Steady-state voltage in low voltage networks

Perera, Sarath; Elphick, Sean

doi:10.1016/b978-0-323-85467-2.00003-2

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Based on Equation ( 9), the learning rate can be made more effective by designing each layer to track the activity flow over time. To achieve this, an eligibility trace module has been provided to trace the most recent activity value, as presented in Equation (8).…”

Section: Problem Formulation and Solution Methodologymentioning

confidence: 99%

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P2P Coordinated Control between SPV and STATCOM in a Microgrid for Power Quality Compensation Using LSTM–Genetic Algorithm

et al. 2023

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The deployment of a static synchronous compensator within a microgrid can facilitate voltage and reactive power regulation, leading to enhanced stability and reliability. Within a microgrid setting, the effectiveness of a STATCOM in balancing the power supply is influenced by several factors, including the system configuration, the operating conditions, and the specific requirements of the power grid. The capacity, response time, and magnitude of system disturbances also play a role in determining the STATCOM’s ability to balance the power supply. To ensure the successful integration of a STATCOM within a microgrid, coordinating the control system with other distributed energy resources (DER), especially when multiple control strategies are employed, can be a challenging task. Therefore, a meticulously designed control system is indispensable to guarantee the microgrid’s efficient and effective operation. The use of GA in LSTM tuning can accelerate the process of identifying the optimal hyperparameters for a specific task, obviating the need for time-consuming and computationally expensive grid searches or manual tuning. This method can be particularly advantageous when handling large data sets and complex models. In this paper, an attempt has been made to model the STATCOM to communicate with the microgrid, tuned using LSTM–GA, for the effective calculation of real and reactive power support during grid disturbances.

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Section: Problem Formulation and Solution Methodologymentioning

confidence: 99%

“…Sarath Perera et al [8] present a framework for the reduction of power network oscillations with the use of static synchronous compensators and the synthesis of H2/H∞ controllers. The framework employs an H2/H∞ synthesis technique to design the controller and improve the stability of the power system.…”

Section: Introductionmentioning

confidence: 99%

P2P Coordinated Control between SPV and STATCOM in a Microgrid for Power Quality Compensation Using LSTM–Genetic Algorithm

et al. 2023

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“…As per the report, the capacity of wind power generation in microgrids is expected to increase to 9.2 GW by 2024, from 3.6 GW capacity in 2019 [3] , [4] . Furthermore, the report emphasizes that microgrids, including those powered by wind energy, will be more widespread in remote regions and islands, as well as in urban areas with high electricity consumption [5] , [6] .…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of DFIG support microgrid using GA optimized restricted Boltzmann Machine algorithm

Bhol,

Swain,

Dash

et al. 2024

Heliyon

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P2P Coordinated Control Between SPV and STATCOM in a Microgrid for Power Quality Compensation using LSTM-Genetic Algorithm

Swain¹,

Viswavandya²,

Dash³

et al. 2023

Preprint

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The deployment of a Static Synchronous Compensator within a microgrid can facilitate voltage and reactive power regulation, leading to enhanced stability and reliability. Within a microgrid setting, a STATCOM can effectively balance power supply and demand, and mitigate voltage fluctuations caused by the intermittent nature of renewable energy sources. To ensure the successful integration of a STATCOM within a microgrid, coordinating the control system with other Distributed Energy Resources, especially when multiple control strategies are employed, can be a challenging task. Therefore, a meticulously designed control system is indispensable to guarantee the microgrid’s efficient and effective operation. The use of GA in LSTM tuning can accelerate the process of finding optimal hyperparameters for a specific task, obviating the necessity for time-consuming and computationally expensive grid search or manual tuning. This method can be particularly advantageous when handling large data sets and complex models. In this paper, an attempt has been made to model the STATCOM to communicate with the microgrid tuned using LSTM-GA for effective calculation of real and reactive power support during grid disturbances.

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Steady-state voltage in low voltage networks

Cited by 3 publications

References 3 publications

P2P Coordinated Control between SPV and STATCOM in a Microgrid for Power Quality Compensation Using LSTM–Genetic Algorithm

P2P Coordinated Control between SPV and STATCOM in a Microgrid for Power Quality Compensation Using LSTM–Genetic Algorithm

Performance analysis of DFIG support microgrid using GA optimized restricted Boltzmann Machine algorithm

P2P Coordinated Control Between SPV and STATCOM in a Microgrid for Power Quality Compensation using LSTM-Genetic Algorithm

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