2022
DOI: 10.3390/en15186641
|View full text |Cite
|
Sign up to set email alerts
|

Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid

Abstract: Power quality (PQ) problems, including voltage sag, flicker, and harmonics, are the main concerns for the grid operator. Among these disturbances, voltage sag, which affects the sensitive loads in the interconnected system, is a crucial problem in the transmission and distribution systems. The determination of the voltage sag relative location as a downstream (DS) and upstream (US) is an important issue that should be considered when mitigating the sag problem. Therefore, this paper proposes a novel approach t… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2

Citation Types

0
4
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
6
1

Relationship

2
5

Authors

Journals

citations
Cited by 9 publications
(4 citation statements)
references
References 55 publications
0
4
0
Order By: Relevance
“…This chapter selects the LSTM network, which is a highly representative deep learning algorithm, as the day ahead point prediction model to obtain photovoltaic output day ahead point prediction data. LSTM networks have excellent performance in processing time series, with many successful cases in time series forecasting such as photovoltaic output prediction, power system load prediction, stock prediction [16], fault prediction [17], etc.…”
Section: Distributed Photovoltaic Output Day Ahead Predictionmentioning
confidence: 99%
See 1 more Smart Citation
“…This chapter selects the LSTM network, which is a highly representative deep learning algorithm, as the day ahead point prediction model to obtain photovoltaic output day ahead point prediction data. LSTM networks have excellent performance in processing time series, with many successful cases in time series forecasting such as photovoltaic output prediction, power system load prediction, stock prediction [16], fault prediction [17], etc.…”
Section: Distributed Photovoltaic Output Day Ahead Predictionmentioning
confidence: 99%
“…This allows the model to save important information to the neuron state while effectively removing irrelevant information. The unique memory and forgetting mode of LSTM can significantly improve the problem of vanishing or exploding gradients during model training [18], solving the problem of dependence on long-term historical data in conventional recurrent neural networks and better adapting to prediction tasks of temporal data.…”
Section: Distributed Photovoltaic Output Day Ahead Predictionmentioning
confidence: 99%
“…It can be a beneficial approach to evaluate power and fuel consumption at this moment to solve difficulties linked to determining ship energy efficiency in the maritime sector [24]. With the advancement of technology, data-driven methods have expanded their field of application and proven their success in a wide variety of industries [25][26][27][28][29]. Obtaining data from a system in the maritime industry can be described as a challenging process until the last ten years.…”
Section: Introduction 1backgroundmentioning
confidence: 99%
“…In the literature, different methods related to artificial intelligence and statistical, which are support vector machine [23,24], multivariable regression method [25,26], artificial neural network [27], deep learning method [28,29], AdaBoost algorithm [30], Attentionbased Recurrent Neural Network [31], pattern recognition [32], multi-label random forest algorithm [33], are implemented to find the relative voltage sag location.…”
Section: Introductionmentioning
confidence: 99%