Fault Diagnosis Model of Photovoltaic Array Based on Least Squares Support Vector Machine in Bayesian Framework

Abstract

Abstract:With the rapid development of the photovoltaic industry, fault monitoring is becoming an important issue in maintaining the safe and stable operation of a solar power station. In order to diagnose the fault types of photovoltaic array, a fault diagnosis method that is based on the Least Squares Support Vector Machine (LSSVM) in the Bayesian framework is put forward. First, based on the elaborate analysis of the change rules of the output electrical parameters and the equivalent circuit internal parameters of photovoltaic array in different fault states, the input variables of the photovoltaic array fault diagnosis model are determined. Second, through the LSSVM algorithm in the Bayesian framework, the fault diagnosis model based on the output electrical parameters and the equivalent circuit internal parameters of the photovoltaic array is built, which can effectively detect the photovoltaic array faults of short circuit, open circuit, and abnormal aging. Then, the simulation model is built to verify the validity of the LSSVM algorithm in the Bayesian framework by comparing it with the model of LSSVM and the Support Vector Machine (SVM). Moreover, a 5 × 3 photovoltaic array and a reference photovoltaic string are established and experimentally tested to validate the performance of the proposed method.

An Effective Evaluation on Fault Detection in Solar Panels

“…The fault identification results in an improvement of the efficiency of solar panels. The ability of a solar panel to convert sunlight into useful energy is measured by its performance [60]. When the sun shines on two solar panels with different ratings for the same amount of time, the more productive panel produces more energy than the less productive panel.…”

Research and Modeling of Photovoltaic Array Channel Noise Characteristics

“…The parallel system of photovoltaic inverters is given by [23]. In order to simplify the analysis of the electromagnetic interference between the inverter and the PV array, we ignore the series resistance and shunt resistance of the PV module equivalent circuit in [1]. When two pairs of bridge arms of a three-phase voltage bridge inverter are alternately turned on and off, the simplified equivalent circuit of the grid and load, inverter and PV array is shown in Figure 4.…”

“…With the rapid development of the photovoltaic industry, fault monitoring in photovoltaic power plants has become an important issue because the performance of photovoltaic modules affects the output characteristics of photovoltaic arrays directly, thus further affecting the stability of the photovoltaic generation system [1]. At present, the Chinese power system is actively constructing smart grids and energy big data centers [2][3][4].…”

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

“…Typical automated failure detection can be categorized as: model‐based, image and data‐driven methods 5–19 . Such implementations mainly utilize electrical and meteorological measurements, signals, or images to capture the behavior of PV systems, uncover patterns, and identify failures.…”

“…2,4 Typical automated failure detection can be categorized as: modelbased, image and data-driven methods. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] Such implementations mainly utilize electrical and meteorological measurements, signals, or images to capture the behavior of PV systems, uncover patterns, and identify failures. Most of the failure detection methods define threshold levels (TLs), which are used to compare a PV performance model against measurements in order to identify fault conditions.…”