This study first explored the effect of shading on the output characteristics of modules in a photovoltaic module array. Next, a modified particle swarm optimization (PSO) method was employed to track the maximum power point of the multiple-peak characteristic curve of the array. Through the optimization method, the weighting value and cognition learning factor decreased with an increasing number of iterations, whereas the social learning factor increased, thereby enhancing the tracking capability of a maximum power point tracker. In addition, the weighting value was slightly modified on the basis of the changes in the slope and power of the characteristic curve to increase the tracking speed and stability of the tracker. Finally, a PIC18F8720 microcontroller was coordinated with peripheral hardware circuits to realize the proposed PSO method, which was then adopted to track the maximum power point of the power-voltage (P-V) output characteristic curve of the photovoltaic module array under shading. Subsequently, tests were conducted to verify that the modified PSO method exhibited favorable tracking speed and accuracy.
This paper presents an interleaved inductor-coupled converter for a fuel cell. It is designed to boost a low input voltage from a fuel cell to a specified voltage level for DC load or high voltage DC link, thus providing a high-voltage conversion ratio. The presented converter mainly involves coupled inductors and capacitor of voltage doublers for boosting purposes, but the voltage ratings of the involved power switches and diodes, in particular, remain unaffected as the output voltage is boosted. Using an interleaving trigger mechanism, this circuit configuration can not only suppress the input current ripple, but also reduce the current ratings of power switches. In simple terms, it is a low-cost but high-voltage gain converter due to a smaller number of required components and the lower current and voltage ratings of power switches. The operation principles and design steps are detailed herein, and the performance simulations are experimentally validated at the end of the work.
In this paper, a novel incremental conductance (INC) maximum power point tracking (MPPT) method based on extension theory is developed to make full use of photovoltaic (PV) array output power. The proposed method can adjust the step size to track the PV array’s maximum power point (MPP) automatically. Compared with the conventional fixed step size INC method, the presented approach is able to effectively improve the dynamic response and steady state performance of a PV system simultaneously. A theoretical analysis and the design principle of the proposed method are described in detail. Some simulation results are performed to verify the effectiveness of the proposed MPPT method.
The main purpose of this paper is to apply the extension method based on the spectrum of the vibration signal for motor mechanical fault diagnosis. The measured data of mechanical fault in induction motors are adopted to carry out the spectrum analysis and then establish the extension element model. According to the distance and the correlation function value concept of extension theory, one can calculate the correlation degree of various fault type and then determine the most possible category of mechanical fault in induction motors. Finally, some simulation and experimental results are made to verify the effectiveness of the proposed motor mechanical fault diagnosis method.
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