A method and system for calculating actual power load of a line based on active power

Abstract

The present invention discloses a method and system for calculating the actual power load of a line based on active power. The method includes the following steps: collecting the current round of distributed photovoltaic data sets; calculating the probability density distribution based on the previous round of distributed photovoltaic historical data sets; constructing a cumulative distribution function to generate a corresponding particle set; updating the particle state; updating the particle weight; judging whether resampling is required based on the validity of the distributed photovoltaic active power particle set after the weight update; calculating the active power estimate; calculating the actual power load of the line based on the active power estimate and the active output particle set of a single distributed power source. The technical solution provided by the present invention can not only improve the statistical accuracy of the actual power load, but also cope with the volatility of distributed power sources and their impact on line load, thereby providing accurate data support for the scheduling, management and optimization of the power system, and has high application value.

Description

Technical Field

[0001] The present invention relates to the technical field of power load calculation, and in particular to a method and system for calculating actual power load of a circuit based on active power. Background Art

[0002] For lines connected to distributed photovoltaic systems, the industry currently lacks the technical means to measure the actual power load of these lines. The active power output at the line outlet observed by the dispatch automation system is the value after the actual power load of the line is offset by the active output of the distributed photovoltaic systems. Furthermore, techniques such as N-1 verification and equipment overload analysis based on these values ​​also fail to reflect the actual operation of the power grid.

[0003] In view of the above, there is an urgent need to design a technical means to calculate the actual power load of the line to meet the user's usage needs. Summary of the Invention

[0004] In order to meet the needs of users, the purpose of the present invention is to provide a method and system for calculating the actual power load of a line based on active power.

[0005] To achieve the purpose of the present invention, the technical solution provided by the present invention is as follows:

[0006] First aspect

[0007] The present application provides a method for calculating the actual power load of a line based on active power, comprising the following steps:

[0008] Step S1: collecting the distributed photovoltaic data set of the current round, wherein the distributed photovoltaic data set includes distributed photovoltaic active power data, distributed photovoltaic irradiance data, distributed photovoltaic inverter efficiency data, distributed photovoltaic line loss rate data, distributed photovoltaic module efficiency data, and active output data of a single distributed power source;

[0009] Step S2: Based on the previous round of distributed photovoltaic historical data sets, the probability density distribution is preliminarily calculated using the kernel density formula;

[0010] Step S3: Based on the probability density distribution, the cumulative distribution function is implicitly constructed in the form of rejection sampling, and the distributed photovoltaic data set is randomly screened to generate the corresponding distributed photovoltaic active power particle set, distributed photovoltaic irradiance particle set, distributed photovoltaic inverter efficiency particle set, distributed photovoltaic line loss rate particle set, distributed photovoltaic module efficiency particle set, and single distributed power source active output particle set;

[0011] Step S4: updating the particle state of the distributed photovoltaic active power particle set;

[0012] Step S5: updating the weights of the particles in the distributed photovoltaic active power particle set after the particle states are updated in the form of an observation likelihood function;

[0013] Step S6: Based on the validity of the distributed photovoltaic active power particle set after weight update, determine whether resampling is required; if resampling is required, obtain the final distributed photovoltaic active power particle set through resampling; if resampling is not required, directly obtain the final distributed photovoltaic active power particle set;

[0014] Step S7: Calculate the active power estimation value based on the final distributed photovoltaic active power particle set;

[0015] Step S8: Calculate the actual power load of the line based on the active power estimation value and the active output particle set of the single distributed power source.

[0016] Second aspect

[0017] Corresponding to the above method, the present application provides a system for calculating the actual power load of a line based on active power, including a current round data set acquisition unit, a probability density distribution calculation unit, a particle set generation unit, a particle state update unit, a particle weight update unit, a resampling unit, an active power estimation value calculation unit, and a line actual power load calculation unit;

[0018] The current round data set acquisition unit is used to acquire the current round of distributed photovoltaic data sets, wherein the distributed photovoltaic data sets include distributed photovoltaic active power data, distributed photovoltaic irradiance data, distributed photovoltaic inverter efficiency data, distributed photovoltaic line loss rate data, distributed photovoltaic module efficiency data, and active output data of a single distributed power source;

[0019] The probability density distribution calculation unit is used to preliminarily calculate the probability density distribution using the kernel density formula based on the previous round of distributed photovoltaic historical data sets;

[0020] The particle set generation unit is used to implicitly construct a cumulative distribution function in the form of rejection sampling based on the probability density distribution, perform particle randomization screening on the distributed photovoltaic data set, and generate corresponding distributed photovoltaic active power particle sets, distributed photovoltaic irradiance particle sets, distributed photovoltaic inverter efficiency particle sets, distributed photovoltaic line loss rate particle sets, distributed photovoltaic component efficiency particle sets, and active output particle sets of single distributed power sources;

[0021] The particle state updating unit is used to update the particle state of the distributed photovoltaic active power particle set;

[0022] The particle weight updating unit is used to update the weights of the particles in the distributed photovoltaic active power particle set after the particle states are updated in the form of an observation likelihood function;

[0023] The resampling unit is used to determine whether resampling is required based on the validity of the distributed photovoltaic active power particle set after weight update; if resampling is required, the final distributed photovoltaic active power particle set is obtained by resampling; if resampling is not required, the final distributed photovoltaic active power particle set is directly obtained;

[0024] The active power estimated value calculation unit is used to calculate the active power estimated value based on the final distributed photovoltaic active power particle set;

[0025] The line actual power load calculation unit is used to calculate the line actual power load based on the active power estimation value and the active output particle set of a single distributed power source.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The technical solution provided by the present invention can not only improve the statistical accuracy of actual power load, but also cope with the volatility of distributed power sources and their impact on line load, thereby providing accurate data support for the scheduling, management and optimization of the power system, and has high application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of a method flow chart provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0030] like Figure 1 As shown, this embodiment provides a method for calculating the actual power load of a line based on active power, comprising the following steps:

[0031] Step S1: collecting the distributed photovoltaic data set of the current round, wherein the distributed photovoltaic data set includes distributed photovoltaic active power data, distributed photovoltaic irradiance data, distributed photovoltaic inverter efficiency data, distributed photovoltaic line loss rate data, distributed photovoltaic module efficiency data, and active output data of a single distributed power source;

[0032] It should be noted that distributed photovoltaic active power data, distributed photovoltaic irradiance data, distributed photovoltaic inverter efficiency data, distributed photovoltaic line loss rate data, and distributed photovoltaic module efficiency data are collected from the control center automation system, and the active output data of a single distributed power source is collected from the marketing procurement system.

[0033] Step S2: Based on the previous round of distributed photovoltaic historical data sets, the probability density distribution is preliminarily calculated using the kernel density formula;

[0034] It should be noted that the previous round of historical datasets includes datasets corresponding to the types and contents of the current round of distributed photovoltaic datasets.

[0035] Wherein, in step S2, the calculation formula of the probability density distribution is as follows:

[0036] ;

[0037] Where, is the probability density distribution; n represents the number of distributed photovoltaic historical dataset results from the previous round; h represents the kernel bandwidth; k represents the index number of the distributed photovoltaic historical dataset results from the previous round; M represents the distributed photovoltaic historical dataset results from the previous round corresponding to the current index number; Represents the kernel function, usually Gaussian kernel or uniform kernel is selected according to the data distribution form; M k Represents the kth result of the distributed photovoltaic historical dataset in the previous round.

[0038] The kernel bandwidth h can be implemented by minimizing the negative log-likelihood, specifically:

[0039] h = arg minutes h [ − ∑ k = 1 n log f ^ − k ( M k ) ] ;

[0040] Where, It represents the kernel density calculated after removing the kth sample from all the data used.

[0041] Step S3: Based on the probability density distribution, the cumulative distribution function is implicitly constructed in the form of rejection sampling, and the distributed photovoltaic data set is randomly screened to generate the corresponding distributed photovoltaic active power particle set, distributed photovoltaic irradiance particle set, distributed photovoltaic inverter efficiency particle set, distributed photovoltaic line loss rate particle set, distributed photovoltaic module efficiency particle set, and single distributed power source active output particle set;

[0042] Wherein, the step S3 specifically includes:

[0043] Step S31: Based on the probability density distribution, the cumulative distribution function is implicitly constructed in the form of rejection sampling, specifically:

[0044] ;

[0045] Where, represents the envelope distribution function, which is usually selected from Gaussian distribution or uniform distribution based on experience; c represents the sampling scale constant; where the sampling scale constant is specifically:

[0046] c = arg max M [ f ^ ( M ) g ( M ) ] .

[0047] Step S32: Generate a candidate point from the envelope distribution function, For candidate points , calculate its acceptance probability :

[0048] ;

[0049] Where, Candidate point The envelope distribution function of

[0050] Step S33: Generate a random probability distribution, ; If it meets the conditions: , then accept As particles, otherwise, candidate points are regenerated until all distributed photovoltaic data sets are initialized, and the distributed photovoltaic active power particle set, distributed photovoltaic irradiance particle set, distributed photovoltaic inverter efficiency particle set, distributed photovoltaic line loss rate particle set, distributed photovoltaic component efficiency particle set, and single distributed power source active output particle set are obtained.

[0051] Step S4: updating the particle state of the distributed photovoltaic active power particle set;

[0052] In step S4, the particle state of the distributed photovoltaic active power particle set is updated in the following form:

[0053] ;

[0054] Where i represents the particle number, , N represents the total number of particles; , represents the active power of the i-th particle at time t and time t-1 respectively; Represents process noise, which is the quantitative expression of system uncertainty. Its form is usually zero-mean Gaussian noise, which is calculated by combining the distributed photovoltaic irradiance particle set, the distributed photovoltaic inverter efficiency particle set, the distributed photovoltaic line loss rate particle set, and the distributed photovoltaic module efficiency particle set; Represents the state update function of the particle; It represents the uncertainty of the i-th particle in the distributed photovoltaic active power particle set at time t.

[0055] The process noise is specifically:

[0056] ;

[0057] Where, Represents the covariance matrix of process noise, specifically:

[0058]

[0059] Where, , , , They represent the noise intensity of the irradiance of the i-th particle at time t, the inverter efficiency, the line loss rate, and the component efficiency respectively;

[0060] Furthermore, the noise intensity is specifically:

[0061]

[0062] Where, , , , They represent the noise intensity proportional factors required for the particleization of the variables collected by the automation system of the control center corresponding to the i-th particle at time t, and the values ​​are set between 0.01 and 0.1 according to actual needs; express t Moment i The irradiance corresponding to each particle is express t -1 moment i The irradiance corresponding to each particle; express t Moment i The inverter efficiency corresponding to each particle is express t -1 moment i The inverter efficiency corresponding to each particle; express t Moment i The line loss rate corresponding to the number of particles is express t -1 moment i The line loss rate corresponding to each particle; express t Moment i The photovoltaic module efficiency corresponding to each particle is express t-1 moment i The photovoltaic module efficiency corresponding to each particle.

[0063] The state update function in step S4 is specifically:

[0064] y ( D P t ( i ) , w t ( i ) ) = G t ( i ) ( 1 + w tG ( i ) ) A ⋅ [ 1 + c G t ( i ) ( 1 + w tG ( i ) ) G t T n ] ⋅ G t ( i ) ( 1 + w tG ( i ) ) G stc ⋅ or inv ( t ) ( i ) ( 1 + w tinv ( i ) ) ⋅ [ 1 − p l ( t ) ( i ) ⋅ ( 1 + w tl ( i ) ) ] ⋅ or m ( t ) ( i ) ( 1 + w tm ( i ) )

[0065] Where, , , , They represent the zero-mean Gaussian noise generation results of the part of the collected variables of the control center automation system corresponding to the i-th particle at time t after particleization; Represents the effective area of ​​the photovoltaic array in the photovoltaic system; represents the temperature power coefficient; Indicates the nominal operating temperature of the PV module.

[0066] Step S5: Update the weights of the particles in the distributed photovoltaic active power particle set after the particle state is updated in the form of an observation likelihood function. After the weight update, the distribution of the particles will be concentrated near the measured value, low-weight particles will be gradually eliminated, and high-weight particles will have a greater impact on the estimation results.

[0067] Wherein, the step S5 specifically includes:

[0068] Step S51. Calculate the estimated weight of the particle using the following formula:

[0069] ;

[0070] Where, represents the normalized weight of the i-th particle at time t-1; represents the measured value of active power at time t; represents the estimated weight of the i-th particle at time t; It is expressed as an observation likelihood function, usually in the form of a Gaussian distribution:

[0071] ;

[0072] Where, represents the measurement error variance at time t and can be updated by the following formula:

[0073] ;

[0074] Where, represents the measurement error variance at the previous moment; Indicates the estimated value of active power at the previous moment; represents the update smoothing factor, which is used to balance the contribution of historical estimates and current errors. The initial value is set to 0.9. For dynamic systems, Generally, it is taken as 0.8~0.9; for steady-state system, Generally, it is taken as 0.95~0.99.

[0075] In particular, the initial value of the measurement error variance It can be determined from historical measurement results, specifically:

[0076] ;

[0077] Where, represents the kth historical estimation result of active power;

[0078] Step S52: Calculate the normalized weight of the particle using the following formula:

[0079] ;

[0080] Where v represents the index variable, which is used to unify the particle number; represents the estimated weight of the vth particle at time t, represents the normalized weight of the i-th particle at time t.

[0081] In particular, the initial value of the weight is uniformly distributed, that is, there is no preference for the state of each particle at the initial moment:

[0082] ;

[0083] Where, represents the weight of the i-th particle in the initial state;

[0084] Step S6: Based on the validity of the distributed photovoltaic active power particle set after weight update, determine whether resampling is required; if resampling is required, obtain the final distributed photovoltaic active power particle set through resampling; if resampling is not required, directly obtain the final distributed photovoltaic active power particle set;

[0085] In step S6, whether resampling is required is determined as follows:

[0086] E s = 1 ∑ i = 1 N [ oh t ( i ) ] 2 < N 2 ;

[0087] Where, Indicates the number of valid samples;

[0088] If the above formula is true, it means that the particle set has been degraded and needs to be resampled; otherwise, it shows that the effectiveness of the particle set is high and the state estimation of the filter is still relatively accurate, so there is no need to resample.

[0089] In step S6, the resampling is performed as follows:

[0090] Step S61: Calculate the cumulative weight of the particles, specifically:

[0091] ;

[0092] Where, represents the cumulative weight of the i-th particle at time t; j represents the particle index number; represents the normalized weight of the jth particle at time t;

[0093] Step S62: To determine the particle resampling position, generate a uniformly distributed random number sequence:

[0094] ;

[0095] Where, and are all random numbers; for each random number , find the corresponding particle index , as follows:

[0096] ;

[0097] According to the particle index , which can achieve resampling.

[0098] Resampling can be achieved by the newly obtained particle index sequence. In particular, after obtaining a new set of particles, the weight of each particle is reset to be equal, that is, each resampled particle has the same weight.

[0099] Step S7: Calculate the active power estimation value based on the final distributed photovoltaic active power particle set;

[0100] In step S7, the method for calculating the active power estimation value is as follows:

[0101] ;

[0102] Where, Represents the estimated active power at time t.

[0103] Step S8: Calculate the actual power load of the line based on the active power estimation value and the active output particle set of the single distributed power source.

[0104] In step S8, the actual power load of the line is calculated as follows:

[0105] ;

[0106] Where, Indicates the active output value of the sth distributed generation connected to this line; Indicates main line

[0107] The number of distributed power sources connected to the road; Indicates the actual power load of the line.

[0108] In addition, corresponding to the above method, this embodiment also provides a system for calculating the actual power load of a line based on active power, including a current round data set acquisition unit, a probability density distribution calculation unit, a particle set generation unit, a particle state update unit, a particle weight update unit, a resampling unit, an active power estimation value calculation unit, and a line actual power load calculation unit;

[0109] The current round data set acquisition unit is used to acquire the current round of distributed photovoltaic data sets, wherein the distributed photovoltaic data sets include distributed photovoltaic active power data, distributed photovoltaic irradiance data, distributed photovoltaic inverter efficiency data, distributed photovoltaic line loss rate data, distributed photovoltaic module efficiency data, and active output data of a single distributed power source;

[0110] The probability density distribution calculation unit is used to preliminarily calculate the probability density distribution using the kernel density formula based on the previous round of distributed photovoltaic historical data sets;

[0111] The particle set generation unit is used to implicitly construct a cumulative distribution function in the form of rejection sampling based on the probability density distribution, perform particle randomization screening on the distributed photovoltaic data set, and generate corresponding distributed photovoltaic active power particle sets, distributed photovoltaic irradiance particle sets, distributed photovoltaic inverter efficiency particle sets, distributed photovoltaic line loss rate particle sets, distributed photovoltaic component efficiency particle sets, and active output particle sets of single distributed power sources;

[0112] The particle state updating unit is used to update the particle state of the distributed photovoltaic active power particle set;

[0113] The particle weight updating unit is used to update the weights of the particles in the distributed photovoltaic active power particle set after the particle states are updated in the form of an observation likelihood function;

[0114] The resampling unit is used to determine whether resampling is required based on the validity of the distributed photovoltaic active power particle set after weight update; if resampling is required, the final distributed photovoltaic active power particle set is obtained by resampling; if resampling is not required, the final distributed photovoltaic active power particle set is directly obtained;

[0115] The active power estimated value calculation unit is used to calculate the active power estimated value based on the final distributed photovoltaic active power particle set;

[0116] The line actual power load calculation unit is used to calculate the line actual power load based on the active power estimation value and the active output particle set of a single distributed power source.

[0117] Finally, it should be noted that the above embodiments are merely examples and illustrations of the present invention and are not intended to limit the present invention to the described embodiments. Furthermore, those skilled in the art will appreciate that the present invention is not limited to the above embodiments and that various variations and modifications may be made based on the teachings of the present invention, all of which fall within the scope of the present invention.

Claims

1. A method for calculating the actual power load of a line based on active power, characterized in that: The following steps are involved: Step S1: collecting the distributed photovoltaic data set of the current round, wherein the distributed photovoltaic data set includes distributed photovoltaic active power data, distributed photovoltaic irradiance data, distributed photovoltaic inverter efficiency data, distributed photovoltaic line loss rate data, distributed photovoltaic module efficiency data, and active output data of a single distributed power source; Step S2: Based on the previous round of distributed photovoltaic historical data sets, the probability density distribution is preliminarily calculated using the kernel density formula; Step S3: Based on the probability density distribution, the cumulative distribution function is implicitly constructed in the form of rejection sampling, and the distributed photovoltaic data set is randomly screened to generate the corresponding distributed photovoltaic active power particle set, distributed photovoltaic irradiance particle set, distributed photovoltaic inverter efficiency particle set, distributed photovoltaic line loss rate particle set, distributed photovoltaic module efficiency particle set, and single distributed power source active output particle set; The step S3 specifically includes: Step S31: Based on the probability density distribution, the cumulative distribution function is implicitly constructed in the form of rejection sampling, specifically: ； Where, represents the envelope distribution function, which can be Gaussian or uniform distribution; c represents the sampling ratio constant; Step S32: Generate a candidate point from the envelope distribution function, For candidate points , calculate its acceptance probability : ； Where, Candidate point The envelope distribution function of Step S33: Generate a random probability distribution, ; If it meets the conditions: , then accept As particles, otherwise, regenerate candidate points until the distributed photovoltaic data sets are initialized, and obtain the distributed photovoltaic active power particle set, distributed photovoltaic irradiance particle set, distributed photovoltaic inverter efficiency particle set, distributed photovoltaic line loss rate particle set, distributed photovoltaic component efficiency particle set, and single distributed power source active output particle set; Step S4: updating the particle state of the distributed photovoltaic active power particle set; Step S5: updating the weights of the particles in the distributed photovoltaic active power particle set after the particle states are updated in the form of an observation likelihood function; Step S6: Based on the validity of the distributed photovoltaic active power particle set after weight update, determine whether resampling is required; if resampling is required, obtain the final distributed photovoltaic active power particle set through resampling; if resampling is not required, directly obtain the final distributed photovoltaic active power particle set; Step S7: Calculate the active power estimation value based on the final distributed photovoltaic active power particle set; Step S8: Calculate the actual power load of the line based on the active power estimation value and the active output particle set of the single distributed power source.

2. The method for calculating the actual power load of a line based on active power according to claim 1, characterized in that: In step S2, the calculation formula of the probability density distribution is as follows: ； Where, is the probability density distribution; n represents the number of distributed photovoltaic historical dataset results in the previous round; h represents the kernel bandwidth; k represents the index number of the distributed photovoltaic historical dataset results in the previous round; M represents the distributed photovoltaic historical dataset results in the previous round corresponding to the current index number; Represents the kernel function, usually Gaussian kernel or uniform kernel is selected according to the data distribution form; M k Represents the kth result of the distributed photovoltaic historical dataset in the previous round.

3. The method for calculating the actual power load of a line based on active power according to claim 2, characterized in that: In step S4, the particle states in the distributed photovoltaic active power particle set are updated in the following form: ； Where i represents the particle number, , N represents the total number of particles; , represents the active power of the i-th particle at time t and time t-1 respectively; represents process noise, which is calculated by combining the distributed photovoltaic irradiance particle set, the distributed photovoltaic inverter efficiency particle set, the distributed photovoltaic line loss rate particle set, and the distributed photovoltaic module efficiency particle set; Represents the state update function of the particle; It represents the uncertainty of the i-th particle in the distributed photovoltaic active power particle set at time t.

4. The method for calculating the actual power load of a line based on active power according to claim 3, characterized in that: The step S5 specifically includes: Step S51. Calculate the estimated weight of the particle using the following formula: ； Where, represents the normalized weight of the i-th particle at time t-1; represents the measured value of active power at time t; Expressed as observation likelihood function; represents the estimated weight of the i-th particle at time t; Step S52: Calculate the normalized weight of the particle using the following formula: ； Where v represents the index variable, which is used to unify the particle number; represents the estimated weight of the vth particle at time t, represents the normalized weight of the i-th particle at time t.

5. The method for calculating the actual power load of a line based on active power according to claim 4, characterized in that: In step S6, whether resampling is required is determined as follows: ； Where, Represents the number of valid samples; if the above formula is true, it means that the particle set has degenerated and needs to be resampled; Otherwise, no resampling is required.

6. The method for calculating actual power load of a line based on active power according to claim 5, characterized in that: In step S6, resampling is performed as follows: Step S61: Calculate the cumulative weight of the particles, specifically: ； Where, represents the cumulative weight of the i-th particle at time t; j represents the particle index number; represents the normalized weight of the jth particle at time t; Step S62: To determine the particle resampling position, generate a uniformly distributed random number sequence: ； Where, and are all random numbers; for each random number , find the corresponding particle index , as follows: ； According to the particle index , which can achieve resampling.

7. The method for calculating actual power load of a line based on active power according to claim 6, characterized in that: In step S7, the active power estimate is calculated as follows: ； Where, Represents the estimated active power at time t.

8. The method for calculating actual power load of a line based on active power according to claim 7, characterized in that: In step S8, the actual power load of the line is calculated as follows: ； Where, Indicates the active output value of the sth distributed generation connected to this line; Indicates the number of distributed power sources connected to this line; Indicates the actual power load of the line.

9. A system for calculating the actual power load of a line based on active power, characterized in that: It includes a current round data set acquisition unit, a probability density distribution calculation unit, a particle set generation unit, a particle state update unit, a particle weight update unit, a resampling unit, an active power estimation value calculation unit, and a line actual power load calculation unit; The current round data set acquisition unit is used to acquire the current round of distributed photovoltaic data sets, wherein the distributed photovoltaic data sets include distributed photovoltaic active power data, distributed photovoltaic irradiance data, distributed photovoltaic inverter efficiency data, distributed photovoltaic line loss rate data, distributed photovoltaic module efficiency data, and active output data of a single distributed power source; The probability density distribution calculation unit is used to preliminarily calculate the probability density distribution using the kernel density formula based on the previous round of distributed photovoltaic historical data sets; The particle set generation unit is used to implicitly construct a cumulative distribution function in the form of rejection sampling based on the probability density distribution, perform particle randomization screening on the distributed photovoltaic data set, and generate corresponding distributed photovoltaic active power particle sets, distributed photovoltaic irradiance particle sets, distributed photovoltaic inverter efficiency particle sets, distributed photovoltaic line loss rate particle sets, distributed photovoltaic component efficiency particle sets, and active output particle sets of single distributed power sources; Specifically used to perform the following steps: Step S31: Based on the probability density distribution, the cumulative distribution function is implicitly constructed in the form of rejection sampling, specifically: ； Where, represents the envelope distribution function, which can be Gaussian or uniform distribution; c represents the sampling ratio constant; Step S32: Generate a candidate point from the envelope distribution function, For candidate points , calculate its acceptance probability : ； Where, Candidate point The envelope distribution function of Step S33: Generate a random probability distribution, ; If it meets the conditions: , then accept As particles, otherwise, regenerate candidate points until the distributed photovoltaic data sets are initialized, and obtain the distributed photovoltaic active power particle set, distributed photovoltaic irradiance particle set, distributed photovoltaic inverter efficiency particle set, distributed photovoltaic line loss rate particle set, distributed photovoltaic component efficiency particle set, and single distributed power source active output particle set; The particle state updating unit is used to update the particle state of the distributed photovoltaic active power particle set; The particle weight updating unit is used to update the weights of the particles in the distributed photovoltaic active power particle set after the particle states are updated in the form of an observation likelihood function; The resampling unit is used to determine whether resampling is required based on the validity of the distributed photovoltaic active power particle set after weight update; if resampling is required, the final distributed photovoltaic active power particle set is obtained by resampling; if resampling is not required, the final distributed photovoltaic active power particle set is directly obtained; The active power estimated value calculation unit is used to calculate the active power estimated value based on the final distributed photovoltaic active power particle set; The line actual power load calculation unit is used to calculate the line actual power load based on the active power estimation value and the active output particle set of a single distributed power source.

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