Abstract: The present disclosure discloses a photovoltaic array fault diagnosis method and apparatus based on a random forest algorithm. A strong classifier is constructed with many weak classifiers by integrating a plurality of decision trees, diagnosis results are generated by voting, and even if the diagnosis result of the most votes is wrong, the diagnosis results of the second and third more votes can be taken for reference of maintenance personnel, thereby improving the maintenance efficiency, and shortening the fault time of a system. The method and the apparatus resolve the problems of large data volume, long training time and the like of the conventional neural network algorithm, and can simply and quickly complete a diagnosis task and quickly implement the fault diagnosis of a small photovoltaic array, especially a 3×2 photovoltaic array.

Abstract: The present disclosure discloses a photovoltaic array fault diagnosis method and apparatus based on a random forest algorithm, belonging to the field of photovoltaic technology. Based on the idea of data driving, the method constructs a photovoltaic array fault diagnosis model by using the random forest algorithm, which is suitable for the characteristics of an actual photovoltaic array. A strong classifier is constructed with many weak classifiers by integrating a plurality of decision trees, diagnosis results are generated by voting, and even if the diagnosis result of the most votes is wrong, the diagnosis results of the second and third more votes can be taken for reference of maintenance personnel, thereby improving the maintenance efficiency, and shortening the fault time of a system.

Abstract: The present invention discloses a decision tree SVM fault diagnosis method of a photovoltaic diode-clamped three-level inverter in view of fault diagnosis problems of the photovoltaic three-level inverter in a photovoltaic microgrid. Taking an inverting state for example, firstly, analyzing running conditions of an inverter main circuit and performing fault classification, then taking the middle, upper and lower bridge leg voltages as measurement signals, extracting feature signals with a wavelet multiscale decomposition method, and thereby generating a decision tree SVM fault classification model with a particle swarm clustering algorithm, to finally achieve multi-mode fault diagnosis of the photovoltaic diode-clamped three-level inverter. Advantages of the present invention are that, this algorithm can obviously distinguish various fault states of the photovoltaic diode-clamped three-level inverter, complete the failure diagnostic task with fewer classification models And the diagnosis precision is high.

Abstract: The present invention discloses a decision tree SVM fault diagnosis method of a photovoltaic diode-clamped three-level inverter in view of fault diagnosis problems of the photovoltaic three-level inverter in a photovoltaic microgrid. Taking an inverting state for example, firstly, analyzing running conditions of an inverter main circuit and performing fault classification, then taking the middle, upper and lower bridge leg voltages as measurement signals, extracting feature signals with a wavelet multiscale decomposition method, and thereby generating a decision tree SVM fault classification model with a particle swarm clustering algorithm, to finally achieve multi-mode fault diagnosis of the photovoltaic diode-clamped three-level inverter. Advantages of the present invention are that, this algorithm can obviously distinguish various fault states of the photovoltaic diode-clamped three-level inverter, complete the failure diagnostic task with fewer classification models And the diagnosis precision is high.