Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis

Abstract

The world today is increasingly dependent directly or indirectly on the power system. Ensuring the quality of power supplied to electrical equipment is essential. The national regulatory framework is for harmonic mitigation in the global power system. This paper discusses the relationship between Efficiency (E), Security (S), and Reliability (R) for Electric Power Quality (EPQ). We measure the harmonic mitigation regulations listed in the IEEE 519 standard. To evaluate the proposed E, S, and R constructs and their relationship to EPQ, a multi-planning approach the method of Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Deep Learning Artificial Neural Network (ANN) analysis were performed. In it, deep Learning Artificial Neural Network (ANN) was performed to complement the PLS-SEM findings and higher prediction accuracy. The study shows that the aspects of efficiency (E), security (S), and reliability (R) have a significant relationship with Electric Power Quality (EPQ). Another result of the study indicates that science, technology, engineering and math (STEM) resource conditions have a significant and positive impact on EPQ.

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“…(3) PLS offers a strong line of defense against biased data and independent factors. There are two techniques for sampling [44]. When doing descriptive research and determining causal relationships, the probabilistic sampling method is chosen since it is errorcontrolled and highly representative of the population.…”

“…Hardness test using Vicker Laryee HVM-30 hardness tester is used according to the JIS hardness measurement value, hardness test with a load of 500 N for one measurement in a running time of 15 seconds. The hardness measurement results are made according to equation (44) and this hardness value is allowed to convert the unit to hardness with HRC units according to JIS standards.…”