Model Free Adaptive Neural Controller for Standalone Photovoltaic Distributed Generation Systems With Disturbances

Korukonda, Meher Preetam; Prakash, Ravi; Samanta, Suvendu; Behera, Laxmidhar

doi:10.1109/tste.2021.3123184

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…This analysis shows that if we compare PSO-STSMC response to the benchmark controllers' response as givem in Table. 5 shows better output because it convinces the suppliers to generate high amount of energy for getting the acceptable profit. This due to the robustness of the PSO-STSMC controller which quickly started tracing the demand, and provides lower energy price both for the consumers and suppliers.…”

Section: B Scenario 2: Response Of Multi Power Generation Model With ...mentioning

confidence: 99%

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An Optimal Adaptive Control Strategy for Energy Balancing in Smart Microgrid Using Dynamic Pricing

et al. 2022

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Energy balancing in smart microgrid plays a vital role to improve the reliability and resolves the load shedding problem to ensure consistent energy supply. However, energy balancing is challenging due to uncertain and intermittent nature of renewable energy integrated in smart microgrid. To solve such problems, dynamic energy pricing mechanism is developed that maintain energy balance for overcoming the gap between demand and supply. Thus, the particle swarm optimization based super twisting sliding mode controller (PSO-STSMC) is developed which uses dynamic energy pricing to control renewable energy resources' generation according to the consumers' demand for real time closed loop energy balancing in an energy market. The proposed PSO-STSMC based model is compared with existing models like proportional integral derivative (PID) controller, proportional integral (PI) controller, proportional derivative (PD) controller, and fractional order proportional derivative (FO-PD) controller and the optimized models of the particle swarm optimization based proportional integral (PSO-PI) controller and particle swarm optimization based proportional integral derivative (PSO-PID) controller. Simulations results demonstrate that energy price regulation by PSO-STSMC consistently controls the elastic demand for real time energy balancing.INDEX TERMS Smart grid, dynamic price server, elastic demand, renewable energy sources, dynamic energy price, elastic demand, demand side load management, energy balance, super twisting sliding mode controller.

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Section: B Scenario 2: Response Of Multi Power Generation Model With ...mentioning

confidence: 99%

“…Future smart grids will contain integrated diverse renewable energy and distributed generation systems [5]. Hence, with integration of diverse energy generation resources power grids will be more volatile in terms of energy supply due to fluctuations and uncertainties in energy generation and trading processes for consumers [6].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Adaptive Control Strategy for Energy Balancing in Smart Microgrid Using Dynamic Pricing

et al. 2022

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“…It only relies on input and output measured data [1]. The application of MFAC has been involved in many fields, such as robots, generation systems, vehicle traffic and surface ships [2][3][4][5]. MFAC is designed via the dynamic linearization model, which has three forms: compact format dynamic linearization (CFDL), partial format dynamic linearization (PFDL) and full format dynamic linearization (FFDL).…”

Section: Introductionmentioning

confidence: 99%

MFAC parameter optimization based on improved sparrow search algorithm

Li²

2022

International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022)

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Model-free adaptive control (MFAC) is based on data and does not depend on the mathematical model of the controlled object. It is simple in structure and easy to implement. At present, there are few methods to determine the parameters of MFAC controller, which brings considerable inconvenience to the research and application. Therefore, an improved sparrow search algorithm (ISSA) is proposed to optimize the parameters of MFAC. The ISSA improves its performance by introducing the piecewise chaotic map operator, and finally realizes the automatic optimization of MFAC. The results show that the ISSA has better searching speed and precision. After using optimized parameters for control, the overshoot is greatly reduced, and the oscillation phenomenon when the desired output changes or the external disturbance occurs is effectively overcome, which makes the system more robust.

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Enactment of Deep Reinforcement Learning Control for Power Management and Enhancement of Voltage Regulation in a DC Micro-Grid System

Nishanthi

Kanakaraj

2023

Electric Power Components and Systems

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Model Free Adaptive Neural Controller for Standalone Photovoltaic Distributed Generation Systems With Disturbances

Cited by 5 publications

References 25 publications

An Optimal Adaptive Control Strategy for Energy Balancing in Smart Microgrid Using Dynamic Pricing

An Optimal Adaptive Control Strategy for Energy Balancing in Smart Microgrid Using Dynamic Pricing

MFAC parameter optimization based on improved sparrow search algorithm

Enactment of Deep Reinforcement Learning Control for Power Management and Enhancement of Voltage Regulation in a DC Micro-Grid System

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