LPV Overall Small-Signal Modeling of Photovoltaic Systems Considering Weather and Load Disturbances

The existing overall linearized model with variable weather parameters (OLM-VWP) has preliminarily established a framework for overall linearization and delay analysis of photovoltaic (PV) systems. However, weather variables are mostly introduced in the form of equivalent parameters, making it difficult to collaboratively describe PV-side power variations, DC-bus dynamics, and load disturbances within a unified state-space framework. To address this issue, this paper proposes an improved linear parameter-varying (LPV) overall small-signal model with explicit weather-disturbance inputs. The proposed model takes the PV-side voltage, Boost converter inductor current, DC-bus voltage, inverter output current, and output voltage as state variables, and incorporates the duty cycle, modulation index, irradiance, temperature, and load disturbance into the state equations in a unified manner. Meanwhile, around the maximum power point (MPP), the PV differential conductance, irradiance sensitivity, and temperature sensitivity are introduced to establish a direct input channel from meteorological disturbances to PV current dynamics. In MATLAB, an OLM-VWP approximate model, the improved LPV model, and a nonlinear averaged benchmark model are developed, and comparative analyses are conducted in terms of output voltage, PV-side power, output power, and duty-cycle scheduling. Under the main test conditions, the relative root mean square errors of the improved model for output voltage, PV-side power, and output power are 0.368%, 0.908%, and 0.830%, respectively, which are reduced by approximately 68.4%, 29.3%, and 64.7% compared with those of the OLM-VWP approximate model. The simulation results demonstrate that the proposed model significantly improves the multivariable dynamic modeling accuracy of PV systems under multiple disturbances while maintaining a low-order structure, thereby providing a more accurate modeling basis for gain-scheduled control, delay compensation, and state observer design.