公开(公告)号：US10234495B2

公开(公告)日：2019-03-19

申请号：US15551299

申请日：2016-12-30

Applicant: Jiangnan University

Inventor： Hongfeng Tao ,  Chaochao Zhou ,  Yan Liu ,  Yajun Tong

IPC: G01R31/02 ,  H02S50/10 ,  G06K9/62 ,  H02S50/00

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. The anti-interference capability is strong.

公开(公告)号：US11975751B2

公开(公告)日：2024-05-07

申请号：US17986431

申请日：2022-11-14

Applicant: Jiangnan University

Inventor： Hongfeng Tao ,  Longhui Zhou ,  Zhihe Zhuang ,  Yande Huang ,  Junyu Wei ,  Rui Wang

IPC: B61L25/02

CPC classification number: B61L25/021 ,  B61L2201/00

Abstract: The present invention discloses an iterative learning control (ILC) method for a multi-particle vehicle platoon driving system, and relates to the field of ILC. The method includes: firstly, discretizing a multi-particle train dynamic equation using a finite difference method to obtain a partial recurrence equation, and then transforming the partial recurrence equation into a spatially interconnected system model; secondly, transforming the spatially interconnected system model into an equivalent one-dimensional dynamic model using a lifting technology, and in order to compensate input delay, designing an ILC law based on a state observer; and thirdly, transforming a controlled object into an equivalent discrete repetitive process according to the ILC law, and converting a controller combination problem into a linear matrix inequality based on stability analysis of the repetitive process. The method is simple and easy to implement, considers structure uncertainty of the system, and has a good control performance and robustness.

公开(公告)号：US11114977B2

公开(公告)日：2021-09-07

申请号：US16557220

申请日：2019-08-30

Applicant: Jiangnan University

Inventor： Hongfeng Tao ,  Chaochao Zhou ,  Jianqiang Shen ,  Qiang Wei ,  Wei Liu ,  Longhui Zhou

IPC: H02S50/10

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.