Leveraging Recent Advancements in Embedded Systems and Wireless Sensor Networks for Cost-Effective Monitoring and Automation in Smart Grids Recent progress in embedded systems and wireless sensor networks (WSNs) has paved the way for affordable monitoring and automation solutions within smart grids. These advancements facilitate the creation of a well-structured network of interconnected subsystems and metasystems, commonly referred to as a "smart grid." The primary goal of a smart grid is to augment the efficiency of traditional power grids while ensuring a consistent and dependable supply of energy. To achieve this, a smart grid necessitates bidirectional communication between utility providers and end users.This study introduces an innovative approach to enhance the security of smart grids and detect industrial faults by employing wireless sensor networks alongside deep learning architectures. The security of the smart grid network is bolstered through the utilization of a blockchain-based routing protocol for smart grid nodes, integrated with an Internet of Things (IoT) module. Furthermore, the research delves into industrial fault detection by utilizing a Q-learning-based transfer convolutional network for network monitoring and analysis. The experimental assessment of this proposed methodology encompasses a range of key performance metrics, including bit error rate, end-to-end delay, throughput rate, spectral efficiency, accuracy, mean average precision (M.A.P.), and root mean square error (RMSE). The attained results demonstrate the effectiveness of the approach, with notable achievements such as a 65% bit error rate, a 57% end-to-end delay, a 97% throughput rate, a 93% spectral efficiency, a 95% accuracy, a 55% M.A.P., and a 75% RMSE.