CN116301183B - Maximum power point tracking method of space power generation system - Google Patents
Maximum power point tracking method of space power generation system Download PDFInfo
- Publication number
- CN116301183B CN116301183B CN202310205177.4A CN202310205177A CN116301183B CN 116301183 B CN116301183 B CN 116301183B CN 202310205177 A CN202310205177 A CN 202310205177A CN 116301183 B CN116301183 B CN 116301183B
- Authority
- CN
- China
- Prior art keywords
- generation system
- power generation
- maximum power
- space
- solar cell
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
- G05F1/00—Automatic systems in which deviations of an electric quantity from one or more predetermined values are detected at the output of the system and fed back to a device within the system to restore the detected quantity to its predetermined value or values, i.e. retroactive systems
- G05F1/66—Regulating electric power
- G05F1/67—Regulating electric power to the maximum power available from a generator, e.g. from solar cell
Abstract
The maximum power point tracking method of the space power generation system solves the problem of how to quickly track the maximum power of the solar cell array in space operation, and belongs to the technical field of control of space power generation systems. The invention comprises the following steps: determining the output voltage range and the maximum power range of the solar cell array according to the space power generation system to be tested and the space environment where the space power generation system to be tested is positioned; the initial positions of the wolves are randomly distributed in the output voltage range of the solar cell array, and the maximum power point of the solar cell array is searched out in the determined maximum power range by utilizing a neural network wolf algorithm, wherein the linear convergence coefficient of the neural network wolf algorithmThe invention adopts an adjustable nonlinear convergence factor searching strategy, so that the system has global searching capability under the condition of high dynamic change, can accurately and rapidly determine the maximum power of the space power generation system, and effectively improves the electrical performance output efficiency of the space power generation system.
Description
Technical Field
The invention relates to a maximum power point tracking method of a space power generation system, and belongs to the technical field of control of space power generation systems.
Background
The rapid development of the aerospace technology makes the importance of the aerospace technology increasingly appear in the fields of national economy and national security, and in the engineering fields of satellite navigation, manned aerospace, deep space exploration, space solar power stations and the like, a long-life, high-power and high-reliability space power generation system is an important component for ensuring the on-orbit operation of a spacecraft. Solar cell arrays are the only energy source for the on-orbit operation of a spacecraft as one of the important components of a space power generation system. In a space environment, the working state of the battery array can be changed along with parameters such as solar spectrum, temperature, electrical load and the like, and the SR (series shunt regulation) control method used by the traditional space power generation system cannot utilize energy to the maximum extent. The Maximum Power Point Tracking (MPPT) technology can comprehensively consider the sun illumination area, the temperature of the solar cell array substrate and the electrical characteristics of the load, and the working point of the solar cell array is adjusted in real time through the control technology, so that the solar cell array always works at the maximum power point, and the aim of optimizing the power output efficiency of the cell array is fulfilled. Compared with the traditional control mode, the MPPT control mode can improve the power output by 20% -30%, can effectively reduce the area and the weight of the solar cell array, and has very important significance for the development of the aerospace power generation system in China.
Solar cell arrays are subject to severe spatial environmental effects when in spatial service. Charged particles in the spatial environment can cause degradation of the solar cell during in-orbit operation of the array, and can cause non-uniform performance output of the array due to varying degrees of degradation of the solar cells in the array. In addition, when the battery array is in spatial service, a temperature gradient may be formed across the longitudinal dimension of the battery array when the spacecraft dissipates less heat than the solar array. Therefore, under the combined action of these factors, the solar cell array will form a multimodal curve of power-voltage (P-V) output characteristics, and if the space power generation system cannot identify the maximum power point and even works in an open state, the battery string with lower voltage in the circuit will be thermally broken down by the reverse current, so that the circuit voltage of the solar cell array unit is clamped, and the battery array cannot normally supply power.
Disclosure of Invention
Aiming at the problem of how to quickly track the maximum power of the solar cell array in space, the invention provides a maximum power point tracking method of a space power generation system.
The invention discloses a maximum power point tracking method of a space power generation system, which comprises the following steps:
s1, determining an output voltage range and a maximum power range of a solar cell array according to a space power generation system to be tested and a space environment where the space power generation system is located;
s2, randomly distributing initial positions of the wolves in an output voltage range of the solar cell array, and searching out a maximum power point of the solar cell array in the maximum power range determined in the S1 by utilizing a neural network wolf algorithm, wherein a linear convergence coefficient of the neural network wolf algorithmk represents the current iteration number, k max Maximum number of iterations, determining vector A from a (k) j ＝2a(k)·a-a(k)，j∈[α，β，δ，…]Alpha, beta, delta, … represent the scheme of the maximum power point, v is the adjustment coefficient, 1>ν>0, a represents coefficient vectors of individuals and wolves, and the distance L between the current wolves and the optimal solution p ＝|A j ·X p (k) X (k) represents the position of the current wolf group, X p (k) Representing the target position vector.
Preferably, in the step S1, a spatial power generation system electrical performance output model based on a fuzzy neural network is established, parameters of the spatial power generation system to be tested and the spatial environment parameters are taken as inputs, an output voltage range and a maximum power range of the solar cell array are taken as outputs, historical data of the spatial power generation system to be tested is adopted to train the spatial power generation system electrical performance output model, and the trained spatial power generation system electrical performance output model is utilized to obtain the output voltage range and the maximum power range of the solar cell array of the spatial power generation system to be tested.
Preferably, the space power generation system parameters to be measured and the space environment parameters comprise seasons, track types, track periods, environment temperatures, environment temperature change rates, environment irradiance change rates, service time, solar cell array open-circuit voltage and short-circuit current.
As a means ofPreferably, the electrical performance output model of the space power generation system based on the fuzzy neural network is based onV (V) m ＝n·V oc Determining;
V oc represents the open circuit voltage of the solar cell array, k represents the boltzmann constant, T represents the temperature, which is determined by the ambient temperature, q represents the charge amount, I sc Representing short-circuit current of solar cell array, which is determined by irradiance, I 0 Represents the dark current of the solar cell array, n represents the ratio of the maximum power point voltage to the open circuit voltage, V m Representing the voltage at the maximum power point of the solar cell array.
Preferably, the electrical performance output model of the space power generation system is realized by adopting an ANN network.
Preferably, the ANN network comprises one input layer, four hidden layers and one output layer.
Preferably, the historical data of the space power generation system to be tested is adopted to train the electric performance output model of the space power generation system:
establishing a training sample database for power generation historical data of the space power generation system during on-orbit running and space environment data collected by the detector;
the maximum power of the solar cell array is evaluated by adopting a space power generation system electrical performance output model under different input categories, and the power loss P of the solar cell array in actual service is calculated sh According to the power loss quantity P sh And obtaining the maximum power dynamic change of the space power generation system in the on-orbit service period, thereby obtaining the maximum power range corresponding to the input.
Preferably, S2 includes:
s21, initializing the population number N and the power value P present Coefficient vector A j 、a、k max Initial position D of gray wolf j (k) A is [0,1 ]]Random number within range, power value P present Each population corresponds to a scheme of maximum power point, j=1, 2,3 … N;
s22, setting the initial position D of the wolf group j (k) Randomly distributed in the output voltage range of the solar cell array, and the power searching range of the population is fixed in the open-circuit voltage range determined by S1, and the maximum power P of the solar cell array is calculated max Maximum power point voltage V m And current I m Collecting;
s23: k=1 for V m And I m Multiplying, and combining the obtained multiplication result with the maximum power P of the solar cell array max Determining D j (k) If only the solution is the only solution, the corresponding power of the solution is recorded as P (D α ) Turning to S28, if the solution is not unique, sorting the corresponding populations according to the merits of the solution: alpha, beta, delta, …, determining the position vector X corresponding to alpha, beta, delta, … α (k)、X β (k)、X δ (k) … determining initial distance of prey from populationK 1 、K 2 And K 3 … represents the known coefficients and is based on the position vector X of alpha, beta, delta, … α (k)、X β (k)、X δ (k) … and updating the position of the wolf groupm represents the number of current solutions; calculating the distance L between the current wolf group and the optimal solution according to X (k+1) p K=k+1, and the process proceeds to S24;
s24, according to the distance L between the current wolf group and the optimal solution p After the population surrounds the hunting, the hunting circle is reduced, and the distance D between the hunting and the population is updated α (k) … recording maximum power point power P (D α )；
S25, determining coefficient vector A j ，j∈[α…]If coefficient vector A j When the absolute value of (1) is greater than the absolute value of (1), then X (k+1) and L are updated p S27, otherwise, P present ＝P(D α )，P present Representing the maximum power value under the current iteration number, and switching to S26;
s26, judging |P present -P previous |＞ξ 1 Wherein xi 1 Limiting the factor, P previous Is the maximum power value of the previous iteration, if yes, the coefficient vector A is updated by using the linear convergence coefficient j If not, turning to S27, the MPPT is considered to be reached, iteration is stopped, and the maximum power point power P (D) α ) S28, switching to;
s27, judging whether k reaches the maximum iteration number k max If so, the maximum power point power P (D α ) If not, k=k+1, and the judgment returns to S24;
s28, the wolf finds the most suitable power P (D α ) Terminating the iteration and outputting a global optimum P (D α )。
The method has the beneficial effects that the method carries out deep learning by calculating the performance degradation rule of the solar battery in the space environment and the rule that the electric output performance of the solar battery is subjected to space temperature in different periods, realizes rapid tracking of the maximum power of the solar battery array in space operation, controls the voltage of the maximum power point through a control circuit and a Maximum Power Point Tracking (MPPT) tracking algorithm, prevents reverse current from thermally breaking down the solar battery while improving the output power of a space power generation system, and improves the reliability of the solar battery array.
Drawings
FIG. 1 is a graph showing P-V curve variation of a solar cell array in an in-orbit environment;
FIG. 2 shows the simulation result of the maximum work point tracking method using the method of the present invention, with the abscissa representing time and the ordinate representing power;
fig. 3 is a graph of output voltage of a solar array, with time indicated by the abscissa and output voltage indicated by the ordinate.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The invention is further described below with reference to the drawings and specific examples, which are not intended to be limiting.
The maximum power point tracking method of the space power generation system of the present embodiment includes:
step 1, determining an output voltage range and a maximum power range of a solar cell array according to a space power generation system to be tested and a space environment where the space power generation system is located;
step 2, randomly distributing initial positions of the wolves in an output voltage range of the solar cell array, and searching out a maximum power point of the solar cell array in the maximum power range determined in the step 1 by utilizing a neural network wolf algorithm, wherein a linear convergence coefficient of the neural network wolf algorithmk represents the current iteration number, k max Maximum number of iterations, determining vector A from a (k) j ＝2a(k)·a-a(k)，j∈[α，β，δ，…]Alpha, beta, delta, … represent the scheme of the maximum power point, v is the adjustment coefficient, 1>ν>0, a represents coefficient vectors of individuals and wolves, and the distance L between the current wolves and the optimal solution p ＝|A j ·X p (k) X (k) represents the position of the current wolf group, X p (k) Representing the target position vector.
According to the method, the adjustable nonlinear convergence factor searching strategy is adopted, so that the method has global searching capability under the condition of high dynamic change, the maximum power of the space power generation system can be accurately and rapidly determined, and the electrical performance output efficiency of the space power generation system is effectively improved.
In step 1 of the present embodiment, a spatial power generation system electrical performance output model based on a fuzzy neural network is established, parameters of the spatial power generation system to be tested and the spatial environment parameters are input, an output voltage range and a maximum power range of a solar cell array are output, historical data of the spatial power generation system to be tested is adopted to train the spatial power generation system electrical performance output model, and the trained spatial power generation system electrical performance output model is utilized to obtain the output voltage range and the maximum power range of the solar cell array of the spatial power generation system to be tested. The parameters of the space power generation system to be measured and the space environment parameters comprise seasons, track types, track periods, environment temperatures, environment temperature change rates, environment irradiance change rates, service time, solar cell array open-circuit voltage and short-circuit current.
In the embodiment, aiming at the power generation system parameters and the spatial environment parameter history samples, a spatial power generation system electrical performance output model based on a fuzzy neural network is established, and power output values of the spatial solar cell array in different seasons or different tracks are counted according to the model. Because the space power generation system is affected by space environment, parameters such as temperature, irradiance, service time and the like are input into the neural network model, a degradation model of the output power of the solar cell array and the track period, season and service time is obtained, and the dynamic output range of electrical performance parameters such as voltage, current, power and the like of the solar cell array is updated, so that the maximum power accuracy and convergence speed of the maximum power point tracking of the space power generation system are improved.
According to the method, the temperature T and the temperature change rate delta T or the irradiation W and the irradiation change rate delta W of the photovoltaic array during working are collected through the sensor, when the spacecraft runs on the orbit, parameters such as the irradiation time and the temperature of the solar cell array are periodically changed, the irradiation period of the solar cell array is recorded, and the parameters of performance output of the solar cell array during on-orbit irradiation are determined. Bringing the change rate of environmental parameters of the space power generation system in the on-orbit service period into an electrical performance output model of the space power generation system according to the electrical performance output model of the space power generation systemV (V) m ＝n·V oc Determined V oc Represents the open circuit voltage of the solar cell array, and k represents Boltzmann's constantThe number, T, represents the temperature, is determined by the ambient temperature, q represents the charge amount, I sc Representing short-circuit current of solar cell array, which is determined by irradiance, I 0 Represents the dark current of the solar cell array, n represents the ratio of the maximum power point voltage to the open circuit voltage, V m Representing the voltage at the maximum power point of the solar cell array.
Calculating the on-orbit output voltage range U of the solar cell array 1 -U 2 And an output power range P 1 -P 2 The solar cell array needs to limit the voltage when searching the maximum power point, and if the voltage is not limited during searching, the solar cell array can be reversely charged to burn the battery. Calculating the ratio n=v of the maximum power point voltage to the open circuit voltage of the solar cell array m /V oc And determining the maximum power range of the solar cell array during the on-orbit period according to the voltage output and the current output range of the photovoltaic array.
In the embodiment, the electric performance output model of the space power generation system is realized by adopting an ANN network. The ANN network forms an input layer, four hidden layers and an output layer. The solar cell array open circuit voltage, short circuit current, temperature, irradiance, service time, season, track period, track type, etc. data are used as input data, and the solar cell array output voltage and power are used as output data. Firstly, a training sample database is established for power generation historical data of a space power generation system in the on-orbit running period and parameters such as solar irradiance, temperature, short-circuit current, open-circuit voltage and the like collected by a detector, a space solar cell array power generation model is established by adopting a fuzzy neural network, the output power of a solar cell array is estimated by adopting models under different categories, and a sample x= [ x ] is input 1 ,x 2 ,…x I ]Setting a neural network error function for irradiance, temperature and service time in sequence, and calculating the power loss P of the solar cell array in actual service sh According to the power loss value, the maximum power dynamic change of the space power generation system in the on-orbit service period can be obtained, so that the maximum power of the solar cell array in different seasons, different tracks and different service times can be more accurately obtainedAnd outputting the interval of the rate.
Step 2 of the present embodiment includes:
step 21, initializing population number N and power value P present Coefficient vector A j 、a、k max Initial position D of gray wolf j (k) A is [0,1 ]]Random number within range, power value P present Each population corresponds to a scheme of maximum power point, j=1, 2,3 … N;
step 22, the initial position D of the wolf group j (k) Randomly distributed in the output voltage range of the solar cell array, and the power searching range of the population is fixed in the open-circuit voltage range determined by S1, and the maximum power P of the solar cell array is calculated max Maximum power point voltage V m And current I m Collecting;
step 23: k=1 for V m And I m Multiplying, and combining the obtained multiplication result with the maximum power P of the solar cell array max Determining D j (k) If only the solution is the only solution, the corresponding power of the solution is recorded as P (D α ) Turning to S28, if the solution is not unique, sorting the corresponding populations according to the merits of the solution: alpha, beta, delta, …, determining the position vector X corresponding to alpha, beta, delta, … α (k)、X β (k)、X δ (k) … determining initial distance of prey from populationK 1 、K 2 And K 3 … represents the known coefficients and is based on the position vector X of alpha, beta, delta, … α (k)、X β (k)、X δ (k) … and updating the position of the wolf groupm represents the number of current solutions; calculating the distance L between the current wolf group and the optimal solution according to X (k+1) p K=k+1, and the process proceeds to S24;
step 24, according to the distance L between the current wolf group and the optimal solution p After the hunting ring is reduced after the hunting ring is surrounded by the population, the hunting ring and the population are updatedDistance D of (2) α (k) … recording maximum power point power P (D α )；
Step 25, determining coefficient vector A j ，j∈[α…]If coefficient vector A j When the absolute value of (1) is greater than the absolute value of (1), then X (k+1) and L are updated p S27, otherwise, P present ＝P(D α )，P present Representing the maximum power value at the current iteration number, and proceeding to step 26;
wherein coefficient vector A j When the number of the coefficient vector A is greater than 1, the wolf group starts searching for the prey, namely starts the early global search, and when the coefficient vector A j Below 1, the wolf flock begins to prey on the prey, i.e. begins during the local optimization phase.
P present Is the maximum power of the current iteration step, P previous Subtracting the maximum power result of the previous iteration from the maximum power result of the previous iteration by using the maximum power of the current iteration, and if the absolute value is larger than a limiting factor, considering that the battery array does not reach the maximum power; if the absolute value is smaller than or equal to the limiting factor, the maximum power is considered to be reached, so that the calculation time can be effectively reduced, and the maximum power point is possibly fluctuated in a small range along with the environmental change;
step 26, judging |P present -P previous |＞ξ 1 Wherein xi 1 Limiting the factor, P previous Is the maximum power value of the previous iteration, if yes, the coefficient vector A is updated by using the linear convergence coefficient j If not, go to step 27, consider that MPPT is reached, stop iteration, and record maximum power point power P (D α ) Go to step 28;
step 27, judging whether k reaches the maximum iteration number k max If so, the maximum power point power P (D α ) If not, k=k+1, the determination returns to step 24;
step 28, the wolf finds the most appropriate power P (D α ) Terminating the iteration and outputting a global optimum P (D α )。
According to the embodiment, through judging and presetting the environmental parameters of the space power generation system, the irradiation intensity distribution of each string of the solar cell array is determined, the temperature change rate and the temperature change range of the solar cell array are determined, the temperature change rate and the temperature change range are input into an algorithm program, and the output range of the voltage, the current and the power of each string of the solar cell array is obtained. In the range of the voltage and the power output of the solar cell array, the working voltage of the solar cell array is limited, and an improved particle swarm algorithm is used in the range of the power output, so that the maximum power point of the power generation system is quickly searched, the output voltage of the solar cell array is limited, reverse voltage is prevented from breaking down the cell string, and the reliability of the space power generation system is effectively improved.
Examples
The spatial environment may cause cumulative damage to the solar cells, thereby reducing the output power of the solar cell array. The level of radiation encountered by a solar cell throughout its period of service in a space environment depends on the type of task. Generally, the geostationary orbit task (GEO) has been continued for 15 years, and the damage to solar cells caused by the charged particle radiation environment is equivalent to 1 x 10 for 1-MeV electrons 15 e/cm 2 For the low-Level Earth Orbit (LEO) task which lasts for about 10 years at a lower height, the equivalent fluence is 5 to 10 times lower, the P-V curve of the solar cell after 1MeV electron irradiation under different fluence is obtained, the damage to the cell caused by 1MeV electron can be observed to be uniform damage, an equivalent relation is established for the solar cell, and the open circuit voltage V during uniform damage oc And maximum power point V m With equivalent relationship V m ≈0.9V oc Therefore, in order to prevent reverse breakdown caused by overlarge voltage of the solar cell array, the open-circuit voltage and the maximum power point of the solar cell array during different track lives are monitored, and the current solar cell array output voltage V oc Recording is carried out, and the voltage range is controlled by MPPT. The algorithm is realized by the following steps:
first, the condition is initially set, and the initial reference voltage value should be slightly higher than the 42V bus voltage, which may be set to 45V. The second step is to collect the output voltage of the solar cell arrayAnd limiting the voltage, in order to prevent reverse breakdown of the solar cell array, setting the voltage to not more than 1.05V oc . And thirdly, searching the global maximum power point of the solar cell array by adopting the method of the embodiment. End condition, no matter whether the irradiation and the temperature are uniform, when the reference voltage is more than 90% of the open-circuit voltage of the PV array, the search is stopped, and the maximum power P of the solar cell array is output max . Fig. 3 is a voltage output curve of a solar cell array during service according to the method of the present embodiment, and the result shows that the voltage interval during MPPT control of the solar cell array can be effectively controlled by using the method. By limiting the voltage of the solar cell array, the reverse voltage can be prevented from breaking down the solar cell string, and performance degradation and even failure can be caused to the solar cell array. In addition, the MPPT control using the method can effectively reduce the voltage output ripple of the solar cell array, which indicates that the invention can effectively improve the reliability and stability of the space power generation system.
The process for searching the maximum power point of the solar cell array within the determined maximum power range by utilizing the neural network gray wolf algorithm comprises the following steps:
initializing an MPPT control method, and randomly distributing the wolf group positions in the output voltage range of the solar cell array. When the solar cell array is started, the gray wolf controller is used for capturing global maximum power points of the solar cell array under different spatial environment conditions. Carrying out iterative updating on the data to obtain the distance between the wolf crowd and the optimal solution in each iteration, and updating according to the iteration times to obtain a historical optimal value and a global optimal value in each iteration;
when coefficient vector A j When the power is smaller than 1, the maximum power value is converged, and the maximum power P of the current iteration step is recorded present And is matched with the last maximum power result P previous And comparing, judging that the power generation system reaches the global optimal value if the absolute value of the difference value is smaller than 0.05 times of the total power, stopping iteration and outputting the global optimal value.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the different dependent claims and the features described herein may be combined in ways other than as described in the original claims. It is also to be understood that features described in connection with separate embodiments may be used in other described embodiments.
Claims (8)
1. A method for tracking a maximum power point of a space power generation system, the method comprising:
s1, determining an output voltage range and a maximum power range of a solar cell array according to a space power generation system to be tested and a space environment where the space power generation system is located;
s2, randomly distributing initial positions of the wolves in an output voltage range of the solar cell array, and searching out a maximum power point of the solar cell array in the maximum power range determined in the S1 by utilizing a neural network wolf algorithm, wherein a linear convergence coefficient of the neural network wolf algorithmk represents the current iteration number, k max Maximum number of iterations, determining vector A from a (k) j ＝2a(k)·a-a(k)，j∈[α，β，δ，…]Alpha, beta, delta, … represent the scheme of the maximum power point, v is the adjustment coefficient, 1>ν>0, a represents coefficient vectors of individuals and wolves, and the distance L between the current wolves and the optimal solution p ＝|A j ·X p (k) X (k) represents the position of the current wolf group, X p (k) Representing a target position vector;
in the S1, a space power generation system electrical performance output model based on a fuzzy neural network is established, parameters of the space power generation system to be tested and the space environment parameters are taken as inputs, an output voltage range and a maximum power range of a solar cell array are taken as outputs, historical data of the space power generation system to be tested is adopted to train the space power generation system electrical performance output model, and the trained space power generation system electrical performance output model is utilized to acquire the output voltage range and the maximum power range of the solar cell array of the space power generation system to be tested;
s2 comprises the following steps:
s21, initializing the population number N and the power value P present Coefficient vector A j 、a、k max Initial position D of gray wolf j (k) A is [0,1 ]]Random number within range, power value P present Each population corresponds to a scheme of maximum power point, j=1, 2,3 … N;
s22, setting the initial position D of the wolf group j (k) Randomly distributed in the output voltage range of the solar cell array, and the power searching range of the population is fixed in the open-circuit voltage range determined by S1, and the maximum power P of the solar cell array is calculated max Maximum power point voltage V m And current I m Collecting;
s23: k=1 for V m And I m Multiplying, and combining the obtained multiplication result with the maximum power P of the solar cell array max Determining D j (k) If only the solution is the only solution, the corresponding power of the solution is recorded as P (D α ) Turning to S28, if the solution is not unique, sorting the corresponding populations according to the merits of the solution: alpha, beta, delta, …, determining the position vector X corresponding to alpha, beta, delta, … α (k)、X β (k)、X δ (k) … determining initial distance of prey from populationK 1 、K 2 And K 3 … represents the known coefficients and is based on the position vector X of alpha, beta, delta, … α (k)、X β (k)、X δ (k) … and updating the position of the wolf groupm represents the number of current solutions; calculating the distance L between the current wolf group and the optimal solution according to X (k+1) p K=k+1, and the process proceeds to S24;
s24, according toDistance L between current wolf group and optimal solution p After the population surrounds the hunting, the hunting circle is reduced, and the distance D between the hunting and the population is updated α (k) … recording maximum power point power P (D α )；
S25, determining coefficient vector A j ，j∈[α，β，δ，…]If coefficient vector A j When the absolute value of (1) is greater than the absolute value of (1), then X (k+1) and L are updated p S27, otherwise, P present ＝P(D α )，P present Representing the maximum power value under the current iteration number, and switching to S26;
s26, judging |P present -P previous |＞ξ 1 Wherein xi 1 Limiting the factor, P previous Is the maximum power value of the previous iteration, if yes, the coefficient vector A is updated by using the linear convergence coefficient j If not, turning to S27, the MPPT is considered to be reached, iteration is stopped, and the maximum power point power P (D) α ) S28, switching to;
s27, judging whether k reaches the maximum iteration number k max If so, the maximum power point power P (D α ) If not, k=k+1, and the judgment returns to S24;
s28, the wolf finds the most suitable power P (D α ) Terminating the iteration and outputting a global optimum P (D α )。
2. The method of claim 1, wherein the spatial power generation system parameters to be measured and the spatial environment parameters include season, track type, track period, ambient temperature change rate, ambient irradiance change rate, time of service, open circuit voltage of solar cell array, and short circuit current.
3. The method for tracking maximum power point of space power generation system according to claim 1, wherein the electrical performance output model of space power generation system based on fuzzy neural network is based onV (V) m ＝n·V oc Determining;
V oc represents the open circuit voltage of the solar cell array, k represents the boltzmann constant, T represents the temperature, which is determined by the ambient temperature, q represents the charge amount, I sc Representing short-circuit current of solar cell array, which is determined by irradiance, I 0 Represents the dark current of the solar cell array, n represents the ratio of the maximum power point voltage to the open circuit voltage, V m Representing the voltage at the maximum power point of the solar cell array.
4. The method for tracking the maximum power point of the space power generation system according to claim 1, wherein the electric performance output model of the space power generation system is realized by adopting an ANN network.
5. The method of maximum power point tracking for a space power generation system of claim 4, wherein the ANN network comprises an input layer, four hidden layers, and an output layer.
6. The method for tracking the maximum power point of the space power generation system according to claim 1, wherein the space power generation system electrical performance output model is trained by using historical data of the space power generation system to be tested:
establishing a training sample database for power generation historical data of the space power generation system during on-orbit running and space environment data collected by the detector;
the maximum power of the solar cell array is evaluated by adopting a space power generation system electrical performance output model under different input categories, and the power loss P of the solar cell array in actual service is calculated sh According to the power loss quantity P sh And obtaining the maximum power dynamic change of the space power generation system in the on-orbit service period, thereby obtaining the maximum power range corresponding to the input.
7. A computer-readable storage device storing a computer program, characterized in that the computer program when executed implements the maximum power point tracking method of the space power generation system according to any one of claims 1 to 6.
8. A maximum power point tracking method apparatus for a space power generation system, comprising a storage device, a processor, and a computer program stored in the storage device and executable on the processor, wherein execution of the computer program by the processor implements the maximum power point tracking method for a space power generation system as claimed in any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310205177.4A CN116301183B (en) | 2023-03-06 | 2023-03-06 | Maximum power point tracking method of space power generation system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310205177.4A CN116301183B (en) | 2023-03-06 | 2023-03-06 | Maximum power point tracking method of space power generation system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116301183A CN116301183A (en) | 2023-06-23 |
| CN116301183B true CN116301183B (en) | 2023-09-08 |
Family
ID=86818016
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310205177.4A Active CN116301183B (en) | 2023-03-06 | 2023-03-06 | Maximum power point tracking method of space power generation system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116301183B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106094972A (en) * | 2016-08-30 | 2016-11-09 | 湖北工业大学 | A kind of maximum power point of photovoltaic power generation system tracking based on function model |
| CN106484026A (en) * | 2016-11-15 | 2017-03-08 | 北京信息科技大学 | Control method and device that a kind of maximum photovoltaic power point based on grey wolf algorithm is followed the tracks of |
| CN107957743A (en) * | 2017-11-13 | 2018-04-24 | 天津大学 | A kind of photovoltaic maximum power point method for tracing |
| CN108539760A (en) * | 2018-04-13 | 2018-09-14 | 昆明理工大学 | A kind of double-fed induction Wind turbines frequency modulation PID control method based on group's grey wolf optimization algorithm |
| CN108646849A (en) * | 2018-07-11 | 2018-10-12 | 东北大学 | Based on the maximum power point of photovoltaic power generation system tracking for improving wolf pack algorithm |
| CN110174919A (en) * | 2019-05-07 | 2019-08-27 | 广州水沐青华科技有限公司 | Photovoltaic system maximum power tracking method, computer readable storage medium under the conditions of part masking based on depth grey wolf algorithm |
| TWI697791B (en) * | 2019-03-20 | 2020-07-01 | 龍華科技大學 | Solar cell maximum power tracking method under shading |
| CN113342124A (en) * | 2021-06-11 | 2021-09-03 | 中国电建集团华东勘测设计研究院有限公司 | Photovoltaic MPPT method based on improved wolf optimization algorithm |
| CN114442725A (en) * | 2022-02-16 | 2022-05-06 | 东南大学 | Photovoltaic maximum power point tracking method, storage medium and tracking device |
-
2023
- 2023-03-06 CN CN202310205177.4A patent/CN116301183B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106094972A (en) * | 2016-08-30 | 2016-11-09 | 湖北工业大学 | A kind of maximum power point of photovoltaic power generation system tracking based on function model |
| CN106484026A (en) * | 2016-11-15 | 2017-03-08 | 北京信息科技大学 | Control method and device that a kind of maximum photovoltaic power point based on grey wolf algorithm is followed the tracks of |
| CN107957743A (en) * | 2017-11-13 | 2018-04-24 | 天津大学 | A kind of photovoltaic maximum power point method for tracing |
| CN108539760A (en) * | 2018-04-13 | 2018-09-14 | 昆明理工大学 | A kind of double-fed induction Wind turbines frequency modulation PID control method based on group's grey wolf optimization algorithm |
| CN108646849A (en) * | 2018-07-11 | 2018-10-12 | 东北大学 | Based on the maximum power point of photovoltaic power generation system tracking for improving wolf pack algorithm |
| TWI697791B (en) * | 2019-03-20 | 2020-07-01 | 龍華科技大學 | Solar cell maximum power tracking method under shading |
| CN110174919A (en) * | 2019-05-07 | 2019-08-27 | 广州水沐青华科技有限公司 | Photovoltaic system maximum power tracking method, computer readable storage medium under the conditions of part masking based on depth grey wolf algorithm |
| CN113342124A (en) * | 2021-06-11 | 2021-09-03 | 中国电建集团华东勘测设计研究院有限公司 | Photovoltaic MPPT method based on improved wolf optimization algorithm |
| CN114442725A (en) * | 2022-02-16 | 2022-05-06 | 东南大学 | Photovoltaic maximum power point tracking method, storage medium and tracking device |
Non-Patent Citations (1)
| Title |
|---|
| 基于改进灰狼算法的最大功率点跟踪;杨丽丽等;《工业自动化技术》;第37卷(第9期);97-100 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116301183A (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Messalti | A new neural networks MPPT controller for PV systems | |
| Behera et al. | A new combined extreme learning machine variable steepest gradient ascent MPPT for PV system based on optimized PI-FOI cascade controller under uniform and partial shading conditions | |
| Edward et al. | The effectiveness of solar tracker use on solar panels to the output of the generated electricity power | |
| Anand et al. | Global maximum power point tracking for PV array under partial shading using cuckoo search | |
| Prasad et al. | An improved method for MPPT using ANN and GA with maximum power comparison through Perturb & Observe technique | |
| Rizzo et al. | Enhanced hybrid global MPPT algorithm for PV systems operating under fast-changing partial shading conditions | |
| Abbassi et al. | A Numerical-Analytical Hybrid Approach for the Identification of SDM Solar Cell Unknown Parameters. | |
| Kiani et al. | Optimal parameter estimation of solar cell using simulated annealing inertia weight particle swarm optimization (SAIW-PSO) | |
| Allahabadi et al. | Neural network based maximum power point tracking technique for PV arrays in mobile applications | |
| Miloudi et al. | Application of artificial neural networks for forecasting photovoltaic system parameters | |
| Hassan Ali et al. | Real-time determination of solar cell parameters | |
| Abubakar et al. | Optimal extraction of photovoltaic cell parameters for the maximization of photovoltaic power output using a hybrid particle swarm grey wolf optimization algorithm | |
| CN116301183B (en) | Maximum power point tracking method of space power generation system | |
| Saad | Enhancement of solar cell modeling with MPPT command practice with an electronic edge filter | |
| Jumaat et al. | Investigate the photovoltaic (PV) module performance using Artificial Neural Network (ANN) | |
| Saadaoui et al. | Modeling of photovoltaic system with maximum power point tracking control by neural networks | |
| Sreedhar et al. | A review on optimization algorithms for MPPT in solar PV system under partially shaded conditions | |
| Azzeddine et al. | An offline trained artificial neural network to predict a photovoltaic panel maximum power point | |
| Sangeetha et al. | An improved technique for predicting characteristics of two-diode based pv model | |
| Bouarroudj et al. | Fuzzy logic controller based maximum power point tracking and its optimal tuning in photovoltaic systems | |
| Shukla et al. | An efficient neural network-based mppt technique for pv array under partial shading conditions | |
| Gupta et al. | Artificial mountain ape optimization algorithm for maximum power point tracking under partial shading condition | |
| Ghedhab et al. | Optimized Photovoltaic Power Generator Using Artificial Neural Network Implementation for Maximum Power Point Tracking | |
| Seddjar et al. | Comprehensive Review of Maximum Power Point Tracking Techniques And Proposed Fuzzy Logic Controller of An Electrical Power System for Nano Satellites | |
| SAIDI et al. | An advanced MPPT scheme for standalone solar powered PV system using neurofuzzy estimator based on measured data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |