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The present study proposes an original hybrid technique that combines H-∞ filtering with an adaptive linear neuron (ADALINE) architecture to estimate flicker components in power distribution networks. By taking advantage of the H-∞ filter’s strong immunity to uncertainty and noise, the approach first recovers the voltage envelope; subsequently, the ADALINE identifies with high precision the relative magnitudes (ΔVi/Vt) of the flicker constituents located at the discrete frequencies specified by IEC standards within that envelope. The resulting cooperation yields fast, reliable time-domain estimation that converges quickly and maintains performance even under heavy noise, thereby resolving the principal weaknesses of most frequency-domain solutions. In contrast to established procedures, this combined framework effectively processes complex power-system disturbances without requiring prior knowledge of noise statistics or prolonged learning phases. Performance evaluation relies on simulations aligned with IEC Standard 61000-4-15, reinforced by statistical metrics, Monte Carlo runs, and actual field recordings. Comparative outcomes reveal markedly better precision, greater tolerance to disturbances, and substantially lower computational requirements than estimators based on the Fast Fourier Transform (FFT) or the Discrete Wavelet Transform (DWT).

This study introduces an improved bidirectional multi-input DC–DC converter (MIPC) tailored for hybrid energy storage systems (HESS) in DC microgrid environments. Traditional batteries exhibit limited charge and discharge rates, which can impose high current stresses, thereby reducing their operational lifespan. Supercapacitors (SCs), due to their high power density, respond rapidly to sudden load variations, mitigating these limitations; however, they are unsuitable for long-term energy storage. Combining batteries with SCs in a HESS leverages their complementary properties, creating an efficient energy storage solution. The proposed MIPC links the HESS to the DC microgrid, enabling independent regulation of battery and SC power as well as efficient energy transfer among storage units. A unified control strategy was developed to manage both charging and discharging of the HESS. A model predictive control (MPC) scheme demonstrated superior DC bus voltage restoration during step changes in photovoltaic (PV) generation and load conditions, compared with conventional proportional-integral (PI) controllers. The MPC reduces current stress, prolongs battery lifespan, and improves overall system performance, ensuring rapid DC grid voltage stabilization. Simulation and experimental tests under varying PV output and load profiles confirmed enhanced DC-link voltage regulation.