EVALUATION OF ARTIFICIAL INTELLIGENCE TECHNIQUE IN IMPROVING AUTOMATIC GENERATION CONTROL

Background: Power system stability demands minimum variation in frequency, so that loadgeneration balance is maintained throughout the operation period. An Automatic Generation Control (AGC) monitors the frequency and varies the generation to maintain the balance. A system with multiple energy sources and use of a fractional controller for efficient control of stability is presented in the paper. At the outset a 2-area thermal system with governor dead band, generation rate constraint and boiler dynamics have been applied. Methods: A variation of load is deliberated for the study of the considered system with Harmony Search (HS) algorithm, applied for providing optimization of controller parameters. Integral Square Time Square Error (ISTSE) is chosen as objective function for handling the process of tuning controller parameters. : A study of similar system with various lately available techniques such as TLBO, hFA-PS and BFOA applied to PID, IDD and PIDD being compared to HS tuned fractional controller is presented under step and dynamic load change. The effort extended to a single area system with reheat thermal plant, hydel plant and a unit of wind plant is tested with the fractional controller scheme. Results: The simulation results provide a clear idea of the superiority of the combination of HS algorithm and FO-PID controller, under dynamically changing load. The variation of load is taken from 1% to 5% of the connected load. Conclusion: Finally, system robustness is shown by modifying essential factors by ± 30%.

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Towards a Customized¬ Awareness Campaign to Educate the Public about COVID-19: Development and Validation of a Messaging Campaign to Promote Precautionary Behavior Using Geospatial Intelligence in Saudi Arabia (Preprint)

10.2196/preprints.26856 ◽

2020 ◽

Author(s):

Mayda Alrige ◽

Hind Bitar Bitar ◽

Maram Meccawi ◽

Balakrishnan Mullachery

Keyword(s):

Artificial Intelligence ◽

Saudi Arabia ◽

Content Validity ◽

Awareness Campaign ◽

Artificial Intelligence Technique ◽

The Public ◽

High Infection ◽

Local Map ◽

The City ◽

Intelligence Technique

BACKGROUND Designing a health promotion campaign is never an easy task, especially during a pandemic of a highly infectious disease, such as Covid-19. In Saudi Arabia, many attempts have been made toward raising the public awareness about Covid-19 infection-level and its precautionary health measures that have to be taken. Although this is useful, most of the health information delivered through the national dashboard and the awareness campaign are very generic and not necessarily make the impact we like to see on individuals’ behavior. OBJECTIVE The objective of this study is to build and validate a customized awareness campaign to promote precautionary health behavior during the COVID-19 pandemic. The customization is realized by utilizing a geospatial artificial intelligence technique called Space-Time Cube (STC) technique. METHODS This research has been conducted in two sequential phases. In the first phase, an initial library of thirty-two messages was developed and validated to promote precautionary messages during the COVID-19 pandemic. This phase was guided by the Fogg Behavior Model (FBM) for behavior change. In phase 2, we applied STC as a Geospatial Artificial Intelligence technique to create a local map for one city representing three different profiles for the city districts. The model was built using COVID-19 clinical data. RESULTS Thirty-two messages were developed based on resources from the World Health Organization and the Ministry of Health in Saudi Arabia. The enumerated content validity of the messages was established through the utilization of Content Validity Index (CVI). Thirty-two messages were found to have acceptable content validity (I-CVI=.87). The geospatial intelligence technique that we used showed three profiles for the districts of Jeddah city: one for high infection, another for moderate infection, and the third for low infection. Combining the results from the first and second phases, a customized awareness campaign was created. This awareness campaign would be used to educate the public regarding the precautionary health behaviors that should be taken, and hence help in reducing the number of positive cases in the city of Jeddah. CONCLUSIONS This research delineates the two main phases to developing a health awareness messaging campaign. The messaging campaign, grounded in FBM, was customized by utilizing Geospatial Artificial Intelligence to create a local map with three district profiles: high-infection, moderate-infection, and low-infection. Locals of each district will be targeted by the campaign based on the level of infection in their district as well as other shared characteristics. Customizing health messages is very prominent in health communication research. This research provides a legitimate approach to customize health messages during the pandemic of COVID-19.

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