Recently, the study of transformer oil-based nanofluids became of great interest, due to their prospective properties as a dielectric and cooling medium. However, agglomeration of nanoparticles limits the beneficial properties that can be obtained from nanofluids. So, the work presented in this paper aims to get enhanced dispersion behavior of nanoparticles within transformer oil-based nanofluids. Then, breakdown strength with the enhanced dispersion behavior is evaluated. In order to get enhanced dispersion behavior, nanofluids were prepared using nanoparticles with a surfactant. The surfactant plays a role in the stabilization of nanoparticles and maintaining suspension stability. The considered type of nanoparticles is TiO₂ nanoparticles. Two series of nanofluid samples were prepared. Through the first series, the effect of surfactant concentration on dispersion behavior and agglomerate size was studied. The dispersion of nanoparticles was characterized using three different techniques. These techniques are optical microscope analysis, transmission electron microscope (TEM) analysis and zeta potential measurements. Based on these techniques, the suitable concentration of surfactant was identified. The second series of nanofluid samples was prepared with different weight percents of nanoparticles for assessing the breakdown strength. Weibull distribution was used to calculate the breakdown probability for the base oil as well as nanofluid samples. The results showed an enhancement in the breakdown strength by about 27% in comparison to the base oil. Based on the obtained results, mechanisms behind the dispersion behavior and breakdown strength were proposed and discussed.

Several methods have been proposed for dissolved gas analysis (DGA) of mineral oil in power transformers. One of the simple and most widely used methods is Duval triangle. However, Duval triangle does not consider the concentrations of two combustible gases, namely ethane (C ₂ H ₆ ) and hydrogen (H ₂ ). As a result, Duval triangle exhibits a lower accuracy in diagnosing certain fault types, for which these gases are the key gases, such as low overheating and corona discharge. Accordingly, this paper proposes a novel graphical technique for DGA based on all the five combustible gases. The proposed graphical technique is developed in the form of a pentagon shape. The pentagon heads represents the percentage concentration of each individual gas to the total combustible gases. The corresponding point for a certain faulty case is determined by the center of mass of all pentagon heads. The knowledge extracted from previous DGA methods and field experiences are used to estimate the preliminary fault regions within the pentagon. The exact boundaries between fault regions are then specified using actual DGA data with corresponding fault types collected from the Egyptian Electricity Network and published cases. The overall performance of the proposed pentagon has been evaluated using a set of fault cases and it is revealed that the proposed pentagon has higher diagnostic accuracy compared to other methods including Duval triangles and IEC standard 60599.

Fault type diagnosis is a very important tool to maintain the continuity of power transformer operation. Dissolved gas analysis (DGA) is one of the most effective and widely used techniques for predicting the power transformer fault types. In this paper, a convolutional neural network (CNN) model is proposed based on the DGA approach to accurately predict transformer fault types under different noise levels in measurements. The proposed model is applied with three categories of input ratios: conventional ratios (Rogers'4 ratios, IEC 60599 ratios, Duval triangle ratios), new ratios (five gas percentage ratios and new form six ratios), and hybrid ratios (conventional and new ratios together). The proposed model is trained and tested based on 589 dataset samples collected from electrical utilities and literature with varying noise levels up to ±20%. The results indicate that the CNN model with hybrid input ratios has superior prediction accuracy. The high accuracy of the proposed model is validated in comparison with conventional and recently published AI approaches. The proposed model is implemented based on MATLAB/toolbox 2020b.

With increased penetration of wind energy as a renewable energy source, there is a need to keep wind turbines connected to the grid during different disturbances such as grid faults. In this paper, the use of superconducting fault current limiter (SFCL) is proposed to reduce fault current level at the stator side and improve the fault ride-through (FRT) capability of the system. To highlight the proposed technique, a doubly fed induction generator (DFIG) is considered as a wind-turbine generator, where the whole system is simulated using PSCAD/EMTDC software. Detailed simulation results are obtained with and without SFCL considering stator and rotor currents. In addition, the voltage profile at the generator terminals is analyzed. The effect of limiting resistance value is also investigated. The obtained results ensure that the SFCL is effective in decreasing the fault current. Moreover, both the voltage dip at the generator terminals and the reactive power consumption from the grid are decreased during the fault. The voltage dip characteristics are discussed in accordance with international grid codes for wind turbines.

This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research.

Transformer oil-based nanofluids have unexpected and distinctive dielectric properties that are different from properties of constituting components. This is believed to be attributed to the effect of interfacial zone between oil and nanoparticles. Accordingly, this paper aims to investigate the role through which the interfacial zone affects the dielectric properties of transformer oil-based nanofluids. Oil-based nanofluids are prepared with two different types of nanoparticles having different surface charging polarity. These types are alumina (Al₂O₃) nanoparticles with cationic surface and titania (TiO₂) nanoparticles with anionic surface. For each type of nanoparticles, two groups are prepared with and without a surface modifier, which is called surfactant. Each group consists of four samples having nanoparticles weight fractions of 0.01, 0.04, 0.07 and 0.1 g/L. Surfactant is chosen with charge opposite to that of nanoparticle surface charge to be adsorbed on the nanoparticle surface. For each nanofluid sample, surfactant is distributed through the oil using magnetic stirrer, and then, nanoparticles are mixed and homogenized using magnetic stirrer and ultrasonic processing. After preparation, dielectric properties including breakdown strength and dielectric constant are measured. Based on the obtained results, the role of interfacial zone could be clarified in conjunction to that developed in solid nanodielectrics.