Browsing by Author "Zhang, Jie"
Now showing 1 - 18 of 18
- Results Per Page
- Sort Options
Item ANN Crowds: Harnessing Collective Wisdom in Design Prediction(December 2023) Adebayo, Oredola Adewale 1993-; Summers, Joshua; Abbas, Waseem; Zhang, JieThis thesis presents and evaluates an approach to early-stage product performance prediction by harnessing the "wisdom of the crowd" embodied in Artificial Neural Network (ANN) Crowds. Unlike traditional crowd wisdom, which leverages responses from large groups of people, this research explores the concept of using 189 distinct ANN architectures, each replicated 100 times, as a collective decision-making entity, thus forming an ANN Crowd. The central inquiry in this study revolves around the notion of whether every agent within the ANN Crowd should possess an equal influence. To address this question, the research conducts a comprehensive exploration of the sensitivity of key influencing factors, including training set selection, the configuration of nodes and layers, and architectural attributes, on the performance of the ANN Crowd. This investigation aims to refine the ANN Crowd, making it more universally applicable. The first aspect explores the impact of training set selection in predictive accuracy. The results clearly demonstrate that training set selection has a statistical and practical influence prediction accuracy, especially when it includes edge cases. This finding provides a crucial guideline for decision-makers, advocating for the strategic inclusion of challenging examples in training sets to improve predictive accuracy. The second facet investigates the complex interplay of architectural attributes within the ANN Crowd. By categorizing architectural attributes based on Normality, Centrality, and Width, the analysis shows that the number of nodes within architectural configurations does not have a statistically significant impact on prediction accuracy. This challenges the conventional belief that complexity leads to improved performance, providing practical insights for architectural design. In conclusion, this research offers valuable insights into the predictive capabilities of ANN Crowds. It extends practical implications to engineering design and decision-making processes, positioning ANN Crowds as a vital tool across diverse industries. The findings and guidelines provide a foundation for data-driven practices, enhancing efficiency, and adding value to businesses. Acknowledging its limitations, this research paves the way for future work, encompassing a broader range of datasets, architectural factors, and validation studies.Item Battery Thermal Management System for Electric Vehicles: Design, Optimization, and Control(2021-12-01T06:00:00.000Z) Liu, Yuanzhi; Zhang, Jie; Du, Dingzhu; Fahimi, Babak; Koeln, Justin; Dai, XianmingWe are witnessing a fast-growing demand in vehicle electrification nowadays due to the widespread environmental consciousness, stringent emission regulations, and carbon neutrality implementation. As one of the most promising energy storage and electrification solutions, lithium-ion battery has been widely employed for electric vehicles (EVs) due to its excellent properties like high energy density, low maintenance, and long cycle life. However, there still exist multiple critical challenges in using lithium-ion battery at large scale as the major power source, such as reliability issues, safety concerns, and especially the range anxiety. Several promising solutions have been explored in the EV industry to mitigate the drawback of range anxiety, such as larger capacity with high energy density and ultra-fast charging. All these approaches challenge the temperature sensitive battery system as a side effect by bringing in extra overburdened waste heat. Given these concerns, battery thermal management system (BTMS) plays an indispensable role in maintaining the maximum temperature and temperature uniformity for EVs. This dissertation proposes a novel J-type air-based cooling structure via re-designing conventional U- and Z- type structures. Aiming to further improve the thermal performance, a surrogate-based optimization framework with two-stage cluster-based resampling is developed for BTMS structural optimization. Compared with the U- and Z- type, the novel J-type structure is proved with significant advancements. Based on the optimized J-type configuration, an operation mode switching module is designed to mitigate the temperature unbalance by controlling the opening degree of two outlet valves. Tested by an integrated driving cycle, results reveal that the J-type structure with its appropriate control strategy is a promising solution for light-duty EVs using an air cooling technology. Improving the energy efficiency is another potential approach to mitigate range anxiety. In this dissertation, a model predictive control (MPC)-based energy management strategy is developed to simultaneously control the BTMS, the air conditioning system, and the regenerative power. A vehicle velocity forecasting framework is integrated with the MPC-based energy management to further improve the energy efficiency. Deep learning and image-based traffic light detection techniques have been leveraged for velocity forecasting. Results show that the proposed energy management method has significantly improved the overall EV energy efficiency.Item Break-Even Analysis of Battery Energy Storage in Buildings Considering Time-Of-Use Rates(IEEE Computer Society) Sun, Mucun; Chang, Chih-Lun; Zhang, Jie; Mehmani, A.; Culligan, P.; Sun, Mucun; Chang, Chih-Lun; Zhang, JieAs energy consumption in residential and commercial buildings continues to grow, demand-side management (DSM) for energy systems becomes crucial, because DSM can shift energy use from peak to off-peak hours. In order to realize peak load shifting, energy storage systems (ESSs) can be integrated into buildings to store energy during off-peak hours and discharge energy in peak hours. However, installing a large number of ESSs in individual buildings can complicate DSM and increase the overall capital cost. In this paper, a cost-effective DSM strategy is proposed to address this energy management challenge. The break-even cost of battery storage in a building is explored through a process of two-step optimization in conjunction with different tariff structures. A number of scenarios are performed to conduct cost analyses of the storage-based building energy system under different time-of-use rate structures. The performance of the DSM strategy in the battery break-even cost, is explored using a particle swarm optimization algorithm based on the size of energy storage and priced-based constraints of the energy system. Results of a case study show that the proposed approach can reduce the peak-to-average ratio of the total energy demand to the total energy costs. In addition, as the percentage reductions in yearly maximum energy peaks increase, the optimal battery cost becomes progressively more economical to building owners.Item Data-enhanced Stochastic Dynamical Modeling for Wind Farms(December 2022) Bhatt, Aditya H. 1997-; Zare, Armin; Leonardi, Stefano; Rotea, Mario A.; Zhang, JieLow-fidelity analytical models of turbine wakes have traditionally been used to demonstrate the utility of advanced control algorithms in increasing the annual energy production of wind farms. In practice, however, it remains challenging to achieve significant performance improvements using closed-loop strategies that are based on conventional low-fidelity models. This is due to the over-simplified static nature of wake predictions from models that are agnostic to the complex aerodynamic interactions among turbines. In this thesis, we offer a stochastic dynamical modeling framework to improve the predictive capability of low-fidelity models while remaining amenable to control design. The framework is capable of capturing the effect of atmospheric turbulence on the thrust force and power generation as determined by the actuator disk concept. In this approach, we use stochastically forced linear models of the turbulent velocity field to augment the analytically computed wake velocity and achieve consistency with higher-fidelity models in capturing power and thrust force measurements. The power-spectral densities of our stochastic models are identified via convex optimization to ensure statistical consistency while preserving model parsimony. We demonstrate the utility of our approach in estimating the thrust force and power signals generated by large- eddy simulations of the flow over a cascade of turbines. We also evaluate the capability of our models in predicting turbulence intensities at the hub height of a multi-turbine wind farm.Item Design a J-Type Air-Based Battery Thermal Management System Through Surrogate-Based Optimization(Elsevier Ltd, 2019-06-11) Liu, Yuanzhi; Zhang, Jie; Liu, Yuanzhi; Zhang, JieBattery thermal management system is of great importance to the performance and safety of electric vehicles. The conventional U- and Z-type air-based structures may fail to meet the thermal requirements under changing working conditions. This paper proposes a novel J-type air-based battery thermal management system by integrating the U-type and Z-type structures. A comparative parametric study of key design variables and priori optimized structures is first conducted with a newly developed battery electro-thermal model. Based on the parametric analyses, the grouped-channel optimizations are performed using surrogate-based optimization. Results show that there are 35.3%, 46.6%, and 31.18% reduction in temperature rise for U-, Z-, and J-type, respectively. The pros and cons of the J-type structure are further explored by comparing with the optimal U- and Z-type structures. A further J-type optimization regarding the manifold configuration is also conducted to show that the optimal settings of the air-based cooling system vary across working conditions, and the J-type structure is able to be adaptively controlled to satisfy the cooling requirement. Corresponding experiments are also conducted to validate the modeling and optimization results. © 2019 Elsevier LtdItem Effects of Aluminum Plate Residual Stress on Machined-part Distortion(May 2023) Seger, Michael Brett; Malik, Arif; Fumagalli, Andrea; Qian, Dong; Zhang, Jie; Kumar, GoldenDimensional tolerance requirements for high-speed-machined aluminum products continue to tighten due to strong demand for automated assembly of complex monolithic aluminum parts in aerospace and other industries. Understanding the contribution of inherent residual stress in wrought aluminum 7050-T7451 plate, a common alloy in aircraft manufacture, in the distortion of high-aspect-ratio machined monolithic parts is critical but remains problematic. The difficulty stems from the alloy’s low magnitude of residual stress, distributed over relatively large geometries. The numerous prior studies aimed at investigating residual stress effects on machined part distortion, however, suffer from inadequate characterization of the inherent stress field within the wrought material—because of low fidelity issues due to slitting methods of residual stress measurement, confounding effects from machined-layer removal methods, or because of small number of measurements when using neutron diffraction (ND). In this work, inherent residual stress is measured using ND at over 860 locations throughout the volume of a 90.5 mm thick 7050-T7454 aluminum plate having dimensions 399 mm in the longitudinal (rolling) direction and 335 mm in the transverse direction. Unlike prior studies, the ND residual stress field is reconstructed using an iterative stress reconstruction algorithm to ensure a fully compatible and equilibrated 3D field prior to examining its effect on the distortion of a high- aspect-ratio monolithic part. Validation of the equilibrated stress field is accomplished by comparison of corresponding aggregate fields generated by both experimental and simulated slitting techniques. To isolate and study the potential contribution of residual stress on part distortion, an element deletion technique to simulate material removal is performed to avoid confounding with any machining-induced effects. The findings reveal that the inherent residual stress is not negligible, and alone is sufficient to distort a high-aspect-ratio part beyond tolerances necessary to meet current aerospace industry manufacturing requirements (>0.75 mm distortion over 400 mm span). Moreover, the work reveals that a residual stress field developed only from slitting data, per the literature, underrepresents both residual stress and part distortion. The results show that parts created from different locations within the plate thickness can lead to reversed distortion patterns due to the corresponding residual stress induced effects. The research gives insight into fixturing and shimming to compensate for distortion as well as provides an algorithm to further address distortion of the finished part by applying weights to in accordance with industry practices.Item Electrochemical impedance spectroscopy wearable systems for reporting biomarker modulation in sweat(December 2021) Sankhala, Devangsingh; Prasad, Shalini; Bhatia, Dinesh; Zhang, Jie; Balsara, Poras T.; Muthukumar, SriramThe commercial wearable device market today majorly consists of activity trackers and smartwatches: that enable the monitoring of user states such as walking, sleeping, and exercising using sensors relying on physically measurable quantities. These devices are the ones that make a huge impact on the lives of people suffering from chronic illnesses and their quality of life. Integrating a sweat-based electrochemical biosensor with a wearable device opens new avenues in health management and decision support systems for healthcare providers as they can provide a physiologically relevant and clinically acceptable output. Integrating a glucose-sensing sweat biosensor adds more value in the lives of diabetics, who require support in terms of balancing quality of life using good diet and exercise routines. This work is a methodology of understanding the aspects of making such a wearable platform, starting from understanding the needs of the wearable device user population. The current market technology is thoroughly studied to pick relevant aspects and an electronic front end is designed within the bounds of good design practice to enable good accuracy, ease of use, and 1-week battery life. This in turn is utilized to collect human subject data to get an understanding of the performance of the sensors in varying environmental conditions and user states. Finally, mathematical modeling approaches are used to build correlations between the outcome to be presented to the user against change in the recorded data features as per the human subject experimentation.Item EMG-based Finger Movement Classification for Prosthetic Hand Control(December 2022) Azhiri, Reza Bagherian; Nourani, Mehrdad; Overzet, Lawrence; Hassanipour, Fatemeh; Zhang, Jie; Koeln, JustinMillions of people around the world are suffering from the consequences of amputation. In particular, advanced prosthetic hands could retrieve some functionality and improve quality of life of those who lost it. The most challenging part in the prosthetic hands is their finger control. Ideally, when the brain sends the signal for moving a finger, the corresponding finger is detected in a very short time. Therefore, not only the accuracy but time to reach that accuracy should be considered. EMG signal is found as a promising mechanism to control prosthetics. Unfortunately, EMG signal is easily contaminated with noise and various artifacts factors that deteriorates the accuracy of classification. In this research, we addressed key challenges in EMG classification. First, we proposed 5 new features to enhance the accuracy. Second, we advocated EVM as a powerful classifier and compared its performance with other EMG classifiers in the literature. Third, for online classification, we employed various recurrent neural network (RNN) structures, and showed that bidirectional-RNNs with sequential inputs could achieve higher accuracies in a shorter time. Fourth, to make the system robust against noise, we proposed a convolutional neural network feature learning (CNNFL) structure combined with EVM. Fifth, to address the drawback of the Bayesian fusion method, we proposed a novel postprocessing technique. Overall, in this research, we have customized machine learning techniques for EMG analysis that can produce accurate classification in realtime and real world noisy condition.Item Energy Trading in Local Electricity Markets With Distributed Energy Resources(2022-12-01T06:00:00.000Z) He, Li; Nguyen, Tien; Zhang, Jie; Akin, Bilal; Al-dhahir, Naofal; Li, YaoyuThe energy system has been under dramatic transformation in recent years because of the advent of smart grid technologies and the increasing penetration of distributed energy resources (DERs), such as distributed photovoltaic (PV), wind turbines, energy storage systems, electric vehicles (EV), smart appliances, and others. While the increasing penetration of DERs helps the grid decarbonization, it imposes challenges on power system operation and economics. Some regions such as California that possesses the largest PV installation in the U.S., will have to deal with the fast solar capacity expansion. When the sun shines, the systems must make frequent regulations to offset the imbalance between demand and renewable energy production. After sunset, utility companies must rapidly increase other forms of generators to compensate for the loss of solar power. Besides, DERs have also disturbed the traditional electricity market by introducing a larger proportion of flexibility to the demand side. Therefore, it calls for a reconsideration of the economic model for future electricity markets. The concept of local electricity market (LEM) has emerged as a promising trading framework for future smart grids. Different from the wholesale electricity market, LEM enables participants to share their resources such as excess renewable generation, unused energy storage capacity, extra rooftop space, flexible appliances, etc., to other entities who are in shortage. LEM can be leveraged not only to efficiently manage the local supply and demand, but also to decrease the local community’s reliance on the main grid. This dissertation proposes to mitigate the over-generation issues with ever-increasing DERs in foreseeable future by designing and evaluating different LEM architectures considering the characteristics of distributed solar, residential load, multiple market entities/stakeholders, and their interactions. More specifically, in this dissertation LEMs are categorized into two main groups: peer-to-peer (P2P) models and subscription models, to investigate the interactions between different participating entities with various supply/demand and optimization goals. Besides, different pricing strategies are explored, to incentivize local customers to actively manage their energy resources. For P2P markets, we explore potential cooperative and non-cooperative energy sharing and trading strategies among prosumers and consumers. For subscription markets, unique and discriminated dynamic pricing strategies that take in account of customers’ different consumption flexibility with centralized ES are developed. In addition, different data availability and privacy concerns in the LEMs are also investigated. Experimental results indicate that the proposed LEM schemes are beneficial and efficient, which are practical and supportive for the future grid decentralization and decarbonization with the capacity expansion of DERs.Item Evacuated Tube Solar Collector Integrated with Multifunctional Absorption-Storage Materials(2017-05) Sobhansarbandi, Sarvenaz; Hassanipour, Fatemeh; Zakhidov, Anvar; Fadda, Dani; Ashuri, Turaj; Zhang, JieSolar water heaters (SWHs) are a well-established renewable energy technology that have been widely adopted around the world. This work presents a novel method of integrating Phase Change Materials (PCMs) within the evacuated solar tube collectors for solar water heaters (SWHs). In this method, the heat pipe is immersed inside the phase change material, where heat is effectively accumulated and stored for an extended period of time due to thermal insulation of evacuated tubes. The benefit of this method includes improved functionality by delayed release of heat, thus providing hot water during the hours of high demand or when solar intensity is insufficient. The proposed solar collector utilizes two distinct phase change materials (dual-PCM), namely Tritriacontane and Erythritol, with melting temperatures of 72◦C and 118◦C respectively. The operation of solar water heater with the proposed solar collector is investigated during both normal and on-demand stagnation) operation. Beyond the improved functionality for solar water heater systems, the results from this study show efficiency improvement of 26% for the normal operation and 66% for the stagnation mode, compared with standard solar water heaters that lack phase change materials. The obtained results from the experimental work is validated by computational fluid dynamic model of one single evacuated tube solar collector (large scale size) with ANSYS Fluent. Also the concept of enhancing the convective heat transfer and uniform melting process by silicone oil as a heat transfer medium in direct contact with PCMs is investigated. In addition, the Evacuated Tube solar Collectors (ETCs) are significantly improved by utilizing the “dry-drawable” Carbon Nanotube (CNT) sheet coatings to increase the solar energy absorption and Phase Change Materials (PCMs) to increase the heat accumulation for application in solar water heaters. The proposed solar collector utilizes a phase change material namely Octadecane paraffin, with melting temperatures of 28 ◦C which is categorized as nontoxic with long-term chemical stability PCM. As PCMs particularly in powder form may not be effective by itself due to the poor heat transfer rate, low thermal diffusivity and thermal conductivity, by combining CNT layers with the high thermal diffusivity and thermal conductivity compare to phase change materials, we are able to overcome the shortcomings of PCMs and design an innovative and efficient solar water heater. With the current technology, we can provide a near ideal “black body” surface, absorbing a maximum of 98%, between 600-1100 nm, of solar light striking the surface, and providing additional spectral absorption which improves the performance of the solar heater. Applying CNT sheets in conjunction with PCM enables heat storage directly on the collector for a more constant output, even on a cloudy day and prolonged output of heat at night.Item Fault Handling for Medium-voltage (MV) Grids(2022-12-01T06:00:00.000) Nourmohamadi, Hesam 1993-; Balsara, Poras T.; Moheimani, S.O. Reza; Gohil, Ghanshyamsinh; Zhang, Jie; Panahi, Issa M. S.This thesis provides an overview of the fault detection and protection methods for medium- voltage grids. First, it discusses and review the evolving direct-current medium-voltage (MVDC) grids and their application for various on-shore and off-shore cases. It then explores provided techniques and solutions in the literature to study challenges related to the short-circuit faults that a grid might be prone to them. Advantages and disadvantages of each technique are investigated. This is done with the ultimate goal to propose a new and fast fault detection, classification and location control method to be implemented for any given medium-voltage grid for prompt fault analysis. The so called proposed Grid Transient Classifier-Active Impedance Estimation (GTC-AGIE) provides a two-step fault detection, classification and location method based on the artificial neural network (ANN), wavelet transform (WT) and active high-frequency signal injection. The GTC part decomposes voltage and current signals using WT to extract feature vectors. Then, by the aid of two separate ANN, fault type and an estimation of its location (zone and side where fault has occurred) can be identified. The AGIE plays a complementary role to calculate fault resistance and its distance in a particular zone and side, which are identified by GTC. The AGIE performs its function by injecting a small duration high-frequency signal into the grid and then calculates corresponding impedance to retrieve fault distance and resistance. Shipboard MVDC system is considered as the case study to investigate applicability of the proposed method. Shipboard grid includes several power and voltage stages with various interconnections and load zones in a compact structure with small distances. Compared to alternative current (AC) system, fault current rises quickly in DC ones and a very fast fault analysis is required. Hence, shipboard MVDC is considered as a good case study to examine the effectiveness of the proposed GTC-AGIE. In the following, some solid-state fault current limiter (FCL) topologies for grid protection are reviewed and their advantages and disadvantages are assessed to identify potential areas for improvements. Finally, a novel intelligent multi-functional fault current limiter (IMFCL) topology is proposed to provide protection over short circuit faults and also address any voltage sag/swell by operating as dynamic voltage restorer (DVR) in a hybrid medium voltage alternative current (MVAC) and MVDC grid. A simple fault disturbance detector is proposed to quickly identify voltage sag/swell or fault current conditions. Furthermore, in case of any fault occurrence, control system in IMFCL injects a short duration high-frequency signal into the grid to quickly calculate system impedance in new condition. By knowing the impedance, it is possible to calculate fault resistance and estimate fault location. Both simulation and hardware-in-the-loop (HIL) results are presented throughout the thesis to evaluate performance of the proposed GTC-AGIE and IMFCL.Item Grid Optimization of Shared Energy Storage Among Wind Farms Based on Wind Forecasting(Institute Of Electrical And Electronics Engineers Inc.) Zhu, K.; Chowdhury, S.; Sun, Mucun; Zhang, Jie; Sun, Mucun; Zhang, JieEnergy storage is crucial for source-side renewable energy power plants for enhancing output stability and reducing mismatch between power generation and demand. However, installing large size energy storage systems for renewable energy plants may not be economic, due to high capital cost and ever-increasing human resources and maintenance cost. As a result, in this paper, a shared energy storage system among multiple wind farms is proposed to address this energy management challenge. A state-of-the-art wind power forecasting method with ensemble numerical weather prediction models is used to optimally determine the size of a shared energy storage system (ESS). A number of scenarios are performed to optimize and explore the energy storage size under different economic and storage resource sharing circumstances. The performance of ESS, namely the net revenue of power plants, is explored subject to ESS size and operating constraints of wind farms and power systems. Results of a case study show that sharing of energy storage among multiple wind farms and lower cost of storage progressively enhance the economic benefits of using storage to mitigate over-production/under-forecasting (thus curtailment) and under-production/over-forecasting scenarios.Item Machine Learning-Based Renewable and Load Forecasting in Power and Energy Systems(2020-08) Feng, Cong; Zhang, JieIn the past decades, the power electricity industry has undergone tremendous evolution, which ends up with the development and establishment of electricity markets. This transformation breaks up generation services into a separate, more competitive part of the industry, and facilitates advanced techniques, such as the smart grid techniques and the integration of high-penetration levels of renewable energies, which introduce more uncertainty into the systems. To balance the electricity supply and demand at every moment, power system load and renewable energy forecasting have emerged as one of the major research fields in power and energy engineering. The development of the smart grid provides opportunities for accurate forecasting, such as the utilization of machine learning. However, the current machine learning-based forecasting techniques have several nonnegligible deficiencies, such as the over-reliance on single-algorithm machine learning models, the lack of concern of weather effects, and the neglect of heterogeneity between macroscopic superiority and local performance. This dissertation proposes to mitigate power system uncertainty by improving power system forecasting accuracy utilizing advanced machine learning techniques that are capable of providing robust, weather-aware, and widely applicable forecasting services to power system operators. Considering the unique characteristics of wind, solar, and load forecasting, this research develops advanced machine learning-based forecasting methodologies for the three forecasting tasks from different perspectives. Specifically, we first improve short-term wind forecasting accuracy by adaptively ensembling multiple machine learning models (M3) by another machine learning model, and assess the forecastability of wind sites in the United States by this enhanced M3 method. Then, short-term and very short-term solar forecasting methodologies that are aware of different weather conditions and embraces state-of-theart deep learning techniques based on sky imagery are developed. At last, we compare different aggregate strategies in short-term load forecasting and develop a reinforcement learning based dynamic model selection (QMS) methodology that is able to select the best forecasting models at every single forecasting step from a deterministic forecasting model pool or probabilistic forecasting model pool. Numerical simulations show that the developed forecasting models significantly improve forecasting accuracy, which brings benefits to various power system individuals.Item Probabilistic Renewable Energy Forecasting by Considering Spatial-Temporal Correlation(2020-08) Sun, Mucun; 0000-0002-7527-6410 (Sun, M); Zhang, JieRenewable energy forecasts can help reduce the amount of operating reserves needed for the system, reducing costs of balancing the system, and improving the reliability of the system. Conventional deterministic forecasts might not be sufficient to characterize the inherent uncertainty of renewable energy. Probabilistic forecasts that provide quantitative uncertainty information associated with renewable energy are therefore expected to better assist power system operations. Meanwhile, studies have shown that the integration of geographically dispersed and correlated wind/solar farms could reduce extreme power output, which is referred to as smoothing effect. In addition, power produced from one wind/solar farm at different times is typically temporally correlated. The impacts of spatial-temporal correlation between wind/solar farms on renewable energy forecasting are not well studied in the literature. This dissertation aims at mitigating power system uncertainty by improving probabilistic renewable energy forecasting accuracy utilizing spatial-temporal correlation modeling. In this dissertation, a variety of methods, such as predictive distribution optimization, ensemble learning, deep learning, and scenario generation-based methods, are developed to assist spatial-temporal correlation modeling, and improve probabilistic forecasting accuracy. Computational experiments indicate that the presented study can provide scholars and engineers with critical insights to the usage of spatial-temporal modeling in probabilistic renewable energy forecasting and anomaly detection, and also serve as a valuable reference for practical industry forecasting systems.Item Probabilistic Short-term Wind Forecasting Based on Pinball Loss Optimization(Institute of Electrical and Electronics Engineers Inc.) Sun, Mucun; Feng, Cong; Zhang, Jie; Chartan, E. K.; Hodge, B. -M; Sun, Mucun; Feng, Cong; Zhang, JieProbabilistic wind power forecasts that quantify the uncertainty in wind output have the potential to aid in the economic grid integration of wind power at large penetration levels. In this paper, a novel probabilistic wind forecasting approach based on pinball loss optimization is proposed, in conjunction with a multi-model machine learning based ensemble deterministic forecasting framework. By assuming the point-forecasted value as the mean at each point, one unknown parameter (i.e., standard deviation) of a predictive distribution at each forecasting point is determined by minimizing the pinball loss. A surrogate model is developed to represent the unknown distribution parameter as a function of deterministic forecasts. This surrogate model can be used together with deterministic forecasts to predict the unknown distribution parameter and thereby generate probabilistic forecasts. Numerical results of case studies show that the proposed method has improved the pinball loss by up to 35% compared to a baseline quantile regression forecasting model. © 2018 IEEE.Item Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures(MDPI Ag, 2016-02-02) G©mez-L©zaro, E.; Bueso, M. C.; Kessler, M.; Mart©n-Mart©nez, S.; Zhang, Jie; Hodge, B. -M; Molina-Garc©a, A.; Zhang, JieThe Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.Item The Protective Role of Dot1L in UV-Induced Melanomagenesis(Nature Publishing Group, 2018-11-05) Zhu, Bo; Chen, Shuyang; Wang, Hongshen; Yin, Chengqian; Han, Changpeng; Peng, Cong; Liu, Zhaoqian; Wan, Lixin; Zhang, Xiaoyang; Zhang, Jie; Lian, Christine G.; Ma, Peilin; Xu, Zhi-xiang; Prince, Sharon; Wang, Tao; Gao, Xiumei; Shi, Yujiang; Liu, Dali; Liu, Min; Wei, Wenyi; Wei, Zhi; Pan, Jingxuan; Wang, Yongjun; Xuan, Zhenyu; Hess, Jay; Hayward, Nicholas K.; Goding, Colin R.; Chen, Xiang; Zhou, Jun; Cui, Rutao; 0000-0001-9344-8493 (Xuan, Z); Xuan, ZhenyuThe DOT1L histone H3 lysine 79 (H3K79) methyltransferase plays an oncogenic role in MLL-rearranged leukemogenesis. Here, we demonstrate that, in contrast to MLL-rearranged leukemia, DOT1L plays a protective role in ultraviolet radiation (UVR)-induced melanoma development. Specifically, the DOT1L gene is located in a frequently deleted region and undergoes somatic mutation in human melanoma. Specific mutations functionally compromise DOT1L methyltransferase enzyme activity leading to reduced H3K79 methylation. Importantly, in the absence of DOT1L, UVR-induced DNA damage is inefficiently repaired, so that DOT1L loss promotes melanoma development in mice after exposure to UVR. Mechanistically, DOT1L facilitates DNA damage repair, with DOT1L-methylated H3K79 involvement in binding and recruiting XPC to the DNA damage site for nucleotide excision repair (NER). This study indicates that DOT1L plays a protective role in UVR-induced melanomagenesis.Item Structural Design and Optimization of Sub-scale and Extreme-scale Wind Turbine Rotors(2021-12-01T06:00:00.000Z) Yao, Shulong; Griffith, D. Todd; Cho, Kyeongjae; Qian, Dong; Zhang, Jie; Malik, ArifThe main objectives of this dissertation are to develop some new design and optimization solutions for large/extreme-scale (up to 50 MW) wind turbine rotors, as well as a structural design method for a sub-scale wind turbine blade for manufacturing and field testing. To accomplish these objectives, a series of 13.2 MW downwind rotors is designed and optimized. A key question to enable large rotor designs is how to configure and optimize structural designs to constrain blade mass and cost while satisfying a growing set of challenging structural design requirements. In this dissertation, the performance of a series of three two-bladed downwind rotors with different blade lengths (104.3 meters, 122.9 meters, and 143.4 meters) all rated at 13.2 MW is investigated. The primary goals are to achieve 25% rotor mass and 25% LCOE (levelized cost of energy) reduction. A comparative analysis of the structural performance and economics of this family rotors is presented. To further explore optimization opportunities for large rotors, the new results in a root optimization and a spar cap design study are presented. The structural design solutions that achieve 25% rotor mass reduction in a SUMR13i design (104.3 meters) and 25% LCOE reduction in a SUMR13C design (143.4 meters) are provided. A new sub-scale field-prototype design solution is also developed to realize the dynamics, structural response, and distributed loads (gravitational, aerodynamic, centrifugal) that are characteristics of a full-scale large, modern wind turbine rotor. The challenge lies in producing a structural design meeting two competing objectives: novel scaling objectives that prescribe the sub-scale blade to have low mass and stiffness; and traditional structural safety objectives that drive the design to have high stiffness and mass. A 20% gravo-aeroelastically scaled wind turbine blade is developed successfully that satisfies these competing objectives. First, it achieves close agreements for non-dimensional tip deflection and flap-wise blade frequency (both within 2.1% error) with a blade mass distribution constrained to produce target gravitational and centrifugal loads. Second, the entire blade structure is optimized to ensure a safe, manufacturable solution meeting strict strength requirements for a testing site that can experience up to 45 m/s wind gusts. Next, 50 MW wind turbine rotors with blades’ length over 250 meters are designed and optimized. Key questions in this work include: what is the structural limit for the size of a wind turbine rotor to be feasible or cost-effective, and what are the technologies and approaches needed to achieve large rotors. The largest wind turbine design in prior work is a 25 MW rotor, and here a 50 MW rotor design is considered, the largest ever design with blades’ length over 250 meters, which is 2.5 times the length of a football field. This dissertation shows that a 50 MW design is indeed possible from a detailed engineering perspective and presents a series of aero-structural blade designs for 50 MW wind turbine rotors, and a critical assessment of technology pathways and challenges for such extreme-scale rotors. The rotor design for the 50 MW rotor begins with Monte Carlo simulations focused on optimizing the carbon spar cap design, which is found to be a major cost driver in the blade design. Further, a study of blade root fatigue performance is performed, which is found to be the key limitation for the extreme-scale machine at 50 MW scale. A baseline, initial design results in a 250-meter blade with a mass of 500 metric tons. This initial study indicates a significant opportunity for improvement through the aero-structural design; thus, an aero-structural design and optimization study is performed to reduce the blade mass/cost and achieves more mass/cost-effective 50 MW rotors that result in more than 25% mass reduction and over 30% cost reduction by determining the optimal blade chord and the optimal airfoil thickness for the best aerodynamic and structural performance. Wind turbine blade reliability is critical once blades go into service in operation to avoid costly repairs and lost revenue due to turbine downtime resulting from blade damage. With increased size of the blade, especially for the extreme-scale blades, the blade can experience a more complicated loading. A new method utilizing the panel behavior for structural health monitoring and nondestructive damage detection is examined in this dissertation. The localized panel resonance (panel mode) is identified by the experimental modal test of the BSDS blades at Sandia National Laboratories and numerical analysis of an open-source BSDS design model. Then the study is extended to a larger, novel concept SUMR-D blade design, which was presented in Chapter 3. Some classical wind turbine damage modes are simulated based on the SUMR-D ANSYS model, including shear webs disbonding simulations and trailing edge disbonding simulations. A relation is established to correlate the panel mode with the damage size, which can be applied to structural health monitoring applications. For the shear webs damage cases, a relation is established to correlate the panel mode with panel buckling performance as a function of damage size based on numerical results and analytical formulae, which has potential applications in the nondestructive buckling capacity evaluation.