Analysis of smart grid power flow system with Gauss-Seidel method

Putri, Syarifah Muthia; Maizana, Dina; Bahri, Zulkifli

doi:10.1088/1755-1315/753/1/012005

Cited by 2 publications

(3 citation statements)

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“…This is due to the addition of an active power supply of 10 kW from the solar cell. This will also add up to 16 kW of reactive power losses [2]. However, in the case 3, the active power of the transformer decreased again to 378 kW.…”

Section: The Case 6 Where the Pln Is Off And The Solar Cell And The B...mentioning

confidence: 95%

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Stability analysis of smart grid management system on campus building

Maizana

Rezky

Putri

et al. 2023

IJEECS

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Medan Area University is one of the private universities in North Sumatra that is also involved in the development of renewable energy technology. This technological development has initiated the University's efforts to develop an electrical system by developing a smart grid system in order to reduce the University's operational costs. However, in its realization, it has its own challenges, such as power flow stability and transient stability. Both problems can be analyzed using ETAP 12.6 software. In one of the cases analyzed, the smart grid system can reduce the use of the active power of the transformer by 11 kW and a current of 19.7 Amp. Meanwhile, in the Indonesian State Electricity Company (PLN) off condition, the smart grid system has not been able to supply the load because the availability of renewable energy sources is not sufficient for the load requirement. As well as the results of the voltage and frequency analysis, the highest voltage is 376.4 Volts, the lowest is 375.9 Volts, which is still in the stable category of 380 Volt voltage deviation because it is not +5% and -10%, and the overall frequency of the cases discussed is consistent stable at nominal 50 Hz.

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Section: The Case 6 Where the Pln Is Off And The Solar Cell And The B...mentioning

confidence: 95%

“…The processing of solar radiation into electrical energy is carried out through solar cells. With the existence of electrical energy from solar cells can ensure the availability of electrical energy [2], [3]. Electricity is an important component in a building [4].…”

Section: Introductionmentioning

confidence: 99%

Stability analysis of smart grid management system on campus building

Maizana

Rezky

Putri

et al. 2023

IJEECS

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“…Non-iterative algorithms include Gauss-Seidel methods and fast-decoupled methods. The Gauss-Seidel method sequentially updates the voltage magnitudes and angles of each bus until convergence is achieved [25]. While it is computationally less intensive than iterative methods, it may converge slowly or fail to converge for certain system configurations, particularly those with high levels of nonlinearity or ill-conditioned matrices [26].…”

Section: Related Workmentioning

confidence: 99%

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

Tang,

Zhang,

et al. 2024

Energies

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Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications.

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Analysis of smart grid power flow system with Gauss-Seidel method

Cited by 2 publications

References 1 publication

Stability analysis of smart grid management system on campus building

Stability analysis of smart grid management system on campus building

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

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