Implementation of interleaved soft switching boost converter and H-bridge inverter for solar pv power generation system to attain maximum output voltage and reduced harmonics

With the widening application of solar PV power generation, a way of solar utilization, the safe operation of PV power generation system is increasingly valued. To assure the operating safety and reliability of PV power plant, monitoring system has to be installed to identify and eliminate faults of the plant immediately. Solar remote monitoring system in the architecture of internet of things (IoT) is comprised of sensing layer, information transmission layer and application layer. The sensing layer consists of sensors for monitoring environment, PV module and inverter; the information transmission layer consists of ZigBee access unit, access network, WEB server and Internet; and the application layer consists of IoT application fault diagnosis module. The remote monitoring system provides scientific decision-making reference to the safe operation and daily maintenance and management of PV power generation system through data acquisition, fusion, analysis and evaluation.

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Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Protection and Control of Modern Power Systems ◽

10.1186/s41601-021-00210-1 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Yingying Zhao ◽

Aimin An ◽

Yifan Xu ◽

Qianqian Wang ◽

Minmin Wang

Keyword(s):

Power Generation ◽

Model Predictive Control ◽

Predictive Control ◽

Current Control ◽

Pi Control ◽

Maximum Output ◽

Generation System ◽

Pv Modules ◽

Power Generation System ◽

Pv Power Generation

AbstractBecause of system constraints caused by the external environment and grid faults, the conventional maximum power point tracking (MPPT) and inverter control methods of a PV power generation system cannot achieve optimal power output. They can also lead to misjudgments and poor dynamic performance. To address these issues, this paper proposes a new MPPT method of PV modules based on model predictive control (MPC) and a finite control set model predictive current control (FCS-MPCC) of an inverter. Using the identification model of PV arrays, the module-based MPC controller is designed, and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature. An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors, the optimal voltage vector is selected according to the optimal value function, and the corresponding optimal switching state is applied to power semiconductor devices of the inverter. The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified, and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink. The results show that MPC has better tracking performance under constraints, and the system has faster and more accurate dynamic response and flexibility than conventional PI control.

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