Enhanced-Online-Random-Forest Model for Static Voltage Stability Assessment Using Wide Area Measurements

Su, Heng-Yi; Liu, Tzu-Yi

doi:10.1109/tpwrs.2018.2849717

Cited by 78 publications

(30 citation statements)

References 25 publications

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“…Then they perform curve fitting for each subset in order to infer a relation between the features and the voltage stability margin and they average the results to obtain one prediction. In [137], the authors use PMU data for online voltage stability assessment and deal with the problem of frequent model update. Frequently updating the learning model is useful to take system changes into account but it can take time.…”

Section: A Prediction Of Power Flowsmentioning

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 Prediction Of Power Flowsmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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“…RFs algorithm, which combines many decision trees, is an ensemble machine‐learning algorithm for classification and prediction [35, 36]. RFs algorithm is commonly used to train the fault diagnosis classifier with the fault data samples.…”

Section: Fault Diagnosis Classifier Based On Integrated Knowledge‐dmentioning

confidence: 99%

Fault diagnosis for open‐circuit faults in NPC inverter based on knowledge‐driven and data‐driven approaches

Kou

Liu

Cai

et al. 2020

IET Power Electronics

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In this study, the open-circuit faults diagnosis and location issue of the neutral-point-clamped (NPC) inverters are analysed. A novel fault diagnosis approach based on knowledge driven and data driven was presented for the open-circuit faults in insulated-gate bipolar transistors (IGBTs) of NPC inverter, and Concordia transform (knowledge driven) and random forests (RFs) technique (data driven) are employed to improve the robustness performance of the fault diagnosis classifier. First, the fault feature data of AC in either normal state or open-circuit faults states of NPC inverter are analysed and extracted. Second, the Concordia transform is used to process the fault samples, and it has been verified that the slopes of current trajectories are not affected by different loads in this study, which can help the proposed method to reduce overdependence on fault data. Moreover, then the transformed fault samples are adopted to train the RFs fault diagnosis classifier, and the fault diagnosis results show that the classification accuracy and robustness performance of the fault diagnosis classifier are improved. Finally, the diagnosis results of online fault diagnosis experiments show that the proposed classifier can locate the open-circuit fault of IGBTs in NPC inverter under the conditions of different loads.

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“…Previous research divided voltage stability status into two categories, voltage stable and voltage unstable, and applied the decision tree for classification [38][39][40][41][42][43]. Reference [39] employed the CART and C4.5 algorithms to build DTs, and combined the bagging and adaptive boosting methods to increase accuracy.…”

Section: Introductionmentioning

confidence: 99%

Decision Tree for Online Voltage Stability Margin Assessment Using C4.5 and Relief-F Algorithms

Meng

Zhang

2020

Energies

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In practical power system operation, knowing the voltage stability limits of the system is important. This paper proposes using a decision tree (DT) to extract guidelines through offline study results for assessing system voltage stability status online. Firstly, a sample set of DTs is determined offline by active power injection and bus voltage magnitude (P-V) curve analysis. Secondly, participation factor (PF) analysis and the Relief-F algorithm are used successively for attribute selection, which takes both the physical significance and the classification capabilities into consideration. Finally, the C4.5 algorithm is used to build the DT because it is more suitable for handling continuous variables. A practical power system is implemented to verify the feasibility of the proposed online voltage stability margin (VSM) assessment framework. Study results indicate that the operating guidelines extracted from the DT can help power system operators assess real time VSM effectively.

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Enhanced-Online-Random-Forest Model for Static Voltage Stability Assessment Using Wide Area Measurements

Cited by 78 publications

References 25 publications

Recent Developments in Machine Learning for Energy Systems Reliability Management

Recent Developments in Machine Learning for Energy Systems Reliability Management

Fault diagnosis for open‐circuit faults in NPC inverter based on knowledge‐driven and data‐driven approaches

Decision Tree for Online Voltage Stability Margin Assessment Using C4.5 and Relief-F Algorithms

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