An Online Generator Start-Up Algorithm for Transmission System Self-Healing Based on MCTS and Sparse Autoencoder

Sun, Runjia; Liu, Yutian; Wang, Liang

doi:10.1109/tpwrs.2018.2890006

Cited by 44 publications

(8 citation statements)

References 34 publications

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“…The goal of the optimization of restoration paths is to maximize the load served at all times, to minimize the restoration time, to minimize the number and magnitude of the control actions to be taken, and eventually to form a skeleton network 9 . The restoration paths from the BS unit to NBS units are built by restoring transmission lines one by one to start NBS units 93‐95 . The objective of the optimization of the restoration path is to maximize the average importance degree of restoration path and formulated 11 and discussed in Equation (7).…”

Section: Optimization Models and Methodologies For Transmission Netwomentioning

confidence: 99%

Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience

Aziz

Liu

Waseem

et al. 2020

Int Trans Electr Energ Syst

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Background When a power system blackout occurs, it affects the economy of the country and every aspect of human life. Cascading failures can easily occur and cause a major blackout in the power grid due to the breakdown or failure of important nodes or links. Recently, transmission network reconfiguration (TNR) becomes a hot topic and has made many concerns after major blackouts of power systems. Aims TNR is the second‐stage action plan to restore power systems and plays a major role in the process of power system restoration. On the other hand, grid resilience involves a quick dynamic reconfiguration of power systems to minimize the propagation of attack influences on the grid. The motivations to include the works in this survey are based on the quality of the research performed in the transmission network reconfiguration problem for grid resilience. In this article, the state‐of‐the‐art review of recent progress in the network reconfiguration problem of the transmission system for grid resilience is discussed with practical challenges, technical issues, and power industry practices. Materials & Methods In this paper, complex network theory‐based indices with advantages, disadvantages, and their applications have been discussed to assess the important nodes and lines for network reconfiguration problem during sudden disturbances in power systems. Furthermore, optimization models have been presented with objective functions as well as their constraints. Taken together, optimization methodologies have been discussed to solve network reconfiguration problem with merits and demerits. Results This survey paper presents current trends in research and future research directions concerning transmission network reconfiguration for academic researchers and practicing engineers. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations. Discussion Based on a thorough comparison of literature some future perspectives are also discussed for transmission network reconfiguration problem for grid resilience. Conclusion This review paper provides a comprehensive review of current practices applied to transmission network reconfiguration. The core focus of this paper will remain on complex network theory‐based indices, optimization models, optimization methodologies, challenges, and technical issues, and discusses future direction for transmission network reconfiguration problem for grid resilience. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations.

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Section: Optimization Models and Methodologies For Transmission Netwomentioning

confidence: 99%

Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience

Aziz

Liu

Waseem

et al. 2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…In this scenario, the initial conditions and related parameter settings of the power system are the same as in the literature (SUN et al, 2018): the generator located at the No. 33 bus is set as a black-start generator.…”

Section: Simulation Of the Ieee 39 Bus Systemmentioning

confidence: 99%

“…To validate the effectiveness and accuracy of the proposed method, the results obtained by the proposed method are compared with those obtained by two methods in (Liu and Gu, 2007;SUN et al, 2018), as shown in Table 4. Method proposed by (SUN et al, 2018) is an online method which obtains a generator restoration sequence by searching the next transmission line to be restored based on real-time situation. Method proposed by (Liu and Gu, 2007) obtains a skeleton network based on the topological characteristics of power Frontiers in Energy Research frontiersin.org network.…”

Section: Comparison With Some Published Workmentioning

confidence: 99%

A Monte Carlo tree search-based method for decision making of generator serial restoration sequence

Chen²,

Han³

et al. 2023

Front. Energy Res.

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Reasonable generator serial restoration sequence is a key issue to the system restoration following blackouts. This paper proposed an optimization method for the decision making of generator serial restoration sequence based on Monte Carlo tree search algorithm. First, the generator serial restoration sequence mechanism during the restoration process is analyzed. Considering the maximization of the total power generation capacity as the objective function, this paper also consider generator’s hot start. Second, the Monte Carlo tree search algorithm (MCTS) is applied to decide the generator serial restoration sequence. In the simulation stage of MCTS, the Dijkstra’s algorithm is utilized to determine the shortest path between the selected generator and the recovered power system. Finally, the IEEE 39 bus system and Hebei power grid system are used to validate the proposed algorithm. Simulation results show that the proposed method is efficiency and it can provide an reasonable generator serial restoration sequence to maximizing power generation during the restoration process.

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“…In the LSTM and CNN models, the relationship between adjacent hidden layers is too close to migrate weights easily, 17 whereas in the SDAE model it is easy to transfer the weight of each layer because of its stacked structure. Therefore, the SDAE was chosen as the basis of the transfer learning algorithm in this paper 18 …”

Section: Introductionmentioning

confidence: 99%

Short‐term wind power prediction based on stacked denoised auto‐encoder deep learning and multi‐level transfer learning

Peng,

Yang,

et al. 2023

Wind Energy

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Wind power prediction (WPP) has an important impact on the security and reliability operations of the power grid. The major difficulty in power prediction of new, expanded, or reconstructed wind farms is the lack of operational data, which leads to insufficient training of the model and makes the prediction error of wind power become enormous. A short‐term WPP model based on stacked denoised auto‐encoder (SDAE) deep learning and multilevel transfer learning is proposed in this paper. First, the correlation coefficient between the samples of source wind farms and the target wind farm is calculated by using a network trained with the samples from the target wind farm. Second, the samples with high correlation coefficients in source wind farms are graded and migrated to the target wind farm to assist multilevel transfer learning. Finally, the samples from different grades are each used to train a layer of SDAE, and their weights and thresholds are migrated to the final network. The case study shows that the 24‐h‐day‐ahead normalized root‐mean‐square error (NRMSE) and 96‐h‐short‐term NRMSE obtained by the proposed method are 4.48% and 5.11% lower, respectively, compared with the model without transfer learning, which proves the effectiveness of the proposed model.

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An Online Generator Start-Up Algorithm for Transmission System Self-Healing Based on MCTS and Sparse Autoencoder

Cited by 44 publications

References 34 publications

Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience

Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience

A Monte Carlo tree search-based method for decision making of generator serial restoration sequence

Short‐term wind power prediction based on stacked denoised auto‐encoder deep learning and multi‐level transfer learning

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