2023 WSU Annual CGRS Abstracts

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    Valuing distributed energy resource location based on the impact on transmission network
    (Wichita State University, 2023-03-22) Rajendran, Sarangan; Aravinthan, Visvakumar
    The accelerated growth of distributed energy resources (DER), including solar PV, results in unanticipated electric grid operating conditions, as they are traditionally not considered in planning studies by the transmission utilities. The impacts can vary depending on the location at which the DER connects to the existing grid. We have developed three metrics to realize the impacts caused to the grid by the addition of a DER, and find a quantitative value for the location of that DER. This method can be used as a planning tool by the transmission utilities of Kansas to identify the favorable locations for upcoming DERs with respect to their system requirements. A test system developed based on the western Kansas region was used to analyze the impact of DERs and demonstrate the usefulness of the proposed method.
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    Analysing the impact of distributed energy resources on bulk power systems
    (Wichita State University, 2023-03-22) Melagoda, Adithya; Manoharan, Arun Kaarthick; Aravinthan, Visvakumar
    The transition from conventional generation to renewables is continuously speeding up in the state of Kansas given its higher potential for solar due to the larger reception of sunlight around the year. A considerable percentage of these renewables include Distributed Energy Resources (DER) which are distributed in smaller individual capacities throughout the network. Although these deployments of DERs bring out ample environmental and technological benefits, the planning, control and operation of power system are getting more complex due to their intermittency. So far, the most common practice of adding DERs to the system is embedding them within the distribution system as a passive load and analysing the impacts on the Bulk Power System (BPS). However, this practice can no longer be accepted due to the increasing integration of DERs and their capability to provide advanced support services as non-synchronous inverter-based resources. Hence, this work provides a novel approach to modelling and analysis of the impact of DERs on BPS performance using four different scenarios of the distribution system. The proposed models are tested in the IEEE 37-bus system along with the transmission-distribution (T-D) interface. The results of this work will be highly beneficial for power system planners and operators in appropriate decision-making to maintain a reliable power system.
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    Developing a power system resilience planning framework to ensure energy security for critical resources during power outages
    (Wichita State University, 2023-03-22) Shanthanam, Sangar; Aravinthan, Visvakumar
    Uninterrupted electricity supply is an essential resource for the US economy. Critical infrastructures such as hospitals and fire stations heavily rely on electricity. However, current trends in weather related events such as polar vortex and tornadoes impact the continuous power supply. When power outages occur, electric utilities try to shed part of the load. These could have both economic impact and power outage to the consumers including critical loads such as hospitals and fire stations. Existing metrics cannot differentiate critical loads and other normal loads during load shedding. For example, 100 kWh of power loss to a hospital and 100kWh of power loss to normal households are considered the same. In this work a metric-based framework is proposed to prioritize critical and important loads. A new quantitative metric called Cumulative Value of Expected Energy Not Served is proposed to value the load based on the criticality. The proposed metric incorporates several types of loads and estimates the total monetary value of expected energy not served. Total value of expected energy not served is determined using survey methods and probability theory. This value can be used to prioritize the critical loads. The developed metric framework could capture the resilience of electric grid effectively. Further, it can give high priority for critical loads therefore energy security for these resources is increased.
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    Decision-making at intersections
    (Wichita State University, 2023-03-22) Sutton, Rachel; Vangsness, Lisa
    Over 90 percent of automobile crashes are primarily due to driver behavior. According to 2015 traffic crash data in the state of Kansas, the number one contributing circumstance of crashes was that the driver failed to give their full time and attention. Accidents regarding the vehicle and driver accounted for approximately 30% of car crashes in the state of Kansas in 2015. Therefore, as vehicles become increasingly automated, the amount of car crashes related to vehicles may increase in the future, if they are not appropriately calibrated. The current study was designed to assess perceptions of trust and risk by asking individuals to make braking judgements with and without an automated braking system within the context of a driver approaching a yellow light at an intersection. The hypotheses were tested with a Qualtrics survey, where drivers viewed images that portrayed a car at various distances from the light. Each image was paired with a Likert-style question to assess drivers' endorsement of braking at the point depicted in the figure. The results suggested that participants had a bias to endorse braking more strongly when imaging driving a car without automation. By understanding how people's behavior affects their trust in automated systems, it can aid in creating an appropriate reliance, which could reduce crash rates and increase safety in the state of Kansas.
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    Machine learning to improve the performance of computer-aided diagnostic systems used for detecting skin diseases
    (Wichita State University, 2023-03-22) Thompson, Christian; Asaduzzaman, Abu
    Early detection is critical in enhancing the patient's survival rate due to deadly skin diseases such as melanoma. Most skin diseases are ascertained by extracting sample cells for evaluation. Dermatologists often experience difficulties identifying features during the early stages of skin disease development, which may result in high false positive and false negative rates. The introduction of Computer-Aided Diagnosis (CAD) systems aids a second reader in interpreting medical images. Recent studies show that machine learning (ML) can improve CAD performance. In this work, we implement three ML modules for CAD systems to detect melanoma cancer. ML libraries from TensorFlow and 10,000 training images from Kaggle are used to test the ML modules. The preliminary results indicate that Convolutional Neural Network (CNN) performs better than Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) techniques in detecting melanoma. This work's result will help improve the accuracy and reliability of CAD systems, ultimately leading to better patient outcomes and higher melanoma survival rates for Kansas and the United States.