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  • 1.
    Foorginezhad, Sahar
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science. Faculty of Advanced Technologies, Nano-Chemical Engineering Department, Shiraz University, Shiraz, 71348-51154, Iran.
    Zerafat, Mohammad Mahdi
    Faculty of Advanced Technologies, Nano-Chemical Engineering Department, Shiraz University, Shiraz, 71348-51154, Iran.
    Mohammadi, Younes
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Asadnia, Mohsen
    School of Engineering, Macquarie University, Sydney, New South Wales, 2109, Australia.
    Fabrication of tubular ceramic membranes as low-cost adsorbent using natural clay for heavy metals removal2022In: Cleaner Engineering and Technology, ISSN 2666-7908, Vol. 10, article id 100550Article in journal (Refereed)
    Abstract [en]

    Due to high toxicity and non-biodegradability, heavy metals pollution is between the major concerns of today's world. Among various techniques, membrane separation technology has taken precedence over other counterparts due to reduced separation units, low energy consumption, facile upscaling, and continuous separation. This study aims to fabricate ultrafiltration membranes made from abundant natural materials to reduce fabrication/operational costs, including precursors, sintering temperature, and filtration pressure. Moreover, SnO2/Montmorillonite nanocomposite is synthesized via the hydrothermal procedure and incorporated into the membrane matrix to decrease membrane fouling, enhance water flux, and improve heavy metals rejection rate. Results delineate 97.88–99.26%, 76.79–92.23%, and 24.97–64.74% of Cu (II), Zn (II), and Ni (II) removal from aqueous solutions in the 5–50 ppm range. An enhancement up to ∼40% is observed upon nanocomposite incorporation. Furthermore, ∼30% increase in Cu (II) removal is obtained for SnO2/MMT-incorporated membranes. Moreover, utilization of abundant natural minerals results in decreased fabrication/operational cost. Therefore, the obtained removal results and the estimated overall cost provide guidance for the large-scale utilization of low-cost membranes. As a result, the demand for heavy metals removal from wastewaters before their discharge to protect and govern the environment and implementation for agricultural purposes are fulfilled. 

  • 2.
    Mohammadi, Younes
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math H. J.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Voltage Sag Source Location Methods' Performance during Transient and Steady-state Periods2022In: 2022 20th International Conference on Harmonics & Quality of Power (ICHQP) Proceedings: “Power Quality in the Energy Transition”, IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    Online locating of voltage sag sources requires fast and accurate methods. In this context, this paper describes and compares the positive-sequence phasor-based (PB) and instantaneous-based (IB) methods during both transient and steady-state periods of voltage sags caused by grid faults and transformer energizing (TE). The methods are evaluated by applying 1992 voltage sags in a Brazilian power network. For fault cases, PB methods work better than IB. Some methods perform well within the transient period, and some do not work good enough and need improvements for online applications. For TE cases, IB methods are ahead of PB and most IB methods work very well in a half-cycle window of the transient period, whereas the PB methods must be improved. Regardless of the sag sources, the real current component method using IB values during the half-cycle window of transient period is the best method currently available, which can be used in online applications.

  • 3.
    Mohammadi, Younes
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Leborgne, Roberto C.
    Universidade Federal do Rio Grande do Sul, Osvaldo Aranha, 99, 90035-190, Porto Alegre, RS, Brazil.
    Polajžer, Boštjan
    University of Maribor, Koroška c. 46, 2000 Maribor, Slovenia.
    Modified methods for voltage-sag source detection using transient periods2022In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 207, article id 107857Article in journal (Refereed)
    Abstract [en]

    Real-time detection of the voltage sag sources' relative location requires fast and accurate methods. Therefore, in this paper, the transient period of voltage sags is used with useful detection information, which is not considered in the literature. In this context, this work firstly analyses the main positive-sequence phasor-based (PB) and instantaneous-based (IB) methods within both transient and steady-state periods of voltage sags caused by network faults and transformer energizing. Secondly, new methods are proposed using five different modifiers, applied in the transient period of voltage sags, i.e., half and one cycle time windows, to achieve a faster and more accurate response. These modifiers use the PB/IB criteria obtained from the existing methods, such as power, impedance, and current, and are applied as: The mean of the criterion changes, the first largest peak (FLP) on the criterion changes, the mean of the zero-mean criteria during a sag, the FLP of the zero-mean criteria during a sag, and the Trend (slope) of criteria's trajectories versus time. Voltage sag source detection methods are evaluated by applying 1992 simulated voltage sag events in a Brazilian regional power network. The results reveal that the proposed modifiers, used in the new methods, improve the ineffective existing methods by taking half/one cycle within a transient period of voltage sags. The modifiers also show an accuracy equal to other existing enhanced methods due to employing them within the transient period, thus evidencing their appropriateness. Correspondingly, a selection is made amongst the new modified methods in order to choose the most accurate time window (half or one cycle) for the methods. The selected modified methods are also tested by applying field measurements in a Slovenian power network to confirm their effectiveness in the transient short periods. According to a recommendation of the fastest and most accurate new methods in this study, an important application can be using the recommended methods as the directional function in the relays, along with an accurate voltage sag/fault inception time detection algorithm in real-time.

  • 4.
    Mohammadi, Younes
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Miraftabzadeh, Seyed Mahdi
    Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy.
    Bollen, Math H.J.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Longo, Michela
    Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy.
    Seeking patterns in rms voltage variations at the sub-10-minute scale from multiple locations via unsupervised learning and patterns’ post-processing2022In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 143, article id 108516Article in journal (Refereed)
    Abstract [en]

    This paper addresses the issue of seeking sub-10-min patterns in fast rms voltage variations from time-limited measurement data at multiple locations worldwide. This is a rarely considered time scale in studies that could be important for the incorrect operation of end-user equipment. Moreover, measurements from multiple locations could be significant from the view of seeking pattern methods. To learn more about this time scale, we propose an unsupervised learning method that employs a Kernel Principal Component Analysis (KPCA) with a Cosine kernel to extract principal features from 10-min time series of voltage variations with a 1-s resolution followed by a k-means clustering to group the features. The scheme is applied to measurements from 57 low-voltage locations in 19 countries from 2009 to 2018. Fifteen initial clusters/patterns are then extracted and converted to ten new (general) patterns using a clusters’ merging strategy with highly similar patterns employed in a new post-processing approach useful for multiple locations. Utilizing data from multiple locations in multiple countries ensures a level of generality of the patterns. It also allows comparing the locations. Next to the ten general patterns, some typical patterns are extracted separately for every location. A statistical indices analysis confirms that a complete picture of sub-10-min oscillations needs both statistical indices (quantifying level and variations) and the proposed framework (quantifying patterns). The extracted patterns could be used as a reference for testing/putting requirements on the grid-connected equipment and quantifying the grid’s hosting capacity for different types of new distributed generations connected to the grid. The framework is scalable and computationally cheap, making it appropriate for seeking typical patterns in the big data domain. Applying the framework to the much less understood phenomenon will result in providing general knowledge in the field of power quality.

  • 5.
    Mohammadi, Younes
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Miraftabzadeh, Seyed Mahdi
    Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Longo, Michela
    Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy.
    An unsupervised learning schema for seeking patterns in rms voltage variations at the sub-10-minute time scale2022In: Sustainable Energy, Grids and Networks, ISSN 2352-4677, Vol. 31, article id 100773Article in journal (Refereed)
    Abstract [en]

    This paper proposes an unsupervised learning schema for seeking the patterns in rms voltage variations at the time scale between 1 s and 10 min, a rarely considered time scale in studies but could be relevant for incorrect operation of end-user equipment. The proposed framework employs a Kernel Principal Component Analysis (KPCA) followed by a k-means clustering. The schema is applied on 10-min time series with a 1-s time resolution obtained from 44 different periods of a location south of Sweden. Then, ten patterns are obtained by reconstructing the 10-min time series from each cluster center. The results of the proposed schema show a good separation of cluster centers. Moreover, some statistical power-quality indices are applied to the whole dataset, showing voltage variation between (0.5–3) V over a 10-min window. Obtaining the most suitable indices and applying them to the ten obtained cluster centers and their belonging time series shows that the existing statistical indices may not be enough to show a complete picture of the sub-10 min actual variations. This outcome shows the necessity of extracting 10-min patterns through our proposed schema besides the existing statistics to quantify the voltage variations, levels, and patterns together. Findings of this paper are: Not forgetting the sub-10-min time scale; The necessity of employing both statistics and the proposed schema; Extraction of ten typical patterns; The need for the statistics and patterns that are justified as changes in equipment connected to the grid; and compressing a huge amount of data from power-quality monitoring. The proposed schema is applied to a much less understood phenomena/disturbance type so that this work will result in general knowledge beyond the specific case study.

  • 6.
    Mohammadi, Younes
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Miraftabzadeh, Seyed Mahdi
    Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Longo, Michela
    Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy.
    Voltage-sag source detection: Developing supervised methods and proposing a new unsupervised learning2022In: Sustainable Energy, Grids and Networks, E-ISSN 2352-4677, Vol. 32, article id 100855Article in journal (Refereed)
    Abstract [en]

    Recognition and analysis of voltage sags (dips) allow network operators to predict and prevent problems in real-life applications. Clearing the voltage sag source by direction detection methods is the most effective way to solve and improve the voltage sags and their related problems. However, the existing analytical methods use single or two input features as phasor-based (PB) or instantaneous-based (IB) values. Hence, their limited maximum accuracy is given at 93% and 84% when using PB features for noiseless and high-level noise signals, respectively. To increase the detection accuracy, the main contributions of this research by proposing machine learning (ML) methods include: (a) Developing nine supervised methods including support vector machine (SVM)-based, tree-based, others, and an ensemble learning of said methods, and providing a comparative analysis (b) Employing a set of PB, IB, and both PB and IB input features as noiseless and noisy; (c) Finding the best developed supervised methods by highest possible accuracy under subsets said in (b); (d) Proposing a new unsupervised method fed by both PB and IB features using a sparse principal component analysis (SPCA) applied to a k-means clustering with an internal SPCA approach. The proposed unsupervised schema does not use the upstream/downstream labels in developed supervised methods. Extensive simulations of voltage sags due to fault and transformer energizing on a Brazilian regional network show that regardless of the sag sources, input feature subset, and noise levels, the random forest (RF) models yield the best performance so that noiseless-RF (99.84%) using both PB and IB features is the most effective one. The proposed unsupervised method outcomes an overall accuracy of 99.17%-noiseless and about 90% for high-level noises. This performance is higher than analytical methods, very close to SVM-based supervised methods, and uses no predefined labels. Moreover, the results of Slovenian field measurements confirm the effectiveness of the best-developed supervised methods and the proposed unsupervised learning.

  • 7.
    Mohammadi, Younes
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science. Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
    Salarpour, Amir
    Sirjan University of Technology, Kerman, Iran.
    Chouhy Leborgne, Roberto
    Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
    Comprehensive strategy for classification of voltage sags source location using optimal feature selection applied to support vector machine and ensemble techniques2021In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 124, article id 106363Article in journal (Refereed)
    Abstract [en]

    The classification of voltage sags source location as downstream (DS) or upstream (US) of a monitor is a significant issue that should be taken into account when establishing mitigation strategies. Given the weak accuracy -bellow 90%- of single or two criteria analytical methods that are usually applied to locate sags, the application of intelligent methods is highly desirable. Therefore, this paper presents two classifiers of the Support Vector Machine (SVM) (three kernels) and Ensemble (three learners and nine methods) using genetic algorithm (GA) and a 10-fold cross-validation (CV). These methods throught extensive simulations on a Brazilian regional utility showed a 96.28% classification performance with Polynomial-SVM and a 99.11% performance for Decision Tree (DT)-Ensemble with the Totally Corrective Boosting (TotalBoost) method. Also, a comprehensive strategy to enhance the SVM accuracy and to keep the Ensemble performance by fewer appropriate features (which determine relative location of voltage sags sources) is presented. After testing three different feature selectors, an effective forward selection applied to the Polynomial-SVM concluded five appropriate optimum features and improved the accuracy of SVM up to 98.6%. The obtained optimum features applied to Ensemble showed a 99.2% performance in the DT-Ensemble-TotalBoost. Using the minimum obtained optimum features, a novel analytical rule based on maximum wins strategy has been proposed as well.

  • 8.
    Morais, Guilherme S.
    et al.
    Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
    Resener, Mariana
    Simon Fraser University Surrey, B.C., Canada.
    Ferraz, Bibiana M. P.
    Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
    Zanatta, Ana P.
    Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
    Ramos, Maicon J. S.
    Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
    Mohammadi, Younes
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Transient Stability and Protection Evaluation of Distribution Systems with Distributed Energy Resources2022In: Intelligent Control and Smart Energy Management: Renewable Resources and Transportation / [ed] Maude Josée Blondin; João Pedro Fernandes Trovão; Hicham Chaoui; Panos M. Pardalos, Springer Nature, 2022, Vol. 181, p. 343-393Chapter in book (Other academic)
    Abstract [en]

    The connection of distributed energy resources (DERs) in power distribution systems (PDSs) may bring new technical issues that must be analyzed and discussed by distribution companies, and the distribution system operator (DSO) must be aware about them. The issues could be complications in regard to the system voltage profile, power quality, power flow control, energy management, and frequency control and protection. Understanding the impacts on the dynamic behavior of PDSs caused by the presence of DERs is fundamental to guarantee the operation within the criteria established by regulatory agencies. This work presents an analysis of unbalanced distribution systems. Therefore, a modified version of the IEEE 34-node system in the presence of a synchronous distributed generation (SG) and some photovoltaic generation systems (PVs) is chosen. After multiple simulations carried out using DIgSILENT PowerFactory software, a total of five scenarios were selected to show the voltage stability analysis and the influences of protection system in the stability and integrity of the machines and to demonstrate the behavior of synchronous machine in a true way. The events include faults, reclosing operation, islanding, and changing the number of PVs connected, in which the operational limits of the SG are evaluated. In addition, the protection schemes must satisfy the performance requirements of selectivity, reliability, and sensitivity in order to ensure the safety of the system. Thus, this work focuses beyond the conventional protection schemes based on overcurrent detection, being introduced with other complementary functions. The results show some changes with regard to the voltage profile along the feeder due to the variation of PVs connected to the system, in which a greater number increase the voltage system. Besides, the behavior of one of the PVs is analyzed, being observed the contribution of reactive power during the short-circuit event. Other important achievements are related to the protection scheme adopted, in which using more sensitive adjustments by the protection devices may prevent excessive torsional efforts and help to avoid the loss of system stability.

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