物理代写|光电技术代写Photovoltaic Technology代考|Fuzzy Reasoning Arithmetic

After fuzzy quantity, we develop an operational rule based on the rules of expert knowledge. As discussed above, it is concluded that the fuzzy control output process in fact is the process of fuzzy reasoning algorithm, and it is concluded that the output is still in the form of fuzzy quantity. According to the above analysis of the different combination of $e$ and $\Delta e$, in order to follow to the MPP, to make corresponding changes to cope with the change of output voltage value, i.e., the change of $\mathrm{d} U$ should make the operation point to the direction search close to the MPP.
We can obtain the fuzzy rules reasoning table which is shown in Table 1 through the logic of MPPT control rules, the table reflect the fact that when the input variables $e$ and $\Delta e$ change, the corresponding output variable rules of $\mathrm{d} U$ change. Thus the corresponding linguistic variables are obtained. For example, it shows the slope of two sample points of attachment is negative when $e$ is NB (negative big), and the absolute value is larger, to show that the operation point on the left side of the MPP and is far away from the MPP. At this point if $\Delta e$ also is NB (negative big) that was followed by the voltage change and further away from the MPP. This can make the output variable $\mathrm{d} U$ for PB (positive big), thus the operation point voltage is sharp increase and close to the MPP quickly.

物理代写|光电技术代写Photovoltaic Technology代考|Neural Network-Based Control

The typical three-layer feedforward neural network structure is shown in Fig. 12, which is used to identify the MPP voltage $U_{\mathrm{MPP}}^$ of PV cells. The neural network comprises three layers: the input layer, the hidden layer and the output layer, where the numbers of neurons in the three layers are $3,5,1$, respectively. The input signal of the input layer neurons is the open-circuit voltage $U_{\mathrm{OC}}$ obtained from the detection unit and time constant $T_{\mathrm{p}}$ from the controller. The output of the input layer directly transmits to the neurons in the hidden layer, and the output of the output layer is the estimated voltage $U_{\text {MPP }}^$ at the MPP. For each neuron in the hidden layer and output layer, the used activation function is
$$O_i(k)=\frac{1}{1+\mathrm{e}^{-\lambda_i(k)}}$$

where the function $O_i(k)$ is used to define the input-output characteristics of neurons, and $\lambda_i(k)$ is the input signal of neuron $i$ when using the $k$ th sample data. The input signal $\lambda_i(k)$ is the weighted summation of the output of the previous layer, namely
$$I_i(k)=\sum_j \omega_{i j}(k) O_j(k)$$
where $w_{i j}$ is the connection weights between neurons $i$ and $j$, and $O_j(k)$ is the output signal of the neuron $j$.

In order to accurately determine the MPP, the weights must be determined according to the training of typical sample data. The training of the neural network needs a set of input-output sample data. All calculations in the training process are done offline. The weights are adaptively updated until they satisfy the input-output mode based on the sample data. When the mean square error reaches its minimum value, the training is finished.
$$E=\sum_{k=1}^N[t(k)-O(k)]^2$$
where $N$ is the total number of training samples, $t(k)$ is the desired output, and $O(k)$ is the actual output. In order to verify the feasibility of the control scheme, the neural network-based control can be applied to track the MPP in the PV system, and the following formula can be used to evaluate the estimation error

物理代写|光电技术代写光伏技术代考|基于神经网络的控制

$$O_i(k)=\frac{1}{1+\mathrm{e}^{-\lambda_i(k)}}$$

，其中函数$O_i(k)$用于定义神经元的输入输出特性，$\lambda_i(k)$为使用第$k$个样本数据时神经元$i$的输入信号。输入信号$\lambda_i(k)$是上一层输出的加权和，即
$$I_i(k)=\sum_j \omega_{i j}(k) O_j(k)$$
，其中$w_{i j}$是神经元$i$和$j$之间的连接权值，$O_j(k)$是神经元$j$的输出信号

$$E=\sum_{k=1}^N[t(k)-O(k)]^2$$

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