In this work, we investigate the robustness and consistency of the Equilibrium Propagation (EqProp) algorithm for training analog neural networks, addressing previous research limitations. We analyze the algorithm's robustness concerning learning parameter variations and examine the relationship between power calculations and convergence behavior. By accounting for all relevant components and their impact on learning, we aim to develop a reliable and consistent algorithm based on EqProp principles.