Intelligent System for Voltage Fault Detection in Electrical Networks: A Neuro-Fuzzy Approach with Hybrid Transmission (Fiber, 4G, VSAT)

The reliability of the power supply depends heavily on the ability of operators to quickly detect and classify voltage disturbances. In the context of Congo-Brazzaville, structural limitations in communication infrastructure make this challenge particularly complex. This article proposes an innovative approach combining an ANFIS neuro-fuzzy system with a hybrid remote transmission architecture combining fiber optics, 4G/LTE, and VSAT. The model is based on the joint integration of electrical indices (RMS values, symmetrical components, THD) and network quality of service metrics (latency, jitter, losses) into fuzzy premises, in order to strengthen decision-making robustness in the face of heterogeneous transmission conditions. MATLAB/Simulink simulations demonstrate that ANFIS significantly outperforms conventional RMS threshold and ANN approaches: classification accuracy reaches 97.8% over fiber and remains at 94.3% over 4G and 88.6% over VSAT, with a median detection delay reduced to 12 ms over fiber and 41 ms over 4G. This performance complies with regulatory recommendations (EN 50160, IEC 61000-4-30) and confirms the value of near real-time deployment. Beyond the experimental results, the study paves the way for the modernization of African electrical grids by combining artificial intelligence and communications resilience. It establishes a credible scientific basis for the implementation of Smart Grid solutions adapted to constrained environments.