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Dr. Michał Jasiński
Wroclaw University of Science and Technology

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Research Keywords & Expertise

1 Cluster Analysis
1 Data Mining
1 Distributed Generation
1 Power Quality
1 Renewable Energy

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Power Quality
Cluster Analysis
Distributed Generation
Data Mining
Renewable Energy
renewable energy sources

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Journal article
Published: 03 August 2021 in Energy
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Introducing new technologies in co-generation and tri-generation systems has led to a rapid growth toward the energy hubs (EHs) as an effective way for coupling among various energy types. On the other hand, the energy systems have usually been exposed to uncertain environments due to the presence of renewable energy sources (RESs) and interaction with the electricity markets. Hence, this paper develops a novel optimization framework based on a hybrid information gap decision theory (IGDT) and robust optimization (RO) to handle the optimal self-scheduling of the EH within a medium-term horizon for large consumers. The proposed mixed-integer linear programming (MILP) framework aims to capture the advantages of both the IGDT and RO techniques in dealing with the complicated binary variables and achieving the worst-case realization arisen from wind turbine generation and day-ahead (DA) electricity market uncertainties. The RO optimization approach is presented to model the DA electricity price uncertainty while the uncertainty related to the wind turbine generations is taken into account by the IGDT. Numerical results validate the capability of the model facing uncertainties. The amount of total operation cost of the EH increases by 8.6% taking into account the worst-case realization of uncertainties through the proposed hybrid IGDT-RO compared to the case considering perfect information. Besides, the results reveal that optimal decisions can be taken by the operator using the proposed hybrid IGDT-RO model.

ACS Style

Arsalan Najafi; Mahdi Pourakbari-Kasmaei; Michal Jasiniski; Matti Lehtonen; Zbigniew Leonowicz. A medium-term hybrid IGDT-Robust optimization model for optimal self scheduling of multi-carrier energy systems. Energy 2021, 238, 121661 .

AMA Style

Arsalan Najafi, Mahdi Pourakbari-Kasmaei, Michal Jasiniski, Matti Lehtonen, Zbigniew Leonowicz. A medium-term hybrid IGDT-Robust optimization model for optimal self scheduling of multi-carrier energy systems. Energy. 2021; 238 ():121661.

Chicago/Turabian Style

Arsalan Najafi; Mahdi Pourakbari-Kasmaei; Michal Jasiniski; Matti Lehtonen; Zbigniew Leonowicz. 2021. "A medium-term hybrid IGDT-Robust optimization model for optimal self scheduling of multi-carrier energy systems." Energy 238, no. : 121661.

Journal article
Published: 19 June 2021 in Energies
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The paper presents a power-quality analysis in the utility low-voltage network focusing on harmonic currents’ pollution. Usually, to forecast the modern electrical and electronic devices’ contribution to increasing the current total harmonic distortion factor (THDI) and exceeding the regulation limit, analyses based on tests and models of individual devices are conducted. In this article, a composite approach was applied. The performance of harmonic currents produced by sets of devices commonly used in commercial and residential facilities’ nonlinear loads was investigated. The measurements were conducted with the class A PQ analyzer (FLUKE 435) and dedicated to the specialized PC software. The experimental tests show that the harmonic currents produced by multiple types of nonlinear loads tend to reduce the current total harmonic distortion factor (THDI). The changes of harmonic content caused by summation and/or cancellation effects in total current drawn from the grid by nonlinear loads should be a key factor in harmonic currents’ pollution study. Proper forecasting of the level of harmonic currents injected into the utility grid helps to maintain the quality of electricity at an appropriate level and reduce active power losses, which have a direct impact on the price of electricity generation.

ACS Style

Łukasz Michalec; Michał Jasiński; Tomasz Sikorski; Zbigniew Leonowicz; Łukasz Jasiński; Vishnu Suresh. Impact of Harmonic Currents of Nonlinear Loads on Power Quality of a Low Voltage Network–Review and Case Study. Energies 2021, 14, 3665 .

AMA Style

Łukasz Michalec, Michał Jasiński, Tomasz Sikorski, Zbigniew Leonowicz, Łukasz Jasiński, Vishnu Suresh. Impact of Harmonic Currents of Nonlinear Loads on Power Quality of a Low Voltage Network–Review and Case Study. Energies. 2021; 14 (12):3665.

Chicago/Turabian Style

Łukasz Michalec; Michał Jasiński; Tomasz Sikorski; Zbigniew Leonowicz; Łukasz Jasiński; Vishnu Suresh. 2021. "Impact of Harmonic Currents of Nonlinear Loads on Power Quality of a Low Voltage Network–Review and Case Study." Energies 14, no. 12: 3665.

Journal article
Published: 09 June 2021 in Electronics
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The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

ACS Style

Sk Hassan; Arnab Maji; Michał Jasiński; Zbigniew Leonowicz; Elżbieta Jasińska. Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach. Electronics 2021, 10, 1388 .

AMA Style

Sk Hassan, Arnab Maji, Michał Jasiński, Zbigniew Leonowicz, Elżbieta Jasińska. Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach. Electronics. 2021; 10 (12):1388.

Chicago/Turabian Style

Sk Hassan; Arnab Maji; Michał Jasiński; Zbigniew Leonowicz; Elżbieta Jasińska. 2021. "Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach." Electronics 10, no. 12: 1388.

Journal article
Published: 21 May 2021 in Electronics
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A considerable fraction of the female workforce worldwide is making ends meet by doing various jobs informally at home or in nearby places, rather than at employers’ premises. The contribution of these female home-based workers (FHBWs) is significant to the country’s economic growth. FHBWs are often confronted with numerous occupational diseases due to a lack of awareness of occupational safety and health measures, and unhealthy living and working conditions. The informality of FHBWs prevents them from getting proper healthcare, safety, and other dispensations enjoyed by formal employees. Despite their undeniable importance, health issues of FHBWs are still overlooked. This study is an attempt to discover the frequent co-occurring occupational diseases encountered by FHBWs in Punjab, a province of Pakistan. Frequent itemset mining (FIM) or co-occurrence grouping is a technique of data science that identifies the associations among different entities in the data. Based on FIM, the D-GENE algorithm is applied in this study to efficiently discover frequent co-occurring diseases in the data obtained from the Punjab Home-based Workers Survey (2016). The far-reaching goal of the study is to bring awareness of the occupational health issues and safety risks to the health authorities as well as to the FHBWs.

ACS Style

Muhammad Yasir; Ayesha Ashraf; Muhammad Chaudhry; Farhad Hassan; Jee-Hyong Lee; Michał Jasiński; Zbigniew Leonowicz; Elżbieta Jasińska. D-GENE-Based Discovery of Frequent Occupational Diseases among Female Home-Based Workers. Electronics 2021, 10, 1230 .

AMA Style

Muhammad Yasir, Ayesha Ashraf, Muhammad Chaudhry, Farhad Hassan, Jee-Hyong Lee, Michał Jasiński, Zbigniew Leonowicz, Elżbieta Jasińska. D-GENE-Based Discovery of Frequent Occupational Diseases among Female Home-Based Workers. Electronics. 2021; 10 (11):1230.

Chicago/Turabian Style

Muhammad Yasir; Ayesha Ashraf; Muhammad Chaudhry; Farhad Hassan; Jee-Hyong Lee; Michał Jasiński; Zbigniew Leonowicz; Elżbieta Jasińska. 2021. "D-GENE-Based Discovery of Frequent Occupational Diseases among Female Home-Based Workers." Electronics 10, no. 11: 1230.

Journal article
Published: 10 May 2021 in IEEE Access
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The current paper has presented the study on theoretical dependences of the optimization parameter (the degree of transmission transparency) on the design factors of elastic-damping mechanism (EDM) in the transmission for tractors of small traction class (14 kN). The purpose of the study was to obtain a unified functional relationship that integrates early study results. The determination of a unified function was based on reaching a compromise by the method of indefinite Lagrange multipliers. With the help of the function, there have been obtained the dependences of the effect of EDM design factors on transmission processes when performing the main agricultural operations. Using a method based on finding a compromise solution (method of indefinite Lagrange multipliers), there have been substantiated design parameters of the elastic-damping mechanism in the tractor transmission and there has been identified an optimization parameter of the Lagrange function. For a better understanding of the obtained dependence, there has been carried out a three-dimensional visual analysis. Each factor used in the Lagrange function was a physical parameter of an elastic-damping mechanism. The resulting equation has given an opportunity to control mechanism parameters in natural operating conditions. The obtained Lagrange value has demonstrated that the mechanism absorbs 32% of fluctuations in the external traction load of a tractor, when working with the main agricultural tools.

ACS Style

Sergey Senkevich; Vadim Bolshev; Ekaterina Ilchenko; Prasun Chakrabarti; Michal Jasinski; Zbigniew Leonowicz; Mikhail Chaplygin. Elastic Damping Mechanism Optimization by Indefinite Lagrange Multipliers. IEEE Access 2021, 9, 71784 -71804.

AMA Style

Sergey Senkevich, Vadim Bolshev, Ekaterina Ilchenko, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz, Mikhail Chaplygin. Elastic Damping Mechanism Optimization by Indefinite Lagrange Multipliers. IEEE Access. 2021; 9 (99):71784-71804.

Chicago/Turabian Style

Sergey Senkevich; Vadim Bolshev; Ekaterina Ilchenko; Prasun Chakrabarti; Michal Jasinski; Zbigniew Leonowicz; Mikhail Chaplygin. 2021. "Elastic Damping Mechanism Optimization by Indefinite Lagrange Multipliers." IEEE Access 9, no. 99: 71784-71804.

Journal article
Published: 21 April 2021 in International Journal of Electrical Power & Energy Systems
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This paper presents an investigation into finding an optimal location for a planned Electrical Vehicle Charging Station (EVCS) with coordinated charging within the Microgrid installation at Wroclaw University of Science and Technology. The study uses a hybrid optimization algorithm built to combine the speed of MATPOWER and the search capabilities of a meta-heuristic optimization algorithm based on an extended ant colony which serves as the Energy Management System (EMS) of the Microgrid. The location obtained is based on an analysis of energy savings calculated on representative days of the year obtained through clustering and placing the EVCS on different nodes of the Microgrid consisting of distributed energy sources.

ACS Style

Vishnu Suresh; Najmeh Bazmohammadi; Przemyslaw Janik; Josep M. Guerrero; Dominika Kaczorowska; Jacek Rezmer; Michal Jasinski; Zbigniew Leonowicz. Optimal location of an electrical vehicle charging station in a local microgrid using an embedded hybrid optimizer. International Journal of Electrical Power & Energy Systems 2021, 131, 106979 .

AMA Style

Vishnu Suresh, Najmeh Bazmohammadi, Przemyslaw Janik, Josep M. Guerrero, Dominika Kaczorowska, Jacek Rezmer, Michal Jasinski, Zbigniew Leonowicz. Optimal location of an electrical vehicle charging station in a local microgrid using an embedded hybrid optimizer. International Journal of Electrical Power & Energy Systems. 2021; 131 ():106979.

Chicago/Turabian Style

Vishnu Suresh; Najmeh Bazmohammadi; Przemyslaw Janik; Josep M. Guerrero; Dominika Kaczorowska; Jacek Rezmer; Michal Jasinski; Zbigniew Leonowicz. 2021. "Optimal location of an electrical vehicle charging station in a local microgrid using an embedded hybrid optimizer." International Journal of Electrical Power & Energy Systems 131, no. : 106979.

Journal article
Published: 16 March 2021 in Electronics
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Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier’s performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.

ACS Style

Yogendra Solanki; Prasun Chakrabarti; Michal Jasinski; Zbigniew Leonowicz; Vadim Bolshev; Alexander Vinogradov; Elzbieta Jasinska; Radomir Gono; Mohammad Nami. A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches. Electronics 2021, 10, 699 .

AMA Style

Yogendra Solanki, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz, Vadim Bolshev, Alexander Vinogradov, Elzbieta Jasinska, Radomir Gono, Mohammad Nami. A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches. Electronics. 2021; 10 (6):699.

Chicago/Turabian Style

Yogendra Solanki; Prasun Chakrabarti; Michal Jasinski; Zbigniew Leonowicz; Vadim Bolshev; Alexander Vinogradov; Elzbieta Jasinska; Radomir Gono; Mohammad Nami. 2021. "A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches." Electronics 10, no. 6: 699.

Journal article
Published: 10 March 2021 in Electronics
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Analysis of the connection between different units that operate in the same area assures always interesting results. During this investigation, the concerned area was a virtual power plant (VPP) that operates in Poland. The main distributed resources included in the VPP are a 1.25 MW hydropower plant and an associated 0.5 MW energy storage system. The mentioned VPP was a source of synchronic, long-term, multipoint power quality (PQ) data. Then, for five related measurement points, the conclusion about the relation in point of PQ was performed using correlation analysis, the global index approach, and cluster analysis. Global indicators were applied in place of PQ parameters to reduce the amount of analyzed data and to check the correlation between phase values. For such a big dataset, the occurrence of outliers is certain, and outliers may affect the correlation results. Thus, to find and exclude them, cluster analysis (k-means algorithm, Chebyshev distance) was applied. Finally, the correlation between PQ global indicators of different measurement points was performed. It assured general information about VPP units’ relation in point of PQ. Under the investigation, both Pearson’s and Spearman’s rank correlation coefficients were considered.

ACS Style

Michał Jasiński. Combined Correlation and Cluster Analysis for Long-Term Power Quality Data from Virtual Power Plant. Electronics 2021, 10, 641 .

AMA Style

Michał Jasiński. Combined Correlation and Cluster Analysis for Long-Term Power Quality Data from Virtual Power Plant. Electronics. 2021; 10 (6):641.

Chicago/Turabian Style

Michał Jasiński. 2021. "Combined Correlation and Cluster Analysis for Long-Term Power Quality Data from Virtual Power Plant." Electronics 10, no. 6: 641.

Journal article
Published: 04 March 2021 in IEEE Access
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Distribution transformer is the most vital component in the power system. Failure of a transformer leads to loss of revenue besides affecting the reliability of power supply to consumers. It can lead to the non-availability of the transformer for a long duration. Due to this, it is important to maintain the good quality of mineral oil. Thus, if the quality of the mineral oil is reduced then its dielectric strength/quality is degraded. Finally, it can affect the services of the transformer, in terms of continuity of power supply. This paper entails the development of a mathematical MATLAB/Simulink model which able to calculate the life cycle of distribution transformer and exact oil changing frequency. With the help of proposed Matlab/Simulink models, the plot curves between furan content formation versus time, pollution index versus time, and dielectric strength of oil versus time are also prepared. The article methodology uses the newly proposed equations, that are in accordance with IEEE standards: IEEE Guide for Loading Mineral-Oil-Immersed Transformers and Step-Voltage Regulators (IEEE Std. C57.91-2011) and IEEE Guide for the Reclamation of Insulating Oil and Criteria for Its Use (IEEE Std C57.637-2015). Then the case study for a 100 kVA distribution transformer is realized. So, with the input values in the Simulink model of load current of the transformer, dielectric constant of oil and flash point of oil we can estimate the life of the distribution transformer. Harmonic load factor in our research work is not included, in order to reduce influence of harmonic load we need to installed the active filter, which is not covered in this paper.

ACS Style

Rajkumar Soni; Prasun Chakrabarti; Zbigniew Leonowicz; Michał Jasiński; Krzysztof Wieczorek; Vadim Bolshev. Estimation of Life Cycle of Distribution Transformer in Context to Furan Content Formation, Pollution Index, and Dielectric Strength. IEEE Access 2021, 9, 37456 -37465.

AMA Style

Rajkumar Soni, Prasun Chakrabarti, Zbigniew Leonowicz, Michał Jasiński, Krzysztof Wieczorek, Vadim Bolshev. Estimation of Life Cycle of Distribution Transformer in Context to Furan Content Formation, Pollution Index, and Dielectric Strength. IEEE Access. 2021; 9 ():37456-37465.

Chicago/Turabian Style

Rajkumar Soni; Prasun Chakrabarti; Zbigniew Leonowicz; Michał Jasiński; Krzysztof Wieczorek; Vadim Bolshev. 2021. "Estimation of Life Cycle of Distribution Transformer in Context to Furan Content Formation, Pollution Index, and Dielectric Strength." IEEE Access 9, no. : 37456-37465.

Journal article
Published: 20 February 2021 in Electronics
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This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

ACS Style

Imayanmosha Wahlang; Arnab Maji; Goutam Saha; Prasun Chakrabarti; Michal Jasinski; Zbigniew Leonowicz; Elzbieta Jasinska. Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography. Electronics 2021, 10, 495 .

AMA Style

Imayanmosha Wahlang, Arnab Maji, Goutam Saha, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz, Elzbieta Jasinska. Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography. Electronics. 2021; 10 (4):495.

Chicago/Turabian Style

Imayanmosha Wahlang; Arnab Maji; Goutam Saha; Prasun Chakrabarti; Michal Jasinski; Zbigniew Leonowicz; Elzbieta Jasinska. 2021. "Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography." Electronics 10, no. 4: 495.

Journal article
Published: 12 February 2021 in Energies
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One of the recent trends that concern renewable energy sources and energy storage systems is the concept of virtual power plants (VPP). The majority of research now focuses on analyzing case studies of VPP in different issues. This article presents the investigation that is based on a real VPP. That VPP operates in Poland and consists of hydropower plants (HPP), as well as energy storage systems (ESS). For specific analysis, cluster analysis, as a representative technique of data mining, was selected for power quality (PQ) issues. The used data represents 26 weeks of PQ multipoint synchronic measurements for 5 related to VPP points. The investigation discusses different input databases for cluster analysis. Moreover, as an extension to using classical PQ parameters as an input, the application of the global index was proposed. This enables the reduction of the size of the input database with maintaining the data features for cluster analysis. Moreover, the problem of the optimal number of cluster selection is discussed. Finally, the assessment of clustering results was performed to assess the VPP impact on PQ level.

ACS Style

Michał Jasiński; Tomasz Sikorski; Dominika Kaczorowska; Jacek Rezmer; Vishnu Suresh; Zbigniew Leonowicz; Paweł Kostyła; Jarosław Szymańda; Przemysław Janik; Jacek Bieńkowski; Przemysław Prus. A Case Study on Data Mining Application in a Virtual Power Plant: Cluster Analysis of Power Quality Measurements. Energies 2021, 14, 974 .

AMA Style

Michał Jasiński, Tomasz Sikorski, Dominika Kaczorowska, Jacek Rezmer, Vishnu Suresh, Zbigniew Leonowicz, Paweł Kostyła, Jarosław Szymańda, Przemysław Janik, Jacek Bieńkowski, Przemysław Prus. A Case Study on Data Mining Application in a Virtual Power Plant: Cluster Analysis of Power Quality Measurements. Energies. 2021; 14 (4):974.

Chicago/Turabian Style

Michał Jasiński; Tomasz Sikorski; Dominika Kaczorowska; Jacek Rezmer; Vishnu Suresh; Zbigniew Leonowicz; Paweł Kostyła; Jarosław Szymańda; Przemysław Janik; Jacek Bieńkowski; Przemysław Prus. 2021. "A Case Study on Data Mining Application in a Virtual Power Plant: Cluster Analysis of Power Quality Measurements." Energies 14, no. 4: 974.

Journal article
Published: 09 February 2021 in Energies
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The integration of virtual power plants (VPP) has become more popular. Thus, research on VPP for different issues is highly desirable. This article addresses power quality issues. The presented investigation is based on multipoint, synchronic measurements obtained from five points that are related to the VPP. This article provides a proposition and discussion of using one global index in place of the classical power quality (PQ) parameters. Furthermore, in the article, one new global power quality index was proposed. Then the PQ measurements, as well as global indexes, were used to prepare input databases for cluster analysis. The mentioned cluster analysis aimed to detect the short-term working conditions of VPP that were specific from the point of view of power quality. To realize this the hierarchical clustering using the Ward algorithm was realized. The article also presents the application of the cubic clustering criterion to support cluster analysis. Then the assessment of the obtained condition was realized using the global index to assure the general information of the cause of its occurrence. Furthermore, the article noticed that the application of the global index, assured reduction of database size to around 74%, without losing the features of the data.

ACS Style

Michał Jasiński; Tomasz Sikorski; Dominika Kaczorowska; Jacek Rezmer; Vishnu Suresh; Zbigniew Leonowicz; Paweł Kostyła; Jarosław Szymańda; Przemysław Janik; Jacek Bieńkowski; Przemysław Prus. A Case Study on a Hierarchical Clustering Application in a Virtual Power Plant: Detection of Specific Working Conditions from Power Quality Data. Energies 2021, 14, 907 .

AMA Style

Michał Jasiński, Tomasz Sikorski, Dominika Kaczorowska, Jacek Rezmer, Vishnu Suresh, Zbigniew Leonowicz, Paweł Kostyła, Jarosław Szymańda, Przemysław Janik, Jacek Bieńkowski, Przemysław Prus. A Case Study on a Hierarchical Clustering Application in a Virtual Power Plant: Detection of Specific Working Conditions from Power Quality Data. Energies. 2021; 14 (4):907.

Chicago/Turabian Style

Michał Jasiński; Tomasz Sikorski; Dominika Kaczorowska; Jacek Rezmer; Vishnu Suresh; Zbigniew Leonowicz; Paweł Kostyła; Jarosław Szymańda; Przemysław Janik; Jacek Bieńkowski; Przemysław Prus. 2021. "A Case Study on a Hierarchical Clustering Application in a Virtual Power Plant: Detection of Specific Working Conditions from Power Quality Data." Energies 14, no. 4: 907.

Journal article
Published: 22 January 2021 in IEEE Access
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Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is required as soon as possible. Machine learning technique has become reliable for medical treatment. With the help of a machine learning classifier algorithms, the doctor can detect the disease on time. For this perspective, Chronic Kidney Disease prediction has been discussed in this article. Chronic Kidney Disease dataset has been taken from the UCI repository. Seven classifier algorithms have been applied in this research such as artificial neural network, C5.0, Chi-square Automatic interaction detector, logistic regression, linear support vector machine with penalty L1 & with penalty L2 and random tree. The important feature selection technique was also applied to the dataset. For each classifier, the results have been computed based on (i) full features, (ii) correlation-based feature selection, (iii) Wrapper method feature selection, (iv) Least absolute shrinkage and selection operator regression, (v) synthetic minority over-sampling technique with least absolute shrinkage and selection operator regression selected features, (vi) synthetic minority over-sampling technique with full features. From the results, it is marked that LSVM with penalty L2 is giving the highest accuracy of 98.86% in synthetic minority over-sampling technique with full features. Along with accuracy, precision, recall, F-measure, area under the curve and GINI coefficient have been computed and compared results of various algorithms have been shown in the graph. Least absolute shrinkage and selection operator regression selected features with synthetic minority over-sampling technique gave the best after synthetic minority over-sampling technique with full features. In the synthetic minority over-sampling technique with least absolute shrinkage and selection operator selected features, again linear support vector machine gave the highest accuracy of 98.46%. Along with machine learning models one deep neural network has been applied on the same dataset and it has been noted that deep neural network achieved the highest accuracy of 99.6%.

ACS Style

Pankaj Chittora; Sandeep Chaurasia; Prasun Chakrabarti; Gaurav Kumawat; Tulika Chakrabarti; Zbigniew Leonowicz; Michal Jasinski; Lukasz Jasinski; Radomir Gono; Elzbieta Jasinska; Vadim Bolshev. Prediction of Chronic Kidney Disease - A Machine Learning Perspective. IEEE Access 2021, 9, 17312 -17334.

AMA Style

Pankaj Chittora, Sandeep Chaurasia, Prasun Chakrabarti, Gaurav Kumawat, Tulika Chakrabarti, Zbigniew Leonowicz, Michal Jasinski, Lukasz Jasinski, Radomir Gono, Elzbieta Jasinska, Vadim Bolshev. Prediction of Chronic Kidney Disease - A Machine Learning Perspective. IEEE Access. 2021; 9 ():17312-17334.

Chicago/Turabian Style

Pankaj Chittora; Sandeep Chaurasia; Prasun Chakrabarti; Gaurav Kumawat; Tulika Chakrabarti; Zbigniew Leonowicz; Michal Jasinski; Lukasz Jasinski; Radomir Gono; Elzbieta Jasinska; Vadim Bolshev. 2021. "Prediction of Chronic Kidney Disease - A Machine Learning Perspective." IEEE Access 9, no. : 17312-17334.

Journal article
Published: 19 January 2021 in Sustainability
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Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to forecast earthquakes is one of the biggest issues in geoscience. Machine learning technology can play a vital role in the field of geoscience for forecasting earthquakes. We aim to develop a method for forecasting the magnitude range of earthquakes using machine learning classifier algorithms. Three different ranges have been categorized: fatal earthquake; moderate earthquake; and mild earthquake. In order to distinguish between these classifications, seven different machine learning classifier algorithms have been used for building the model. To train the model, six different datasets of India and regions nearby to India have been used. The Bayes Net, Random Tree, Simple Logistic, Random Forest, Logistic Model Tree (LMT), ZeroR and Logistic Regression algorithms have been applied to each dataset. All of the models have been developed using the Weka tool and the results have been noted. It was observed that Simple Logistic and LMT classifiers performed well in each case.

ACS Style

Papiya Debnath; Pankaj Chittora; Tulika Chakrabarti; Prasun Chakrabarti; Zbigniew Leonowicz; Michal Jasinski; Radomir Gono; Elżbieta Jasińska. Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers. Sustainability 2021, 13, 971 .

AMA Style

Papiya Debnath, Pankaj Chittora, Tulika Chakrabarti, Prasun Chakrabarti, Zbigniew Leonowicz, Michal Jasinski, Radomir Gono, Elżbieta Jasińska. Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers. Sustainability. 2021; 13 (2):971.

Chicago/Turabian Style

Papiya Debnath; Pankaj Chittora; Tulika Chakrabarti; Prasun Chakrabarti; Zbigniew Leonowicz; Michal Jasinski; Radomir Gono; Elżbieta Jasińska. 2021. "Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers." Sustainability 13, no. 2: 971.

Journal article
Published: 17 December 2020 in Energies
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A virtual power plant (VPP) can be defined as the integration of decentralized units into one centralized control system. A VPP consists of generation sources and energy storage units. In this article, based on real measurements, the charging and discharging characteristics of the battery energy storage system (BESS) were determined, which represents a key element of the experimental virtual power plant operating in the power system in Poland. The characteristics were determined using synchronous measurements of the power of charge and discharge of the storage and the state of charge (SoC). The analyzed private network also includes a hydroelectric power plant (HPP) and loads. The article also examines the impact of charging and discharging characteristics of the BESS on its operation, analyzing the behavior of the storage unit for the given operation plans. The last element of the analysis is to control the power flow in the private network. The operation of the VPP for the given scenario of power flow control was examined. The aim of the scenario is to adjust the load of the private network to the level set by the function. The tests of power flow are carried out on the day on which the maximum power demand occurred. The analysis was performed for four cases: a constant value limitation when the HPP is in operation and when it is not, and two limits set by function during normal operation of the HPP. Thus, the article deals not only with the issue of determining the actual characteristics of charging and discharging the storage unit, but also their impact on the operation of the entire VPP.

ACS Style

Dominika Kaczorowska; Jacek Rezmer; Michal Jasinski; Tomasz Sikorski; Vishnu Suresh; Zbigniew Leonowicz; Pawel Kostyla; Jaroslaw Szymanda; Przemyslaw Janik. A Case Study on Battery Energy Storage System in a Virtual Power Plant: Defining Charging and Discharging Characteristics. Energies 2020, 13, 6670 .

AMA Style

Dominika Kaczorowska, Jacek Rezmer, Michal Jasinski, Tomasz Sikorski, Vishnu Suresh, Zbigniew Leonowicz, Pawel Kostyla, Jaroslaw Szymanda, Przemyslaw Janik. A Case Study on Battery Energy Storage System in a Virtual Power Plant: Defining Charging and Discharging Characteristics. Energies. 2020; 13 (24):6670.

Chicago/Turabian Style

Dominika Kaczorowska; Jacek Rezmer; Michal Jasinski; Tomasz Sikorski; Vishnu Suresh; Zbigniew Leonowicz; Pawel Kostyla; Jaroslaw Szymanda; Przemyslaw Janik. 2020. "A Case Study on Battery Energy Storage System in a Virtual Power Plant: Defining Charging and Discharging Characteristics." Energies 13, no. 24: 6670.

Journal article
Published: 14 December 2020 in Energies
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The concept of virtual power plants (VPP) was introduced over 20 years ago but is still actively researched. The majority of research now focuses on analyzing case studies of such installations. In this article, the investigation is based on a VPP in Poland, which contains hydropower plants (HPP) and energy storage systems (ESS). For specific analysis, the power quality (PQ) issues were selected. The used data contain 26 weeks of multipoint, synchronic measurements of power quality levels in four related points. The investigation is concerned with the application of a global index to a single-point assessment as well as an area-related assessment approach. Moreover, the problem of flagged data is discussed. Finally, the assessment of VPP’s impact on PQ level is conducted.

ACS Style

Michal Jasiński; Tomasz Sikorski; Dominika Kaczorowska; Jacek Rezmer; Vishnu Suresh; Zbigniew Leonowicz; Paweł Kostyla; Jarosław Szymańda; Przemysław Janik. A Case Study on Power Quality in a Virtual Power Plant: Long Term Assessment and Global Index Application. Energies 2020, 13, 6578 .

AMA Style

Michal Jasiński, Tomasz Sikorski, Dominika Kaczorowska, Jacek Rezmer, Vishnu Suresh, Zbigniew Leonowicz, Paweł Kostyla, Jarosław Szymańda, Przemysław Janik. A Case Study on Power Quality in a Virtual Power Plant: Long Term Assessment and Global Index Application. Energies. 2020; 13 (24):6578.

Chicago/Turabian Style

Michal Jasiński; Tomasz Sikorski; Dominika Kaczorowska; Jacek Rezmer; Vishnu Suresh; Zbigniew Leonowicz; Paweł Kostyla; Jarosław Szymańda; Przemysław Janik. 2020. "A Case Study on Power Quality in a Virtual Power Plant: Long Term Assessment and Global Index Application." Energies 13, no. 24: 6578.

Journal article
Published: 21 September 2020 in Energies
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This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small.

ACS Style

Tuan Pham Van; Dung Vo Tien; Zbigniew Leonowicz; Michal Jasinski; Tomasz Sikorski; Prasun Chakrabarti. Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive. Energies 2020, 13, 4946 .

AMA Style

Tuan Pham Van, Dung Vo Tien, Zbigniew Leonowicz, Michal Jasinski, Tomasz Sikorski, Prasun Chakrabarti. Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive. Energies. 2020; 13 (18):4946.

Chicago/Turabian Style

Tuan Pham Van; Dung Vo Tien; Zbigniew Leonowicz; Michal Jasinski; Tomasz Sikorski; Prasun Chakrabarti. 2020. "Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive." Energies 13, no. 18: 4946.

Journal article
Published: 24 July 2020 in IEEE Access
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The need for monitoring the electrical network parameters is identified to use methods and means to improve power supply reliability and power quality. The article lists the exiting sensors for monitoring electrical parameters and substantiates the necessity of monitoring the parameters at both sides of switching devices. In the paper, there is basic information on the structure, operation and capabilities of the monitoring system for power supply reliability and power quality. A functional electrical circuit of the device for monitoring the number and duration of power outages and voltage deviations is proposed for monitoring the parameters at both sides of switching devices. An algorithm for the device operation has also been developed, which allows detecting the main emergency modes in the consumer’s internal network. The article also describes laboratory tests of a prototype of the device for monitoring the number and duration of power outages and voltage deviations, which is based on the Arduino NANO V3 ATmega 328 microprocessor.

ACS Style

Vadim Bolshev; Alexander Vinogradov; Michal Jasinski; Tomasz Sikorski; Zbigniew Leonowicz; Radomir Gono. Monitoring the Number and Duration of Power Outages and Voltage Deviations at Both Sides of Switching Devices. IEEE Access 2020, 8, 137174 -137184.

AMA Style

Vadim Bolshev, Alexander Vinogradov, Michal Jasinski, Tomasz Sikorski, Zbigniew Leonowicz, Radomir Gono. Monitoring the Number and Duration of Power Outages and Voltage Deviations at Both Sides of Switching Devices. IEEE Access. 2020; 8 (99):137174-137184.

Chicago/Turabian Style

Vadim Bolshev; Alexander Vinogradov; Michal Jasinski; Tomasz Sikorski; Zbigniew Leonowicz; Radomir Gono. 2020. "Monitoring the Number and Duration of Power Outages and Voltage Deviations at Both Sides of Switching Devices." IEEE Access 8, no. 99: 137174-137184.

Journal article
Published: 15 June 2020 in Energies
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The article presents calculations and power flow of a real virtual power plant (VPP), containing a fragment of low and medium voltage distribution network. The VPP contains a hydropower plant (HPP), a photovoltaic system (PV) and energy storage system (ESS). The purpose of this article is to summarize the requirements for connection of generating units to the grid. Paper discusses the impact of the requirements on the maximum installed capacity of distributed energy resource (DER) systems and on the parameters of the energy storage unit. Firstly, a comprehensive review of VPP definitions, aims, as well as the characteristics of the investigated case study of the VPP project is presented. Then, requirements related to the regulation, protection and integration of DER and ESS with power systems are discussed. Finally, investigations related to influence of DER and ESS on power network condition are presented. One of the outcomes of the paper is the method of identifying the maximum power capacity of DER and ESS in accordance with technical network requirements. The applied method uses analytic calculations, as well as simulations using Matlab environment, combined with real measurement data. The obtained results allow the influence of the operating conditions of particular DER and ESS on power flow and voltage condition to be identified, the maximum power capacity of ESS intended for the planed VPP to be determined, as well as the influence of power control strategies implemented in a PV power plant on resources available for the planning and control of a VPP to be specified. Technical limitations of the DER and ESS are used as input conditions for the economic simulations presented in the accompanying paper, which is focused on investigations of economic efficiency.

ACS Style

Tomasz Sikorski; Michal Jasiński; Edyta Ropuszyńska-Surma; Magdalena Węglarz; Dominika Kaczorowska; Paweł Kostyla; Zbigniew Leonowicz; Robert Lis; Jacek Rezmer; Wilhelm Rojewski; Marian Sobierajski; Jarosław Szymańda; Daniel Bejmert; Przemysław Janik; Beata Solak. A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept: Technical Aspects. Energies 2020, 13, 3086 .

AMA Style

Tomasz Sikorski, Michal Jasiński, Edyta Ropuszyńska-Surma, Magdalena Węglarz, Dominika Kaczorowska, Paweł Kostyla, Zbigniew Leonowicz, Robert Lis, Jacek Rezmer, Wilhelm Rojewski, Marian Sobierajski, Jarosław Szymańda, Daniel Bejmert, Przemysław Janik, Beata Solak. A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept: Technical Aspects. Energies. 2020; 13 (12):3086.

Chicago/Turabian Style

Tomasz Sikorski; Michal Jasiński; Edyta Ropuszyńska-Surma; Magdalena Węglarz; Dominika Kaczorowska; Paweł Kostyla; Zbigniew Leonowicz; Robert Lis; Jacek Rezmer; Wilhelm Rojewski; Marian Sobierajski; Jarosław Szymańda; Daniel Bejmert; Przemysław Janik; Beata Solak. 2020. "A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept: Technical Aspects." Energies 13, no. 12: 3086.

Journal article
Published: 29 May 2020 in Energies
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This paper presents the analysis of power supply restoration time after failures occurring in power lines. It found that the power supply restoration time depends on several constituents, such as the time for obtaining information on failures, the time for information recognition, the time to repair failures, and the time for connection harmonization. All these constituents have been considered more specifically. The main constituents’ results values of the power supply restoration time were analyzed for the electrical networks of regional power supply company “Oreolenergo”, a branch of Interregional Distribution Grid Company (IDGC) of Center. The Delphi method was used for determining the time for obtaining information on failures as well as the time for information recognition. The method of mathematical statistics was used to determine the repair time. The determined power supply restoration time (5.28 h) is similar to statistical values of the examined power supply company (the deviation was equal to 9.9%). The technical means of electrical network automation capable of the reduction of the power supply restoration time have also been found. These means were classified according to the time intervals they shorten.

ACS Style

Alexander Vinogradov; Vadim Bolshev; Alina Vinogradova; Michał Jasiński; Tomasz Sikorski; Zbigniew Leonowicz; Radomir Goňo; Elżbieta Jasińska. Analysis of the Power Supply Restoration Time after Failures in Power Transmission Lines. Energies 2020, 13, 2736 .

AMA Style

Alexander Vinogradov, Vadim Bolshev, Alina Vinogradova, Michał Jasiński, Tomasz Sikorski, Zbigniew Leonowicz, Radomir Goňo, Elżbieta Jasińska. Analysis of the Power Supply Restoration Time after Failures in Power Transmission Lines. Energies. 2020; 13 (11):2736.

Chicago/Turabian Style

Alexander Vinogradov; Vadim Bolshev; Alina Vinogradova; Michał Jasiński; Tomasz Sikorski; Zbigniew Leonowicz; Radomir Goňo; Elżbieta Jasińska. 2020. "Analysis of the Power Supply Restoration Time after Failures in Power Transmission Lines." Energies 13, no. 11: 2736.