Microgrid (MG) technologies offer users attractive characteristics such as enhanced power quality, stability, sustainability, and environmentally friendly energy through a control and Energy Management System (EMS). Microgrids are enabled by integrating such distributed energy sources into the utility grid. The microgrid concept is proposed to create a self-contained system composed of distributed energy resources capable of operating in an isolated mode during grid disruptions. With the Internet of Things (IoT) daily technological advancements and updates, intelligent microgrids, the critical components of the future smart grid, are integrating an increasing number of IoT architectures and technologies for applications aimed at developing, controlling, monitoring, and protecting microgrids. Microgrids are composed of various distributed generators (DG), which may include renewable and non-renewable energy sources. As a result, a proper control strategy and monitoring system must guarantee that MG power is transferred efficiently to sensitive loads and the primary grid. This paper evaluates MG control strategies in detail and classifies them according to their level of protection, energy conversion, integration, benefits, and drawbacks. This paper also shows the role of the IoT and monitoring systems for energy management and data analysis in the microgrid. Additionally, this analysis highlights numerous elements, obstacles, and issues regarding the long-term development of MG control technologies in next-generation intelligent grid applications. This paper can be used as a reference for all new microgrid energy management and monitoring research.

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The choice of the voting algorithm in N-version programming directly affects the evaluation of the results of N software versions and determines the correct result. The result of the voting algorithm is also the outcome of the N-version software. Therefore, the choice of the voting algorithm is vital. However, many voting algorithms were already developed and they may be selected for implementation, based on the specifics of the analysis of input data of these algorithms. This article presents a brief overview of major fuzzy voting algorithms.

Abstract. Multiversion or N-version programming is well known as an effective approach, ensuring high level of software reliability. This approach is based on two fundamental strategies for enhancing the reliability of a software system -redundancy and diversity. Modules solving critical tasks are redundant and implemented in the form of functionally equivalent versions. In this connection versions can be developed by different programmer teams, in different languages, in different environment and can implement different methods and algorithms for solution of identical tasks in order to provide versions diversity. Complex software systems, as a rule, include a set of programs which can call the same modules for solving their target tasks, or to be more precise, versions of these modules. According to diversity concept call of different module versions allows to avoid identical failures. This article presents a technique of selecting optimal multiversion software system to minimize simultaneous usage of the same module versions.