Recent Advances in Electrical & Electronic Engineering - Volume 15, Issue 5, 2022

Rapid and sudden increase in the use of small Distributed Renewable Energy Resources (DRER) and Distributed Energy Storage (DES), reduced compact dynamic electrical power consumer-like electrical vehicle loads, the requirement of a bidirectional power flow communication network to exchange critical information, other ancillary services, etc. has to be envisaged and fulfilled by a future smart distribution grid system. A solid-State Transformer (SST) has more potential solutions to provide stable and efficient distribution system operation to overcome the above problem. From various designs of converter control schemes, the solid-state transformer concept has been evaluated and summarized. The benefits and downsides of converters in the AC/DC, DC/DC, and DC/AC stages are studied for the best configuration. The High-Frequency Transformer (HFT) is a main part of SST. The design and optimization of an HFT are approached to minimize the weight and size of the SST. Researchers also face several challenges in the prototype design and implementation of SST before operating effectively in the distribution system which is presented. Some expected solutions and future recommendations for the establishment of the solid-state transformer for future smart electrical distribution systems are discussed.

Next-generation phasor measurement units (PMUs) are critical for proper distribution network monitoring in future smart grids. In the highly dynamic environments of future distribution systems, more advanced algorithms for detecting amplitude, phase, and frequency changes in power waveforms will be required. In this context, the goal of this study is to discuss the advantages and disadvantages of commonly used PMU algorithms. Many commonly used approaches for power system phasors and frequency estimation algorithms for distribution networks are reviewed and evaluated in this work from the perspective of phasor and frequency identification. According to various performance criteria, e-IpDFT (Enhanced Interpolated Discrete Fourier Transform) and DDPE (Dictionary-based Dynamic Phasor Estimator) provided the best performance among the algorithms. This study provides some insights into the shortcomings of current phasor estimation algorithms and will be useful in the development of future estimation techniques.

Background & Objective: In single-phase solar PV applications, the module voltage level is limited; therefore, a coupled step-up transformer is a mandatory portion to interconnect into the grid. However, due to the presence of a transformer, the overall cost and size of the single-phase grid-tied solar PV system are higher. A multilevel inverter is an alternative solution to solve these issues, but it requires more switches. Methods: In order to overcome this drawback, this particular paper proposes a novel Asymmetric Multilevel Inverter (AMLI) with a reduced number of switches for a solar PV grid-tied system. The operational details of the proposed converter are explained in this paper. In addition, the MPPT control has been implemented in the proposed novel AMLI and presented in this paper. The hysteresis current control mechanism is applied to the proposed converter and corresponding control blocks are reported in this paper. Results: Finally, to validate the proposed system, the simulation results are performed and correlated with a theoretical approach. Furthermore, to verify the feasibility of proposed converter for solar PV grid-tied system, the experimental setup was made, and experimental results have been measured and presented in this paper. Conclusion: From the measured results, it is concluded that the proposed asymmetric MLI can be the most promising converter for solar PV grid-connected systems.

Background: Coronavirus refers to a large group of RNA viruses that infects the respiratory tract in humans and also causes diseases in birds and mammals. SARS-CoV-2 gives rise to the infectious disease “COVID-19”. In March 2020, coronavirus was declared a pandemic by the WHO. The transmission rate of COVID-19 has been increasing rapidly; thus, it becomes indispensable to estimate the number of confirmed infected cases in the future. Objective: The study aims to forecast coronavirus cases using three ML algorithms, viz., support vector regression (SVR), polynomial regression (PR), and Bayesian ridge regression (BRR). Methods: There are several ML algorithms like decision tree, K-nearest neighbor algorithm, Random forest, neural networks, and Naïve Bayes, but we have chosen PR, SVR, and BRR as they have many advantages in comparison to other algorithms. SVM is a widely used supervised ML algorithm developed by Vapnik and Cortes in 1990. It is used for both classification and regression. PR is known as a particular case of Multiple Linear Regression in Machine Learning. It models the relationship between an independent and dependent variable as nth degree polynomial. Results: In this study, we have predicted the number of infected confirmed cases using three ML algorithms, viz. SVR, PR, and BRR. We have assumed that there are no precautionary measures in place. Conclusion: In this paper, predictions are made for the upcoming number of infected confirmed cases by analyzing datasets containing information about the day-wise past confirmed cases using ML models (SVR, PR and BRR). According to this paper, as compared to SVR and PR, BRR performed far better in the future forecasting of the infected confirmed cases owing to coronavirus.

Power electronics converter is widely used in various applications. When the converter failure occurs, the system will be damaged, so it is of great significance to timely detect the fault and apply effective protection. In this paper, the open-circuit fault of the three-phase inverter is taken as the main object. The time convolutional neural network (TCN) model is designed to realize the fault diagnosis strategy of the inverter based on feature extraction. The simulated experimental results show that the neural network model can realize high accuracy fault diagnosis for different inverter circuits. The overall classification performance is good, and the loss is small. It is shown that the time convolutional neural network method can diagnose different IGBT fault intelligently in the inverter. It is proved that the neural network model is effective and feasible for inverter circuit fault diagnosis. Background: Power electronics converter is widely used in various applications. When the converter failure occurs, the system will be damaged, so it is of great significance to timely detect the fault and apply effective protection. Objective: The open-circuit fault of the three-phase inverter is taken as the main object. Method: The time convolutional neural network (TCN) model is designed to realize the fault diagnosis strategy of the inverter based on feature extraction. Results: The simulated experimental results show that the neural network model can realize high accuracy fault diagnosis for different inverter circuits. The overall classification performance is good, and the loss is small. Conclusion: It is shown that the time convolutional neural network method can diagnose different IGBT fault intelligently in the inverter. It has been proved that the neural network model is effective and feasible for inverter circuit fault diagnosis.

Background: High Voltage Direct Current (HVDC) is a well-proven technology used to transmit electricity over long distances by overhead transmission lines or submarine cables. It is also used to interconnect separate power systems, where traditional Alternating Current (AC) connections cannot be used. Objective: In this paper, an HVDC supply has been proposed with the Tesla coil using hybrid energy sources, such as solar panels, batteries, and AC main supply with an AC-DC converter. Methods: DC voltage is obtained from various hybrid sources and is supplied to the input of the Cockcroft-Walton (C-W) voltage multiplier circuit through DC/AC inversion. Results: The produced HVDC is provided to the Tesla coil to obtain a stable HVDC output. Fractional Order PID (FOPID) controller is used in the system for control purposes to stabilize the output voltage due to load variations. The model is designed in Matlab / Simulink environment. The simulation results show that the proposed design successfully achieved a stable 15 kV HVDC. Conclusion: The advantage of the proposed system is the availability of variable output voltage as per load requirement compared to the fixed limited output voltage generated by conventional HVDC systems.

Aim: The aim of the study was to implement low-power approximate arithmetic circuits for IoT devices. Background: Information transmitted via electronic media is exposed to security threats. With the advancement of internet technology, the devices linked to the internet are growing, leading to the Internet of Things (IoT). Objective: IoT and big data are the prominent computing paradigms that employ approximate computing. It takes the benefit of various applications' error-tolerable features to lower the amount of resources necessary to deliver a specific degree of computation quality. An IoT device has to receive and transmit a lot of data. If this data size can be reduced by approximate computing, then a lot of power can be saved, which provides the dual benefit of data protection and power consumption. Methods: The approximated adder and multiplier using AIF is proposed that helps in the reduction of power consumption and security threats that occur in IoT devices. Results: The proposed approximated adder and multiplier consumes 2.81% to 32.95% less power as compared to conventional technique. Conclusion: For the protection of data communication in IoT devices from security threats, approximate arithmetic circuits play a fundamental role. To attain this issue, in this paper, authors have proposed approximate adder and multiplier using AIF, which also provides reduction in power consumption. The proposed circuits can be used as a basic block for security purposes in IoT devices. In future, approximation algorithms will be implemented for mitigation of security threats.