﻿ 一种改进变步长自适应扰动观察法的研究 A Study of Improved Variable Step Size Adaptive Disturbance Observation

Smart Grid
Vol.08 No.01(2018), Article ID:23760,9 pages
10.12677/SG.2018.81003

A Study of Improved Variable Step Size Adaptive Disturbance Observation

Dexiao Han, Yanping Wang, Zhisen Wang

School of Information Science and Engineering, Dalian Polytechnic University, Dalian Liaoning

Received: Jan. 22nd, 2018; accepted: Feb. 6th, 2018; published: Feb. 14th, 2018

ABSTRACT

Photovoltaic panels are effective means of solar energy utilization, and maximum power tracking is an important way to optimize this method. At present, the disturbance observing method is a widely used method of MPPT. However, the traditional disturbance observing method has the problems of tracking speed and energy fluctuation loss. In view of this, a variable step adaptive MPPT tracking method is designed. The method uses the power difference between the front and the rear moment as the duty cycle adjustment factor, and sets the threshold value, leaving a certain power fluctuation margin, which effectively solves the above existing problems; besides, it can also carry out adaptive control. By Simulink modeling and simulation analysis, compared with the traditional methods, the conclusion is drawn that the method has obvious advantages in terms of tracking stability, rapidity and energy loss reduction.

Keywords:MPPT, Variable Step, Duty Cycle, Threshold, Adaptive

1. 引言

${g}_{k}=g\left({U}_{k}\right)=\frac{\text{d}p}{\text{d}U}$ (1)

${U}_{k+1}={U}_{k}+{a}_{k}{g}_{k}$ (2)

Figure 1. P-U characteristic graph

2. 光伏电池建模

$I={I}_{sc}\left[1-{C}_{1}\left({\text{e}}^{\frac{U}{{C}_{2}{U}_{oc}}}-1\right)\right]$ (3)

${C}_{1}=\left(1-\frac{{I}_{m}}{{I}_{sc}}\right){\text{e}}^{\frac{-U}{{C}_{2}{U}_{oc}}}$ (4)

${C}_{2}=\left(\frac{{U}_{m}}{{U}_{oc}}-1\right){\left[\mathrm{ln}\left(1-\frac{{I}_{m}}{{I}_{sc}}\right)\right]}^{-1}$ (5)

Figure 2. Photovoltaic cell equivalent model

$\Delta S=\frac{S}{{S}_{ref}}-1$ (6)

$\Delta T=T-{T}_{ref}$ (7)

${I}_{sc}^{*}={I}_{sc}\frac{S}{{S}_{ref}}\left(1+\alpha \Delta T\right)$ (8)

${U}_{oc}^{*}={U}_{oc}\left(1-\gamma \Delta T\right)\mathrm{ln}\left(e+\beta \Delta S\right)$ (9)

${I}_{m}^{*}={I}_{m}\frac{S}{{S}_{ref}}\left(1+\alpha \Delta T\right)$ (10)

${U}_{m}^{*}={U}_{m}\left(1+\gamma \Delta T\right)\mathrm{ln}\left(e+\beta \Delta S\right)$ (11)

3. 最大功率跟踪模型分析

${Z}_{in}={Z}_{L}$ (12)

${U}_{in}{I}_{s}={U}_{L}{I}_{L}$ (13)

${U}_{L}=\frac{1}{1-\alpha }{U}_{S}$ (14)

${I}_{L}=\left(1-\alpha \right){I}_{S}$ (15)

Figure 3. Photovoltaic cell physics model

Figure 4. Impedance conversion circuit

${R}_{in}=\frac{{U}_{S}}{{I}_{S}}=\frac{\left(1-\alpha \right){U}_{L}}{\frac{{I}_{L}}{1-\alpha }}={\left(1-\alpha \right)}^{2}{R}_{L}$ (16)

4. 变步长自适应扰动法原理

$\Delta D=a×|{P}_{k}-{P}_{k-1}|$ (17)

$\Delta D$ 为占空比增量，a为步长因子系数。式(17)可得，采用前后时刻功率差值作为变步长的扰动步长因子，随着功率趋近最大值的程度，差值的模也会随之改变，从而实现自适应跟踪目。

5. 仿真及数据分析

5.1. 系统∆P曲线变化分析

$\Delta P$ 曲线变化如图6所示。(a)图为改进后的功率波动图，(b)图为最优梯度法功率波动图。由于改进型方法设定了功率阈值，所以功率的波动被限制在50W以内。结合图(a)和图7中可以得出，随着光照强度的增加，系统功率在不断增加，但系统的 $\Delta P$ 也在增大，但都没有超出阈值范围。

5.2. 系统功率跟踪分析

5.2.1. 追踪速率方面

5.2.2. 最大功率方面

5.3. 占空比分析

6. 结论

Figure 5. MPPT controller schematic

(a) (b)(c)

Figure 6. Power fluctuation diagram. (a) Improved step-by-step adaptive method ΔP curve; (b) optimal gradient method ΔP curve; (c) set the threshold of the optimal gradient method ΔP curve

(a) (b)

Figure 7. Power tracking process diagram. (a) Improved variable step adaptive tracking process diagram; (b) the best gradient method to track the process

(a) (b)

Figure 8. Duty Cycle Variation Graph. (a) Improved variable step size adaptive duty cycle curve; (b) optimum gradient method duty cycle curve

A Study of Improved Variable Step Size Adaptive Disturbance Observation[J]. 智能电网, 2018, 08(01): 16-24. http://dx.doi.org/10.12677/SG.2018.81003

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