Solar energy has emerged as a cornerstone in the quest for renewable energy sources, with its low carbon footprint and abundant availability propelling its adoption. The proliferation of solar power generation devices across the globe is a testament to the commitment to a sustainable energy future. These devices, however, are subject to a myriad of challenges that can impair their operation. Environmental factors such as extreme weather, dust accumulation, and shading, along with human-induced damages or technical malfunctions, can precipitate faults that degrade the performance of solar arrays and, by extension, the robustness of the power grid they support. The intermittent nature of solar power already poses a challenge to grid stability; device failures exacerbate this issue, potentially leading to fluctuations in power supply and even outages. To mitigate these risks and enhance the reliability of solar power systems, we have introduced a sophisticated hierarchical failure detection strategy grounded in Support Vector Machine (SVM) algorithms. This innovative approach organizes solar power devices into clusters, optimizing the monitoring process. At the helm of each cluster, a dedicated failure detector scrutinizes operational data transmitted by the devices, employing SVM classification to discern the functional status of each unit. We have subjected our hierarchical SVM-based failure detection method to rigorous testing within a controlled simulation environment. The empirical evaluation of our system reveals a marked improvement in detecting device anomalies, as evidenced by substantial gains in Detection Accuracy (DA) and a reduction in the False Positive Rate (FPR). These advancements signify not only a stride forward in fault diagnosis in solar power networks but also a step toward ensuring the continuous, reliable delivery of clean energy. Our method promises to bolster the resilience of solar power infrastructure, thereby supporting the broader integrat...