<![CDATA[The use of solar panels is being pursued as a solution to reduce dependence on fossil fuels. However, solar panels face challenges such as power fluctuations due to environmental conditions and partial shading. To address these issues, an MPPT technique using Zebra Optimization Algorithm (ZOA) has been developed, which integrates foraging behaviour and defensive strategies to achieve GMPP. Simulation testing results show the superiority of ZOA over PSO in achieving GMPP. ZOA's contribution in addressing this problem is to efficiently perform a global search to find the optimal MPP, even under varying partial shading conditions. The algorithm mimics the behaviour of zebras in foraging and defending against predator attacks, enabling a fast solution search process and higher precision. ZOA can overcome the local maxima trap by expanding the search space, allowing solar panels to function close to optimal efficiency even if there is shading on a portion of the module. This improves system stability and performance and reduces energy loss due to partial shading. ZOA achieved a tracking accuracy of 99.99% with an average tracking time of 0.779 seconds and with a power gain of 28.5%, surpassing PSO with an accuracy of 95.18%, an average trcking time of 0.850 seconds with a power gain of 24.68%. In hardware testing, ZOA is also superior to PSO with an average tracking accuracy of 98.96% while PSO is 97.22%. These results underline the outstanding performance of the ZOA algorithm in optimising the power output of solar panels.]]>
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