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Mr. Arash Moradzadeh
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

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Research Keywords & Expertise

0 Data Mining
0 Deep Learning
0 Energy
0 Energy Management
0 power transformers

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Energy
Deep Learning
Load forecasting
machine learning
Load Disaggregation
Energy Management
power transformers

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Short Biography

Arash Moradzadeh was born in Tabriz, Iran in 1991. He received a B.S. degree in electrical power engineering and an M.S. degree in power electronics and electrical machines from Islamic Azad University of Tabriz in Tabriz, Iran in 2016 and 2019, respectively. He is currently working toward a Ph.D. degree in power electrical engineering at the University of Tabriz in Tabriz, Iran. His current research interests include power and energy systems, cyber-physical systems, energy management, intelligent energy systems, transients in power systems, diagnostics and condition monitoring of power transformers, frequency response analysis, the application of data mining methods to design, and the optimization and analysis of energy systems.

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Review
Published: 02 June 2021 in Sustainable Cities and Society
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In recent years, energy saving has attracted the attention of researchers due to environment, energy, and reliability issues. Energy saving due to these advantages is one of the major steps toward sustainable cities and society. In this regard, the low-voltage section of the distribution system, including buildings and public lighting systems (PLSs), has great energy-saving potential. Accordingly, the present work reviews the potential of different energy-saving options and their environmental impact on buildings of different sectors and PLSs. In addition to direct energy-saving options such as using renewable energy sources and energy-efficient luminaries, available indirect options such as transactive energy, using energy storage systems and demand response programs are reviewed. For both the building and PLS sectors, available control strategies and technologies and related energy and emission saving potential are discussed. The detailed highlights of the previous works associated with the location of each research or experimental study are given in this review study. Finally, the key findings regarding the gap in the literature of the energy saving topic are discussed. This study is influential for policy-makers to take effective actions for energy saving through existing approaches and technologies, and is beneficial for researchers of the energy saving topic.

ACS Style

Omid Sadeghian; Arash Moradzadeh; Behnam Mohammadi-Ivatloo; Mehdi Abapour; Amjad Anvari-Moghaddam; Jeng Shiun Lim; Fausto Pedro Garcia Marquez. A comprehensive review on energy saving options and saving potential in low voltage electricity distribution networks: Building and public lighting. Sustainable Cities and Society 2021, 72, 103064 .

AMA Style

Omid Sadeghian, Arash Moradzadeh, Behnam Mohammadi-Ivatloo, Mehdi Abapour, Amjad Anvari-Moghaddam, Jeng Shiun Lim, Fausto Pedro Garcia Marquez. A comprehensive review on energy saving options and saving potential in low voltage electricity distribution networks: Building and public lighting. Sustainable Cities and Society. 2021; 72 ():103064.

Chicago/Turabian Style

Omid Sadeghian; Arash Moradzadeh; Behnam Mohammadi-Ivatloo; Mehdi Abapour; Amjad Anvari-Moghaddam; Jeng Shiun Lim; Fausto Pedro Garcia Marquez. 2021. "A comprehensive review on energy saving options and saving potential in low voltage electricity distribution networks: Building and public lighting." Sustainable Cities and Society 72, no. : 103064.

Journal article
Published: 03 February 2021 in Inventions
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Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting.

ACS Style

Arash Moradzadeh; Hamed Moayyed; Sahar Zakeri; Behnam Mohammadi-Ivatloo; A. Aguiar. Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid. Inventions 2021, 6, 15 .

AMA Style

Arash Moradzadeh, Hamed Moayyed, Sahar Zakeri, Behnam Mohammadi-Ivatloo, A. Aguiar. Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid. Inventions. 2021; 6 (1):15.

Chicago/Turabian Style

Arash Moradzadeh; Hamed Moayyed; Sahar Zakeri; Behnam Mohammadi-Ivatloo; A. Aguiar. 2021. "Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid." Inventions 6, no. 1: 15.

Journal article
Published: 18 January 2021 in IEEE Access
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Accurate and fast fault detection in transmission lines is of high importance to maintain the reliability of power systems. Most of the existing methods suffer from false detection of high-impedance faults. In this paper, the transfer function (TF) method is introduced to evaluate the effect of impedance and location of faults by analyzing the voltage and current signals in the frequency domain. Interpretation of the results of the TF method is considered as a weakness of this method. In order to alleviate this problem, a convolutional neural network (CNN) and the hybrid model of deep reinforcement learning (DRL) are utilized to identify and locate single-phase to ground short circuit faults in transmission lines. Single-phase to ground short circuit faults with various fault impedances are applied on an IEEE standard transmission line system. Then, the TF traces are calculated and are collected as input datasets for the proposed models. The fault location results for each network are evaluated via various statistical performance metrics such as correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE). The R-value of the CNN and DRL models in fault identification is presented as 96.12% and 98.04%, respectively. Finally, in the early detection of single-phase to ground short circuit fault location (high impedance), the results revealed the efficiency of the DRL model with R=96.61% compared to CNN with R=95.21%.

ACS Style

Hamid Teimourzadeh; Arash Moradzadeh; Maryam Shoaran; Behnam Mohammadi-Ivatloo; Reza Razzaghi. High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions. IEEE Access 2021, 9, 15796 -15809.

AMA Style

Hamid Teimourzadeh, Arash Moradzadeh, Maryam Shoaran, Behnam Mohammadi-Ivatloo, Reza Razzaghi. High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions. IEEE Access. 2021; 9 ():15796-15809.

Chicago/Turabian Style

Hamid Teimourzadeh; Arash Moradzadeh; Maryam Shoaran; Behnam Mohammadi-Ivatloo; Reza Razzaghi. 2021. "High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions." IEEE Access 9, no. : 15796-15809.

Original research
Published: 02 January 2021 in Journal of Ambient Intelligence and Humanized Computing
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In recent years, the introduction of practical and useful solutions to solve the non-intrusive load monitoring (NILM) as one of the sub-sectors of energy management has posed many challenges. In this paper, an effective and applicable solution based on deep learning called convolutional neural network (CNN) is employed for this purpose. The proposed method with the layer-to-layer structure and extraction of features in the power consumption (PC) curves of each household appliances will be able to detect and distinguish the type of electrical appliances (EAs). Likewise, the load disaggregation for the total home PC will be based on identifying the PC patterns of each EA. To do this, experimental evaluation of reference energy data disaggregation dataset (REDD) related to real-world data and measurement at low frequency is used. The PC curves of each EA are used as input data for training and testing the network. After initial training and testing by the PC data of EAs, the total PC of building obtained from the smart meter are used as input for each network in order to load disaggregation. The trained networks prove to be able to disaggregate the total PC for REDD houses 1, 2, 3, and 4 with a 96.17% mean accuracy. The presented results show the precision and efficiency of the suggested technique for solving NILM problems compared to other used methods.

ACS Style

Arash Moradzadeh; Behnam Mohammadi-Ivatloo; Mehdi Abapour; Amjad Anvari-Moghaddam; Saeid Gholami Farkoush; Sang-Bong Rhee. A practical solution based on convolutional neural network for non-intrusive load monitoring. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -15.

AMA Style

Arash Moradzadeh, Behnam Mohammadi-Ivatloo, Mehdi Abapour, Amjad Anvari-Moghaddam, Saeid Gholami Farkoush, Sang-Bong Rhee. A practical solution based on convolutional neural network for non-intrusive load monitoring. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-15.

Chicago/Turabian Style

Arash Moradzadeh; Behnam Mohammadi-Ivatloo; Mehdi Abapour; Amjad Anvari-Moghaddam; Saeid Gholami Farkoush; Sang-Bong Rhee. 2021. "A practical solution based on convolutional neural network for non-intrusive load monitoring." Journal of Ambient Intelligence and Humanized Computing , no. : 1-15.

Journal article
Published: 30 August 2020 in Sustainability
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Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model.

ACS Style

Arash Moradzadeh; Sahar Zakeri; Maryam Shoaran; Behnam Mohammadi-Ivatloo; Fazel Mohammadi. Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms. Sustainability 2020, 12, 7076 .

AMA Style

Arash Moradzadeh, Sahar Zakeri, Maryam Shoaran, Behnam Mohammadi-Ivatloo, Fazel Mohammadi. Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms. Sustainability. 2020; 12 (17):7076.

Chicago/Turabian Style

Arash Moradzadeh; Sahar Zakeri; Maryam Shoaran; Behnam Mohammadi-Ivatloo; Fazel Mohammadi. 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms." Sustainability 12, no. 17: 7076.

Journal article
Published: 17 August 2020 in IEEE Transactions on Industrial Informatics
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Power transformers usually confront various mechanical and electromagnetic stresses during operation that may lead to defects in their windings. The short circuit in the windings is one of those severe defects. Early detection of short circuits is necessary as extra heating in the shorted location can lead to progressive damage in windings insulation. In this paper, Isometric feature mapping (Isomap) is used as a nonlinear dimensionality reduction technique to locate turn-to-turn short circuit faults in transformer windings due to its capability of capturing the nonlinear phenomena in FRT of power transformers. It is revealed that, after constructing the Isometric mapping for a transformer, there is no need for any expertise to detect fault location even in non-direct (high-impedance) short circuits. In other words, it can be the first step for the automated interpretation of FRA of power transformers.

ACS Style

Arash Moradzadeh; Kazem Pourhossein; Behnam Mohammadi-Ivatloo; Fazel Mohammadi. Locating Inter-Turn Faults in Transformer Windings Using Isometric Feature Mapping of Frequency Response Traces. IEEE Transactions on Industrial Informatics 2020, 17, 6962 -6970.

AMA Style

Arash Moradzadeh, Kazem Pourhossein, Behnam Mohammadi-Ivatloo, Fazel Mohammadi. Locating Inter-Turn Faults in Transformer Windings Using Isometric Feature Mapping of Frequency Response Traces. IEEE Transactions on Industrial Informatics. 2020; 17 (10):6962-6970.

Chicago/Turabian Style

Arash Moradzadeh; Kazem Pourhossein; Behnam Mohammadi-Ivatloo; Fazel Mohammadi. 2020. "Locating Inter-Turn Faults in Transformer Windings Using Isometric Feature Mapping of Frequency Response Traces." IEEE Transactions on Industrial Informatics 17, no. 10: 6962-6970.

Conference paper
Published: 04 August 2020 in 2020 28th Iranian Conference on Electrical Engineering (ICEE)
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Considering the importance of energy and the necessity of its management, this paper examines residential energy disaggregation/non-intrusive load monitoring. Support Vector Machine (SVM) has been proposed as one of the most powerful machine learning applications to solve this problem. The advantage of this method over other methods is the feature extraction of data and their classification based on recognized patterns. The proposed method is conducted on two REDD and AMPds datasets, which are related to real-world measurements. In the proposed method, SVM is trained by each of the characteristics of a particular electrical appliance. Then, the trained network shows the closest recognition to identify the given electrical appliance and predict the total power consumption of homes. The accuracy obtained for the datasets shows the applicability of the proposed method for load disaggregation.

ACS Style

Arash Moradzadeh; Sevda Zeinal-Kheiri; Behnam Mohammadi-Ivatloo; Mehdi Abapour; Amjad Anvari-Moghaddan. Support Vector Machine-Assisted Improvement Residential Load Disaggregation. 2020 28th Iranian Conference on Electrical Engineering (ICEE) 2020, 1 -6.

AMA Style

Arash Moradzadeh, Sevda Zeinal-Kheiri, Behnam Mohammadi-Ivatloo, Mehdi Abapour, Amjad Anvari-Moghaddan. Support Vector Machine-Assisted Improvement Residential Load Disaggregation. 2020 28th Iranian Conference on Electrical Engineering (ICEE). 2020; ():1-6.

Chicago/Turabian Style

Arash Moradzadeh; Sevda Zeinal-Kheiri; Behnam Mohammadi-Ivatloo; Mehdi Abapour; Amjad Anvari-Moghaddan. 2020. "Support Vector Machine-Assisted Improvement Residential Load Disaggregation." 2020 28th Iranian Conference on Electrical Engineering (ICEE) , no. : 1-6.

Journal article
Published: 16 July 2020 in Electronics
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Transformation of the energy sector due to the appearance of plug-in electric vehicles (PEVs) has faced the researchers with challenges in recent years. The foremost challenge is uncertain behavior of a PEV that hinders operators determining a deterministic load profile. Load forecasting of PEVs is so crucial in both operating and planning of the energy systems. PEV load demand mainly depends on traveling behavior of them. This paper tries to present an accurate model to forecast PEVs’ traveling behavior in order to extract the PEV load profile. The presented model is based on machine-learning techniques; namely, a generalized regression neural network (GRNN) that correlates between PEVs’ arrival/departure times and traveling behavior is considered in the model. The results show the ability of the GRNN to communicate between arrival/departure times of PEVs and the distance traveled by them with a correlation coefficient (R) of 99.49% for training and 98.99% for tests. Therefore, the trained and saved GRNN model is ready to forecast PEVs’ trip length based on training and testing with historical data. Finally, the results indicate the importance of implementing more accurate methods to predict PEVs to gain the significant advantages in the importance of electrical energy in vehicles in the years to come.

ACS Style

Amin Mansour-Saatloo; Arash Moradzadeh; Behnam Mohammadi-Ivatloo; Ali Ahmadian; Ali Elkamel. Machine Learning Based PEVs Load Extraction and Analysis. Electronics 2020, 9, 1150 .

AMA Style

Amin Mansour-Saatloo, Arash Moradzadeh, Behnam Mohammadi-Ivatloo, Ali Ahmadian, Ali Elkamel. Machine Learning Based PEVs Load Extraction and Analysis. Electronics. 2020; 9 (7):1150.

Chicago/Turabian Style

Amin Mansour-Saatloo; Arash Moradzadeh; Behnam Mohammadi-Ivatloo; Ali Ahmadian; Ali Elkamel. 2020. "Machine Learning Based PEVs Load Extraction and Analysis." Electronics 9, no. 7: 1150.

Journal article
Published: 03 June 2020 in Energies
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Yearly generation maintenance scheduling (GMS) of generation units is important in each system such as combined heat and power (CHP)-based systems to decrease sudden failures and premature degradation of units. Imposing repair costs and reliability deterioration of system are the consequences of ignoring the GMS program. In this regard, this research accomplishes GMS inside CHP-based systems in order to determine the optimal intervals for predetermined maintenance required duration of CHPs and other units. In this paper, cost minimization is targeted, and violation of units’ technical constraints like feasible operation region of CHPs and power/heat demand balances are avoided by considering related constraints. Demand-response-based short-term generation scheduling is accomplished in this paper considering the maintenance intervals obtained in the long-term plan. Numerical simulation is performed and discussed in detail to evaluate the application of the suggested mixed-integer quadratic programming model that implemented in the General Algebraic Modeling System software package for optimization. Numerical simulation is performed to justify the model effectiveness. The results reveal that long-term maintenance scheduling considerably impacts short-term generation scheduling and total operation cost. Additionally, it is found that the demand response is effective from the cost perspective and changes the generation schedule.

ACS Style

Omid Sadeghian; Arash Moradzadeh; Behnam Mohammadi-Ivatloo; Mehdi Abapour; Fausto Pedro Garcia Marquez. Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach. Energies 2020, 13, 2840 .

AMA Style

Omid Sadeghian, Arash Moradzadeh, Behnam Mohammadi-Ivatloo, Mehdi Abapour, Fausto Pedro Garcia Marquez. Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach. Energies. 2020; 13 (11):2840.

Chicago/Turabian Style

Omid Sadeghian; Arash Moradzadeh; Behnam Mohammadi-Ivatloo; Mehdi Abapour; Fausto Pedro Garcia Marquez. 2020. "Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach." Energies 13, no. 11: 2840.

Journal article
Published: 31 May 2020 in Applied Sciences
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Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

ACS Style

Arash Moradzadeh; Amin Mansour-Saatloo; Behnam Mohammadi-Ivatloo; Amjad Anvari-Moghaddam. Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings. Applied Sciences 2020, 10, 3829 .

AMA Style

Arash Moradzadeh, Amin Mansour-Saatloo, Behnam Mohammadi-Ivatloo, Amjad Anvari-Moghaddam. Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings. Applied Sciences. 2020; 10 (11):3829.

Chicago/Turabian Style

Arash Moradzadeh; Amin Mansour-Saatloo; Behnam Mohammadi-Ivatloo; Amjad Anvari-Moghaddam. 2020. "Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings." Applied Sciences 10, no. 11: 3829.

Journal article
Published: 14 April 2020 in Sustainability
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The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM.

ACS Style

Arash Moradzadeh; Omid Sadeghian; Kazem Pourhossein; Behnam Mohammadi-Ivatloo; Amjad Anvari-Moghaddam. Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis. Sustainability 2020, 12, 3158 .

AMA Style

Arash Moradzadeh, Omid Sadeghian, Kazem Pourhossein, Behnam Mohammadi-Ivatloo, Amjad Anvari-Moghaddam. Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis. Sustainability. 2020; 12 (8):3158.

Chicago/Turabian Style

Arash Moradzadeh; Omid Sadeghian; Kazem Pourhossein; Behnam Mohammadi-Ivatloo; Amjad Anvari-Moghaddam. 2020. "Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis." Sustainability 12, no. 8: 3158.

Conference paper
Published: 01 September 2019 in 2019 54th International Universities Power Engineering Conference (UPEC)
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In this paper, disk space variations (DSV) as one of common transformer winding defects, has been practically applied to a transformer winding in some specific locations and with various extents. To locate DSV faults, Convolutional neural networks (CNN) has been applied to frequency response traces of the tested winding. It has been presented that the proposed method has accurate fault location capability. Convolutional Neural Networks was utilized to extract important features from frequency response traces to detect DSV location in transformer winding.

ACS Style

Arash Moradzadeh; Kazem Pourhossein. Location of Disk Space Variations in Transformer Winding using Convolutional Neural Networks. 2019 54th International Universities Power Engineering Conference (UPEC) 2019, 1 -5.

AMA Style

Arash Moradzadeh, Kazem Pourhossein. Location of Disk Space Variations in Transformer Winding using Convolutional Neural Networks. 2019 54th International Universities Power Engineering Conference (UPEC). 2019; ():1-5.

Chicago/Turabian Style

Arash Moradzadeh; Kazem Pourhossein. 2019. "Location of Disk Space Variations in Transformer Winding using Convolutional Neural Networks." 2019 54th International Universities Power Engineering Conference (UPEC) , no. : 1-5.

Conference paper
Published: 01 September 2019 in 2019 54th International Universities Power Engineering Conference (UPEC)
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Turn-to-turn short circuit fault is one of the most important failures in transformer windings. Turn-to-Turn fault can be occurred due to removal of insulation between winding turns. Early detection of Turn-to-Turn fault (high impedance short circuit) can prevent a direct short circuit. Frequency Response Analysis (FRA) as a well-known method, introduced for transformer winding mechanical defect recognition, is able to recognize the turn-to-turn fault potential but its interpretation to detect exact location of high impedance short circuit fault is difficult. In this regard, producing a mapping between the frequency response and the exact location of each fault can be helpful. In this paper, this mapping was made using support vector regression (SVR). Extracted features from frequency responses are used to train and test SVR. The results show that this method is able to detect turn-to-turn faults in the transformer winding even in their early stages.

ACS Style

Arash Moradzadeh; Kazem Pourhossein. Application of Support Vector Machines to Locate Minor Short Circuits in Transformer Windings. 2019 54th International Universities Power Engineering Conference (UPEC) 2019, 1 -6.

AMA Style

Arash Moradzadeh, Kazem Pourhossein. Application of Support Vector Machines to Locate Minor Short Circuits in Transformer Windings. 2019 54th International Universities Power Engineering Conference (UPEC). 2019; ():1-6.

Chicago/Turabian Style

Arash Moradzadeh; Kazem Pourhossein. 2019. "Application of Support Vector Machines to Locate Minor Short Circuits in Transformer Windings." 2019 54th International Universities Power Engineering Conference (UPEC) , no. : 1-6.

Conference paper
Published: 01 August 2019 in 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)
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Turn-to-turn insulations of transformer windings may degrade gradually because of mechanical forces, thermal stresses or chemical corrosion. Degradation decreases impedances of inter-turn insulations that finally may lead to a solid turn-to-turn short circuit. In this paper, early detection of turn-to-turn faults in transformers windings has been studied, in its high-impedance stage, using Artificial Neural Networks (ANN) based on its Frequency Response (FR). For this purpose, a model winding has been used as test object to approve capability of the proposed approach. A variety of low impedance and high impedance short circuit faults were tested on the model winding. Then the frequency response of winding in both intact and defected conditions is measured using Low Voltage Impulse (LVI) test. A mapping between frequency response and exact location of each fault was made using multi-layer perceptron (MLP) neural network. Extracted features from frequency responses are used to train and test the proposed MLP. The results show that this method is able to detect turn-to-turn faults in transformer winding even in their early stages.

ACS Style

Arash Moradzadeh; Kazem Pourhossein. Early Detection of Turn-to-Turn Faults in Power Transformer Winding: An Experimental Study. 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) 2019, 199 -204.

AMA Style

Arash Moradzadeh, Kazem Pourhossein. Early Detection of Turn-to-Turn Faults in Power Transformer Winding: An Experimental Study. 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). 2019; ():199-204.

Chicago/Turabian Style

Arash Moradzadeh; Kazem Pourhossein. 2019. "Early Detection of Turn-to-Turn Faults in Power Transformer Winding: An Experimental Study." 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) , no. : 199-204.

Conference paper
Published: 01 August 2019 in 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)
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A turn-to-turn short circuit fault is One of the most important defects in transformer windings that is most difficult to diagnosis. Degradation decreases impedances of inter-turn insulations that finally may lead to a solid turn-to-turn short circuit. In this paper, early detection of turn-to-turn faults in transformers windings has been studied, in its high-impedance stage, using Convolutional Neural Network (CNN) based on extracting features from frequency response traces. For this purpose, a model winding has been used as test object to approve capability of the proposed approach. A variety of low impedance and high impedance short circuit faults were tested on the model winding. The results show that this method is able to detect turn-to-turn faults in transformer winding even in their early stages.

ACS Style

Arash Moradzadeh; Kazem Pourhossein. Short Circuit Location in Transformer Winding Using Deep Learning of Its Frequency Responses. 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) 2019, 268 -273.

AMA Style

Arash Moradzadeh, Kazem Pourhossein. Short Circuit Location in Transformer Winding Using Deep Learning of Its Frequency Responses. 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). 2019; ():268-273.

Chicago/Turabian Style

Arash Moradzadeh; Kazem Pourhossein. 2019. "Short Circuit Location in Transformer Winding Using Deep Learning of Its Frequency Responses." 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) , no. : 268-273.

Journal article
Published: 10 January 2018 in Emerging Science Journal
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The use of electric vehicles in addition to reducing environmental concerns can play a significant role in reducing the peak and filling the characteristic valleys of the daily network load. In other words, in the context of smart grids, it is possible to improve the battery of electric vehicles by scheduling charging and discharging processes. In this research, the issue of controlling the charge and discharge of electric vehicles was evaluated using a variety of neural models, until the by examining the effect of the growth rate of the penetration level of electric vehicles of the hybrid type that can be connected to the distribution network, the results of the charge management and discharge model of the proposed response are examined. The results indicate that due to increased penetration of these cars is increased the amount of responses to charge and discharge management. In this research, a variety of neural network methods, a) neural network method using Multilayer Perceptron Training (MLP), b) neural network method using Jordan Education (RNN), c) neural network method using training (RBF ) Was evaluated based on parameters such as reduction of training error, reduction of network testing error, duration of run and number of replications for each one. The final results indicate that electric vehicles can be used as scattered power plants, and can be useful for regulating the frequency and regulation of network voltages and the supply of peak traffic. This also reduces peak charges and incidental costs, which ultimately helps to further network stability. Finally, the charge and discharge management response reflects the fact that intelligent network-based models have the ability to manage the charge and discharge of electric vehicles, and among the models the amount of error reduction training and testing is very favourable for both RNN, MLP.

ACS Style

Arash Moradzadeh; Kamran Khaffafi. Comparison and Evaluation of the Performance of Various Types of Neural Networks for Planning Issues Related to Optimal Management of Charging and Discharging Electric Cars in Intelligent Power Grids. Emerging Science Journal 2018, 1, 1 .

AMA Style

Arash Moradzadeh, Kamran Khaffafi. Comparison and Evaluation of the Performance of Various Types of Neural Networks for Planning Issues Related to Optimal Management of Charging and Discharging Electric Cars in Intelligent Power Grids. Emerging Science Journal. 2018; 1 (4):1.

Chicago/Turabian Style

Arash Moradzadeh; Kamran Khaffafi. 2018. "Comparison and Evaluation of the Performance of Various Types of Neural Networks for Planning Issues Related to Optimal Management of Charging and Discharging Electric Cars in Intelligent Power Grids." Emerging Science Journal 1, no. 4: 1.