The growing dependence of solar energy infrastructure on digital systems has created new cybersecurity concerns in renewable derivatives and challenging the reliability and stability of these power generation units. When smart photovoltaic (PV) networks are connected to IoT devices, cloud-based monitoring and SCADA platforms, challenges like false data injection (FDI), DoS and malicious control tampering pose as potential cyber threats. This paper introduces a deep learning–based intrusion detection system for protecting solar energy infrastructures in the face of emerging cyber threats. The hybrid model is developed to integrate Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), so that both spatial dependencies of communication traffic and temporal dynamics of system behavior can be learned. The benchmark data sets CIC-IDS2017 and UNSW-NB15 were complemented with simulated PV-SCADA data to increase domain relevance. Experimental results demonstrate that the CNN–RNN model achieves accuracy rate 97.4% and AUC 0.98, which is higher than classical algorithms e.g., SVM and Random Forest. The system was highly robust and carried out good generalization to various types of attacks with low false-alarm rates and short detection delay, suggesting the capability of our control node for real-time edge-level security monitoring in distributed PV systems. These results indicate that applying deep learning approached in the energy control center can effectively improve the resilience of solar power systems against cyber-attacks. The paper concludes with a recommendation that AI-based security be adopted as a strategic approach to achieving sustainable, secure and autonomous renewable energy infrastructures.