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The Order of Polonia Restituta (Order of the Rebirth of Poland) is a Polish state order established 4 February 1921, as a secondary award to the Order of the White Eagle. It is conferred on both military and civilians as well as on foreigners for outstanding achievements in the fields of education, science, sport, culture, art, economics, national defense, social work, civil service, or for furthering good relations between countries.
President of Poland
Institute, Department or Faculty Head
01 January 2020 - 30 August 2021
University Lecturer
01 February 2001 - 01 December 2012
Research or Laboratory Scientist
01 October 1997 - 30 August 2021
A graduate of the Electrical Department of the Wrocław University of Technology. In 2001 obtained the doctorate, in 2012 obtained the habilitation degree, in 2016 became a university professor. In 2019 received the full professor nomination in Czech Republic and in Poland.
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.
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 StyleArsalan 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 StyleArsalan 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.
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
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 StyleŁukasz 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 StyleŁukasz 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.
Waveforms distortion is a pressing concern in Smart Grids where a massive presence of new technologies in distributed energy resources and in advanced smart metering systems is expected. In this context, the increasing diffusion of high switching frequencies static converters and the growing usage of Power Line Communication push for research dealing with the assessment of waveforms with spectral components up to 150 kHz. The analysis of such waveforms is a challenge for researchers due to the contemporaneous presence of a high number of spectral components in the range of low- (up to 2 kHz) and high- (up to 150 kHz) frequencies, with their opposite needs in term of time window length (and frequency resolution). The main idea of this paper is to improve the performances of existing methods by using a joint method of analysis based on a profitable strategy of divide and conquer; the method guarantees the best compromise between accuracy and computational efforts. A Discrete Wavelet Transform initially divides the original waveform to obtain two frequency bands: the wavelet suitability for conducting multi-resolution time-frequency analysis on waveforms in different frequency bands with different frequency resolution is effectively exploited. Then, the sliding-window modified ESPRIT method and the sliding-window Discrete Fourier Transform which uses a Nuttal window are used for the analysis of the low- and high-frequency bands, respectively; the positive characteristics of each method are exploited, minimizing the drawbacks and integrating their behavior so that the whole joint method allows an accurate estimation of each low- and high-frequency spectral component with the required acceptable computational efforts. The proposed method is tested on synthetic and measured waveforms in terms of accuracy and computational efforts. The analysis of the numerical application results clearly reveals that the proposed method improves the performances of existing methods of analysis in the examined cases.
Guido Carpinelli; Antonio Bracale; Pietro Varilone; Tomasz Sikorski; Pawel Kostyla; Zbigniew Leonowicz. A new advanced method for an accurate assessment of harmonic and supraharmonic distortion in power system waveforms. IEEE Access 2021, 9, 1 -1.
AMA StyleGuido Carpinelli, Antonio Bracale, Pietro Varilone, Tomasz Sikorski, Pawel Kostyla, Zbigniew Leonowicz. A new advanced method for an accurate assessment of harmonic and supraharmonic distortion in power system waveforms. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleGuido Carpinelli; Antonio Bracale; Pietro Varilone; Tomasz Sikorski; Pawel Kostyla; Zbigniew Leonowicz. 2021. "A new advanced method for an accurate assessment of harmonic and supraharmonic distortion in power system waveforms." IEEE Access 9, no. : 1-1.
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.
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 StyleSk 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 StyleSk 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.
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.
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 StyleMuhammad 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 StyleMuhammad 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.
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.
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 StyleSergey 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 StyleSergey 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.
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.
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 StyleVishnu 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 StyleVishnu 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.
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.
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 StyleYogendra 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 StyleYogendra 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.
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.
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 StyleRajkumar 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 StyleRajkumar 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.
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.
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 StyleImayanmosha 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 StyleImayanmosha 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.
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.
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 StyleMichał 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 StyleMichał 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.
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.
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 StyleMichał 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 StyleMichał 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.
The Internet of Things (IoT) refers to a network of physical devices, which collects data and processes into a system without human intervention. In the commercialized market, IoT architectures are upgrading day by day to reduce data transmission costs, latency, and bandwidth usage for various application requirements. The extensively available IoT architectures and their specification resist the researchers to select a system-on-chip (SoC) for heterogeneous IoT applications. This paper seeks to comprehend the various IoT device specifications and their characteristics to support multiple applications. Moreover, microprocessor architectures and their components are detailed to facilitate developer knowledge in advanced methodology and technology. The various instructions set architectures (ISA) are implemented in a Zynq-7000 (xc7Zz20clg484-1) FPGA device to examine the feasibility of design space requirements for real-time hardware execution. To select specific system-on-chip (SoC) architecture for heterogeneous IoT applications, a genetic algorithm (GA) based optimization method is implemented in MATLAB. The proposed algorithm identifies the optimized SoC architecture concerning device parameters such as a clock, cache, RAM space, external storage, network support, etc. Further, the confusion matrix method evaluates the proposed algorithm’s accuracy, which yields 84.62% accuracy. The outcome of SoCs attained through the GA are tested by analyzing their execution time and performance using various evaluation benchmarks. This article helps the researchers and field engineers to comprehend the microarchitecture device configurations and to identify the superior SoC for next-generation IoT practices.
Ramesh Krishnamoorthy; Kalimuthu Krishnan; Bharatiraja Chokkalingam; Sanjeevikumar Padmanaban; Zbigniew Leonowicz; Jens Bo Holm-Nielsen; Massimo Mitolo. Systematic Approach for State-of-the-Art Architectures and System-on-Chip Selection for Heterogeneous IoT Applications. IEEE Access 2021, 9, 25594 -25622.
AMA StyleRamesh Krishnamoorthy, Kalimuthu Krishnan, Bharatiraja Chokkalingam, Sanjeevikumar Padmanaban, Zbigniew Leonowicz, Jens Bo Holm-Nielsen, Massimo Mitolo. Systematic Approach for State-of-the-Art Architectures and System-on-Chip Selection for Heterogeneous IoT Applications. IEEE Access. 2021; 9 (99):25594-25622.
Chicago/Turabian StyleRamesh Krishnamoorthy; Kalimuthu Krishnan; Bharatiraja Chokkalingam; Sanjeevikumar Padmanaban; Zbigniew Leonowicz; Jens Bo Holm-Nielsen; Massimo Mitolo. 2021. "Systematic Approach for State-of-the-Art Architectures and System-on-Chip Selection for Heterogeneous IoT Applications." IEEE Access 9, no. 99: 25594-25622.
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%.
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 StylePankaj 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 StylePankaj 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.
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.
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 StylePapiya 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 StylePapiya 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.
With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.
Sunny Katyara; Muhammad Fawad Shaikh; Shoaib Shaikh; Zahid Hussain Khand; Lukasz Staszewski; Veer Bhan; Abdul Majeed; Madad Ali Shah; Leonowicz Zbigniew. Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System. Electronics 2021, 10, 172 .
AMA StyleSunny Katyara, Muhammad Fawad Shaikh, Shoaib Shaikh, Zahid Hussain Khand, Lukasz Staszewski, Veer Bhan, Abdul Majeed, Madad Ali Shah, Leonowicz Zbigniew. Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System. Electronics. 2021; 10 (2):172.
Chicago/Turabian StyleSunny Katyara; Muhammad Fawad Shaikh; Shoaib Shaikh; Zahid Hussain Khand; Lukasz Staszewski; Veer Bhan; Abdul Majeed; Madad Ali Shah; Leonowicz Zbigniew. 2021. "Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System." Electronics 10, no. 2: 172.
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.
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 StyleDominika 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 StyleDominika 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.
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.
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 StyleMichal 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 StyleMichal 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.
This paper presents an energy management system for the microgrid present at Wroclaw University of Science and Technology. It has three components: a forecasting system, an optimizer and an optimized electrical vehicle charging station as a separate load for the system. The forecasting system is based on a deep learning model utilizing a Long Short-Term Memory (LSTM) – Autoencoder based architecture. The study provides a statistical analysis of its performance over several runs and addresses reliability and running time issues thereby building a case for its adoption. A MIDACO – MATPOWER combined optimization algorithm has been used as the optimization algorithm for energy management which intends to harness the speed of MATPOWER and the search capabilities of Mixed Integer Distributed Ant Colony Optimization (MIDACO) in finding an appropriate global minimum solution. The objective of the system is to minimize the import of power from the main grid resulting in improved self-sufficiency. Finally, an optimized electrical vehicle charging station model to maximize the renewable energy utilization within the facility is incorporated into the same.
Vishnu Suresh; Przemyslaw Janik; Josep M. Guerrero; Zbigniew Leonowicz; Tomasz Sikorski. Microgrid Energy Management System With Embedded Deep Learning Forecaster and Combined Optimizer. IEEE Access 2020, 8, 202225 -202239.
AMA StyleVishnu Suresh, Przemyslaw Janik, Josep M. Guerrero, Zbigniew Leonowicz, Tomasz Sikorski. Microgrid Energy Management System With Embedded Deep Learning Forecaster and Combined Optimizer. IEEE Access. 2020; 8 (99):202225-202239.
Chicago/Turabian StyleVishnu Suresh; Przemyslaw Janik; Josep M. Guerrero; Zbigniew Leonowicz; Tomasz Sikorski. 2020. "Microgrid Energy Management System With Embedded Deep Learning Forecaster and Combined Optimizer." IEEE Access 8, no. 99: 202225-202239.
This paper presents a two stage optimization approach for solving the excessive network fault currents and resetting of overcurrent relays based on Adaptive Protection Scheme (APS) concept. The first stage consists of Fault Current Limiter (FCL) optimization to restore fault currents within Circuit Breaker (CB) thermal capacity whether caused by Distributed Generation (DG) or not. The second stage consists of Directional Overcurrent Relay (DOCR) coordination based on the adaptive protection concept to reset DOCR in order to function properly in presence of FCL and DG. This is of great importance since FCL may cause over limitation of fault current during minimum load operation which may degrade DOCR performance. The optimization process has been achieved by formulating both the FCL sizing and location problem and DOCR coordination problem using Differential Evolution Multi-Objective (DEMO) algorithm. The proposal has effectively re-coordinated the DOCRs contemplating the effects of FCLs and DGs which mitigated the faults surpassing CB thermal limit, DOCR miscoordination and degraded performance caused by FCL over limitation.
Meng Yen Shih; Arturo Conde; Cesar Ángeles-Camacho; Erika Fernández; Zbigniew Leonowicz; Francisco Lezama; Jorge Chan. A two stage fault current limiter and directional overcurrent relay optimization for adaptive protection resetting using differential evolution multi-objective algorithm in presence of distributed generation. Electric Power Systems Research 2020, 190, 106844 .
AMA StyleMeng Yen Shih, Arturo Conde, Cesar Ángeles-Camacho, Erika Fernández, Zbigniew Leonowicz, Francisco Lezama, Jorge Chan. A two stage fault current limiter and directional overcurrent relay optimization for adaptive protection resetting using differential evolution multi-objective algorithm in presence of distributed generation. Electric Power Systems Research. 2020; 190 ():106844.
Chicago/Turabian StyleMeng Yen Shih; Arturo Conde; Cesar Ángeles-Camacho; Erika Fernández; Zbigniew Leonowicz; Francisco Lezama; Jorge Chan. 2020. "A two stage fault current limiter and directional overcurrent relay optimization for adaptive protection resetting using differential evolution multi-objective algorithm in presence of distributed generation." Electric Power Systems Research 190, no. : 106844.