Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method

Huang, Wanjun; Zhang, Xinran; Hill, David J.

doi:10.1109/isgt-asia.2019.8881311

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…In addition, the transient time of STVS is very short, it is difficult for modelbased methods to assess STVS in real-time [32], while datadriven methods can realize real-time STVSA through offline training. Reference [33] proposed an STVSA method based on an integrated graph metric set and an artificial neural network. Reference [34] developed an efficient time series (TS) data-driven scheme and a hierarchical clustering method to improve the speed of STVSA.…”

Section: Data-driven Stvsa Methodsmentioning

confidence: 99%

A Review of Data-Driven Short-Term Voltage Stability Assessment of Power Systems: Concept, Principle, and Challenges

Cao

2021

Mathematical Problems in Engineering

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With the rapid growth of power market reform and power demand, the power transmission capacity of a power grid is approaching its limit and the secure and stable operation of power systems becomes increasingly important. In particular, in modern power grids, the proportion of dynamic loads with fast recovery characteristics such as air conditioners, refrigerators, and industrial motors is increasing. As well as there is an increasing proportion of different forms of renewable energy in power systems. Therefore, the short-term voltage stability (STVS) of power systems cannot be ignored. This article comprehensively sorts out the STVS problems of power systems from the perspective of data-driven methods and discusses existing challenges.

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Section: Data-driven Stvsa Methodsmentioning

confidence: 99%

A Review of Data-Driven Short-Term Voltage Stability Assessment of Power Systems: Concept, Principle, and Challenges

Cao

2021

Mathematical Problems in Engineering

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“…It concentrates on one fault diagnosis method instead of all techniques. ANN is a pivotal AIT and has been widely used in numerous areas of power systems, such as insulated equipment fault diagnosis in power grids [21], current harmonic suppression [22], economic dispatch [23][24][25][26], fault diagnosis of power systems [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41], load prediction [42][43][44][45], and voltage stability analysis [46][47][48][49][50], due to its strong self-learning ability and good generalization performance. In addition, ANN-based fault diagnosis of PV systems has also shown excellent performance.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey

Yuan

Xiong

2022

Energies

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Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to ensure the reliability and efficiency of energy conversion. Therefore, an effective fault diagnosis method is essential. Artificial neural networks, a pivotal technique of artificial intelligence, have been developed and applied in many fields including the fault diagnosis of PV systems, due to their strong self-learning ability, good generalization performance, and high fault tolerance. This study reviews the recent research progress of ANN in PV system fault diagnosis. Different widely used ANN models, including MLP, PNN, RBF, CNN, and SAE, are discussed. Moreover, the input attributes of ANN models, the types of faults, and the diagnostic performance of ANN models are surveyed. Finally, the main challenges and development trends of ANN applied to the fault diagnosis of PV systems are outlined. This work can be used as a reference to study the application of ANN in the field of PV system fault diagnosis.

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Resilient power network structure for stable operation of energy systems: A transfer learning approach

2021

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Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method

Cited by 6 publications

References 16 publications

A Review of Data-Driven Short-Term Voltage Stability Assessment of Power Systems: Concept, Principle, and Challenges

A Review of Data-Driven Short-Term Voltage Stability Assessment of Power Systems: Concept, Principle, and Challenges

Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey

Resilient power network structure for stable operation of energy systems: A transfer learning approach

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