Investigation of (Eu2+)–(Mn2+) Energy Transfer and Y2O3 Scattering Effects in Phosphor-Converted White LEDs

2026-09-03T14:25:29+03:00

Phosphor-converted white light-emitting diodes (WLEDs) require both efficient luminescence conversion and effective control of photon propagation within the phosphor layer. Eu²⁺–Mn²⁺ co-doped phosphors are of interest because Eu²⁺ can act as a sensitizer for Mn²⁺ through an energy-transfer process. In addition, scattering materials can modify photon propagation and consequently affect the optical performance of WLEDs. This work discusses the Eu²⁺→Mn²⁺ energy-transfer process in Ba₂MgSi₂O₇ (BMSO) phosphors based on previously reported spectroscopic and mechanistic studies, while experimentally examining the effect of Y₂O₃ scattering conditions on WLED optical characteristics. The effects of Y₂O₃ on correlated color temperature (CCT), ΔCCT, and luminous output are analyzed, and its scattering properties are compared with those of KBr and TiO₂. The results show that Y₂O₃ produces a moderate scattering effect and influences both color characteristics and luminous output. The Y₂O₃-based WLED exhibits a CCT of 3000 K, a ΔCCT of 48.82 K, a CRI of 56.32, a CQS of 42.46, and a luminous output of 73.11 lm. Compared with the investigated scattering materials, TiO₂ provides stronger scattering and higher luminous output, whereas Y₂O₃ provides a more moderate scattering response with warm-white emission characteristics. The discussion of Eu²⁺–Mn²⁺ energy transfer and Y₂O₃ scattering provides complementary perspectives on luminescence conversion and photon propagation in phosphor-converted WLED systems.

Machine Learning-Based Multi-Objective Optimization for Maximum Power Point Tracking and Fault Diagnosis in Intelligent Solar Photovoltaic Systems

2026-08-23T16:29:22+03:00

Photovoltaic (PV) systems require effective maximum power point tracking (MPPT) under rapidly changing environmental conditions while simultaneously maintaining reliable fault detection. Existing approaches commonly address MPPT optimization and fault diagnosis independently, limiting their ability to coordinate energy extraction and system health. This study proposes an integrated machine learning–multi-objective particle swarm optimization (ML-MOPSO) framework that combines environmental prediction, adaptive MPPT, converter optimization, and fault diagnosis within a unified closed-loop architecture. MOPSO simultaneously optimizes power extraction, convergence time, steady-state oscillation, tracking efficiency, fault-detection rate, and false-alarm rate, while machine learning predicts irradiance and temperature variations and identifies abnormal operating conditions. Simulation results demonstrate an average power extraction efficiency of 98.7%, with MPPT convergence time and steady-state oscillations reduced by approximately 34% and 41%, respectively. Under partial shading, the proposed framework achieved a 99.1% global maximum power point detection rate. The fault-diagnosis subsystem achieved 97.9% overall classification accuracy, with detection times below approximately 0.18 s. Robust performance was maintained under rapid irradiance changes, partial shading, measurement noise, parameter uncertainty, and multiple PV fault conditions. These findings demonstrate that coordinated predictive MPPT and fault-aware optimization can improve PV energy extraction, dynamic response, and operational reliability within a unified control framework.

International Journal of Electrical and Computer Engineering Research

Investigation of (Eu2+)–(Mn2+) Energy Transfer and Y2O3 Scattering Effects in Phosphor-Converted White LEDs

Machine Learning-Based Multi-Objective Optimization for Maximum Power Point Tracking and Fault Diagnosis in Intelligent Solar Photovoltaic Systems