A new deep learning method for the classification of power quality disturbances in hybrid power system

Eristi, Belkis; Eri̇şti̇, Hüseyin

doi:10.1007/s00202-022-01581-w

Cited by 12 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several frameworks have been used to solve graph-based problems, such as graph attention networks 78 , graph convolutional networks 79 , heterogeneous graph neural network 80 , heterogeneous graph attention network 81 , and so on. As well as classic neural network models 82 or models based on deep learning 83 – 85 , the choice of the appropriate framework is critical for successful application.…”

Section: Proposed Methodsmentioning

confidence: 99%

Interference recommendation for the pump sizing process in progressive cavity pumps using graph neural networks

Starke,

Hoppe,

Sartori

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Pump sizing is the process of dimensional matching of an impeller and stator to provide a satisfactory performance test result and good service life during the operation of progressive cavity pumps. In this process, historical data analysis and dimensional monitoring are done manually, consuming a large number of man-hours and requiring a deep knowledge of progressive cavity pump behavior. This paper proposes the use of graph neural networks in the construction of a prototype to recommend interference during the pump sizing process in a progressive cavity pump. For this, data from different applications is used in addition to individual control spreadsheets to build the database used in the prototype. From the pre-processed data, complex network techniques and the betweenness centrality metric are used to calculate the degree of importance of each order confirmation, as well as to calculate the dimensionality of the rotors. Using the proposed method a mean squared error of 0.28 is obtained for the cases where there are recommendations for order confirmations. Based on the results achieved, it is noticeable that there is a similarity of the dimensions defined by the project engineers during the pump sizing process, and this outcome can be used to validate the new design definitions.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Interference recommendation for the pump sizing process in progressive cavity pumps using graph neural networks

Starke,

Hoppe,

Sartori

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…DL is a type of machine learning and AI that consists essentially of a NN with several layers. The DL algorithms have the ability to automatically learn the best features from the original input signal without being specifically coded [212,213]. A neural network with more hidden layers allows for improving accuracy compared to the NN with only one layer.…”

Section: Machine Learning-based Techniquesmentioning

confidence: 99%

Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review

2023

View full text Add to dashboard Cite

Several factors affect existing electric power systems and negatively impact power quality (PQ): the high penetration of renewable and distributed sources that are based on power converters with or without energy storage, non-linear and unbalanced loads, and the deployment of electric vehicles. In addition, the power grid needs more improvement in the performances of real-time PQ monitoring, fault diagnosis, information technology, and advanced control and communication techniques. To overcome these challenges, it is imperative to re-evaluate power quality and requirements to build a smart, self-healing power grid. This will enable early detection of power system disturbances, maximize productivity, and minimize power system downtime. This paper provides an overview of the state-of-the-art signal processing- (SP) and pattern recognition-based power quality disturbances (PQDs) characterization techniques for monitoring purposes.

show abstract

“…However, improving the quality and distinctive characteristics of the data presented to a CNN algorithm will increase the classification performance. erefore, it is of great importance to search for an optimal time-frequency method in many deep learning method-based studies [7,9,18,47]. In this paper, scalograms representing the distinctive time-frequency features of PQD signals are obtained using the CWT method.…”

Section: The Proposed Recognition Systemmentioning

confidence: 99%

Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Eristi

Eri̇şti̇

2022

International Transactions on Electrical Energy Systems

Self Cite

View full text Add to dashboard Cite

Nowadays, due to the increase in the demand for electrical energy and the development of technology, the electrical devices have a more complex structure. This situation has increased the importance of concept of the power quality in the electrical power system. This paper presents a deep learning-based system to recognize the power quality disturbances (PQDs) in the solar photovoltaic (SPV) plant integrated with power system networks. The PQDs are analyzed using continuous wavelet transform (CWT) and image files are obtained from scalograms of CWT. Then, these image files are used to recognize PQDs with the help of a hybrid deep learning approach based on convolutional neural network (CNN), neighbor component analysis (NCA), and support vector machine (SVM). In this hybrid deep learning approach, the image files are given as input to AlexNet and GoogLeNet. The NCA is applied to the features obtained from the last dropout layer of each architecture. The distinctive features obtained from the NCA process are classified using the SVM algorithm. In order to evaluate the proposed approach, PQD data are obtained from a modified IEEE 13-bus test system including the SPV system. Several analyses and comparisons are carried out to verify the success of the proposed approach. It has been found that the proposed hybrid deep learning approach has the ability to accurately recognize the PQDs even if the SPV plant integrated power system has a negative effect on power quality.

show abstract

A new deep learning method for the classification of power quality disturbances in hybrid power system

Cited by 12 publications

References 31 publications

Interference recommendation for the pump sizing process in progressive cavity pumps using graph neural networks

Interference recommendation for the pump sizing process in progressive cavity pumps using graph neural networks

Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review

Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Contact Info

Product

Resources

About