Abstract
This paper presents an on-line diagnosis method for large photovoltaic (PV) power plants by using a machine learning algorithm. Most renewable energy output power is decreased due to the lack of management tools and the skills of maintenance engineers. Additionally, many photovoltaic power plants have a long down-time due to the absence of a monitoring system and their distance from the city. The IEC 61724-1 standard is a Performance Ratio (PR) index that evaluates the PV power plant performance and reliability. However, the PR index has a low recognition rate of the fault state in conditions of low irradiation and bad weather. This paper presents a weather-corrected index, linear regression method, temperature correction equation, estimation error matrix, clearness index and proposed variable index, as well as a one-class Support Vector Machine (SVM) method and a kernel technique to classify the fault state and anomaly output power of PV plants.
Highlights
Due to the interest in renewable energy, photovoltaic (PV) power plants have been penetrating power systems
This paper presents a weather-corrected index, linear regression method, temperature correction equation, estimation error matrix, clearness index and proposed variable index, as well as a one-class Support Vector Machine (SVM) method and a kernel technique to classify the fault state and anomaly output power of PV plants
If a fault occurs in a photovoltaic power plant, the fault state can be identified through a Performance Ratio (PR) index that has a lower value than that in the normal state
Summary
Due to the interest in renewable energy, photovoltaic (PV) power plants have been penetrating power systems. Some of the recent research on PV fault detection algorithms is based on the irradiance-power linear regression method This method improves the low recognition rate problem for small irradiation values [8,9]. The research in [15] proposes a detecting algorithm for a particular fault by utilizing the Voltage Ratio and Power Ratio indices This method calculates the high and low limit values of the PR and VR indices and classifies the fault of Grid-Connected. The author of [27] proposed the BNN (Bayesian Neural Network) AI method to detect the anomaly pattern (soiling effect) This AI method classifies the soiling effect by learning the dirty and clean modules of generation data sets on sunny and cloudy days. The recognition rate of the fault state is improved by utilizing the kernel technique of a variable index that separates the fault state from the fluctuations in data of normal generation
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