Browsing by Subject "Decision trees"

Now showing 1 - 7 of 7
  • Thumbnail Image
    Item
    Algorithms for Detecting Nearby Loss of Generation Events for Decentralized Controls
    (IEEE Xplore, 2021-04) Dahal, Niraj; Rovnyak, Steven M.; Electrical and Computer Engineering, School of Engineering and Technology
    The paper describes algorithms to screen realtime frequency data for detecting nearby loss of generation events. Results from Fourier calculation are combined with other features to effectively distinguish a nearby loss of generation from similar remote disturbances. Nearby in this context usually refers to an event occurring around 50-100 miles from the measurement location. The proposed algorithm can be trained using pattern recognition tools like decision trees to enable smart devices including appliances like residential air conditioners and dryers to autonomously detect and estimate the source of large frequency disturbances. An area of application of this strategy is to actuate controls such as location targeted under frequency load shedding (UFLS) so that loads closest to a tripped generator are the most likely to shut down.
  • Thumbnail Image
    Item
    Applying Different Wide-Area Response-Based Controls to Different Contingencies in Power Systems
    (IEEE Xplore, 2021) Iranmanesh, Shahrzad; Rovnyak, Steven M.; Electrical and Computer Engineering, School of Engineering and Technology
    Electrical disturbances in the power system can threaten stability. One-shot control is an effective method for stabilizing some events. In this paper, predetermined amounts of loads are increased or decreased around the network. Determining the amounts of loads, and the location for shedding is crucial. This paper is completed in two different sections. First, finding the effective control combinations, and second, finding an algorithm for applying different control combinations to different contingencies in real time. The particle swarm optimization (PSO) algorithm is used to find the effective control combinations. Next, decision trees (DT) are trained to assess the benefits of applying each of the three most effective control combinations found by PSO method. The DT outputs are combined into an algorithm for selecting the best control in real time. Finally, the algorithm is evaluated using a test set of contingencies. The results reveal a 46% improvement in comparison to previous studies.
  • No Thumbnail Available
    Item
    Comparison of Supervised Machine Learning and Probabilistic Approaches for Record Linkage
    (AMIA Informatics summit 2019 Conference Proceedings., 2020-03-25) McNutt, Andrew T.; Grannis, Shaun J.; Bo, Na; Xu, Huiping; Kasthurirathne, Suranga N.
    Record linkage is vital to prevent fragmentation of patient data. Machine learning approaches present considerable potential for record linkage. We compared the performance of three machine learning algorithms to an established probabilistic record linkage technique. Machine learning approaches exhibited results that were comparable, or statistically superior to the established probabilistic approach. It is unclear if the cost of manually reviewing datasets for supervised learning is justified by the performance improvements they yield.
  • Thumbnail Image
    Item
    Machine Learning Techniques for Prediction of Early Childhood Obesity
    (Schattauer, 2015-08-12) Dugan, T.M.; Mukhopadhyay, S.; Carroll, A.; Downs, S.; Department of Computer and Information Science, School of Science
    Objectives This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Methods Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Results Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. Conclusions This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.
  • Thumbnail Image
    Item
    Methods of Handling Missing Data in One Shot Response Based Power System Control
    (2019-08) Dahal, Niraj; Rovnyak, Steven; Li, Lingxi; Santos, Euzeli Dos
    The thesis extends the work done in [1] [2] by Rovnyak, et al. where the authors have described about transient event prediction and response based one shot control using decision trees trained and tested in a 176 bus model of WECC power system network. This thesis contains results from rigorous simulations performed to measure robustness of the existing one shot control subjected to missing PMU's data ranging from 0-10%. We can divide the thesis into two parts in which the first part includes understanding of the work done in [2] using another set of one-shot control combinations labelled as CC2 and the second part includes measuring their robustness while assuming missing PMU's data. Previous work from [2] involves use of decision trees for event detection based on different indices to classify a contingency as a 'Fault' or 'No fault' and another set of decision trees that decides either to actuate 'Control' or 'No control'. The actuation of control here means application of one-shot control combination to possibly bring the system to a new equilibrium point which would otherwise attain loss of synchronism. The work done in [2] also includes assessing performance of the one shot control without event detection. The thesis is organized as follows- Chapter 1 of the thesis highlights the effect of missing PMUs' data in a power system network and the need to address them appropriately. It also provides a general idea of transient stability and response of a transient fault in a power system. Chapter 2 forms the foundation of the thesis as it describes the work done in [1] [2] in detail. It describes the power system model used, contingencies set, and different indices used for decision trees. It also describes about the one shot control combination (CC1) deduced by Rovnyak, et.al. of which performance is later tested in this thesis assuming different missing data scenarios. In addition to CC1, the chapter also describes another set of control combination (CC2) whose performance is also tested assuming the same missing data scenarios. This chapter also explains about the control methodology used in [2]. Finally the performance metrics of the DTs are explained at the end of the chapter. These are the same performance metrics used in [2] to measure the robustness of the one shot control. Chapter 2 is thus more a literature review of previous work plus inclusion of few simulation results obtained from CC2 using exactly the same model and same control methodology. Chapter 3 describes different techniques of handling missing data from PMUs most of which have been used in and referred from different previous papers. Finally Chapter 4 presents the results and analysis of the simulation. The thesis is wrapped up explaining future enhancements and room for improvements.
  • Thumbnail Image
    Item
    Pandemic-Aware Day-Ahead Demand Forecasting Using Ensemble Learning
    (IEEE, 2022) Arjomandi-Nezhad, Ali; Ahmadi, Amirhossein; Taheri, Saman; Fotuhi-Firuzabad, Mahmud; Moeini-Aghtaie, Moein; Lehtonen, Matti; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    Electricity demand forecast is necessary for power systems’ operation scheduling and management. However, power consumption is uncertain and depends on several factors. Moreover, since the onset of covid-19, the electricity consumption pattern went through significant changes across the globe, which made the forecasting demand more challenging. This is mainly due to the fact that pandemic-driven restrictions changed people’s lifestyles and work activities. This calls for new forecasting algorithms to more effectively handle these conditions. In this paper, ensemble-based machine learning models are utilized for this task. The lockdown temporal policies are added to the feature set in order to make the model capable of correcting itself in pandemic situations and enhance data quality for the forecasting task. Several ensemble-based machine learning models are examined for the short-term country-level demand prediction model. Besides, the quantile random forest regression is implemented for a probabilistic point of view. For case studies, the models are trained for predicting Germany’s country-level demand. The results indicate that ensemble models, especially boosting and bagging-boosting models, are capable of accurate country-level demand forecast. Besides, the majority of these models are robust against missing the pandemic policy data. However, utilizing the pandemic policy data as features increases the forecasting accuracy during the pandemic situation significantly. Furthermore, the probabilistic quantile regression demonstrated high accuracy for the aforementioned case study.
  • Thumbnail Image
    Item
    POLYNOMIAL CURVE FITTING INDICES FOR DYNAMIC EVENT DETECTION IN WIDE-AREA MEASUREMENT SYSTEMS
    (2013-08-14) Longbottom, Daniel W.; Rovnyak, Steven; Li, Lingxi; Chen, Yaobin
    In a wide-area power system, detecting dynamic events is critical to maintaining system stability. Large events, such as the loss of a generator or fault on a transmission line, can compromise the stability of the system by causing the generator rotor angles to diverge and lose synchronism with the rest of the system. If these events can be detected as they happen, controls can be applied to the system to prevent it from losing synchronous stability. In order to detect these events, pattern recognition tools can be applied to system measurements. In this thesis, the pattern recognition tool decision trees (DTs) were used for event detection. A single DT produced rules distinguishing between and the event and no event cases by learning on a training set of simulations of a power system model. The rules were then applied to test cases to determine the accuracy of the event detection. To use a DT to detect events, the variables used to produce the rules must be chosen. These variables can be direct system measurements, such as the phase angle of bus voltages, or indices created by a combination of system measurements. One index used in this thesis was the integral square bus angle (ISBA) index, which provided a measure of the overall activity of the bus angles in the system. Other indices used were the variance and rate of change of the ISBA. Fitting a polynomial curve to a sliding window of these indices and then taking the difference between the polynomial and the actual index was found to produce a new index that was non-zero during the event and zero all other times for most simulations. After the index to detect events was chosen to be the error between the curve and the ISBA indices, a set of power system cases were created to be used as the training data set for the DT. All of these cases contained one event, either a small or large power injection at a load bus in the system model. The DT was then trained to detect the large power injection but not the small one. This was done so that the rules produced would detect large events on the system that could potentially cause the system to lose synchronous stability but ignore small events that have no effect on the overall system. This DT was then combined with a second DT that predicted instability such that the second DT made the decision whether or not to apply controls only for a short time after the end of every event, when controls would be most effective in stabilizing the system.