Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff

Ren, Chao; Xu, Yan; Zhang, Yuchen

doi:10.1186/s41601-018-0091-3

Cited by 34 publications

(27 citation statements)

References 24 publications

(52 reference statements)

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“…The database generation is then usually done offline, given the extensive simulation cost to build it, while the application of the resulting model trained on the dataset can be done offline or online, depending on the application and the context. [15], [16], [19], [22], [27]- [29], [32], [34], [35], [37], [38], [41], [43], [45], [47], [49]- [52], [54]- [56], [63], [64], [67]- [73], [75], [76], [81]- [83], [85]- [88], [90], [92], [93], [98], [102], [105], [107], [113], [115]- [117] Voltage stability [26], [30], [39], [40], [42], [44], [46], [48], [53],…”

Section: A Database Buildingmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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Section: A Database Buildingmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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“…28,29 This case study simulates the conditions when the generator of system ceases operation and compares these two kinds of methods in transient frequency prediction. The most directly related faults for transient frequency problem are the system critical transmission line and generator exiting operation.…”

Section: Case Studymentioning

confidence: 99%

“…The most directly related faults for transient frequency problem are the system critical transmission line and generator exiting operation. 28,29 This case study simulates the conditions when the generator of system ceases operation and compares these two kinds of methods in transient frequency prediction. The results show the effectiveness and adaptability of this proposed method.…”

Section: Case Studymentioning

confidence: 99%

Data inheritance–based updating method and its application in transient frequency prediction for a power system

Wang

Zhang

Ying³

et al. 2019

Int Trans Electr Energ Syst

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Summary Transient frequency prediction helps formulate and take emergency control measures in a timely way, which is of great significance to the security and stability of a power system. Conventional frequency prediction methods encounter challenges when taking both speed and accuracy into account. Machine–learning‐based prediction methods cannot readily achieve satisfactory prediction accuracy based on historical samples of transient faults; only by updating the prediction model online can these methods accommodate a transient fault, which may occur at any time. Aiming at the online update of the prediction model, the concept of data inheritance is presented and applied to the process of model updating. Based on this update method, a transient frequency prediction method considering model inheritance is proposed. In this prediction method, the historical transient fault samples of the power system are used to train the initial prediction model; in this way, the prediction performance can be quickly improved according to the newly incremented transient fault samples by inheriting the historical prediction model. Validations on the New England 39‐bus and NPCC 140‐bus systems demonstrate the effectiveness of the proposed method.

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“…The IEEE 118-bus system consists of 54 generators and 186 branches and is divided into three voltage levels, i.e., 138 kV, 161 kV, and 345 kV. The IEEE 300-bus system is constituted by 69 generators and 411 branches, with 13 voltage levels, i.e., 0.6 kV, 2.3 kV, 6.6 kV, 13.8 kV, 16.5 kV, 20 kV, 27 kV, 66 kV, 86 kV, 115 kV, 138 kV, 230 kV, and 345 kV [39][40][41]. The number of controlled variables in IEEE 118-bus system was 25, and the number of controlled variables in IEEE 300-bus system was 111.…”

Section: Case Studiesmentioning

confidence: 99%

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

Zhang

Yang

et al. 2019

Processes

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A novel transfer bees optimizer for reactive power optimization in a high-power system was developed in this paper. Q-learning was adopted to construct the learning mode of bees, improving the intelligence of bees through task division and cooperation. Behavior transfer was introduced, and prior knowledge of the source task was used to process the new task according to its similarity to the source task, so as to accelerate the convergence of the transfer bees optimizer. Moreover, the solution space was decomposed into multiple low-dimensional solution spaces via associated state-action chains. The transfer bees optimizer performance of reactive power optimization was assessed, while simulation results showed that the convergence of the proposed algorithm was more stable and faster, and the algorithm was about 4 to 68 times faster than the traditional artificial intelligence algorithms.

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Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff

Cited by 34 publications

References 24 publications

Recent Developments in Machine Learning for Energy Systems Reliability Management

Recent Developments in Machine Learning for Energy Systems Reliability Management

Data inheritance–based updating method and its application in transient frequency prediction for a power system

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

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