Incremental conductance method, fuzzy control method and neural network control method
Maximum power point tracking technology

Incremental conductance method, fuzzy control method and neural network control method

①Incremental conductance method
Incremental conductance method is to realize the tracking of the maximum power point by adjusting the voltage of the working point to gradually approach the voltage of the maximum power point. The incremental conductance method avoids the blindness of the disturbance observation method. It can determine the relationship between the operating point voltage and the maximum power point voltage. As we all know, the power-voltage curve of the photovoltaic array is a single-peak curve, so when the output power is the largest At the point, the derivative of power with respect to voltage is zero. For power Ppv:
Taking the derivation of Vpv at both ends of the above formula, and taking Ipv as a function of Vpv, we can get:
It can be seen from formula (2) that when (dPpv/dVpv)>0, it means that the current is in the left area of ​​the maximum power point, and Vpv is less than the maximum power point voltage; when (dPpv/dVpv)<0, it means that the current is at the maximum power point. In the area to the right of the point, Vpv is greater than the maximum power point voltage: when (dPpv/dVpv)=0, it means that Vpv is equal to the maximum power point voltage at the current maximum power point; according to the above analysis, we can get:

When Vpv(I pv /Vpv): When Vpv>Vmmp, (dIpv/dVpv)<(Ipv/Vpv); When Vpv=Vmpp, (dIpv/dVpv)=(Ipv /Vpv)
Here you need to obtain information about four parameters: the current output voltage and current of the photovoltaic array, the difference dVpv between the current voltage and the last collected voltage, and the difference dIpv between the current current and the last collected current. Therefore, according to the relationship between (dIpv/dVpv) and (Ipv/Vpv), the operating point voltage is adjusted to achieve maximum power point tracking. The software flow of the incremental conductance method is shown in Figure 1.

Incremental conductance method, fuzzy control method and neural network control method
Figure 1 Flow chart of incremental conductance method

Incremental conductance method has precise control and fast response speed, which can be used in occasions wherethe external environment changes rapidly. In practical applications, the algorithm will be affected by the sampling accuracy, especially under weaker light intensity, the actual voltage of the photovoltaic array has increased, and the output power has been reduced, but at this time the output current of the photovoltaic array is small, so the output The current change is also very small. If the current sampling accuracy is not enough, the system considers that the current has not changed, and the calculated power generation increases instead. Therefore, in this case, the wrong sampling information leads to an incorrect judgment, which may lead to an error in the maximum power point tracking. In addition, due to measurement noise and discrete quantization errors, equation (2) cannot always be equal to zero, and the voltage at the operating point will fluctuate at the maximum power point, resulting in a loss of power generation. In short, the incremental conductance method has higher requirements for hardware, especially the higher requirements for the sampling accuracy of the sensor. At the same time, the incremental conductance method also has the problem of difficulty in selecting the tracking step length.

② Fuzzy control method
The fuzzy control method does not require precise mathematical models, has a fast response, and is less affected by changes in the external environment. Therefore, the use of fuzzy logic control method for maximum power point tracking will obtain a more ideal tracking effect. Fuzzy control usually has three stages: fuzzification, fuzzy inference, and output defuzzification.

(1) Fuzzification of input and output
Figure 2 is a block diagram of the fuzzy control system based on the principle of dPpv/dVpv=0. Integrate and limit the control step △d to obtain the duty cycle d of the DC/DC converter.

Incremental conductance method, fuzzy control method and neural network control method
Figure 2 Block diagram of fuzzy control

The input is the output voltage Vpv and current Ipv of the photovoltaic array. Calculate the PV array output power Ppv from Vpv and Ipv. The maximum power point is reached when dPpv/dVpv=0 between the output power of the photovoltaic array and the output voltage. Therefore, dPpv/dVpv and △(dPpv/dVpv) are selected as the input variables E and CE of the fuzzy control. The output variables of the fuzzy control are The step length is marked per unit Ad. The definition of input and output is shown in formula (3). The fuzzy subsets of the input quantities E, CE and the output quantity Ad are (NB, NS, ZE, PS, PB), and the input quantity and output quantity are expressed by the unit value, and the membership function is shown in Figure 3(a) and (B) Representation. The membership function of the output △d is shown in Figure 3(c).
CE=△{(dPpv/dVpv)(k)}=[(dPpv/dVpv)(k)]﹣[(dPpv/dVpv)(k﹣1)] ——(3)

Incremental conductance method, fuzzy control method and neural network control method
Figure 3 Membership functions of E, CE and Ad

(2) Fuzzy reasoning
Determine the step change value according to the state of E and CE, make the power close to the maximum point, and establish the fuzzy rules in Table 1.

△d △d EEEEE
Table 1 Fuzzy rule table

(3) Unblur
The fuzzy quantity obtained by the above reasoning is transformed into a clear control quantity. Usually, there are maximum membership method, median method and weight method for defuzzification. The weighting method is adopted, as shown in formula (4).

△d=[C(i)μ(i)+C(j)μ(j)+…+C(k)μ(k)]/[μ(i)+μ(j)+…+μ(k )]——(4)

In the formula, C(i), C(j),…, C(k) and μ(i), μ(j),…, μ(k) are respectively the i-th and j-th satisfying fuzzy inference ,…, the clarity value and the degree of membership of the control output corresponding to the k rules.
Using PSIM simulation software, the fuzzy control method is simulated. Simulation conditions: Under standard conditions, the open circuit voltage of the photovoltaic array is 38V, and the short-circuit current is 3.2A..
The simulation result is shown in Figure 4. It can be seen from the figure that the light intensity changes from 1000W/㎡ to 1200W/㎡ in 1s. Using this fuzzy control method, the output power of the photovoltaic array is quickly tracked to the maximum power point, and the power fluctuation is very small, so the power of the tracking process The loss is correspondingly small.

Incremental conductance method, fuzzy control method and neural network control method
Figure 4 The output power and light intensity waveform of the photovoltaic array

The power and voltage output waveforms of the photovoltaic array are shown in Figure 5. Point a is the maximum power point of the photovoltaic array when the light intensity is 100/㎡. When the light intensity changes to 1200W/㎡, the maximum power point output by the photovoltaic array also changes accordingly, tracking to the maximum power point b point.

Incremental conductance method, fuzzy control method and neural network control method
Figure 5 P-V characteristic curve of photovoltaic array when illumination changes

It can be seen from Figure 4 and Figure 5 that the designed fuzzy control maximum power point tracking algorithm can track the maximum power point very well, with small power fluctuations, and can also track the maximum power point quickly and accurately when the light changes suddenly, which verifies the control The correctness of the method design. However, the fuzzy control algorithm also has shortcomings, that is, it depends on the pros and cons of empirical rules, and human factors are relatively large.

③ Neural network control method
Neural networks usually have three layers: input, hidden, and output layers. The nodes of each layer are changed and determined by the user. Input variables can be PV array parameters, such as open circuit voltage, short circuit current, light intensity, and temperature. Generally, there is only one output variable, that is, the duty cycle of the DC/DC converter, and the driving power converter runs at or near the maximum power point.

Since most photovoltaic arrays have their own characteristics, the neural network is only suitable for a certain photovoltaic array that has been tested, and the characteristics of the photovoltaic array will change over time, which also means that the neural network is specific to a certain photovoltaic array. A specific photovoltaic cell module, but not universal.