The existing Maximum Power Point Tracking (MPPT) method has low tracking efficiency and poor stability. It is easy to fall into the Local Maximum Power Point (LMPP) in Partial Shading Condition (PSC), resulting in the degradation of output power quality and efficiency. It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms, and their performance in tracking the Global Maximum Power Point (GMPP) varies. Thus, a Cuckoo search algorithm (CSA) combined with the Incremental conductance Algorithm (INC) is proposed (CSA-INC) is put forward for the MPPT method of photovoltaic power generation. The method can improve the tracking speed by more than 52% compared with the traditional Cuckoo Search Algorithm (CSA), and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization (PSO) and the Gravitational Search Algorithm (GSA). CSA-INC has an average tracking efficiency of 99.99% and an average tracking time of 0.19 s when tracking the GMPP, which improves PV power generation’s efficiency and power quality.

Human civilization faces many challenges due to the excessive use of non-renewable energy resources such as coal, oil, etc. The exponential growth of greenhouse gases (mainly carbon dioxide emissions) is a significant existential and practical problem, leading to severe environmental damage, thus threatening human society and the entire ecosystem [

Currently, Hill Climbing (HC) [

In recent years, scholars from various countries have proposed numerous advanced MPPT algorithms to solve the GMPP problem under PSC. Jiang et al. [

The CSA is a new biomimetic intelligent optimization algorithm proposed by Yang et al. in 2009 based on the hatching parasitic behavior of the cuckoo [

However, the CSA, similar to other intelligent heuristics, suffers from the problem of inefficient search. The simulation verifies that the CSA has slow convergence speed, apparent power fluctuation, inaccurate tracking of PV MPPT, and easy to fall into LP under PSC. Based on the above problems, this paper proposes a Cuckoo search algorithm combined with the Incremental conductance Algorithm (CSA-INC). The main features and focuses of this paper are as follows:

This paper proposed a CSA-INC applied to MPPT in PV systems.

The algorithm utilizes the efficient global search capability of CSA to track to the vicinity of GMPP quickly and then uses the good local search and fast convergence properties of INC to quickly and accurately track to GMPP. Improves the convergence speed and tracking efficiency.

The proposed algorithm can track true global power in less time and minimizes the oscillations under five different shading patterns, and compared with GSA, CSA, and PSO, it shows its superiority.

The P-V, I-V characteristics, and maximum power point parameters of photovoltaic arrays are related to external environmental factors, such as temperature, light intensity, etc. [

As double diodes can better improve the model's accuracy, this model is used to model in this paper. The output voltage-current characteristics of the photovoltaic cell are:

_{D1} and _{D2} are the reverse saturation currents of diodes D_{1} and D_{2}, respectively, and the calculation formula is shown in

_{SC_STC} and _{OC_STC} are the short-circuit current and the open-circuit voltage of the photovoltaic cell in the ideal state, respectively. _{1} and _{2} are the current temperature coefficient and the voltage temperature coefficient.

Under conditions of uniform irradiance, the P-V characteristics of the PV module show a unique peak which is the MPP. However, this is not the case for the PV module with bypass diodes under PSCs. In case the PV cells receive different irradiance levels, the P-V curve exhibits several peaks. The multi-peak characteristic is obtained using the bypass diodes, and the number of the peaks depends on the total irradiance levels. The peak corresponding to the highest power is treated as the global MPP, whereas the remaining peaks are LMPPs [^{2}; At this point, the P-V curve has only one wave peak. PSC 2: The light received by the five battery modules are 1000, 1000, 800, 600, and 600 W/m^{2}, respectively. The P-V curve has three wave peaks. PSC 3: The light received by the five battery modules is 1000, 800, 600, 400, and 200 W/m^{2}, respectively. The P-V curve has five-wave peaks. Under three different solar radiation conditions, simulations are performed. The I-V and P-V curves under three different shading patterns are illustrated in

The convergence speed of CSA is not sensitive to the selected parameters to a certain extent. It has the characteristics of simplicity, efficiency, superior search path, and strong global search capability. However, CSA also has a problem regarding how the convergence speed in the later optimization stage has a tendency to slow down. To overcome this shortcoming, we combine the CSA with the INC algorithm in this paper. We first use the cuckoo search algorithm to quickly find the range interval of the global maximum power point and then apply the conductivity increment algorithm to find the maximum power point in that interval.

The Cuckoo search algorithm comes from the following three rules:

Each cuckoo lays an egg each time and puts it in a randomly selected host bird nest;

The birds’ nest with the best quality will appear in the next generation;

The number of available host birds is fixed, and the host bird finds the eggs laid by the cuckoo with probability

In this paper, the cuckoo egg position update adopts the Lévy Flight method, improving the global search ability, having fewer adjustment parameters, fast convergence, and prevent the algorithm from falling into the optimal local solution to a large extent [

In the formula,

The basic process of CSA is as follows:

Initialize the parameters of the cuckoo algorithm, the number of bird nests after initialization is N, and the probability of bird eggs found by other birds

The position of the bird’s nest is initialized. Randomly generate the position of the bird’s nest _{i} = [_{1},_{2},…,_{S}], i=1,2,…,

Evaluate the quality of the bird’s nest. Calculate the quality of the bird’s nest, which is the corresponding fitness value, and record the optimal quality _{best} and the position of the bird’s nest;

Improve and update the position of the bird's nest through Lévy Flight;

The improved bird's nest quality is calculated and compared with the previous bird’s nest quality, and N bird’s nests with better quality are retained;

Improve and update the position of the bird's nest according to the probability

Update and record the quality and location of the bird's nest;

Output the best nest quality value, judge whether the end condition is met, and return to Step 3 if the end condition is not met;

Applying the Cuckoo algorithm to the maximum power point tracking control in the photovoltaic system, the position of the bird's nest corresponds to the duty cycle, and the quality of the bird's nest is the corresponding power.

The PSO algorithm is equivalent to the particular case of the cuckoo algorithm (equivalent to the specific case where other birds find cuckoo probability

The flow chart of the MPPT method based on the CSA is shown in

The Incremental conductance Algorithm is a conventional maximum power point tracking algorithm. The principle of the algorithm is to track the maximum power by differentiating the power and voltage equal to zero. Its main principles are as follows:

The photovoltaic power formula is shown in

Taking the derivation of V at both ends of the above equation simultaneously, it can get

The power is on the left of the maximum powerpoint. At this time, the control duty cycle D is reduced, and the parallel voltage increases.

The power is on the right of the maximum powerpoint. At this time, the control duty cycle D is increased, and the parallel voltage is reduced.

The power is at the maximum powerpoint. The control duty cycle D remains unchanged, and the maximum power point is tracked.

This paper combines the cuckoo search algorithm and Incremental conductance Algorithm for MPPT. The Cuckoo search algorithm is first applied to perform a global search and converge quickly to the vicinity of the MPP. Then the conductivity increment algorithm is introduced to optimize and improve the problem that the Cuckoo algorithm tends to fall into local optimums and slow convergence at a later stage using the conductivity increment algorithm's good local search and fast convergence characteristics.

To speed up the convergence speed of the CSA-INC algorithm, we need to set the CSA’s termination condition and introduce the INC algorithm in time. When the maximum position difference of the bird’s nest is less than a small threshold δ, the operation of the cuckoo algorithm is ended, the Incremental conductance Algorithm is started, and δ = 0.05 is set. In the tracking process of the Incremental conductance Algorithm, the termination discriminant is as follows:

When _{mpp} is output.

When moving clouds, dust, and bird excrement cover part of the shadow or light intensity changes, the output power of the photovoltaic system will also change. To reduce the power mismatch and reduce the power generation efficiency, the algorithm needs to be restarted. The output power changes to

The formula

The flow chart of the MPPT method based on CSA-INC is shown in

MATLAB/SIMULINK is used to perform simulations and check the performance of the CSA-INC based MPPT. The PV system model contains 5 × 1 PV arrays, and each PV module contains four parallel and two series-connected PV cells. The photovoltaic module is an Aavid Solar ASMS-165P, and detailed Specifications of the PV module used in _{in} = 10 uF, C_{out} = 60 uF, and L = 1 mH. The switching frequency is 30 kHz, and the MPPT controller’s sampling time is set to 0.01 s.

Parameter name | Value | Parameter name | Value |
---|---|---|---|

_{max}(W) |
213.15 | Temperature coefficient of _{oc}(%/deg.C) |
−0.36 |

_{m}(V) |
29 | Temperature coefficient of _{sc} (%/deg.C) |
0.102 |

_{m}(A) |
7.35 | Parallel strings | 4 |

_{oc}(V) |
36.3 | Series-connected modules per string | 2 |

_{SC}(A) |
7.84 |

The model of MPPT system based on CSA-INC fusion algorithm is shown in

Case | Irradiances^{2}) |
Power at GMPP (W) | Wave peaks of PV curve under PSCs |
---|---|---|---|

1-PSC 1 | 1000 1000 1000 1000 1000 | 8517.81 | 1 |

2-PSC 2 | 1000 1000 800 600 600 | 5632 | 3 |

3-PSC 3 | 1000 800 600 400 200 | 3374.52 | 5 |

4-PSC 1-PSC 2-PSC 3 | – | 8517.81–5632–3374.52 | 1–3–5 |

5-PSC 3-PSC 2-PSC 1 | – | 3374.52–5632–8517.81 | 5–3–1 |

CSA-INC: _{a} = 0.25,

CSA: _{a} = 0.25,

GSA:

PSO: _{1} = 1.2, _{2} = 1.6.

The simulation results of the different algorithms' PV power and duty cycle for the first case of PSC 1 are presented in

The simulation results of the different algorithms' PV power and duty cycle for the second case of PSC 2 are presented in

The third case, PSC 3, shows a GMPP of 3374.52 W and five-wave peaks of the P-V curve. The obtained simulation results for the four algorithms are presented in

The PSO, GSA, CSA, and CSA-INC simulation results are tabulated in

Case | Algorithms | Power at GMPP (W) | Power |
Efficiency (%) | Tracking time (s) | GMPP |
---|---|---|---|---|---|---|

PSC 1 | PSO | 8517.81 W | 8516.82 W | 99.98% | 0.75 s | Yes |

GSA | 8517.33 W | 99.99% | 0.25 s | Yes | ||

CSA | 8517.26 W | 99.99% | 0.26 s | Yes | ||

CSAGA | 8517.58 W | 99.99% | 0.18 s | Yes | ||

PSC 2 | PSO | 5632 W | 5221.84 W | 92.72% | 0.13 s | No |

GSA | 4376.82 W | 77.71% | 0.28 s | No | ||

CSA | 5532.52 W | 98.23% | 0.52 s | Yes | ||

CSAGA | 5631.99 W | 99.99% | 0.22 s | Yes | ||

PSC 3 | PSO | 3374.52 W | 3372.46 W | 99.93% | 0.62 s | Yes |

GSA | 3373.77 W | 99.97% | 0.28 s | Yes | ||

CSA | 3373.21 W | 99.96% | 0.52 s | Yes | ||

CSAGA | 3374.47 W | 99.99% | 0.17 s | Yes |

As shown in

As shown in

As can be observed through the simulation results, the performance of the PSO and CSA algorithms is similar. However, in case of sudden changes in light intensity, the PSO saves the best global solution and the best solution reached by each particle before the current iteration, which requires memory when implementing the algorithm. Whereas CSA only needs to save the worst solution. Therefore, the CSA algorithm outperforms the PSO algorithm when there is a sudden change in light intensity. Although the GSA has fast convergence and stable power fluctuations, the tracking efficiency is low. CSA-INC takes advantage of the efficient global search capability of CSA to track to the vicinity of the GMPP quickly and then take advantage of the good local search and fast convergence properties of INC, which has faster tracking speed, less power fluctuation, and higher tracking efficiency than the other three algorithms.

In this paper, we address the problems of existing MPPT methods, such as low tracking efficiency, poor stability, and ease of falling into the local maximum power point under PSC, resulting in the degradation of output power quality and efficiency. We propose a CSA-INC algorithm with excellent MPPT performance, which utilizes the efficient global search capability of CSA to fast track to the GMPP range and then utilizes the good local search and fast convergence properties of INC to fast track to the GMPP. The proposed MPPT algorithm is compared with PSO, GSA, and CSA algorithms in terms of MPPT performance. The MPPT performance of CSA-INC is better than the above methods, and CSA-INC has higher tracking performance in all cases regardless of the light intensity variations. In addition, the average convergence time is reduced by more than 52%, and the power fluctuation is significantly reduced compared to CSA. The convergence speed is within 0.19 s. The results demonstrate the feasibility of the proposed method and its ability to track GMPP with high efficiency of over 99.99% in all test cases.