Clustering Methods for Power Quality Measurements in Virtual Power Plant

Aksan, Fachrizal; Jasiński, Michał; Sikorski, Tomasz; Kaczorowska, Dominika; Rezmer, J.; Suresh, Vishnu; Leonowicz, Zbigniew; Kostyła, Paweł; Szymańda, Jarosław M.; Janík, P.

doi:10.3390/en14185902

Cited by 19 publications

(8 citation statements)

References 38 publications

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“…The Passport Business Database (Passport) [66] was used as the source of data for the K-Means Clustering Algorithm method [111]. Data with exceptions identified and previously excluded from tabular comparisons were downloaded for the full analysis.…”

Section: Clusterization Resultsmentioning

confidence: 99%

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

2022

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The Industry 4.0 idea influences the development of both charging stations and electromobility development, due to its emphasis on device communication, cooperation, and proximity. Therefore, in electromobility development, growing attention is paid to chargers’ infrastructure density and automotive electric vehicles’ accessibility. The main goal of this scientific paper was to present the electromobility development represented in the number of charging stations and its infrastructure development calculations. In this study, the sequence of methods was used to indicate and explore the research gap. The first was the Structured Literature Review (SLR) variation method. The second method was the classical tabular comparison of gathered results. The third research method was a cluster analysis based on secondary data with cross-country comparisons of the number of charging stations and electric cars. Therefore, this paper presents a theoretical discussion and practical business implications based on the achieved results of clusters and rankings. The main finding of this paper is that charging stations play a pivotal role in electromobility development in countries with already developed road infrastructure and maritime transportation. The charging stations can support energetic infrastructure, especially in countries with vast geographical distances. The charging stations and electric vehicles statistics presented in ratios and ranks proved similarities in the electromobility development patterns in the analyzed countries. This paper also presents the limitations of the performed study and identifies possible future research avenues.

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Section: Clusterization Resultsmentioning

confidence: 99%

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

2022

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“…The main objective of this phase is to prepare and convert the raw data into a format suitable for the HDL model. The implementation of data pre-processing is very important for any type of deep learning model as it can improve the model accuracy by improving the quality of the data and extracting valuable information from the data [ 36 ]. In this work, various data pre-processing techniques were used, ranging from normalising the data to splitting the dataset.…”

Section: Methodsmentioning

confidence: 99%

CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany

Aksan

Suresh

et al. 2023

Sensors

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The massive installation of renewable energy sources together with energy storage in the power grid can lead to fluctuating energy consumption when there is a bi-directional power flow due to the surplus of electricity generation. To ensure the security and reliability of the power grid, high-quality bi-directional power flow prediction is required. However, predicting bi-directional power flow remains a challenge due to the ever-changing characteristics of power flow and the influence of weather on renewable power generation. To overcome these challenges, we present two of the most popular hybrid deep learning (HDL) models based on a combination of a convolutional neural network (CNN) and long-term memory (LSTM) to predict the power flow in the investigated network cluster. In our approach, the models CNN-LSTM and LSTM-CNN were trained with two different datasets in terms of size and included parameters. The aim was to see whether the size of the dataset and the additional weather data can affect the performance of the proposed model to predict power flow. The result shows that both proposed models can achieve a small error under certain conditions. While the size and parameters of the dataset can affect the training time and accuracy of the HDL model.

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“…Поскольку он позво-ляет не только качественно, но и количественно распределить события на группы. Суть метода заключается в том, что он стремится уменьшить сумму квадратичных отклонений точек кластеров от центров кластеризации [15].…”

Section: проведение кластерного анализа с использованием библиотеки S...unclassified

Using the Scikit-Learn Library and Our Own Clustering Program to Process Statistical Data on Failures of Double-Circuit Gas Boilers

Shcherban,

Abu-Hamdi,

Karagadyan

2023

System Analysis &Amp; Mathematical Modeling

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At the present stage of technology development, a large number of industrial enterprises and service organizations often have the problem of processing arrays of statistical data on emergency situations and equipment failures. The increase of information quantity and its complexity, the multifactorial nature of the occurred failures and the risks, which are associated with it, do not allow us to use primitive mechanisms of statistical analysis and accounting. Clustering of statistical information is one of the ways to stratify incoming data, identify certain patterns in equipment failures and establish the weight of influencing factors. For small and medium-sized enterprises involved in the maintenance and repair process, this is especially important, because along with identifying the causes of malfunctions and failures, the application of clustering to an array of statistical data provides information for strategic planning - the volume of repair work, the volume of materials and components procurement, time management, and in general, prognostics. Processing of an array of data requires the involvement of appropriate software. For this purpose, both freely available packages (for example, the scikit-learn library with the Jupyter Notebook interactive computing environment used in the work) and proprietary software products designed for investigation of specific problem can be used. In the study, we conducted sequential processing and normalization of the initial data on boilers equipment failures for cluster analysis. Then the cluster analysis itself is performed and its results are compared, obtained on the basis of the scikit-learn library with Jupyter Notebook public interactive computing environment and the data received from clustering program of our own design.

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Clustering Methods for Power Quality Measurements in Virtual Power Plant

Cited by 19 publications

References 38 publications

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany

Using the Scikit-Learn Library and Our Own Clustering Program to Process Statistical Data on Failures of Double-Circuit Gas Boilers

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