Abstract
One of the most important aspects of power quality for a distribution network's operation is the voltage sag issue. Simultaneous starting of irrigation motors fed from a distribution network leads to a voltage drop, which degrades the network's power quality. Mitigation of the voltage sag was carried out before by using superconducting magnetic energy storage (SMES) with a pre-defined capacity. The innovation of the present research work is optimal design of SMES including optimal sizing of SMES and its controller parameters with the consideration of its optimal cost for mitigating voltage sag resulting from simultaneous starting of irrigation motors in a real Egyptian distribution network. This is made by minimizing a multi-objective function formulated by a weighted-sum voltage sag and SMES cost. A new optimization technique called Mountain Gazelle Optimizer (MGO) is used to optimize the sizing of fuzzy logic controller (FLC)-SMES as well as the weight factors of multi-objective function. These factors are not free for the decision maker to choose. An actual Egyptian electric distribution network named Karot feeding 16 induction-driven irrigation motors and residential loads, is implemented as a test case study to exhibit the performance of the suggested optimal FLC-SMES strategy in mitigating the voltage sag. For validating the performance of the MGO technique, the obtained results are compared with those determined by particle swarm optimization (PSO). The present results show that the MGO-optimized SMES unit with a capacity of 0.135 MJ and actual cost of 0.2483 M$ successfully mitigated the voltage-sag in the investigated network due to simultaneous starting of the irrigation motors where the voltage never drops below 0.9 p.u. against 0.625 MJ capacity and 0.6934 M$ actual cost for the non-optimized SMES. This corresponds to about 78 % and 64 % reduction in capacity and actual cost of the SMES unit.
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