Power System Transient Stability Control Method Based on Deep Learning Hybrid Model

Zhang, Xin; Wang, Yawen; Xie, Penghai; Lin, Sajia; Luo, Hanxing; Ling, Hao; Li, Weixia

doi:10.1109/icpsasia52756.2021.9621750

Cited by 7 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The flowchart of the procedure proposed by Ashraf is shown in Figure 7. In [204], in order to determine the voltage stability of the system, a hybrid model based on machine learning techniques such as RF and LSTM was proposed. Then, in order to verify the proposed method, a simulation analysis was performed on a 68-node system.…”

Section: Stability Issues In the Power Systemmentioning

confidence: 99%

“…Both proposed methods were tested on the energy system of the Spanish island of Las Palmas, and the results obtained by the authors indicate that their methods are characterised by accuracy and high flexibility. In [209] the authors propose In [204], in order to determine the voltage stability of the system, a hybrid model based on machine learning techniques such as RF and LSTM was proposed. Then, in order to verify the proposed method, a simulation analysis was performed on a 68-node system.…”

Section: Stability Issues In the Power Systemmentioning

confidence: 99%

“…Deep learning [204,206,209,210] Neural networks [192,[194][195][196][198][199][200][201][202][203]208,211] Classification [192,197,205] Regression [207,208] Ensemble methods Bagging [204,208]…”

Section: Supervised Learningmentioning

confidence: 99%

See 2 more Smart Citations

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

View full text Add to dashboard Cite

The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network is being expanded and modernised. This contributes to the occurrence of various types of network operating states in practice. The appearance of a significant number of objects with random generation in the power system complicates the process of planning and controlling the operation of the power system. It is therefore necessary to constantly search for new methods and algorithms that allow operators to adapt to the changing operating conditions of the power grid. There are many different types of method in the literature, with varying effectiveness, that have been or are used in practice. So far, however, no one ideal, universal method or methodology has been invented that would enable (with equal effectiveness) all problems faced by the power system to be solved. This article presents an overview and a short description of research works available in the literature in which the authors have used modern methods to solve various problems in the field of power engineering. The article is an introduction to the special issue entitled Advances in the Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering. It is an overview of various current problems and the various methods used to solve them, which are used to cope with difficult situations. The authors also pointed out potential research gaps that can be treated as areas for further research.

show abstract

Section: Stability Issues In the Power Systemmentioning

confidence: 99%

Section: Stability Issues In the Power Systemmentioning

confidence: 99%

See 1 more Smart Citation

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

View full text Add to dashboard Cite

show abstract

“…In the study [24], the RF algorithm was used to select CAs. The solution to the EC problem using EPS modes was divided into two stages:…”

mentioning

confidence: 99%

Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements

Senyuk,

Safaraliev,

Pazderin

et al. 2023

Mathematics

View full text Add to dashboard Cite

Modern electrical power systems place special demands on the speed and accuracy of transient and steady-state process control. The introduction of renewable energy sources has significantly influenced the amount of inertia and uncertainty of transient processes occurring in energy systems. These changes have led to the need to clarify the existing principles for the implementation of devices for protecting power systems from the loss of small-signal and transient stability. Traditional methods of developing these devices do not provide the required adaptability due to the need to specify a list of accidents to be considered. Therefore, there is a clear need to develop fundamentally new devices for the emergency control of power system modes based on adaptive algorithms. This work proposes to develop emergency control methods based on the use of deep machine learning algorithms and obtained data from synchronized vector measurement devices. This approach makes it possible to ensure adaptability and high performance when choosing control actions. Recurrent neural networks, long short-term memory networks, restricted Boltzmann machines, and self-organizing maps were selected as deep learning algorithms. Testing was performed by using IEEE14, IEEE24, and IEEE39 power system models. Two data samples were considered: with and without data from synchronized vector measurement devices. The highest accuracy of classification of the control actions’ value corresponds to the long short-term memory networks algorithm: the value of the accuracy factor was 94.31% without taking into account the data from the synchronized vector measurement devices and 94.45% when considering this data. The obtained results confirm the possibility of using deep learning algorithms to build an adaptive emergency control system for power systems.

show abstract

Automatic Verification Method for Control Strategy of Regional Stability Control System Based on Cloud Host -Virtual Device

Wang,

Kong,

Chen

et al. 2023

2023 2nd Asian Conference on Frontiers of Power and Energy (ACFPE)

View full text Add to dashboard Cite

Power System Transient Stability Control Method Based on Deep Learning Hybrid Model

Cited by 7 publications

References 12 publications

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements

Automatic Verification Method for Control Strategy of Regional Stability Control System Based on Cloud Host -Virtual Device

Contact Info

Product

Resources

About