Combined ANFIS–Wavelet Technique to Improve the Estimation Accuracy of the Power Output of Neighboring PV Systems during Cloud Events

Abstract

The short-term variability of photovoltaic (PV) system-generated power due to ambient conditions, such as passing clouds, represents a key challenge for network planners and operators. Such variability can be reduced using a geographical smoothing technique based on installing multiple PV systems over certain locations at distances of meters to kilometers. To accurately estimate the PV system’s generated power during cloud events, a variability reduction index (VRI), which is a function of several parameters, should be calculated precisely. In this paper, the Wavelet Transform Technique (WTT) along with Adaptive Neuro Fuzzy Inference System (ANFIS) are used to develop new models to estimate the PV system’s power output during cloud events. In this context, irradiance data collected from one PV system along with other parameters, including ambient conditions, were used to develop the proposed models. Ultimately, the models were validated through their application on a 0.7 km2 PV plant with 16 rooftop PV systems in Brisbane, Australia.

A Modified LADRC-Based DC-Link Voltage Controller for Photovoltaic Grid-Connected Inverters

“…However, in the presence of active power disturbances, the dc-link voltage is prone to fluctuations. These active power disturbances are mainly divided into three categories, namely: (1) Variations in climatic conditions, such as temperature and solar irradiance [9,10]; (2) voltage sag fault on the grid; and (3) inevitable power exchange fluctuations between the dc side of the inverter and the grid. Besides, the inverter itself is also a sensitive system with non-constant parameters and non-linear variations.…”