An Average Voltage Approach to Control Energy Storage Device and Tap Changing Transformers Under High Distributed Generation

Tshivhase, Ndamulelo; Hasan, Ali N.; Shongwe, Thokozani

doi:10.1109/access.2021.3101463

Cited by 11 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ However, to maintain voltage within permissible limit in power system there are several control strategies used by power system operators to mitigate the effect of the aforementioned factors that affect the voltage profile in the network. According to [51]- [55]. The control strategies include: I) Voltage Regulators These devices automatically adjust the tap settings on transformers to regulate the voltage within desired limits, which maintain a stable voltage profile.…”

Section: Elm Ccelmnmentioning

confidence: 99%

Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review

Makanju,

Shongwe,

Famoriji

2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

Prediction in the power system network is very crucial as expansion is needed in the network. Several methods have been used to predict the load on a network, from short to long time load prediction, to ensure adequate planning for future use. Since the power system network is dynamic, other parameters, such as voltage and frequency prediction, are necessary for effective planning against contingencies. Also, most power systems are interconnected networks; using isolated variables to predict any part of the network tends to reduce prediction accuracy. This review analyzed different machine learning approaches used for load, frequency, and voltage prediction in power systems and proposed a machine learning predictive approach using network topology behavior as input variables to the model. The analysis of the proposed model was tested using a regression model, Decision tree regressor, and long short-term memory. The analysis results indicate that with network topology behavior as input to the model, the prediction will be more accurate than when isolated variables of a particular Bus in a network are used for prediction. This work suggests that network topology behavior data should be used for prediction in a power system network rather than the use of isolated data of a particular bus or exogenous data for prediction in a power system. Therefore, this research recommends that the accuracy of different predictive models be tested on power system parameters by hybridizing the network topology behavior dataset and the exogenous dataset.

show abstract

Section: Elm Ccelmnmentioning

confidence: 99%

Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review

Makanju,

Shongwe,

Famoriji

2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…The calculation of the trust degree is based on the user's initial trust degree and the historical behavior trust degree calculated from the historical access behavior [11]. A historical access behavior record item L(u, Mi, Mj, t, c, d), where u is the identity number of the user, Mi is the number of the domain where the user is located, and Mj is the number of the domain where the service or resource requested by the user is located., t is the time point, c is the penalty coefficient when the service or resource requested by the user fails, and d is the evaluation result of the access request behavior, such as "pass" or "fail" [12].…”

Section: Distributed Processing Systemmentioning

confidence: 99%

Trust Metrics Model for Distributed System Access Control

2021

DPS

View full text Add to dashboard Cite

With the massive growth of Internet technology and data resources, the scale of entities involved in distributed systems and the complexity of the system continue to increase, and higher requirements are placed on the quality of data transmission. Provide support for the integration and integration of information resources and services. However, the access security of the system needs to be considered when sharing information, so it is necessary to implement access control on information resources to avoid computer access security problems. In this paper, a distributed system access control model system is constructed, and the inter-domain access control module is tested for concurrent access. The test results show that with the increase of concurrent access, the system response time and throughput change smoothly, and the system is relatively stable. In the simulation experiment of the trust quantification model, with the increase of the number of successful accesses, the trust degree will also increase; with the rejection of the access request, the trust degree will decrease.

show abstract

State-of-the-art technologies for volt-var control to support the penetration of renewable energy into the smart distribution grids

Gholami

Islam²,

Rahman³

et al. 2022

Energy Reports

View full text Add to dashboard Cite

An Average Voltage Approach to Control Energy Storage Device and Tap Changing Transformers Under High Distributed Generation

Cited by 11 publications

References 39 publications

Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review

Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review

Trust Metrics Model for Distributed System Access Control

State-of-the-art technologies for volt-var control to support the penetration of renewable energy into the smart distribution grids

Contact Info

Product

Resources

About