The universal islanding detectionmethods (IDMs) for photovoltaic (PV) power systems require manually thresholds setting. That will lead to a certain non-detection zone (NDZ). Moreover, disturbance signals injected by...
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The universal islanding detectionmethods (IDMs) for photovoltaic (PV) power systems require manually thresholds setting. That will lead to a certain non-detection zone (NDZ). Moreover, disturbance signals injected by active detection methods may adversely affect power quality. Aiming at the above problems, this study proposes a passive intelligent IDM for parallel multi-PV system based on improved Adaptive Boosting (Adaboost) algorithm. Using Adaboost algorithm to generate classification models for islanding detection can theoretically avoid the NDZ of passive methods. The proposed method takes advantage of the electrical connection between characteristic parameters to adjust the classification model and improves the detection ability by redistributing the weight of each sub-model. Simulation results show that when adopted to a multi-PV system, the proposed method can effectively distinguish islanding operation in the NDZs of conventional passive IDMs. The method can also achieve accurate detection in the case of short-term power quality interferences, line faults and disturbance signal interference injected by activemethods.
With the increasing of the capacity of grid-connected photovoltaic (PV) power system, islanding detection becomes more prominent and significant. At present, islanding detectionmethods used in grid-connected photovol...
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ISBN:
(纸本)9783037855515
With the increasing of the capacity of grid-connected photovoltaic (PV) power system, islanding detection becomes more prominent and significant. At present, islanding detectionmethods used in grid-connected photovoltaic system can be divided into passive detectionmethods and active detection methods these two categories, which can also be divided into a variety of methods. This paper shows a comprehensive review of islanding detectionmethods, classifies the methods of islanding detection, analyzes the principles and characteristics of various islanding detectionmethods, indicates their appropriate situations, and pointes out the prospect of islanding detectionmethods. In practical applications, according to the actual situation, selects one or more islanding detectionmethods can attain better detection effect.
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