Power electronic systems have a great impact on modern society. Their applications target a more sustainable future by minimizing the negative impacts of industrialization on the environment, such as global warming effects and greenhouse gas emission. Power devices based on wide band gap (WBG) material have the potential to deliver a paradigm shift in regard to energy efficiency and working with respect to the devices based on mature silicon (Si). Gallium nitride (GaN) and silicon carbide (SiC) have been treated as one of the most promising WBG materials that allow the performance limits of matured Si switching devices to be significantly exceeded. WBG-based power devices enable fast switching with lower power losses at higher switching frequency and hence, allow the development of high power density and high efficiency power converters. This paper reviews popular SiC and GaN power devices, discusses the associated merits and challenges, and finally their applications in power electronics.

In this article, a three-phase transformerless inverter (TLI) for a solar photovoltaic (PV) system connected to a high-power grid are proposed, which has advantages of better performance and lower cost. The primary concern about the TLI is fluctuations in the common-mode voltage, which impacts switching frequency leakage current and grid interface system. An improved H7 common-mode voltage (CMV) clamped TLI with discontinuous pulse width modulation (DPWM) is designed using a conventional neural network (CNN)based deep learning approach. In this, a completely minimized leakage current is obtained to avoid CMV transients. The proposed PV-connected improved H7-TLI provides low-loss DC-side decoupling, which further reduces leakage current and isolation of the PV system during off-grid. In addition, the effects of several factors on CNN deep learning performance are explored, including training data size, image resolution, and network configuration. The proposed technique has the potential to be used in a test instrument for intelligent signal analysis or used in an artificial intelligence system. Switching loss is analyzed using proposed and existing H7 inverters under different load conditions. To verify the theoretical explanation, existing H7 inverters is analyzed by MATLAB/Simulink, and the outcomes are tested experimentally. The total harmonic distortion (THD) analysis of proposed and existing topology is analyzed and compared. The THD values of the existing and proposed topology are 3.74% and 3.23%, respectively.

Cloud computing is becoming one of the next industry buzz words. In the current cloud computing scenario, the need for establishing an SLA is highly essential, since it expresses the commitment of provider to create an affordable balance between hosting costs and high levels of service availability during the service offerings. It is also highly necessary to monitor dynamically whether the QoS mentioned in the SLA, is met by the service provider. Moreover, a reliable trust model is required to give a quantitative measure of the trust to the requester before choosing a service provider. In this paper, we portray a dynamic trust establishment framework, wherein the Third-party SLA monitor provides an on-demand QoS assessment module, on a real time basis. Based on the computed trust value, the service provider is chosen. Then, the concept of Adaptive window-based state monitoring is introduced, negating the drawbacks associated with the existing techniques, by reducing the amount of data sent over the network. The proposed approach is implemented and it proves to be efficacious than the existing techniques in most cases.

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