Arash Moradzadeh

• Ph.D. Candidate in Power Electrical Engineering
• Research Assistant at University of Tabriz

Accurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of mode...

Hybrid energy systems (HESs) are the most important sources of energy demand-supply, have developed significantly around the world. Microgrids, renewable energy sources, remote telecommunications stations, greenhouses, etc., are being considered as HESs applications. Optimal sizing of these systems is considered as one of the important issues relat...

Uncertainty in renewable energy generation and energy demand reduces generation flexibility, which should be compensated by increasing flexibility on the supply grid and demand sides. Dynamic line rating (DLR) forecasting, in which the maximum current carrying capacity of overhead transmission lines is accurately forecasted, is important to enable...

Recently, the increasing prevalence of solar energy in power and energy systems around the world has dramatically increased the importance of accurately predicting solar irradiance. However, the lack of access to data in many regions and the privacy concerns that can arise when collecting and transmitting data from distributed points to a central s...

Improving the accuracy of photovoltaic (PV) power forecasting is crucial to ensure more effective use of energy resources. Improvements are especially important for regions for which historical solar radiation data do not exist. This paper proposes a cyber-secure forecasting model called federated deep learning (FDL) to forecast PV power generation...

Historically, the combination of generous subsidies along with extreme climate has led to unsustainable domestic electricity consumption in Saudi Arabia. The residential sector constitutes a significant portion of this consumption. Amid the economic challenges, the country enforced a new electricity tariff for residential consumers in 2018. This st...

In recent years, there has been a tremendous increase in the application of information and communication technologies (ICTs) in critical infrastructures, such as energy grids and communication infrastructures. Smart grids, smart cities, including novel power and energy systems, etc. are current ICT developments that are becoming more complex by th...

Today, introducing useful and practical solutions to residential load disaggregation as subsets of energy management has created numerous challenges. In this study, an intelligence hybrid solution based on manifold learning and deep learning applications is presented. The proposed solution presents a combined structure of Laplacian eigenmaps (LE),...

Precise electric load forecasting at different time horizons is an essential aspect for electricity producers and consumers who participate in energy markets in order to maximize their economic efficiency. Moreover, accurate prediction of the electric load contributes toward robust and resilient power grids due to the error minimization of generato...

Due to its advantages and the continual availability of solar energy, photovoltaic (PV) systems have become the most popular energy production equipment in various business and residential structures. This chapter proposes solar radiation forecasting to manage solar power generation in residential and commercial buildings using deep learning algori...

Accurate wind power forecasting is one of the most important operations within the economic dispatch problem to increase the performance of power and energy systems. Accordingly, this study proposes a cyber-resilient hybrid approach based on the Federated Learning and Convolutional Neural Network (CNN) procedure for short-term wind power generation...

Nowadays, the world is facing energy crisis and environmental issues. This is why the energy demand is increasing in different energy sections. The buildings as a large energy consumer are critical to face with these issues. To overcome these challenges, the conventional active buildings are moving toward the active building. Demand flexibility and...

Increased integration of renewable energy resources into the grid may create new difficulties for ensuring a sustainable power grid which drives electric utilities to use a number of cost-effective techniques such as Dynamic line rating (DLR) that enable them to run power networks more efficiently and reliably. DLR forecasting is a technique devise...

Electricity load forecasting is a key aspect for power producers to maximize their economic efficiency in deregulated markets. So far, many solutions have been employed to forecast the consumption load in power grids. However, most of these methods have suffered in modeling the time-series state of data and removing noise from real-world data. Thus...

Non-uniform irradiance due to partial shading conditions (PSCs) reduces the power delivered by the photovoltaic (PV) cell. The output power reduction in the PV arrays directly depends on the shading pattern and type of array configuration which is selected. So far, many dynamic and static reconfiguration methods have been used for maximum power poi...

This paper introduces a novel reconfiguration technique, called Knight's tour to extract maximum power from photovoltaic (PV) arrays in partial shading conditions. The Knight's tour reconfigures the PV arrays based on the Knight's movements on the chessboard. The proposed procedure achieves the maximum power values by spreading partial shadows in a...

Timely and accurate detection of transmission line faults is one of the most important issues in the reliability of the power systems. In this paper, in order to assess the effects of impedance and location of the fault in identifying and classifying it, the frequency response analysis (FRA) method is utilized. This method clearly shows the smalles...

The Frequency Response Analysis (FRA) technique has advantages in identifying faults related to power transformers, but it suffers from the interpretation of frequency responses. This paper presents an approach based on statistical indices and Artificial Neural Network (ANN) methods to interpret frequency responses. The proposed procedure divides f...

Recently, with the establishment of new thermal regulation, the energy efficiency of buildings has increased significantly, and various deep learning-based methods have been presented to accurately forecast the heating load demand of buildings. However, all of these methods are executed on a dataset with specific distribution and do not have the pr...

Dynamic line rating (DLR) is a technology introduced to rectify an overhead line's current carrying capacity based on climatic conditions. Reducing congestion costs, increasing the penetration of renewable energy, and network stability are the most important benefits of this modification. The DLR forecasting is a significant issue and can be very u...

Prediction of building energy consumption plays an important role in energy conservation, management, and planning. Continuously improving and enhancing the performance of forecasting models is the key to ensuring the performance sustainability of energy systems. In this connection, the current paper presented a new improved hybrid model of machine...

With more sensors being installed by utilities for accurate control of power grids, there is a growing risk of vulnerability to sophisticated data integrity attacks such as false data injection (FDI), circumventing current bad data detection schemes resulting in inaccurate state estimation solutions. While diverse automated detectors to battle FDI...

Early fault detection in power electronic systems (PESs) to maintain reliability is one of the most important issues that has been significantly addressed in recent years. In this paper, after reviewing various literature based on fault detection in PESs, data mining-based techniques including artificial neural network, machine learning, and deep l...

Nowadays, the increment of energy demand in the world as well as the development of smart grids and the combination of different types of energy systems have led to the complexity of power systems. On the other hand, ever-expanding energy consumption, development of industry and technology systems, and high penetration of renewable energies have ma...

In recent years, the development and influence of wind power in the power system have witnessed, which has led to a significant increase in the production and use of wind energy worldwide. Considering the variability of wind velocity, planning, and managing wind intermittency are important parts of wind energy development, so predicting wind speeds...

Nowadays, the increasing energy demand, development of smart grids, and the combination of different energy systems have led to the complexity of power systems. On the other hand, ever-expanding energy consumption, development of industry and technology systems, and high penetration of solar and wind energies have made electricity networks operate...

Smart heating system is one of the most efficient ways to realize indoor heating comfort. As the most energy consumption in the residential buildings is related to the heating, introducing an efficient energy management algorithm is so important for heating operation management. Accordingly, this chapter focuses on residential buildings’ heat load...

Over many decades, the electric power industry has evolved from a single low-power generator serving a small area to highly interconnected networks serving a large number of countries, or even continents. Nowadays, an electric power system is one of the largest man-made systems ever created, consisting of an enormous number of components ranging fr...

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 propose...

Non-uniform solar irradiance on the photovoltaic arrays decreases the output power due to the partial shading conditions. In this paper, the distribution of shade in the photovoltaic array rows is done by reconfiguring the shaded modules related to the photovoltaic array. The method proposed in this paper is a static approach based on prime numbers...

Frequency response analysis (FRA) suffers from the interpretation of results despite its potential ability to detect faults related to the power transformer windings. This paper presents a technique for interpreting frequency responses, which is based on image processing and a deep learning method called Graph Convolutional Neural Network (CNN). Th...

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...

A power transformer winding is usually subject to mechanical stress and tension because of improper transportation or operation. Radial deformation (RD) is an example of mechanical stress that can impact power transformer operation through short circuit faults and insulation damages. Frequency response analysis (FRA) is a well-known method to diagn...

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...

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 volt...

Energy management is of paramount importance due to rising energy demand in the world and energy consumption costs. As one of the energy management processes, energy storage systems (ESSs) are known as essential equipment throughout energy markets. Energy can be produced and used in a variety of types in the electricity markets, each having its own...

A power transformer winding is usually subject to mechanical stress and tension because of improper transportation or operation. Radial deformation (RD) is an example of mechanical stress that can impact power transformer operation through short circuit faults and insulation damages. Frequency response analysis (FRA) is a well-known method to diagn...

The Frequency Response Analysis (FRA) is an efficient tool to diagnosis mechanical defects in transformer windings. After measurement of frequency responses, they must be compared to find any defects in the winding. The lack of clarity in the interpretation of measured frequency responses is the main problem of this method. This paper tries to solv...

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 ord...

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 insulati...

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 e...

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...

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 resear...

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...

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 lo...

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 s...

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...

Turn-to-turn short circuit fault is one of the most important failures in transformer winding. 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, intro...

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. the problem of charge and discharge management of electrical vehicles was evaluated using a variety of neural networks. By assessing the effect of the penetrat...

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,...

Power losses reduction and voltage profile improvement are goals for electricity utilities. Distribution system reconfiguration and optimal capacitor placement are two of the low Cost available tools to Power losses reduction and voltage profile improvement. Reconfiguring the network means altering its topology by changing the status of normally op...

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...