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Recent Developments in Photovoltaic Materials and Devices || A Quick Maximum Power Point Tracking Method Using an Embedded Learning Algorithm for Photovoltaics on Roads
摘要： This chapter presents a new approach to realize quick maximum power point tracking (MPPT) for photovoltaics (PVs) bedded on roads. The MPPT device for the road photovoltaics needs to support quick response to the shadow flickers caused by moving objects. Our proposed MPPT device is a microconverter connected to a short PV string. For real-world usage, several sets of PV string connected to the proposed microconverter will be connected in parallel. Each converter uses an embedded learning algorithm inspired by the insect brain to learn the MPPs of a single PV string. Therefore, the MPPT device tracks MPP via the perturbation and observation method in normal circumstances and the learning machine learns the relationships between the acquired MPP and the temperature and magnitude of the Sun irradiation. Consequently, if the magnitude of the Sun beam incident on the PV panel changes quickly, the learning machine yields the predicted MPP to control a chopper circuit. The simulation results suggested that the proposed MPPT method can realize quick MPPT.
关键词： microconverter,maximum power point tracking (MPPT),incremental learning,modal regression on a fixed memory budget,photovoltaics bedded on road,insect brain,shadow flicker,embedded learning algorithm,partial shading
更新于2025-09-12 10:27:22
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[IEEE 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) - Winterton, South Africa (2019.8.5-2019.8.6)] 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) - Failure Analysis of Photovoltaic Equipment Based on ISOMPNN
摘要： ISOMPNN, a new incremental learning method based on the self-organizing map (SOM) and probabilistic neural network (PNN) is proposed to tackle the problem of model self-adaptation when new categories of equipment failures occur. It uses a modular SOM to learn each category of photovoltaic device data and then constructs a PNN using the prototype vector of each category of data after training as a model neuron of that category. Incremental SOMPNN can incrementally learn new classes of different data to complement existing models. In the incremental learning process, only the new data is used to adjust the model, instead of reusing the original data, reducing training time and reducing storage space. Its effectiveness can be well verified in known photovoltaic device data.
关键词： self-organization map,photovoltaic equipment,probabilistic neural network,incremental learning
更新于2025-09-11 14:15:04