A Novel Polyester Varnish Nanocomposites for Electrical Machines with Improved Thermal and Dielectric Properties Using Functionalized TiO2 Nanoparticles.

Recently, there has been a growing interest in polymer insulating materials that incorporate nanoscale inorganic additives, as they have shown significantly improved dielectric, thermal, and mechanical properties, making them highly suitable for application in high-voltage insulating materials for electrical machines. This study aims to improve the dielectric and thermal properties of a commercial polyester varnish by incorporating different concentrations of titanium dioxide nanoparticles (TiO2) with proper surface functionalization. Permafil 9637 dipping varnish is the varnish used for this investigation, and vinyl silane is the coupling agent used in the surface functionalization of TiO2 nanoparticles. First, nanoparticles are characterized through Fourier transform infrared spectroscopy to validate the success of their surface functionalization. Then, varnish nanocomposites are characterized through field emission scanning electron microscopy to validate the dispersion and morphology of nanoparticles within the varnish matrix. Following characterization, varnish nanocomposites are evaluated for thermal and dielectric properties. Regarding thermal properties, the thermal conductivity of the prepared nanocomposites is assessed. Regarding dielectric properties, both permittivity and dielectric losses are evaluated over a wide frequency range, starting from 20 Hz up to 2 MHz. Moreover, the AC breakdown voltage is measured for varnish nanocomposites, and the obtained data are incorporated into a finite element method to obtain the dielectric breakdown strength. Finally, the physical mechanisms behind the obtained results are discussed, considering the role of nanoparticle loading and surface functionalization.

Condition Assessment of Natural Ester-Mineral Oil Mixture Due to Transformer Retrofilling via Sensing Dielectric Properties.

Mineral oil (MO) is the most popular insulating liquid that is used as an insulating and cooling medium in electrical power transformers. Indeed, for green energy and environmental protection requirements, many researchers introduced other oil types to study the various characteristics of alternative insulating oils using advanced diagnostic tools. In this regard, natural ester oil (NEO) can be considered an attractive substitute for MO. Although NEO has a high viscosity and high dielectric loss, it presents fire safety and environmental advantages over mineral oil. Therefore, the retrofilling of aged MO with fresh NEO is highly recommended for power transformers from an environmental viewpoint. In this study, two accelerated aging processes were applied to MO for 6 and 12 days to simulate MO in service for 6 and 12 years. Moreover, these aged oils were mixed with 80% and 90% fresh NEO. The dielectric strength, relative permittivity, and dissipation factor were sensed using a LCR meter and oil tester devices for all prepared samples to support the condition assessment performance of the oil mixtures. In addition, the electric field distribution was analyzed for a power transformer using the oil mixtures. Furthermore, the dynamic viscosity was measured for all insulating oil samples at different temperatures. From the obtained results, the sample obtained by mixing 90% natural ester oil with 10% mineral oil aged for 6 days is considered superior and achieves an improvement in dielectric strength and relative permittivity by approximately 43% and 48%, respectively, compared to fresh mineral oil. However, the dissipation factor was increased by approximately 20% but was at an acceptable limit. On the other hand, for the same oil sample, due to the higher molecular weight of the NEO, the viscosities of all mixtures were at a higher level than the mineral oil.

Influence of Mineral Oil-Based Nanofluids on the Temperature Distribution and Generated Heat Energy Inside Minimum Oil Circuit Breaker in Making Process.

The enhancement of the thermal properties of insulating oils has positively reflected on the performance of the electrical equipment that contains these oils. Nanomaterial science plays an influential role in enhancing the different properties of liquids, especially insulating oils. Although a minimum oil circuit breaker (MOCB) is one of the oldest circuit breakers in the electrical network, improving the insulating oil properties develops its performance to overcome some of its troubles. In this paper, 66 kV MOCB is modeled by COMSOL Multiphysics software. The internal temperature and the internally generated heat energy inside the MOCB during the making process of its contacts are simulated at different positions of the movable contact. This simulation is introduced for different modified insulating oils (mineral oil and synthetic ester oil) with different types of nanoparticles at different concentrations (0.0, 0.0025, 0.005, and 0.01 wt%). From the obtained results, it is noticed that the thermal stress on the MOCB can be reduced by the use of high thermal conductivity insulating oils. Nano/insulating oils decrease internal temperature and generate heat energy inside the MOCB by about 17.5%. The corresponding physical mechanisms are clarified considering the thermophoresis effect.

Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System.

In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 × 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems.

Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors.

Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV-40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings.

Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper's innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic.

This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters.

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters' data. The data monitoring is carried based on the industrial digital twins' platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

Recent Advances in Polymer Nanocomposites Based on Polyethylene and Polyvinylchloride for Power Cables.

Polymer nanocomposites used in underground cables have been of great interest to researchers over the past 10 years. Their preparation and the dispersion of the nanoparticles through the polymer host matrix are the key factors leading to their enhanced dielectric properties. Their important dielectric properties are breakdown strength, permittivity, conductivity, dielectric loss, space charge accumulation, tracking, and erosion, and partial discharge. An overview of recent advances in polymer nanocomposites based on LDPE, HDPE, XLPE, and PVC is presented, focusing on their preparation and electrical properties.