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This paper addresses multiphase matrix converter (MPMCs) modulated with Venturini's classical scalar approach. The solution to obtain time-varying modulation functions was limited to 3 × 3 configuration, which was later extended to 3 × n configuration. The equations had four constants that were common to all modulation functions; out of which two were defined arbitrarily and other two were found analytically by reducing the equation to a two-variable optimisation problem. However, in this work, an attempt is made to provide flexibility in finding other solutions by taking all the constants as variables. Thus, Differential Evolution (DE) algorithm is adopted in the proposed modulation approach to solve the complex equations thus obtained. Another aim is to find the solution for the MPMC, where inputs are more than three. It is found that the optimal solutions exist only for 3 × n configuration and are in close approximation with the results of analytical approach. Other solutions were found for certain input and output frequencies and voltage transfer ratios whose results are discussed. It is also found that the solution does not exist for configurations with input phases are other than three. The discussion is supported by simulation and experimental results on a 3 × 7 matrix converter at various values of voltage transfer ratios and input/output frequencies.

The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.

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