Design and dynamic analysis of superconducting magnetic energy storage-based voltage source active power filter using deep Q-learning

Mangaraj, M.; Pilla, Ramana; Kumar, Polamarasetty P.; Nuvvula, Ramakrishna S. S.; Verma, Aanchal; Ali, Ahmed; Khan, Baseem

doi:10.1007/s00202-023-02062-4

Cited by 1 publication

(3 citation statements)

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“…K p , K i of FOPID will be tuned by the ANN during the online process., As well, the error and emotional signals are symbolized by e(t) and E(t). Accordingly, by combining Equations ( 4) and (11), the final characteristics function of the closed loop system is carried by…”

Section: Brain Emotional Learning-based Intelligent Controllermentioning

confidence: 99%

“…As the overall closed loop of the system determined, the neural implementations of the BELBIC can be performed to output the final control signal. Referring to (5-7) and (11):…”

Section: Brain Emotional Learning-based Intelligent Controllermentioning

confidence: 99%

“…Efforts have been made to develop cost-effective coil designs and efficient power electronic converters to maximize the benefits of SMES in power applications [10]. The research conducted in [11,12] integrated SMES with power compensation techniques to enhance the dynamic performance of power distribution networks. By deploying SMES alongside, for example, Distribution Static Synchronous Compensator (DSTATCOM), these studies aim to extend the range of compensation capabilities while reducing DC link voltage.…”

Section: Introductionmentioning

confidence: 99%

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The Regulation of Superconducting Magnetic Energy Storages with a Neural-Tuned Fractional Order PID Controller Based on Brain Emotional Learning

Safari,

Sorouri,

Oshnoei

2024

Fractal Fract

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Intelligent control methodologies and artificial intelligence (AI) are essential components for the efficient management of energy storage modern systems, specifically those utilizing superconducting magnetic energy storage (SMES). Through the implementation of AI algorithms, SMES units are able to optimize their operations in real time, thereby maximizing energy efficiency. To have a more advanced understanding of this issue, DynamoMan is presented in this paper. For use with SMES systems, DynamoMan, an Artificial Neural Network (ANN)-tuned Fractional Order PID Brain Emotional Learning-Based Intelligent Controller (ANN-FOPID-BELBIC), has been developed. ANN tuning is employed to optimize the key settings of the reward/penalty generator of a BELBIC, which are important for its overall efficacy. Following this, DynamoMan is integrated into the SMES control system and compared to scenarios in which a BELBIC, PID, PI, and P are utilized. The findings indicate that DynamoMan performs considerably better than other models, demonstrating robust and control attributes alongside a considerably reduced period of settling time, especially when incorporated with the power grid.

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Section: Brain Emotional Learning-based Intelligent Controllermentioning

confidence: 99%

“…As the overall closed loop of the system determined, the neural implementations of the BELBIC can be performed to output the final control signal. Referring to (5-7) and (11):…”

Section: Brain Emotional Learning-based Intelligent Controllermentioning

confidence: 99%