CN106202914A - Based on the photovoltaic cell parameter identification method improving particle cluster algorithm - Google Patents
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Abstract
The invention discloses a kind of based on the photovoltaic cell parameter identification method improving particle cluster algorithm, implementing procedure framework includes: sets up the recursive least-squares model form of photovoltaic cell and determines parameter to be identified, initialize position and the speed of population, calculate particle fitness value, individual extreme value, colony's extreme value, the position of more new particle and speed, individual extreme value adds Gauss operator, calculate fitness value and update individual extreme value, calculate the distance of each particle and global extremum, calculate particle fitness value, carry out individual extreme value and colony's extreme value updates, finally export the optimum value of photovoltaic cell parameter undetermined.The present invention is the Parameter analysis of connection in series-parallel m × N-shaped photovoltaic module array in grid-connected photovoltaic system, it is possible to the undetermined parameter of identification photovoltaic cell I V equation, the I V mathematical model determining photovoltaic cell and the fault cause of parsing photovoltaic cell.
Description
Technical field
The present invention relates to technical field of photovoltaic power generation, particularly relate to distinguishing of photovoltaic cell parameter in grid-connected photovoltaic system
Knowledge method.
Background technology
The utilization of solar energy and the research of photovoltaic cell characteristic have become focus, along with research deeply, the most domestic
Outer scholar proposes the different photovoltaic cell model describing I-V curve.I-V curve is the macroscopical description of photovoltaic cell characteristic, its
In parameter be the reflection of model intrinsic characteristic.Being possible not only to determine I-V equation by identification photovoltaic cell parameter, utilization is tried to achieve
The output of I-V prediction equation photovoltaic array；And photovoltaic can be studied further by analyzing the change of these parameters
The cause of cell malfunctions.Therefore the identification carrying out photovoltaic cell inner parameter is to have very much for studying and improving its characteristic
Meaning.
At present, the parameter identification method of photovoltaic cell is broadly divided into parameter Approximate Solution and parameter based on optimized algorithm
Method of estimation.The characteristic equation of photovoltaic cell model is one and complicated surmounts nonlinear function, it is impossible to straight by simple computation
Connecing and solve, parameter Approximate Solution utilizes the mathematical methods such as differential derivation and simplified model to process I-V characteristic equation exactly, in the hope of
Obtain parameter approximation.Although the method utilizing mathematical analysis approximate solution parameter is intuitively simple, but the ginseng that this method is tried to achieve
Number approximation error is relatively big, inapplicable when required precision is higher.
Method for parameter estimation based on optimized algorithm carries out parameter identification mainly by intelligent algorithm to photovoltaic cell.Example
As, some scholars propose to apply to genetic algorithm photovoltaic cell parameter identification field, on the premise of ensureing identification precision, and will
The many groups result obtained after photovoltaic cell parameter identification converges to one group of parameter value, and its advantage is to utilize iteration to reduce error, from
And obtain the optimal estimation value of parameter；And minimal gradient searching method in traditional genetic algorithm, be the formation of improving
Blending inheritance algorithm, it is possible to increase the accuracy and speed of parameter identification, but genetic algorithm still cannot be overcome easily to be absorbed in early
Ripe defect.For another example, particle cluster algorithm is introduced photovoltaic cell parameter identification field by some scholars, but traditional particle cluster algorithm
Complete extreme value optimizing by following individual extreme value and colony's extreme value, although simple to operate, but be as the continuous increasing of iterations
Adding, while convergence in population is concentrated, each particle is more and more similar, cannot may jump out at locally optimal solution periphery, and after
Phase convergence rate is slow, convergence precision is poor, is difficult to meet reality need.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of based on the photovoltaic cell parameter identification improving particle cluster algorithm
Method, the Parameter analysis of connection in series-parallel m × N-shaped photovoltaic module array in grid-connected photovoltaic system, it is possible to identification photovoltaic electric
The undetermined parameter of pond I-V equation, the I-V mathematical model determining photovoltaic cell and the fault cause of parsing photovoltaic cell.
For solving above-mentioned technical problem, the technical solution used in the present invention is as follows.
Photovoltaic cell parameter identification method based on improvement particle cluster algorithm, sets up the recursive least-squares mould of photovoltaic cell
Type form also determines parameter to be identified, initializes position and the speed of population, calculates particle fitness value, individual extreme value, group
The position of body extreme value, more new particle and speed, individual extreme value adds Gauss operator, calculates fitness value and updates individual extreme value,
Calculating the distance of each particle and global extremum, if distance is less than threshold value, then this particle and global extremum intersect the solution of this particle
Become the solution after intersecting, if distance is not less than threshold value, then retains the solution of this particle, then calculate particle fitness value, carry out
Individual extreme value and colony's extreme value update, if the most not up to maximum iteration time, then return and calculate particle fitness value, individuality
Extreme value, colony's extreme value continue iteration, until it reaches maximum iteration time, export the optimum value of photovoltaic cell parameter undetermined.
As a preferred technical solution of the present invention, the method includes implementing as follows step:
Step 1: set up the photovoltaic cell I-V characteristic equation shown in following formula, determine parameter to be identified；Wherein, ULAnd ILIt is respectively output voltage and the output electricity of photovoltaic module
Stream, IscFor photogenerated current, I0Saturation current when shining for photovoltaic module is unglazed, RsFor the series resistance of photovoltaic module, RshFor light
The bleeder resistance of photovoltaic assembly, q is electron charge, and A is invariant, and K is Boltzmann constant, and T is the certain operating mode of photovoltaic module
Under kelvin rating；Determine that parameter to be identified is Isc、I0、A、Rs、Rsh；
Step 2: obtained the output voltage U of photovoltaic module by measurement and measuring and calculating modeLWith output electric current IL；
Step 3: input initial data, including the output voltage of day part photovoltaic module, electric current, constraints, and initially
Change each parameter to be identified, make iterations k=0；
Step 4: initialization of population, the position x of initial search pointi0And speed vi0In the restriction range meeting variable
Randomly generate, for the parameter identification of photovoltaic cell, the value of position individuality that is five parameter to be identified；
Step 5: update position and the speed of each particle, and make out-of-limit process；The speed of particle swarm optimization algorithm and position
Putting renewal equation is: Its
In, ω is inertia weight；D=1,2 ..., D, D are the dimension of search volume；I=1,2,3,4,5；K is current iteration number of times；c1
And c2For accelerated factor, it it is the constant of non-negative；r1And r2The random number interval for being distributed in [0,1]；vidFor particle rapidity；For
Particle i is the current location of d dimension in kth time iteration；It it is the position of the global extremum point that whole population is tieed up at d；
It it is the position of the individual extreme point that particle i ties up in kth；
Step 6: individual extreme value adds Gauss operator, the most again updates position, and makees out-of-limit process:Wherein, Ni(0,1) be average be 0, variance be 1 Gauss become
Amount；fminFor the minimum fitness function value in m particle, i.e. current optimal value；β is scale factor, takes β=0.6；
Step 7: optimum intersection；Set a threshold value, if the positional distance of a certain particle and current extreme value is less than this
Threshold value, then carry out intersecting operation: C by itself and current optimal solutionh1(Xi)=piPa1(Xi)+(1-pi)Pa2(Xi),Wherein, ChFor filial generation particle, PaFor parent particle, X is particle position, and V is particle
Speed, piIt it is the random number between [0,1]；Again update this particle, and make out-of-limit process；
Step 8: the current location fitness value of each particle is gone through with this particle history optimal location fitness value and the overall situation
The fitness value of history optimal solution compares and updates, and completes fitness value calculation；
Step 9: judge whether iterations reaches maximum, if it is turns to step 10, if otherwise turning to step 5,
Until iterations reaches maximum and turns to step 10；
Step 10: output globally optimal solution, obtains the optimum value of photovoltaic cell parameter undetermined.
As in a preferred technical solution of the present invention, step 1, q value is 1.602e-19C；A is when positive bias-voltage is big
A value is 1, is 2 in positive bias-voltage hour A value, and under general status, A value is 1.3；K value is 1.38e-23J/K。
As a preferred technical solution of the present invention, step 2 includes the most step by step:
Step 2-1: read the output voltage U of photovoltaic arrayL_arrayWith output electric current IL_array；
Step 2-2: the output voltage U of recording light photovoltaic arrayL_arrayWith output electric current IL_array, the output electricity of photovoltaic module
Pressure is UL=UL_array/ m, output electric current is IL=IL_array/n。
As a preferred technical solution of the present invention, step 4 includes the most step by step:
Step 4-1: initialize particle position xi0；When the 0th iteration, if position particle i is xi0=(xi1..., xi5),
xi1..., xi5Randomly generate satisfied following constraints: ximin≤xi≤ximax, i.e. at (xmin, xmaxIn the range of)；Each grain
The x of sonbestCoordinate is set to its current location, calculates its corresponding individual extreme value, best in the most individual extreme value of global extremum
, record the particle sequence number of this optimal value, and by gbestIt is set to the current location of this best particle；
Step 4-2: initialize particle rapidity vi0；If vi0Selecting the biggest, particle may miss optimal solution, if vi0Too
Little, particle may be absorbed in Local Search, selects v during this parameter identificationjmax=0.2 × (vhjmax-vhjmin), in formula,
VhjmaxFor the maximum possible value of jth parameter to be identified, vhjminMinimum for jth parameter to be identified may value.
As a preferred technical solution of the present invention, step 8 includes the most step by step:
Step 8-1: calculate the fitness value of each particle, by fitness value and the particle self of the current location of each particle
The fitness value of desired positions compared in the past, if current location fitness value is better than the fitness value of individual extreme value, the most more
New individual extreme value；
Step 8-2: the fitness value of the individual extreme value of each particle with the globally optimal solution found at present is compared,
Again globally optimal solution is updated.
Use and have the beneficial effects that produced by technique scheme: the photovoltaic cell parameter identification method of the present invention is to change
The particle cluster algorithm entered occupies, and in grid-connected photovoltaic system, the parameter of connection in series-parallel m × N-shaped photovoltaic module array is divided
Analysis, it is possible to the undetermined parameter of identification photovoltaic cell I-V equation, the I-V mathematical model determining photovoltaic cell and parsing photovoltaic cell
Fault cause.Its beneficial effect is described below:
1, the present invention proposes a kind of photovoltaic cell parameter identification method based on improvement particle cluster algorithm, with photovoltaic module
Output voltage, electric current as input quantity, utilize the parameter improved in particle cluster algorithm identification photovoltaic cell archetype, pass through
Iteration obtains optimized parameter value.Can synchronize disposably to pick out whole parameter, it is not necessary to identification parameters step by step.
2, the present invention adds Gauss operator in particle cluster algorithm, often finds body optimal solution one by one just using height about
This operator carries out Local Search；The algorithm introducing Gauss operator is higher than the solving precision of conventional particle group's algorithm early stage, simultaneously
The convergence rate in algorithm later stage is faster.
3, the method updating particle position by tracking extreme value during the present invention has abandoned conventional particle group's algorithm, but
Introduce in genetic algorithm intersection operation, to will be overlapping with current optimal solution individuality intersect, this individuality can be made
Again update, explore new region, it is easier to jump out the local best points of function, there is more preferable global optimizing ability, it is to avoid
It is absorbed in local optimum, so-called Premature convergence occurs.
4, test under the present invention is prone to laboratory condition, be equally applicable to general photovoltaic generating system, highly versatile.
Accompanying drawing explanation
Fig. 1-A is single diode equivalent circuit of the photovoltaic cell constituting photovoltaic module in embodiment 1.
Fig. 1-B is that a number of photovoltaic module connection in series-parallel is arranged in the m × N-shaped photovoltaic module obtained on fixed support
Array.
Fig. 2 is the photovoltaic cell parameter identification method implementing procedure figure in embodiment 2.
Detailed description of the invention
Following example are described in detail the present invention.Various raw material used in the present invention and items of equipment are conventional city
Sell product, all can be bought by market and directly obtain.
M in embodiment 1, grid-connected photovoltaic system × N-shaped photovoltaic module array.
Seeing accompanying drawing 1-A, the photovoltaic cell constituting photovoltaic module is actually a large-area planar diode, its work
Can describe with single diode equivalent circuit of Fig. 1；R in figureLIt is the external load of photovoltaic cell, the output electricity of photovoltaic cell
Pressure is UL, output electric current is IL。
See accompanying drawing 1-B, a number of photovoltaic module connection in series-parallel is arranged on fixed support and i.e. obtains photovoltaic array；
Assume that each photovoltaic module constituting photovoltaic array has preferable concordance, wherein have a m series component, n parallel component, i.e.
For the connection in series-parallel m in grid-connected photovoltaic system × N-shaped photovoltaic module array.
Embodiment 2, the implementing procedure framework of the present invention.
See accompanying drawing 2, this gives the recursive least-squares photovoltaic cell parameter identification method of band forgetting factor
Flow process framework: set up the recursive least-squares model form of photovoltaic cell and determine the position of parameter to be identified → initialization population
Put and speed → calculating particle fitness value, individual extreme value, the position of colony's extreme value → more new particle and speed → individuality extreme value
Add Gauss operator → calculatings fitness value and update the distance of individuality extreme value → calculate each particle and global extremum, if distance
Less than threshold value, then this particle and global extremum intersect the solution of this particle and become the solution after intersecting, if distance is not less than threshold value, then
Retain the solution of this particle → then calculate particle fitness value, if carry out individual extreme value and colony's extreme value update →
Big iterations, then return and calculate particle fitness value, individual extreme value, colony's extreme value continuation iteration, until it reaches greatest iteration
The optimum value of number of times → export photovoltaic cell parameter undetermined.
Embodiment 3, the present invention be embodied as step.
Seeing accompanying drawing 1-2, photovoltaic cell parameter identification method based on improvement particle cluster algorithm, including implementing step as follows
Rapid:
Step 1: set up the photovoltaic cell I-V characteristic equation shown in following formula, determine parameter to be identified；Wherein, ULAnd ILIt is respectively output voltage and the output electricity of photovoltaic module
Stream, IscFor photogenerated current, I0Saturation current when shining for photovoltaic module is unglazed, RsFor the series resistance of photovoltaic module, RshFor light
The bleeder resistance of photovoltaic assembly, q is electron charge, and A is invariant, and K is Boltzmann constant, and T is the certain operating mode of photovoltaic module
Under kelvin rating；Determine that parameter to be identified is Isc、I0、A、Rs、Rsh；Wherein, q value is 1.602e-19C；A is in positively biased
When voltage is big, A value is 1, is 2 in positive bias-voltage hour A value, and under general status, A value is 1.3；K value is 1.38e-23J/
K；
Step 2: obtained the output voltage U of photovoltaic module by measurement and measuring and calculating modeLWith output electric current IL；Including following
The most step by step:
Step 2-1: read the output voltage U of photovoltaic arrayL_arrayWith output electric current IL_array；
Step 2-2: the output voltage U of recording light photovoltaic arrayL_arrayWith output electric current IL_array, the output electricity of photovoltaic module
Pressure is UL=UL_array/ m, output electric current is IL=IL_array/n；
Step 3: input initial data, including the output voltage of day part photovoltaic module, electric current, constraints, and initially
Change each parameter to be identified, make iterations k=0；
Step 4: initialization of population, the position x of initial search pointi0And speed vi0In the restriction range meeting variable
Randomly generate, for the parameter identification of photovoltaic cell, the value of position individuality that is five parameter to be identified；Including dividing in detail below
Step:
Step 4-1: initialize particle position xi0；When the 0th iteration, if position particle i is xi0=(xi1..., xi5),
xi1..., xi5Randomly generate satisfied following constraints: ximin≤xi≤ximax, i.e. at (xmin, xmaxIn the range of)；Each grain
The x of sonbestCoordinate is set to its current location, calculates its corresponding individual extreme value, best in the most individual extreme value of global extremum
, record the particle sequence number of this optimal value, and by gbestIt is set to the current location of this best particle；
Step 4-2: initialize particle rapidity vi0；If vi0Selecting the biggest, particle may miss optimal solution, if vi0Too
Little, particle may be absorbed in Local Search, selects v during this parameter identificationjmax=0.2 × (vhjmax-vhjmin), in formula,
VhjmaxFor the maximum possible value of jth parameter to be identified, vhjminMinimum for jth parameter to be identified may value；
Step 5: update position and the speed of each particle, and make out-of-limit process；The speed of particle swarm optimization algorithm and position
Putting renewal equation is: Its
In, ω is inertia weight；D=1,2 ..., D, D are the dimension of search volume；I=1,2,3,4,5；K is current iteration number of times；c1
And c2For accelerated factor, it it is the constant of non-negative；r1And r2The random number interval for being distributed in [0,1]；vidFor particle rapidity；For
Particle i is the current location of d dimension in kth time iteration；It it is the position of the global extremum point that whole population is tieed up at d；
It it is the position of the individual extreme point that particle i ties up in kth；
Step 6: individual extreme value adds Gauss operator, the most again updates position, and makees out-of-limit process:Wherein, Nj(0,1) be average be 0, variance be 1 Gauss become
Amount；fminFor the minimum fitness function value in m particle, i.e. current optimal value；β is scale factor, takes β=0.6；
Step 7: optimum intersection；Set a threshold value, if the positional distance of a certain particle and current extreme value is less than this
Threshold value, then carry out intersecting operation: C by itself and current optimal solutionh1(Xi)=piPa1(Xi)+(1-pi)Pa2(Xi),Wherein, ChFor filial generation particle, PaFor parent particle, X is particle position, and V is particle
Speed, piIt it is the random number between [0,1]；Again update this particle, and make out-of-limit process；
Step 8: the current location fitness value of each particle is gone through with this particle history optimal location fitness value and the overall situation
The fitness value of history optimal solution compares and updates, and completes fitness value calculation；Including the most step by step:
Step 8-1: calculate the fitness value of each particle, by fitness value and the particle self of the current location of each particle
The fitness value of desired positions compared in the past, if current location fitness value is better than the fitness value of individual extreme value, the most more
New individual extreme value；
Step 8-2: the fitness value of the individual extreme value of each particle with the globally optimal solution found at present is compared,
Again globally optimal solution is updated；
Step 9: judge whether iterations reaches maximum, if it is turns to step 10, if otherwise turning to step 5,
Until iterations reaches maximum and turns to step 10；
Step 10: output globally optimal solution, obtains the optimum value of photovoltaic cell parameter undetermined.
The present invention improves particle cluster algorithm identification photovoltaic using output voltage, the electric current of photovoltaic module as input quantity, utilization
Parameter in battery model, obtains optimized parameter value by iteration；It adds Gauss operator in particle cluster algorithm, often finds
Body optimal solution is just carrying out Local Search, than conventional particle group's algorithm early stage solving precision more by Gauss operator about one by one
Height, the convergence rate in later stage is faster simultaneously；Which introduce the intersection operation in genetic algorithm, to will be overlapping with current optimal solution
Individuality intersect, this individuality can be made again to update, explore new region, it is easier to jump out the local best points of function,
There is more preferable global optimizing ability, it is to avoid be absorbed in local optimum, so-called Premature convergence occurs.
Foregoing description is only used as the enforceable technical scheme of the present invention and proposes, single not as to its technical scheme itself
Restrictive condition.
Claims (6)
1. photovoltaic cell parameter identification method based on improvement particle cluster algorithm, connection in series-parallel m in grid-connected photovoltaic system
The Parameter analysis of × N-shaped photovoltaic module array, it is possible to the undetermined parameter of identification photovoltaic cell I-V equation, determine photovoltaic cell
I-V mathematical model and the fault cause of parsing photovoltaic cell, it is characterised in that: set up the recursive least-squares model of photovoltaic cell
Form also determines parameter to be identified, initializes position and the speed of population, calculates particle fitness value, individual extreme value, colony
The position of extreme value, more new particle and speed, individual extreme value adds Gauss operator, calculates fitness value and updates individual extreme value, meter
Calculating the distance of each particle and global extremum, if distance is less than threshold value, then this particle and global extremum intersect the solution of this particle and become
For the solution after intersecting, if distance is not less than threshold value, then retains the solution of this particle, then calculate particle fitness value, carry out individual
Body extreme value and colony's extreme value update, if the most not up to maximum iteration time, then return and calculate particle fitness value, individual pole
Value, colony's extreme value continue iteration, until it reaches maximum iteration time, export the optimum value of photovoltaic cell parameter undetermined.
It is the most according to claim 1 based on the photovoltaic cell parameter identification method improving particle cluster algorithm, it is characterised in that:
The method includes implementing as follows step:
Step 1: set up the photovoltaic cell I-V characteristic equation shown in following formula, determine parameter to be identified；Wherein, ULAnd ILIt is respectively output voltage and the output electricity of photovoltaic module
Stream, IscFor photogenerated current, I0Saturation current when shining for photovoltaic module is unglazed, RsFor the series resistance of photovoltaic module, RshFor light
The bleeder resistance of photovoltaic assembly, q is electron charge, and A is invariant, and K is Boltzmann constant, and T is the certain operating mode of photovoltaic module
Under kelvin rating；Determine that parameter to be identified is Isc、I0、A、Rs、Rsh；
Step 2: obtained the output voltage U of photovoltaic module by measurement and measuring and calculating modeLWith output electric current IL；
Step 3: input initial data, including the output voltage of day part photovoltaic module, electric current, constraints, and initializes and treats
Each parameter of identification, makes iterations k=0；
Step 4: initialization of population, the position x of initial search pointi0And speed vi0Produce at random in the restriction range meeting variable
Raw, for the parameter identification of photovoltaic cell, the value of position individuality that is five parameter to be identified；
Step 5: update position and the speed of each particle, and make out-of-limit process；The speed of particle swarm optimization algorithm and position are more
New equation is: Wherein, ω
For inertia weight；D=1,2 ... D, D are the dimension of search volume；I=1,2,3,4,5；K is current iteration number of times；c1And c2For
Accelerated factor, is the constant of non-negative；r1And r2The random number interval for being distributed in [0,1]；vidFor particle rapidity；For particle i
The current location of d dimension in kth time iteration；It it is the position of the global extremum point that whole population is tieed up at d；It it is grain
The position of the individual extreme point that sub-i ties up in kth；
Step 6: individual extreme value adds Gauss operator, the most again updates position, and makees out-of-limit process:Wherein, Nj(0,1) be average be 0, variance be 1 Gauss become
Amount；fminFor the minimum fitness function value in m particle, i.e. current optimal value；β is scale factor, takes β=0.6；
Step 7: optimum intersection；Set a threshold value, if the positional distance of a certain particle and current extreme value is less than this threshold value,
Itself and current optimal solution then carry out intersecting operation: Ch1(Xi)=piPa1(Xi)+(1-pi)Pa2(Xi),Wherein, ChFor filial generation particle, PaFor parent particle, X is particle position, and V is particle
Speed, piIt it is the random number between [0,1]；Again update this particle, and make out-of-limit process；
Step 8: by the current location fitness value of each particle and this particle history optimal location fitness value and global history
The fitness value of excellent solution compares and updates, and completes fitness value calculation；
Step 9: judge whether iterations reaches maximum, if it is turns to step 10, if otherwise turning to step 5, until
Iterations reaches maximum and turns to step 10；
Step 10: output globally optimal solution, obtains the optimum value of photovoltaic cell parameter undetermined.
It is the most according to claim 2 based on the photovoltaic cell parameter identification method improving particle cluster algorithm, it is characterised in that:
In step 1, q value is 1.602e-19C；A A value when positive bias-voltage is big is 1, is 2 in positive bias-voltage hour A value, typically
Under situation, A value is 1.3；K value is 1.38e-23J/K。
It is the most according to claim 2 based on the photovoltaic cell parameter identification method improving particle cluster algorithm, it is characterised in that:
Step 2 includes the most step by step:
Step 2-1: read the output voltage U of photovoltaic arrayL_arrayWith output electric current IL_array；
Step 2-2: the output voltage U of recording light photovoltaic arrayL_arrayWith output electric current IL_array, the output voltage of photovoltaic module is
UL=UL_array/ m, output electric current is IL=IL_array/n。
It is the most according to claim 2 based on the photovoltaic cell parameter identification method improving particle cluster algorithm, it is characterised in that:
Step 4 includes the most step by step:
Step 4-1: initialize particle position xi0；When the 0th iteration, if position particle i is xi0=(xi1..., xi5),
xi1..., xi5Randomly generate satisfied following constraints: ximim≤xi≤ximax, i.e. at (xmin, xmaxIn the range of)；Each grain
The x of sonbestCoordinate is set to its current location, calculates its corresponding individual extreme value, best in the most individual extreme value of global extremum
, record the particle sequence number of this optimal value, and by gbestIt is set to the current location of this best particle；
Step 4-2: initialize particle rapidity vi0；V is selected during this parameter identificationjmax=0.2 × (Vhjmax-Vhjmin), formula
In, VhjmaxFor the maximum possible value of jth parameter to be identified, VhjminMinimum for jth parameter to be identified may value.
It is the most according to claim 2 based on the photovoltaic cell parameter identification method improving particle cluster algorithm, it is characterised in that:
Step 8 includes the most step by step:
Step 8-1: calculate the fitness value of each particle, before the fitness value of the current location of each particle and particle self
The fitness value of desired positions compares, if current location fitness value is better than the fitness value of individual extreme value, then updates individual
Body extreme value；
Step 8-2: the fitness value of the individual extreme value of each particle with the globally optimal solution found at present is compared, again
Update globally optimal solution.
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