Guaranteed Convergence Embedded System for SSSC and IPFC

Singh, Pradeep; Senroy, Nilanjan; Tiwari, Rajive

doi:10.1109/tpwrs.2021.3053146

Cited by 15 publications

(7 citation statements)

References 5 publications

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“…For power system assessment, this study provides a unique power injection model (PIM) of an interline power flow controller (IPFC) [99]. To avoid the mathematical difficulties that standard iterative approaches have, this letter offers a holomorphic embedded load flow method (HELM) scheme for two series FACTS devices, namely SSSC and IPFC [100].…”

Section: D-facts Planningmentioning

confidence: 99%

Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: A technological review

Mousaei,

Gheisarnejad,

Khooban

2023

Journal of Energy Storage

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Section: D-facts Planningmentioning

confidence: 99%

Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: A technological review

Mousaei,

Gheisarnejad,

Khooban

2023

Journal of Energy Storage

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“…Recently several Refs. [34,[52][53][54] have contributed to novel forms of embedded systems considering FACTS. In Ref.…”

Section: Modeling Of Facts Devicesmentioning

confidence: 99%

“…In Ref. [52], several thyristor-based FACTS controllers, e.g., static var compensator (SVC), thyristor controlled switched capacitor (TCSC), thyristor controlled voltage regulator (TCVR), and thyristor controlled phase angle regulator (TCPAR) are integrated into the AC power flow problem. The corresponding embedded systems are formulated in a way that the overall solution functions are holomorphic.…”

Section: Modeling Of Facts Devicesmentioning

confidence: 99%

A Review on Applications of Holomorphic Embedding Methods

Huang,

Sun

2023

iEnergy

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The holomorphic embedding method (HEM) stands as a mathematical technique renowned for its favorable convergence properties when resolving algebraic systems involving complex variables. The key idea behind the HEM is to convert the task of solving complex algebraic equations into a series expansion involving one or multiple embedded complex variables. This transformation empowers the utilization of complex analysis tools to tackle the original problem effectively. Since the 2010s, the HEM has been applied to steady-state and dynamic problems in power systems and has shown superior convergence and robustness compared to traditional numerical methods. This paper provides a comprehensive review on the diverse applications of the HEM and its variants reported by the literature in the past decade. The paper discusses both the strengths and limitations of these HEMs and provides guidelines for practical applications. It also outlines the challenges and potential directions for future research in this field.

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“…The operating system on the processor core can be controlled dynamically using this design. Singh and colleagues developed a bare-metal dual system based on the ZYNQ-7000 and an AMP architecture motion controller based on Linux [12]. Tang et al [13] use architecture, specifically Linux+Nuttx architecture, to achieve robot control on a dual-core processor.…”

Section: Research Status Of Embeddedmentioning

confidence: 99%

Intelligent Identification of Embedded System Equipment State Based on Internet of Things

Xu¹

2022

Mobile Information Systems

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IoT (Internet of things) technology will be more widely used in the manufacturing industry, which will bring new trends to the development of the manufacturing industry. With the continuous advancement of IoT technology in the power field, the integration of IoT and smart grid has become a new topic. In the process of monitoring and troubleshooting the whole EPSE (Embedded Power System Equipment), it is very important to conduct a comprehensive analysis and routine testing on specific equipment and systems, so that the power system can work stably. In this paper, the knowledge sharing and transfer model of EPSE based on IoT is analyzed, and an intelligent identification method of EPSE state based on the GA-C_HMM (Genetic Algorithm-Coupled Hidden Markov Model) algorithm is proposed. After the GA parameters are optimized, the state model library is constructed by fitting each state data of the equipment to C_HMM, and the state of the equipment is determined by calculating the maximum probability value of the signal to be diagnosed. The experimental results show that when the recognition time is 12 hours, the recognition accuracy rate of the existing recognition method is 69.7%, and that of the recognition method in this paper is 98.3%, which shows that the recognition accuracy rate of the recognition method adopted in this study is higher than that of the existing methods and the recognition ability is stronger.

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Guaranteed Convergence Embedded System for SSSC and IPFC

Cited by 15 publications

References 5 publications

Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: A technological review

Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: A technological review

A Review on Applications of Holomorphic Embedding Methods

Intelligent Identification of Embedded System Equipment State Based on Internet of Things

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