A Framework for Prioritizing Tools and Techniques in Electrical Engineering Design and Analysis: An Application of Complex Fuzzy Rough Sets

Decision-making and system optimization are difficult in electrical engineer- ing since the field frequently works with complicated systems that contain uncertainty, nonlinearity, and imprecise data. Unpredictable environmental circumstances and variable demand make it challenging to create precise models for problems like load forecasting, fault detection, and power system stability analysis. Conventional approaches frequently fail to capture the innate ambiguity of these systems. A workable answer is offered by fuzzy logic structures, which make it possible to describe complex relationships and uncertain variables. These issues can be resolved by using fuzzy sets and inference systems. For this purpose, we have delivered the basic operational laws for CFRNs based on Aczel-Alsina t-norm and t-conorm. Moreover, we have delivered the aggregation theory of the CFRNs consisting of complex fuzzy rough Aczel-Alsina weighted aggregation operator, complex fuzzy rough Aczel-Alsina ordered weighted aggregation operator, and complex fuzzy rough Aczel-Alsina hybrid aggregation operator. We have proposed the application of the delivered approaches by defining an algorithm. We provided an illustrative example through which we can classify the different technologies and tools in electrical engineering design and analysis. We have delivered a comparative analysis of the established work with existing theories to show the advantages of the devised theory.