Transformers are the most expensive element of a power system, and so transformer monitoring systems are becoming more important as loads increase. Under heavy loading conditions, the hot-spot temperature of a transformer is the most critical parameter to determine the limit of loading. Therefore, to make an accurate estimation of hot-spot temperature and maintain the reliability of a power system, dynamic thermal models or methods must be employed. Due to the complexity of thermal phenomena, the methods presented so far needs to be improved to reduce computational errors. This is particularly the case for variable loading conditions and power utilities are still in need of good techniques. This paper describes two methods to determine temperature in single-phase, oil-immersed transformers. The first method calculates average windings temperature (AWT) using the voltage and current from primary and secondary windings. The second method, which uses data from the first method, determines the hot-spot temperature of the transformer. The methods have been investigated by experiments on a 30 kVA single-phase transformer and verified the results using COMSOL ™ software. Our methods have proven more accurate than the IEEE Annex G method.

This study introduces the concept of decision tree (DT) classification, a new approach to power transformer differential protection. The proposed technique is based on processing the differential current. The suggested method detects winding insulation failures and distinguishes them from magnetising inrush and sympathetic inrush conditions with classification accuracies of 100% for simulations and 95% for real‐time studies. The internal faults can be accurately recognised from inrush current conditions in a few sampling cycle after the occurrence of a disturbance. Another advantage of the proposed method is that the fault detection algorithm does not depend on the selection of thresholds. Performance analysis of the DT is achieved by the simulation of different faults and switching conditions on a power transformer in power system computer aided design/electromagnetic transients including DC (PSCAD/EMTDC). Furthermore, the proposed method is also tested in laboratory environment. The accuracy of DT is also compared with support vector machine. Both experimental and simulation results are presented and discussed.

Internet of Things (IoT) applications have become popular and are used in innovative device applications. Although radio frequency communication methods, including Bluetooth, zonal intercommunication global standards such as ZigBee, and wireless fidelity (Wi-Fi) and wired communication such as RS485/Modbus, can be used in IoT networks, Wi-Fi is preferred by users due to its ability to connect to the internet and allow users to control devices from anywhere in the world. In addition, numerous do-it-yourself and low-cost solutions have been presented for intelligent home applications in recent decades. However, many solutions use proprietary communication protocols that are not standards-based, so integrating a standardized local home automation server is difficult. We proposed a design based on low-cost Wi-Fi-based IoT-capable control boards that communicate with an IoT network of standard home automation servers and commercial intelligent home devices. The primary focus of the proposed system is to implement control boards for IoT applications by using Espressif systems series microcontrollers that can run in any IoT network used for home automation. Converting traditional houses into smart homes will support demand-side energy management applications in future smart grids and allow customers to use low-cost energy.

An intensive study has been made of the removal efficiency of Cu(II) from industrial leachate by biosorption of montmorillonite. A 2⁴ factorial design and cascade forward neural network (CFNN) were used to display the significant levels of the analyzed factors on the removal efficiency. The obtained model based on 2⁴ factorial design was statistically tested using the well‐known methods. The statistical analysis proves that the main effects of analyzed parameters were significant by an obtained linear model within a 95% confidence interval. The proposed CFNN model requires less experimental data and minimum calculations. Moreover, it is found to be cost‐effective due to inherent advantages of its network structure. Optimization of the levels of the analyzed factors was achieved by minimizing adsorbent dosage and contact time, which were costly, and maximizing Cu(II) removal efficiency. The suggested optimum conditions are initial pH at 6, adsorbent dosage at 10 mg/L, and contact time at 10 min using raw montmorillonite with the Cu(II) removal of 80.7%. At the optimum values, removal efficiency was increased to 88.91% if the modified montmorillonite was used.

Clinoptilolite was investigated for the removal of Cu(II) ions from industrial leachate. Adaptive neural fuzzy interface system (ANFIS) was used for modeling the batch experimental system and predicting the optimal input values, that is, initial pH, adsorbent dosage, and contact time. Experiments were studied under laboratory batch and fixed bed conditions. The outcomes of suggested ANFIS modeling were then compared to a full factorial experimental design (2³), which was utilized to assess the effect of three factors on the adsorption of Cu(II) ions in aqueous leachate of industrial waste. It was observed that the optimized parameters are almost close to each other. The highest removal efficiency was found as about 93.65% at pH 6, adsorbent dosage 11.4 g/L, and contact time 33 min for batch conditions of 2³ experimental design and about 90.43% at pH 5, adsorbent dosage 15 g/L and contact time 35 min for batch conditions of ANFIS. The results show that clinoptilolite is an efficient sorbent and ANFIS, which is easy to implement and is able to model the batch experimental system.