring energy effcient utilization. The research covers renewable AC/DC systems with AC/DC FACTS-based filter capacitor compensation family of extended power electronic devices and converters including GPFC, SFC, NP-SFC, and HSFC schemes, smart grid AC/DC renewable energy schemes with modified dynamic control strategies for electric vehicles, vehicle-to-home (V2H)/vehicle-to-grid (V2G) battery local and utility stations, and hybrid renewable energy PV-FC-battery energy utilization in motor drives, village/island and micro grid renewable/alternative energy utilization. The DC-AC interface schemes for DC and AC EV-drives, V2H/V2G battery chargers and hybrid renewable energy utilization systems with flexible control strategies are digitally simulated and validated using the MATLAB® /Simulink® /SimPowerSystems® environment.
Frequency hopping spread spectrum harmonic radar
(University of New Brunswick, 2014) Alfarra, Omar; Colpitts, Bruce
Portable pulsed harmonic radar systems were built at UNB to track the movement of Colorado potato beetles. These systems use a high power marine magnetron to produce a microwave pulse and it is desired to upgrade the system using lower cost and low power electronics. This thesis is an investigation of an alternative strategy. A Stepped Frequency Continuous Wave Frequency Hopping Spread Spectrum harmonic radar (SFCW-FHSS) was proposed to replace the conventional pulsed harmonic radar system. A mathematical model for the new system is presented and its performance was determined. MATLAB was used to investigate the model and a prototype was constructed and tested. From this, both performance and cost of the spread spectrum design was determined for comparison to the original system. It was found that this laboratory SFCW-FHSS harmonic radar prototype achieved the lower cost and lower power goal but it was only able to detect a signal from a tag that was 4 m away or less.
Impacts of smart grid functions on load-side frequency
(University of New Brunswick, 2019) Wo, Jeffrey W. G; Saleh, Saleh; C.G, Eduardo
Several power systems have made notable progress in implementing smart grid functions as means to achieve load-side control activities. These new control approaches have shown promising potentials to reshape and optimize patterns of energy supply and demand in industrial, commercial, and residential loads. Demand response, peakload management, and direct load control are recognized as smart grid functions that aim to improve the power system efficiency, system reliability, and increase levels of sustainable energy generation and distribution. Several research directions and new business models have been introduced to facilitate and improve the implementation and deployment of smart grid functions in power systems. In general, the fundamental objective of smart grid functions is to regulate the electric power delivered to a set of loads or a set of load centers, where the principal focus is on the active power. Such an objective can introduce new challenges for the operation, control, dynamic conditions, and stability of the host power system. These challenges are centered around the frequency dynamics and margins of frequency stability. As a result, new methods and procedures are sought to carry out accurate assessment of possible impacts from smart grid functions on frequency dynamics and frequency stability margins. The successful development of such methods and procedures will allow setting stable ranges for the load-side control activities, which are associated with smart grid functions. This thesis aims to develop, implement, and test a new method for estimating the frequency changes at load buses that are subject to smart grid functions. The estimation of frequency changes is sought to provide stable ranges for the regulation of load power demands, as carried out by smart grid functions. The developed method is based on utilizing the ZIP model that is capable of providing a relationship between load power demands and frequency changes. Such a relationship can be formulated as a set of nonlinear equations, which can be numerically solved to estimate the frequency changes. The employment of the ZIP model is justified by the implementation of smart grid functions, which require the use of load models. The developed loadmodel based method is implemented for performance evaluation using the Barbados power system, which has 118 buses. The developed method is demonstrated to be accurate, simple, and insensitive to durations and/or magnitudes of power demand changes.
Islanding detection based on measuring impedance at the point of common coupling
(University of New Brunswick, 2015) Yazdkhasti, Pegah; Diduch, Christopher
Detecting and preventing islanding of distributed generators in an electric power system is required to reduce the hazards and risks of damage to equipment and injury to personnel. This thesis will develop a new approach to islanding detection based on computing the frequency dependent impedance at the point of common coupling. To this aim, a frequency dependent model is developed to characterize the change in the circuit interconnection topology when islanding occurs. This approach is used as a basis for selecting features from impedance that distinguish islanding from normal operation. The method is investigated by simulation and the results is verified using an experiment on an inverter-less system. This approach can reduce the non-detection zone and detects the island.
On the feasibility of using pattern recognition based myoelectric control as a human-computer interface for individuals with paralysis
(University of New Brunswick, 2016) Chaulk, Mitchell Charles; Scheme, Erik; Englehart, Kevin
Human-computer interfaces (HCIs), using electromyogram (EMG) data for control, has been studied for decades as a potential means of restoring functional ability to amputees. Often, these HCIs are used to control a powered prosthesis. However, this technology has potential application outside of the scope of prosthetics. The EMG produced by people with neurological damage could contain enough discriminatory information to distinguish between many classes of motion, including those that they cannot functionally perform. In this study, 10 individuals with spinal cord injuries (SCIs) around the C3-C6 level (ASIA A-C) volunteered to have their EMG studied while performing 10 different classes of motion with their dominant upper limb. Preliminary studies, using high-density EMG, were performed on two volunteers before moving on to using an electrode cuff with 8 bipolar channels. Performing pattern recognition, for the 10 classes, using an LDA classifier referencing 5 features (sample entropy, mean absolute value, zero crossings, slope sign change, and wave length) resulted in a total mean accuracy of 91.5%. This accuracy was increased to 98.0% when evaluating a set of 5 classes. These 5 classes were chosen based on the classes available by the Bioness H200 device, which uses functional stimulation to force user contractions. Such a device could benefit from an accurate EMG controller.
Scalable local short-term energy consumption forecasting
(University of New Brunswick, 2019) Buckler, Jay Daniel; S. Ray; E. Castillo-Guerra
Smart meter adoption rates are increasing globally and this has contributed to a rapid increase in the type and volume of data: communication, storage, and processing. These recent advances have created new opportunities for smart grid research, particularly in developing effective methods for processing big data. As power system industry moves towards adapting and implementing smart grid functions, energy demands forecasting is mandated at the distribution level to ensure the balance between energy supply and demand. Unlike system-level forecasting, short term energy demand forecasting at the distribution level needs to be highly scalable, due to the needs for collecting and processing energy demand data for a significant number of loads over a short time. This scalability requirement is magnified if the distribution level forecasting is to be performed centrally where system-level forecasting is being performed. In order to address these challenges, this thesis conducts a systematic study of the scalability and performance of time series forecasting techniques on smart meter data for distribution level short-term energy consumption. The conducted study is based on strategies to parallelize standard and online forecasting algorithms. The developed strategies are converted into algorithms to be implemented for performance evaluation. The performance of these algorithms is evaluated using data collected from several loads during different seasons. Test results demonstrate the challenges of including seasonality terms, and model training when using ARIMA based times series forecasting. Additional results show that the online algorithm achieves better scalability and shorter execution times when compared to the standard ARIMA implementation.
The development of survivability analysis for power systems
(University of New Brunswick, 2021) Chowdhury, Muhammad Rashedul Alam; Saleh, Saleh
The structural and operational natures of power systems make these systems prone to experience various types of transient events. Such undesired events include the loss of generation units, loss of transmission lines, load rejections, sudden and abrupt changes in load power demands, equipment failures, etc. The impacts of transient events start by creating frequency dynamics that can disrupt the generation, transmission and distribution of electric power in any power system. The conventional approaches to mitigate and damp frequency dynamics are set to restore the balance between power generation and demands so that a power system can regain a steadystate condition post any transient event. In general, the conventional approaches are usually designed and operated based on power system dynamics and stability analyses. These analyses are conducted with the assumption that frequency dynamics can be responded to by actions initiated by primary, secondary, or tertiary frequency controllers. The dynamics and stability analyses of power systems are widely used to design power system stabilizers, operate frequency controllers, and select settings for protective device. Conventional responses to frequency dynamics restore the balance between power generation and demands by either adjusting power generation (governor control), changing inter-area power exchange, or disconnecting loads. Such responses have shown a good capacity to effectively damp frequency dynamics and regain steadystate conditions in power systems. However, the recent trends of operating power systems have been shifting towards the de-regulated operation, which can offer integrating distributed generation units and deploy load-side control actions. Despite its advantages, the de-regulated operation of power systems faces several challenges, including the frequency dynamics. This challenge is created by the bi-directional power flows of load buses, as well as load-side control actions that may alter the active power injection into load buses. Frequency dynamics created by such activities can be difficult to damp using conventional responses. This difficulty is due to the fact that the disconnection of loads can (as a response) worsen the frequency dynamics. As a result, new responses are mandated to damp frequency dynamics created by load-side control activities. This thesis develops and tests a survivability-based method to model and analyze the impacts of load-side activities on frequency dynamics in power systems. The developed method introduces a survivability index to quantify the ability of a load bus to regain a steady-state condition, after experiencing a load-side activity. The survivability index is defined as the difference between pre-activity and post-activity power injection into a load bus. The boundary values of the survivability index are also defined in this thesis, and they are used to specify energy storage systems to enhance the survivability of certain load buses. The survivability-based method is implemented as a stand-alone software tool, and is being used by Barbados Power System. Several tests are conducted for integrating solar units and implementing demand response at several load buses. Test results show that the survivability index can accurately quantify the ability of load buses to regain steady-state conditions, and damp frequency dynamics created by load-side activities. Moreover, the survivability index is used to specify battery storage units for load buses with narrow survivability margins. Finally, test results demonstrate that the validity and accuracy of the survivability-based method is not affected by the ratings of integrated solar units and/or times and durations of demand response actions.

Browse

Browsing Open Theses & Dissertations by Subject "Electrical Engineering"

Results Per Page

Sort Options