Recent Advances in Intelligent MPPT Algorithms for Photovoltaic Systems: Performance Comparison and Application Analysis

Abstract

Maximum power point tracking (MPPT) plays a vital role in improving the energy conversion efficiency of photovoltaic systems. In recent years, intelligent MPPT algorithms have advanced rapidly; however, existing studies still lack a sufficiently integrated comparison of their performance characteristics, application suitability, and engineering practicality. This paper reviews recent advances in intelligent MPPT algorithms for photovoltaic systems and classifies them into four categories: rule-based control, prediction-learning, search-optimization, and hybrid-collaborative methods. These methods are comparatively analyzed in terms of tracking speed, tracking accuracy, tracking stability, computational complexity, and application adaptability, with further discussion of typical operating scenarios such as dynamic irradiance variation, partial shading, and resource-constrained control platforms. The review indicates that rule-based methods are generally easier to implement in real-time environments, prediction-learning methods show advantages under dynamic conditions, search-optimization methods are more suitable for multi-peak conditions such as partial shading, and hybrid-collaborative methods offer stronger overall potential under complex operating conditions. Overall, the selection of an MPPT method should be made in accordance with specific operating conditions, control objectives, and engineering constraints