A photovoltaic array line fault diagnosis method based on shadow power tracking

Abstract

The application provides a photovoltaic array line fault diagnosis method based on shadow power tracking, which comprises the following steps: obtaining array current and array voltage of a sampling point of a target photovoltaic array system; calculating power of the sampling point of the target photovoltaic array system; determining whether the target photovoltaic array system has a fault; determining a fault type of the target photovoltaic array system; realizing line fault detection of the target photovoltaic array system; and realizing multi-peak maximum power point tracking control of the target photovoltaic array system.

Description

TECHNICAL FIELD

[0001] The present application relates to renewable energy, artificial intelligence and power electronic conversion technology, and belongs to the field of electrical engineering. BACKGROUND

[0002] As the main way of solar energy use, photovoltaic power generation has an increasing global installed capacity, but the limitation of available land resources makes the photovoltaic installed capacity gradually saturated, so how to improve the operation efficiency of photovoltaic power generation under the condition of limited resources has become a big problem.

[0003] The light intensity and temperature of the external environment are the main factors affecting photovoltaic power generation, and the volatility of the two determines the randomness and volatility of photovoltaic power generation. Conventional maximum power point tracking control technologies such as incremental conductance method and perturbation and observation method can well achieve maximum power point tracking under uniform illumination, but changes in weather, environment, etc. cause the photovoltaic array to be in a long-term multi-peak shadow state, and conventional tracking control technologies are difficult to achieve global maximum power point tracking, reducing the operation efficiency of the photovoltaic system.

[0004] Photovoltaic arrays are exposed to the outdoors for a long time to obtain light energy, and factors such as animal invasion and rain corrosion make the photovoltaic array prone to line faults. The existence of these faults will greatly interfere with the operation efficiency and stability of the system, and in severe cases, even cause fire safety hazards. The current research methods for line fault detection mainly include signal-based, statistical probability and neural network, but it is difficult to locate the fault and the real-time performance cannot be guaranteed.

[0005] Therefore, in view of this problem, it is necessary to realize photovoltaic array line fault diagnosis under complex conditions, reduce the influence of shadow and line fault on the power generation efficiency of the photovoltaic array system, and meet the actual needs. SUMMARY

[0006] In order to solve the problems of the prior art, the present application provides a photovoltaic array line fault diagnosis method based on shadow power tracking, which realizes photovoltaic array line fault diagnosis under complex conditions and reduces the influence of shadow and line fault on the power generation efficiency of the photovoltaic array system.

[0007] A photovoltaic array line fault diagnosis method based on shadow power tracking, characterized by: performing fault preliminary diagnosis on the target photovoltaic array system in real time according to the following steps S1 to S4 to determine whether there is a fault and the fault type:

[0008] Step S1, based on a preset sampling period, sampling the target photovoltaic array system to obtain array current and array voltage corresponding to the current collection time and the adjacent last collection time of each sampling point, and then performing step S2;

[0009] Step S2, the power values corresponding to the current sampling time and the adjacent last sampling time of each sampling point are calculated, and then step S3 is performed;

[0010] Step S3, based on the power values corresponding to the current sampling time and the adjacent last sampling time of each sampling point, the power difference value and the power change percentage value of each sampling point at the current sampling time and the adjacent last sampling time are obtained, and the power difference value preset range and the power change percentage preset range are combined to determine whether the target photovoltaic array system has a fault, and if so, step S4 is performed;

[0011] Step S4, based on the power change percentage value at the current sampling time and the adjacent last sampling time, and combined with the abnormality detection preset range, the type of fault existing in the target photovoltaic array system is determined.

[0012] Further, in step S3, the preset range of the power difference value is ΔP < 0, and the preset range of the power change percentage is Where ΔP is the power difference value, Δ is the power change percentage, and fs is the sampling frequency.

[0013] Further, in step S4, the abnormality detection preset range is Where Δ is the power change percentage, N s is the row value of the target photovoltaic array system, N p is the column value of the target photovoltaic array system, I m is the maximum power point current of the component, U m is the maximum power point voltage of the component.

[0014] Further, based on steps S1 to S4, the line fault of the target photovoltaic array is determined, and further comprising step S5, according to the fault characteristics, the line fault detection of the target photovoltaic array system is realized:

[0015] Step S51, according to the connection mode of the photovoltaic components in the photovoltaic array system, a circuit model of the photovoltaic components is constructed, the working characteristics of the photovoltaic array are obtained, and then step S52 is performed;

[0016] Step S52, based on the circuit model of the photovoltaic components, a fault model is constructed, the collection of fault characteristics is realized, and a fault characteristic table is formed, and then step S53 is performed;

[0017] Step S53, based on the preset test points of each group string in the photovoltaic array system, the test point voltage is selected, and the test point voltage is sorted in the order from small to large, and then step S54 is performed;

[0018] Step S54, according to the test point voltage sorting, each group string preset test point is tested point by point, and the positive and negative electrode current values of each group string are obtained, and then step S55 is performed;

[0019] Step S55, according to the test point voltage and fault characteristic table, combined with the positive and negative electrode current value of each group of strings, the line fault location and line fault type are determined, and the number of fault components is judged.

[0020] Further, when selecting the test point voltage of each group of strings in step S53, the following formula is used:

[0021] U cs (i) = i * U oc + ε

[0022] In the formula: U cs is the test point voltage, U oc is the open circuit voltage of a single component, and ε is a small number greater than 0.

[0023] Further, based on step S3 judging that the target photovoltaic array system does not exist fault, or based on step S1 to step S4 judging that the target photovoltaic array does not exist line fault, or based on step S5 has realized the line fault detection of the target photovoltaic array system, it further includes step S6, according to the hybrid algorithm, realizing the multi-peak maximum power point tracking control of the target photovoltaic array system:

[0024] Step S61, using Circle chaotic mapping to generate an initial population with a size of N, each individual in the population represents a duty cycle particle, and then step S62 is executed;

[0025] Step S62, according to the generation order of the duty cycle particles in the initial population, the fitness of each duty cycle particle is calculated in turn, the output power of each duty cycle particle is obtained, and the best duty cycle particle is determined according to the power optimization principle, and then step S63 is executed;

[0026] Step S63, based on the best duty cycle particle, according to the chaotic mapping principle, update each duty cycle particle, and then execute step S64;

[0027] Step S64, according to the generation order of the updated duty cycle particles, the output power of each duty cycle particle is obtained in turn, and the updated best duty cycle particle is determined combined with the best duty cycle particle of the target historical iteration time, and then step S65 is executed;

[0028] Step S65, if the termination condition is met, the iteration is ended, and the stable output of the maximum power is realized; otherwise, repeat steps S63 to S64 until the termination condition is met.

[0029] Further, the step S61 generates the duty cycle particles according to the following formula:

[0030] x i+1 = αxi (1-x i )0<α≤4

[0031] X i+1 =x i+1 *(u b -l b )+l b

[0032] Where x i is the duty cycle particle generated according to the chaotic mapping formula, α is the mapping parameter, X i Based on the actual range (l b ,u b ) to generate duty cycle particles.

[0033] Furthermore, the step S63 includes:

[0034] Step S631: Based on each duty cycle particle, according to the chaotic mapping principle, generate a pseudo-random number p corresponding to each duty cycle particle and within the range of (0, 1), and then execute step S632;

[0035] In step S632, based on the pseudo-random number corresponding to each duty cycle particle and within a preset pseudo-random number range, it is determined whether each duty cycle particle should be updated according to the spiral predation principle of the Blue Whale algorithm. If so, the update is performed according to the following formula; otherwise, step S633 is executed:

[0036] X i (t+1)=w*e bl cos(2πl)+X best (t)

[0037]

[0038] D′=|X best (t)-X i (t)|

[0039] l∈(-1,1)

[0040] b=1

[0041] Where i is the individual number in the population, i∈{1, 2, ..., N}; N is the population size of the WOA algorithm; b is a constant related to the spiral shape; l is a random number between (-1, 1); D' is the distance vector; t is the current iteration number, t max is the maximum number of iterations; w is the step inertia factor; X best is the particle with the best duty cycle;

[0042] Step S633, based on the iteration number and the sine function, generating the parameter a and the vector parameter A by using the following formula, and then performing step S634:

[0043]

[0044] A = 2a * r1 - a

[0045] In the formula, a is a control parameter related to the iteration number; t is the current iteration number; t max is the maximum iteration number; r1 is a random number of (0, 1);

[0046] Step S634, based on the value of the vector parameter A, according to the range of the preset vector parameter A, judging whether to update each duty cycle particle according to the shrinkage position principle of the blue whale algorithm, if yes, updating each duty cycle particle according to the following formula, otherwise performing step S635:

[0047] X i (t+1) = X best (t) - A * D * w

[0048] D = |C * X best (t) - X i (t)|

[0049] C = 2 * r2

[0050] In the formula, X i is the duty cycle particle generated according to the actual range (l b , u b ); X best is the best duty cycle particle; C is the vector parameter, wherein |A| ∈ (0, 1) in the surrounding stage; r2 is a random number of (0, 1).

[0051] Step S635, based on the value of the vector parameter A, updating each duty cycle particle according to the Levy flight strategy according to the following formula:

[0052]

[0053]

[0054] a = a0 * (X i (t) - X best (t))

[0055] In the formula, X i is the duty cycle particle generated according to the actual range (l b , u b ); X bestFor best duty cycle particles; Levy (β) is the Levy flight strategy, β takes 1.5; α is the distance vector, α0 takes 0.01; μ, v are normal distribution between (0, 1); Γ() is the Gamma distribution function.

[0056] Further, the range of the pseudo-random number in the step S632 is preset as p≥0.5.

[0057] Further, the range of the vector parameter A in the step S634 is preset as |A|<1. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 It is a flowchart of a photovoltaic array line fault diagnosis method based on shadow power tracking, wherein (a) is a line fault anomaly detection diagram, and (b) is a fault location detection diagram.

[0059] Figure 2 It is a fault classification flowchart.

[0060] Figure 3 It is a 5*6 photovoltaic array system under shadow shielding.

[0061] Figure 4 It is a common line fault form of a photovoltaic array system.

[0062] Figure 5 It is a normalized I-V characteristic curve of a line fault under a series blocking diode, wherein (a) is an I-V characteristic curve under a shadow condition, (b) is an I-V characteristic curve under a line short circuit condition, (c) is an I-V characteristic curve under a ground condition, and (d) is an I-V characteristic curve under an interline short circuit condition.

[0063] Figure 6 It is a normalized I-V characteristic curve of a line fault under a non-series blocking diode, wherein (a) is an I-V characteristic curve under a shadow condition, (b) is an I-V characteristic curve under a line short circuit condition, (c) is an I-V characteristic curve under a ground condition, and (d) is an I-V characteristic curve under an interline short circuit condition.

[0064] Figure 7 It is a system parameter change curve diagram of a photovoltaic array system under a ground condition of three components of a non-series blocking diode photovoltaic array system.

[0065] Figure 8 It is a system parameter change curve diagram of a photovoltaic array system under a line short circuit condition of one component of a non-series blocking diode photovoltaic array system.

[0066] Figure 9 It is a system parameter change curve diagram of a photovoltaic array system under a line short circuit condition of two components of a non-series blocking diode photovoltaic array system.

[0067] Figure 10 Graphs of system parameter variation curves for non-series blocking diode photovoltaic array system circuit breakers.

[0068] Figure 11 Flowchart for multi-peak maximum power point tracking control.

[0069] Figure 12 Graphs of normalized array power-voltage curves for strings in a photovoltaic array system under shadow conditions.

[0070] Figure 13 Comparison of steady-state power tracking of improved blue whale algorithm with blue whale algorithm, grey wolf algorithm, and particle swarm algorithm, where (a) is the comparison of steady-state power tracking under single-peak conditions, (b) is the comparison of steady-state power tracking under double-peak conditions, (c) is the comparison of steady-state power tracking under triple-peak conditions, and (d) is the comparison of steady-state power tracking under quadruple-peak conditions. Figure 14 Comparison of multi-peak shadow tracking iteration numbers of improved blue whale algorithm with blue whale algorithm, grey wolf algorithm, and particle swarm algorithm, where (a) is the comparison of multi-peak shadow tracking iteration numbers under single-peak conditions, (b) is the comparison of multi-peak shadow tracking iteration numbers under double-peak conditions, (c) is the comparison of multi-peak shadow tracking iteration numbers under triple-peak conditions, and (d) is the comparison of multi-peak shadow tracking iteration numbers under quadruple-peak conditions. DETAILED DESCRIPTION

[0071] REFERENCE Figure 1 , (a) is a line fault anomaly detection graph, (b) is a fault location detection graph, and Figure 2 Fault classification flowchart for fault diagnosis of a target photovoltaic array system to determine whether a fault exists and the type of fault, and to achieve multi-peak maximum power point tracking control of the target photovoltaic array system:

[0072] Step S1, based on a preset sampling period, sampling the target photovoltaic array system to obtain array current and array voltage of each sampling point corresponding to the current collection time and the adjacent last collection time, respectively, and then executing step S2;

[0073] Step S2, calculating the power value of each sampling point corresponding to the current collection time and the adjacent last collection time, respectively, through the product of the array current and the array voltage, and then executing step S3;

[0074] Step S3, based on the power value of each sampling point corresponding to the current collection time and the adjacent last collection time, respectively, obtaining the power difference value and the power change percentage value of each sampling point at the current collection time and the adjacent last collection time, and combining the power difference value preset range ΔP < 0 and the power change percentage preset range where ΔP is the power difference value, Δ is the power change percentage, and fs is the sampling frequency, to determine whether a fault exists in the target photovoltaic array system, and if so, executing step S4;

[0075] Step S4, based on the current acquisition time and the adjacent last acquisition time power percentage value, combined with abnormal detection preset range Wherein Δ is the power percentage, N s The target photovoltaic array system row value, N p The target photovoltaic array system column value, I m The maximum power point current of the component, U m The maximum power point voltage of the component, determine the fault type of the target photovoltaic array system;

[0076] Based on steps S1 to S4, it is judged that the target photovoltaic array exists line fault, further comprising step S5, according to the fault characteristic, realizing the line fault detection of the target photovoltaic array system:

[0077] Step S51, according to Figure 3 The 5*6 photovoltaic array system under the shadow of the shadow is shown, the circuit model of the component is built, the circuit model of the photovoltaic component is constructed, the working characteristics of the photovoltaic array are obtained, and the related parameters of single photovoltaic component are shown in table 1, and then step S52 is executed;

[0078] Table 1

[0079] Parameter Value Open-circuit voltage U OC / V]]> 44.4 Short circuit current I SC / A]]> 5.4 Maximum power point voltage U m / V]]> 35.4 Maximum power point current I m / A]]> 4.95 Maximum power P m / W]] 175.23

[0080] Step S52, based on the circuit model of the photovoltaic component, the fault model is constructed, that is, the photovoltaic component is randomly processed on the basis of the circuit model of the photovoltaic component, for example, ground fault, see Figure 4 And on this basis, I-V characteristic simulation is carried out for ground fault, and normalized I-V characteristic curve is obtained, see Figure 5 And Figure 6 Realize the collection of fault characteristics, constitute the fault characteristic table, see table 2, table 1 U s Row N p Column photovoltaic array system constitutes the fault characteristic table, see table 2, table 1 U pv Is the array voltage, U C Is the theoretical open circuit voltage of the fault component string, which follows the formula: U C =(N s -N F )*U OC , Wherein N F The number of fault components in the component string, U OC The open circuit voltage of single component, I T The positive current of the component string, I B The negative current of the component string, k is the sequence of the component string, then step S53 is executed;

[0081] Table 2

[0082]

[0083]

[0084] Step S53, based on the preset test points of each group string in the photovoltaic array system, the U cs (i) = i * U oc + ε selection test point voltage, wherein U cs is the test point voltage, U oc is the open circuit voltage of a single component, ε is a small number greater than 0, and a test point voltage U CS (0) = U OC - ε is added for the case of series blocking diode to distinguish between open circuit failure and line short circuit, the test voltage and the corresponding duty ratio selection table of the photovoltaic array system without series blocking diode are shown in Table 3, and the test point voltage is sorted in order from small to large, and then step S54 is executed;

[0085] Table 3

[0086] U CS (1)] U CS (2)] U CS (3)] U CS (4)] Voltage / V 44.4+4.44 88.8+4.44 133.2+4.44 177.6+4.44 Duty cycle D 0.88 0.76 0.65 0.54

[0087] Step S54, according to the test point voltage sorting, the preset test points of each group string are tested in turn, and the positive and negative electrode current values of each group string are obtained, and then step S55 is executed;

[0088] Step S55, according to the test point voltage and the fault feature table, the positive and negative electrode current values of each group string are combined to determine the line fault position and the line fault type, and the number of fault components is judged, see Figure 7 to Figure 10 for details, and the specific process is as follows, wherein k is the group string sequence, I T is the positive electrode current of the group string, I B is the negative electrode current of the group string, N F is the number of fault components in the group string, and N s is the target photovoltaic array system row value:

[0089] ① If any I Tk > 0, there is no line fault;

[0090] In the case of series blocking diode:

[0091] ② If I CS = 0 under U Tk (0), the k sequence group string has an open circuit failure;

[0092] ③ If I Tk = I BK = 0 under the remaining test voltages, the k sequence group string has a line short circuit failure, and the number of fault components NF = N S -i;

[0093] IV. If I Tk > 0, I BK < 0, I Tm = 0, I Bm > 0, then the m-series and k-series group string occurs line-to-line short circuit fault, the number of fault components N F = i;

[0094] V. Otherwise, it is a ground fault, the number of fault components N F = N S -i.

[0095] VI. The detection is ended, and waiting for manual replacement of components;

[0096] Without series blocking diode:

[0097] II. If I Tk = 0, then the k-series group string occurs open circuit fault;

[0098] III. If I Tk < 0, if I Tk = I Bk , then the k-series group string occurs line-to-line short circuit fault, the number of fault components N F = N S -i;

[0099] IV. If I Tk > 0, I BK < 0, I Bm > 0, then the m-series and k-series group string occurs line-to-line short circuit fault, the number of fault components N F = i;

[0100] V. Otherwise, it is a ground fault, the number of fault components N F = N S -i;

[0101] VI. The detection is ended, and waiting for manual replacement of components;

[0102] Based on the line fault detection of the target photovoltaic array system achieved in step S5, further comprising step S6, Table 4 is a light intensity distribution table of group strings in the photovoltaic array system under shadow condition, referring to Figure 11 The multi-peak maximum power point tracking control flow chart, according to the hybrid algorithm, realizes the multi-peak maximum power point tracking control of the target photovoltaic array system:

[0103] Table 4

[0104] Peak case 1000 W / m 2 ]] 600 W / m 2 ]] 400 W / m 2 ]] 200 W / m 2 ]] Single peak 5 0 0 0 Double peak 3 2 0 0 Triple peak 2 2 1 0 Quadruple peak 1 2 1 1

[0105] Step S61, using Circle chaos mapping, generating an initial population of size N according to the following formula, each individual in the population representing a duty cycle particle, then executing step S62:

[0106] x i+1 =αx i (1-x i )0<α≤4

[0107] X i+1 =x i+1 *(u b -l b )+l b

[0108] In the formula, x i is a duty cycle particle generated according to the chaos mapping formula, α is the mapping parameter, X i is a duty cycle particle generated according to the actual range (l b , u b );

[0109] Step S62, according to the generation order of the duty cycle particles in the initial population, sequentially calculating the fitness of each duty cycle particle, obtaining the output power of each duty cycle particle, and determining the best duty cycle particle according to the power optimization principle, then executing step S63;

[0110] Step S63, based on the best duty cycle particle, updating each duty cycle particle according to the chaos mapping principle, specifically including:

[0111] Step S631, based on each duty cycle particle, generating a pseudo-random number p corresponding to each duty cycle particle and within the range (0, 1) according to the chaos mapping principle, then executing step S632;

[0112] Step S632, based on the pseudo-random number corresponding to each duty cycle particle, judging whether to update each duty cycle particle according to the spiral predation principle of the blue whale algorithm according to p≥0.5, yes according to the following formula, otherwise executing step S633:

[0113] X i (t+1)=w*e bl cos(2πl)+X best (t)

[0114]

[0115] D′=|X best (t)-X i (t)|

[0116] l∈(-1,1)

[0117] v=1

[0118] Where i is the individual number in the population, i∈{1, 2, ..., N}; N is the population size of the WOA algorithm; b is a constant related to the spiral shape; l is a random number between (-1, 1); D' is the distance vector; t is the current iteration number, t max is the maximum number of iterations; w is the step inertia factor; X best is the particle with the best duty cycle;

[0119] In step S633, based on the number of iterations and the sine function, the parameter α and the vector parameter A are generated using the following formula, and then step S634 is executed:

[0120]

[0121] A=2α*r1-α

[0122] Where α is a control parameter related to the number of iterations; t is the current number of iterations; t max is the maximum number of iterations; r1 is a random number (0,1);

[0123] In step S634, based on the value of the vector parameter A and according to the condition |A|<1, it is determined whether to update each duty cycle particle according to the shrinking and surrounding position principle of the Blue Whale algorithm. If so, each duty cycle particle is updated according to the following formula; otherwise, step S635 is executed:

[0124] X i (t+1)=X best (t)-A*D*w

[0125] D=|C*X best (t)-X i (t)|

[0126] C=2*r2

[0127] Where, X i Based on the actual range (l b ,u b ) generated duty cycle particles; X best is the particle with the best duty cycle; C is a vector parameter, where |A|∈(0,1) in the encirclement stage; r2 is a random number in (0,1).

[0128] Step S635: Based on the value of the vector parameter A and the Levy flight strategy, update each duty cycle particle according to the following formula:

[0129]

[0130] α=α0*(Xi(t)-Xbest(t))

[0131] In the formula, X i is a duty cycle particle generated according to an actual range (l b , b ) Xbest is the best duty cycle particle; Levy (β) is a Levy flight strategy, β is 1.5; α is a distance vector, α0 is 0.01; μ and v are normal distribution between (0, 1); Γ() is a Gamma distribution function.

[0132] In step S64, according to the generation order of the updated duty cycle particles, the output power of each duty cycle particle is obtained in sequence, the updated best duty cycle particle is determined in combination with the best duty cycle particle at the target historical iteration time, the next iteration value of each duty cycle particle is determined according to the pseudo-random number p and the vector parameter A, and each particle is run after the update position, the output power is sampled, the optimal particle is obtained, and compared with the optimal particle at the previous iteration time, if the output power increases, the optimal particle is replaced, otherwise it is unchanged, and then step S65 is executed;

[0133] In step S65, if the termination condition is met, that is, the maximum iteration number tmax is reached, the iteration is ended, and the stable output of the maximum power is realized; otherwise, steps S63 to S64 are repeated until the termination condition is met.

[0134] The results show that the improved blue whale algorithm based on chaotic mapping and Levy flight strategy is used to realize the multi-peak shadow tracking of the photovoltaic array system, and the inaccuracy of the traditional MPPT algorithm in tracking the maximum power point under the shadow condition is avoided, and details are shown in Figure 12 .

[0135] In addition, through the comparison of the steady-state power tracking, the iteration number of the multi-peak shadow tracking, and the average results of 15 times of multi-peak shadow tracking, it can be seen that the improved blue whale algorithm is superior to the blue whale algorithm (WOA), the grey wolf algorithm (GWO) and the particle swarm algorithm (PSO) in realizing the multi-peak maximum power point tracking, the steady-state power comparison is shown in Figure 13 , the iteration number comparison is shown in Figure 14 , and the average result comparison of 15 times of multi-peak shadow tracking is shown in Table 5.

[0136] Table 5

[0137]

[0138]

[0139] At the same time, the chaos mapping is used to solve the non-uniformity of the random update of the initial particle, and the possibility of the algorithm falling into local optimum is reduced; the Levy flight strategy is used to replace the random search update formula of the blue whale algorithm, and a large number of invalid iterations are avoided, and the convergence rate of the algorithm is improved.

[0140] In addition, the fault characteristics of common photovoltaic array circuit faults are obtained through circuit simulation, the faults are detected, classified and positioned by using the test method of special working voltage points, the real-time diagnosis of the photovoltaic array circuit faults is realized, and the accuracy of the diagnosis is ensured based on the fault characteristics obtained from the I-V characteristics.

[0141] In summary, the application effectively solves the two main problems causing power loss of the photovoltaic power generation system.

[0142] The above is only a preferred embodiment of the application, and does not limit the application, and any simple modification, change and equivalent structural change made according to the technical essence of the application to the above embodiment are still within the protection scope of the technical solution of the application.

Claims

1. A shadow power tracking based photovoltaic array line fault diagnosis method, characterized in that: Real-time fault preliminary diagnosis of the target photovoltaic array system is performed according to steps S1-S4 to determine whether a fault exists and the fault type: In step S1, the target photovoltaic array system is sampled based on a preset sampling period to obtain array current and array voltage corresponding to the current sampling time and the adjacent previous sampling time at each sampling point, and then step S2 is performed; In step S2, the power values corresponding to the current sampling time and the adjacent previous sampling time at each sampling point are calculated, and then step S3 is performed; In step S3, the power difference value and the power change percentage value of each sampling point at the current sampling time and the adjacent previous sampling time are obtained based on the power values corresponding to the current sampling time and the adjacent previous sampling time at each sampling point, and whether the target photovoltaic array system has a fault is determined in combination with the power difference preset range and the power change percentage preset range, and if yes, step S4 is performed; In step S4, the fault type of the target photovoltaic array system is determined based on the power change percentage value at the current sampling time and the adjacent previous sampling time in combination with the abnormality detection preset range; wherein in step S4, the abnormality detection preset range is wherein is the power percentage change, is the target photovoltaic array system row value, is the target photovoltaic array system column value, is the maximum power point current of the component, is the maximum power point voltage of the component; And based on steps S1-S4, whether the target photovoltaic array has a line fault is determined, and step S5 is further included to realize line fault detection of the target photovoltaic array system according to fault characteristics: In step S51, a circuit model of the photovoltaic component is constructed according to the connection mode of the photovoltaic component in the photovoltaic array system to obtain the working characteristics of the photovoltaic array, and then step S52 is performed; In step S52, a fault model is constructed based on the circuit model of the photovoltaic component to realize collection of fault characteristics and form a fault characteristic table, and then step S53 is performed; In step S53, the test point voltage of each preset test point of each string is selected based on the preset test points of each string, and the test point voltage is sorted in descending order to determine the test point voltage sorting, and then step S54 is performed; In step S54, each preset test point of each string is sequentially sampled and tested point by point according to the test point voltage sorting to obtain the positive and negative electrode current values of each string, and then step S55 is performed; In step S55, the line fault position and the line fault type are determined according to the test point voltage and the fault characteristic table in combination with the positive and negative electrode current values of each string, and the number of fault components is determined.

2. The shadow power tracking based photovoltaic array line fault diagnostic method according to claim 1, characterized in that, The preset range of the power difference value in the step S3 is The preset range of the power change percentage is wherein is the power difference value, is the power change percentage, is the sampling frequency.

3. The shadow power tracking based photovoltaic array line fault diagnostic method according to claim 1, characterized in that, When the test point voltage of each preset test point of each string is selected in step S53, the following formula is used: ； wherein: Vp is the voltage at the test point, Voc is the open circuit voltage of the individual component, is a decimal number greater than 0.

4. The shadow power tracking based photovoltaic array line fault diagnostic method according to claim 1, characterized in that, Based on step S3, it is determined that the target photovoltaic array system does not have a fault, or based on steps S1-S4, it is determined that the target photovoltaic array does not have a line fault, or based on step S5, the line fault detection of the target photovoltaic array system has been realized, and step S6 is further included to realize multi-peak maximum power point tracking control of the target photovoltaic array system according to a hybrid algorithm: In step S61, a Circle chaotic mapping is used to generate an initial population with a size of N, and each individual in the population represents a duty cycle particle, and then step S62 is performed; In step S62, the fitness of each duty cycle particle is calculated in sequence according to the generation order of the duty cycle particles in the initial population to obtain the output power of each duty cycle particle, and the best duty cycle particle is determined according to the power optimization principle, and then step S63 is performed; Step S63, based on the optimal duty cycle particle, updating each duty cycle particle according to the chaos mapping principle, and then performing step S64; Step S64, according to the generation order of each duty cycle particle after updating, obtaining the output power of each duty cycle particle in turn, combining the optimal duty cycle particle at the target historical iteration time, determining the updated optimal duty cycle particle, and then performing step S65; Step S65, if the termination condition is met, ending the iteration and realizing the stable output of the maximum power; otherwise, repeating steps S63 to S64 until the termination condition is met.

5. The shadow power tracking based photovoltaic array line fault diagnostic method according to claim 4, characterized in that, The step S61 generates the duty cycle particle according to the following formula: ； ； wherein is a duty cycle particle generated according to a chaotic mapping formula, is a mapping parameter, is a duty cycle particle generated according to a chaotic mapping formula, is a duty cycle particle generated according to a chaotic mapping formula.

6. The shadow power tracking based photovoltaic array line fault diagnostic method according to claim 4, characterized in that, The step S63 includes: Step S631, based on each duty cycle particle, generating a pseudo-random number p corresponding to each duty cycle particle and in the range of (0, 1) according to the chaos mapping principle, and then performing step S632; Step S632, based on the pseudo-random number corresponding to each duty cycle particle, judging whether to update each duty cycle particle according to the spiral predation principle of the blue whale algorithm according to the preset pseudo-random number range, yes according to the following formula, otherwise perform step S633: ； ； ； ； ； wherein, is the number of individuals in the population, ; is the number of population of WOA algorithm; is a constant related to the spiral shape; is a random number between (-1, 1); is the distance vector; t is the current iteration number, t max is the maximum iteration number; is the step size inertia factor; is the best duty cycle particle; Step S633, based on the iteration number and the sine function, the parameter is generated by using the following formula and the vector parameter A, and then step S634 is performed: ； ； wherein is a control parameter related to the iteration number; t is the current iteration number; t max is the maximum iteration number; r 1 is a random number of (0, 1); Step S634, based on the value of the vector parameter A, judging whether to update each duty cycle particle according to the contraction surrounding position principle of the blue whale algorithm according to the range of the preset vector parameter A, yes according to the following formula, otherwise perform step S635: ； ； ； wherein is the actual range generated duty cycle particle; is the optimal duty cycle particle; C is the vector parameter, where in the enclosing phase ; r 2 is a random number of (0,1); Step S635, based on the value of the vector parameter A, updating each duty cycle particle according to the Levy flight strategy according to the following formula: ； ； ； where, is the actual range generated duty cycle particle; is the optimal duty cycle particle; is the Levy flight strategy, take 1.5; is the distance vector, take 0.01; is a normal distribution between (0,1); is a Gamma distribution function.

7. The shadow power tracking based photovoltaic array line fault diagnostic method according to claim 6, characterized in that, The range of the pseudo-random number in the step S632 is preset as .

8. The shadow power tracking based photovoltaic array line fault diagnostic method according to claim 6, characterized in that, The range of the vector parameter A in the step S634 is preset as .

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