Abstract

In the present chapter, the evaluation of the Tennessee Valley Authority (TVA) Markov model transient behavior is derived and studied. It is focused on finding the models of the transient-state availability and unavailability of the four (TVA) models among using an adaptive neuro-fuzzy inference system (ANFIS). The developed ANFIS model for the TVA models is derived, and both availability and unavailability of the four TVA models are derived using the curve fitting technique, where each model of the transient availability of the three-state models of the TVA models is found. Each model is considered as a three-state model, and its equations obtained using the curve fitting technique are helping for the future availabilities and unavailabilities. The availability is a very important measure of performance for the availability of TVA power plants. The technique is used and applied on the four models in the present study to formulate and obtain the TVA models’ results and are compared. In addition, the generation effects on the reliability investigation. The generation study evaluates the improvement in reliability over a time.

Highlights

  • The Tennessee Valley Authority (TVA) is a business organization in the United States

  • In addition to the fourth order Runge-Kutta method, adaptive neuro-fuzzy inference system (ANFIS) technique applied in the present chapter to model the three-state probabilities of the four TVA models which deals to the fifth layer helps to find the availability and unavailability of the model

  • The obtained unavailability equation using the curve fitting technique MyCurveFit is shown in Eq (5) for the four TVA models

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Summary

Introduction

The Tennessee Valley Authority (TVA) is a business organization in the United States. The complete battery energy storage system is used to control the balance of source charging power so that microgrid can operate with high stability and reliability to supply electricity to a variety of customers. The researchers deal with other two simulation models to assess the reliability and flexibility of electrical systems, respectively They introduced the way to measure the results predictability of applying the framework in the new policy which shows that the policy does not guarantee a target level of reliability and increases costs and emissions. Markov models will be developed for three different test policies considering the effects of degradation These models are applied to analyze the availability of essential components of an emergency cooling system for the core of a VVER-1000/ V446 nuclear power plant as a case study. The content of the server availability survey exceeds the shortest content demand on average by 56% and meets up to 98.5% of the total server demand

Power plant model
Applications and results
Findings
Conclusion

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