A Decentralized Higher Order Sliding Mode Control for Islanded Photovoltaic-Storage Systems

Rosini, Alessandro; Procopio, Renato; Bonfiglio, Andrea; Incremona, Gian Paolo; Ferrara, Antonella

doi:10.1016/j.energy.2022.124502

Cited by 13 publications

(2 citation statements)

References 26 publications

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“…An incentive layer can be added by building an incentive layer on top of the blockchain and using native tokens. A smart contract can also be used to store information about storage capacity and agreements between customers and providers [31].…”

Section: Decentralization Of Storagementioning

confidence: 99%

A Comprehensive Survey on Blockchain-Based Decentralized Storage Networks

et al. 2023

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Blockchains are a new approach to creating distributed networks that were first introduced in 2008. It allows the formation of peer-to-peer networks based on consensus, forming chains from accepted blocks without requiring a central authority or centralized controller. A prominent application of this technology is its use in decentralized storage systems. Individuals in decentralized storage networks rent unused hardware storage space to other individuals. A decentralized network utilizing end-to-end encryption eliminates the risk of data loss associated with centralized data control by enabling clients to transmit their files securely. The storage providers must prove that they have kept unaltered files in this network for this time. Many studies have been conducted in this specific domain, most targeting storage capacity and efficiency, but a security, integrity and privacy loophole need to be addressed. This paper presents an overview of blockchain-based storage systems and how they work, followed by a comparison with cloud-based storage networks and a survey of various decentralized storage networks like SIA, File coin, and Storj available on the market. Next, we discuss the advantages and disadvantages of blockchain-based storage. In our final discussion, we examine the security problems of decentralized storage networks and explore potential solutions and research directions for the future.

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Section: Decentralization Of Storagementioning

confidence: 99%

A Comprehensive Survey on Blockchain-Based Decentralized Storage Networks

et al. 2023

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“…In the past, many conventional and advanced control strategies were presented for voltage source to improve the quality of the output voltage as the system works in islanded mode, such as sliding mode controllers (Hajihosseini et al, 2022; Mostafa et al, 2022; Rosini et al, 2022; Yuan et al, 2022), adaptive controllers (Elnady et al, 2022; Ghamari et al, 2020; Jiong et al, 2022) and fuzzy logic controllers (Ayyakrishnan, 2022; Dong et al, 2023; Yang et al, 2022). These controllers effectively solved the problems of the islanded voltage source, including fast response, low steady-state error (SSE), low total harmonic distortion (THD) under the change of loads, the uncertainties of the system and the effect of external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Adaptive optimal sliding mode control for three-phase voltage source inverter: Reinforcement learning approach

Thi-Thuy Vu,

Xuan Nguyen,

Quang Bui

2023

Transactions of the Institute of Measurement and Control

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The operation of the three-phase standalone inverter is affected by many factors, such as heavy changes in load, unbalanced loads, nonlinear loads, system uncertainties and external disturbances. These are critical weaknesses for the control system of the three-phase inverter to reach robust optimal performance. This paper proposes an adaptive optimal sliding mode control (AOSMC) scheme for a three-phase nonlinear uncertain inverter. This AOSMC strategy solves the problems of nonlinear optimization by adaptive dynamic programming, one of the techniques of reinforcement learning, and overcomes the uncertainties and disturbance effects by a disturbance observer–based sliding mode controller. This algorithm uses only one neural network to approximate the critic; therefore, the burden of computation is significantly reduced. Both the weight matrix of the critic network and the disturbance observer are asymptotically stable. The overall system is guaranteed to be ultimately uniformly bounded stable via Lyapunov stable theory. The simulation is conducted to validate the insensitivity of the proposed AOSMC algorithm to working conditions. Also, the competitive results are presented to demonstrate the improvement of the proposed AOSMC scheme in comparison to some other existing controllers.

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