A Synchronous Data Approach to Analyze Cloud-Induced Effects on Photovoltaic Plants Using Ramp Detection Algorithms

Abstract

The proliferation of photovoltaic energy in the electricity grid presents a significant challenge in terms of management, control, and optimization, especially due to its dependence on weather behavior and cloud passing. Even if there are a great number of articles centered on study cloud passing effects, such as voltage flickers, voltage fluctuations, or ramping events, the approaches are quite heterogeneous and lack a broader perspective. A key factor might be the limiting data sets, as wide power generation data sets often omit meteorological data and vice versa. This study uses an advanced monitoring system based on phasor measurement units (PMUs), developed by the authors. The monitoring system is installed at a photovoltaic plant and generates high-quality synchronous irradiance and power data, enabling the joint analysis of irradiance transients, solar power ramp rates, and voltage fluctuations. Therefore, the results of this article present a detailed analysis of the production parameters of photovoltaic plants, focusing on the effects of passing clouds on the photovoltaic plant’s power, current, and voltage. To that end, compression algorithms such as the Swinging Door Algorithm (SDA), commonly used to detect ramp events, were employed. It was found that SDA produces a similar ramp rate output with power and irradiance data, suggesting that both data sets may be complementary. In addition, voltage fluctuations attributable to passing clouds were analyzed.