A method and system for analyzing distribution network loss sensitivity based on power quality

Abstract

The present invention discloses a method and system for analyzing the loss sensitivity of a distribution network based on power quality. The method first obtains the voltage and current information on a branch line, combines the parameters of the transformer and the line on the branch line, and then uses the Sobol index method to perform sensitivity analysis on the obtained function, and solves the first-order sensitivity coefficient of each variable and the overall global sensitivity coefficient, thereby providing a method for quantifying the influence of each power quality index on the loss of the distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of analysis of factors affecting distribution network loss, and in particular relates to a method and system for analyzing distribution network loss sensitivity based on power quality. Background Art

[0002] In recent years, with the widespread access of new energy and distributed generation to the distribution network, and the widespread application of load-side power electronic equipment, the power quality problem of the distribution network is particularly prominent due to its nonlinear, impact and unbalanced characteristics, making the analysis of related issues about network loss more complicated. Therefore, considering the impact of power quality on distribution network loss and analyzing the sensitivity between various power quality sensitivity indicators and network loss is of great significance to improving power quality management measures and improving the accuracy of network loss quantitative analysis.

[0003] In order to evaluate the impact of power quality disturbance on distribution network losses, the equivalent method, power flow calculation and other methods are currently used to quantify the additional losses of the distribution network under power quality disturbance, so as to evaluate the impact of this factor on the network loss. However, it is impossible to intuitively reflect the importance of multiple power quality disturbance factors to the network loss. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a distribution network loss sensitivity analysis method and system based on power quality in response to the above-mentioned deficiencies in the prior art, so as to solve the problem that the existing analysis method only quantifies the loss additional value caused by each power quality disturbance, cannot directly quantify the influence of each disturbance factor on the loss, and does not consider the interaction effect between multiple factors.

[0005] The present invention adopts the following technical solutions:

[0006] A method for analyzing the loss sensitivity of a distribution network based on power quality comprises the following steps:

[0007] S1. Calculate the power quality sensitivity index and the corresponding additional loss value of the distribution network branch according to the basic parameters of the distribution network;

[0008] S2. Determine a sampling sample N according to the power quality sensitivity index obtained in step S1 and the corresponding additional loss value, and pre-process the sampling sample N as an original sample;

[0009] S3, according to the range of the power quality sensitivity index, the original sample pre-processed in step S2 is sampled to obtain the sample X (a) and sample X (b) ;

[0010] S4, the sample X obtained according to step S3 (a) and sample X(b) Calculate the first-order sensitivity coefficient estimate and the global sensitivity coefficient estimate of the input random variables;

[0011] S5. Analyze the factors that are most sensitive to the loss based on the first-order sensitivity coefficient estimate and the global sensitivity coefficient estimate of the input random variable obtained in step S4, and manage the actual distribution network based on the factors that are most sensitive to the loss.

[0012] Specifically, in step S1, the power quality sensitivity indicators include harmonic distortion rate, three-phase imbalance and voltage deviation.

[0013] Specifically, in step S1, the variance D of the additional loss value is:

[0014]

[0015] Among them, D i is the variance of additional loss caused by factor i, D ij is the variance of the additional loss caused by factor ij, D 1Kk is the variance of the additional loss caused by the combined effect of factors 1…k, where k is the number of factors in the space.

[0016] Specifically, in step S2, erroneous values ​​in the sample N are removed through preprocessing.

[0017] Furthermore, the sampling sample N is:

[0018]

[0019] in, are the samples obtained by sampling, For x (a) Contains the sampling results of the i-th factor, is the sampling result including the removal of the i-th factor, Indicates that in x (b) Contains the sampling results of the i-th factor, Contains the sampling results after removing the i-th factor.

[0020] Specifically, in step S3, the range of the power quality sensitivity index is (0, 1).

[0021] Specifically, in step S4, the Sobol index method is used to perform sensitivity analysis to solve the first-order sensitivity coefficient estimation value and the global sensitivity coefficient estimation value of the three variables of harmonic distortion rate, three-phase imbalance and voltage deviation.

[0022] Furthermore, the global sensitivity coefficient estimate and the first-order sensitivity coefficient estimate for:

[0023]

[0024] in, represents the additional loss variance of the two groups of samples, represents the additional loss variance of the i-th factor of the two groups of samples, Represents the additional loss variance of the non-i-th factor of the two groups of samples.

[0025] Specifically, in step S5, the actual distribution network is managed as follows:

[0026] If the harmonic factor is the most sensitive, it can be controlled through active filters or passive filters; if the three-phase imbalance factor is the most sensitive, it can be controlled through on-load capacity regulation and voltage regulation distribution transformer; if the voltage deviation factor is the most sensitive, it can be controlled through reactive power compensation.

[0027] Another technical solution of the present invention is a distribution network loss sensitivity analysis system based on power quality, comprising:

[0028] The calculation module calculates the power quality sensitivity index and the corresponding additional loss value corresponding to the distribution network branch according to the basic parameters of the distribution network;

[0029] The preprocessing module determines the sampling sample N according to the power quality sensitivity index and the corresponding additional loss value obtained by the calculation module, and preprocesses the sampling sample N as the original sample;

[0030] The module is used to sample the original samples preprocessed by the preprocessing module according to the range of the power quality sensitivity index to obtain the sample X (a) and sample X (b) ;

[0031] Coefficient module, based on the sample X obtained by using the module (a) and sample X (b) Calculate the first-order sensitivity coefficient estimate and the global sensitivity coefficient estimate of the input random variables;

[0032] The analysis module analyzes the factors most sensitive to the loss based on the first-order sensitivity coefficient estimate and the global sensitivity coefficient estimate of the input random variable obtained by the coefficient module, and manages the actual distribution network based on the factors most sensitive to the loss.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects:

[0034] The present invention discloses a distribution network loss sensitivity analysis method based on power quality. The first-order sensitivity is used to represent the influence of a single factor on the additional loss. The global sensitivity is used to represent the influence of the interaction of three factors on the additional loss. This method can reflect which power quality factor is more obvious and show how much interaction exists between the various power quality factors. The method can intuitively quantify the influence of specific power quality factors on the additional loss of the distribution network. This method provides a reference for reducing the loss of the distribution network in the future. The first-order sensitivity and the global sensitivity are estimated based on two groups of samples obtained by actual sampling. This method simplifies the complex integral calculation and is also in line with reality to represent the real situation.

[0035] Furthermore, there are many factors that affect power quality, including frequency deviation, voltage deviation, voltage fluctuation and flicker, three-phase imbalance, instantaneous or transient overvoltage, waveform distortion (harmonics), voltage sag, interruption, swell and power supply continuity. Since the impact of power quality on losses in the distribution network is relatively obvious, the ones that are worth studying mainly include harmonic distortion rate, three-phase imbalance and voltage deviation.

[0036] Furthermore, using the variance of the additional loss value to represent the calculated sensitivity is more objective and accurate because the variance has the waveform of all elements.

[0037] Furthermore, the purpose of extraction is to remove erroneous values ​​that seriously deviate from the actual data in order to make the sensitivity calculation results more accurate.

[0038] Furthermore, we use the probability estimation method to calculate. Probability estimation requires sampling. The larger the sample size N, the more accurate the result. However, if the sample size is too large, the workload will be doubled.

[0039] Furthermore, since the actual distribution network is very complex and the voltage and power quality conditions in different areas are not the same, it is necessary to determine the index range of power quality sensitivity based on the actual working conditions, which indicates the actual working conditions and is a prerequisite for sensitivity analysis.

[0040] Furthermore, the first-order sensitivity can represent the influence of a single factor on the additional loss, and the global sensitivity represents the influence of the interaction of the three factors on the additional loss. It can not only reflect which power quality factor is more obvious, but also show how much interaction there is between the various factors of power quality.

[0041] Furthermore, the probability estimation method is used to calculate the first-order sensitivity coefficient and the global sensitivity coefficient, and the estimated value of the global sensitivity coefficient is obtained. and the first-order sensitivity coefficient estimate It can not only solve the problem of no specific functional relationship, but also obtain relatively accurate calculation results.

[0042] Furthermore, the calculation of the first-order sensitivity coefficient and the global sensitivity coefficient requires the corresponding additional loss value. These two coefficients can directly analyze which power quality factor is more obvious and can also show how much interaction there is between the various factors of power quality.

[0043] In summary, the present invention quantifies the sensitivity of specific power quality factors to the additional losses of the distribution network in the form of data, providing a reference for the management of the distribution network; and quantifies the interactive impact of various power quality factors on the additional losses of the distribution network in the form of data, making the coupling effect between the factors more obvious.

[0044] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a specific implementation flow chart of the present invention using the Sobol index method to perform sensitivity analysis on the obtained function;

[0046] Figure 2 It is a first-order sensitivity analysis diagram of the distribution transformer based on the Sobol method of the present invention;

[0047] Figure 3 It is a global sensitivity analysis diagram of the distribution transformer based on the Sobol method of the present invention;

[0048] Figure 4 This is a sensitivity analysis diagram of power quality to transformer loss in the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0051] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0052] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects are in an "or" relationship.

[0053] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0054] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0055] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0056] In view of the actual situation of the distribution network, a global sensitivity analysis method based on the Sobol index method is adopted to deeply analyze the impact of power quality problems on distribution network losses and quantify the sensitivity between power quality disturbances and distribution network losses. This is of great significance for solving power quality problems in a targeted manner in the future, improving the level of line loss management and reducing the operating costs of enterprise power supply and distribution.

[0057] The present invention provides a distribution network loss sensitivity analysis method based on power quality. The voltage and current information on the branch is first obtained, combined with the parameters of the transformer and the line on the branch, and then the Sobol index method is used to perform sensitivity analysis on the obtained function to solve the first-order sensitivity coefficient of each variable and the overall global sensitivity coefficient, thereby providing a method for quantifying the influence of each power sensitivity quality index on the distribution network loss.

[0058] See also Figure 1 The present invention provides a distribution network loss sensitivity analysis method based on power quality, comprising the following steps:

[0059] S1. Calculate the power quality sensitivity index and the corresponding additional loss value of the distribution network branch according to the basic parameters of the distribution network;

[0060] Obtain the basic parameters of the distribution network, refine the distribution transformers and lines of the low-voltage distribution network, select typical extension steps in the distribution network, and calculate the power quality sensitivity index and the corresponding additional loss value of the branch through the voltage and current information on the distribution network branch and the parameters of the transformer and line on the corresponding branch;

[0061] The power quality sensitivity indicators include harmonic distortion rate, three-phase unbalance and voltage deviation. The harmonic distortion rate, three-phase unbalance and voltage deviation are measured using a power quality analyzer (Fluke 1770 series), and the additional loss value is measured using a power meter.

[0062] S2. Determine a sampling sample N according to the power quality sensitivity index obtained in step S1, and pre-process the sampling sample N as an original sample;

[0063] Determine the input variables as the harmonic distortion rate, three-phase imbalance and voltage deviation calculated in step S1, and correct, eliminate and supplement the abnormal data in the distribution network data; the power quality sensitivity index and additional loss value are obtained through actual measurement. Due to the instrument or the surrounding environment, a small part of the values ​​are obviously wrong, and the degree of deviation from other values ​​is particularly large. At this time, they need to be eliminated; remove the erroneous values ​​through step S2. Generally speaking, the amount of measured data is large, so it only needs to be directly eliminated; if the measured data is limited, the data can be smoothed.

[0064] S3, sampling the original sample after preprocessing in step S2 to obtain the sample X (a) and sample X (b) ;

[0065] Feature selection: According to the actual working conditions, determine the input range of each input variable and obtain two sets of sampling samples X (a) and X (b); Since the actual distribution network is very complex and the voltage and power quality conditions in different areas are not the same, it is necessary to determine the range of each power quality sensitivity index based on the actual working conditions.

[0066] S4, the sample X obtained according to step S3 (a) and sample X (b) Calculate the first-order sensitivity coefficient and global sensitivity coefficient of the input random variables;

[0067] The output variables are determined to be the first-order sensitivity coefficient and the global sensitivity coefficient STI, see equations (13) and (14).

[0068] The Sobol index method is used to perform sensitivity analysis, and the first-order sensitivity coefficients and global sensitivity coefficients of the three variables of harmonic distortion rate, three-phase unbalance and voltage deviation are solved, specifically:

[0069] S401, determining the dimension of the factor, where the factor refers to the power quality sensitivity coefficient, and the dimension is 3-dimensional;

[0070] Let x = [x 1 ,K,x k ] is a k-dimensional independent input variable, and the power quality sensitivity index is located in the spatial domain Ω k for:

[0071] Ω k ={x|0≤x i ≤1; i=1,K,k} (1)

[0072] S402, according to the dimensions of the factors obtained in step S401, the dependent variable f(x) is obtained by accumulating the factors individually or interactively, where the dependent variable is the additional loss value caused by each power quality;

[0073] Assuming that the additional loss f(x) caused by power quality is an integrable function, the additional loss f(x) is decomposed into the sum of several sub-items:

[0074]

[0075] Among them, f 0 is a constant. For independent input variables, the integral of each sub-item over any factor it contains must be zero, that is:

[0076]

[0077] S403, according to f(x) determined in step S402, calculate the variance value of the additional loss f(x) corresponding to each factor caused by the shape of each factor alone or in interaction, where D i,L,jrepresents the variance of the additional loss under the interaction of i…j, and D represents the total variance of the additional loss;

[0078] When f 1Λk (x 1 ,K,x k ) satisfies the above formula, the decomposition of formula (3) is unique, and all sub-functions in the above formula are orthogonal, that is, if

[0079] 1≤i 1 <K<i k ≤k,1≤j 1 <K<j k ≤k and (i 1 ,K,i k )≠(j 1 ,K,j k ) (4)

[0080] So:

[0081]

[0082] All decomposition terms are obtained in the form of functional integration:

[0083]

[0084] In formula (6) and (7), after decomposition, we get f 0 , f i , f ij , f j , represents the additional loss caused by i, formula (6) is used to calculate the terms of formula (2), Ω k -i and Ω k -ij represents x i The input variable set, except x i ,x j The set of input variables.

[0085] Then the variance of the function f(x) is:

[0086]

[0087] Among them, D i,L,j represents the variance of the additional loss under the influence of i…j, f 0 2 It represents the square value of the loss without power quality factors, which is a constant. Represents the square value of the additional loss under the influence of i…j;

[0088] S404, dividing the additional loss variance value determined by each factor calculated in step S403 by the total variance obtained in step S403 to obtain the first-order sensitivity coefficient and the global sensitivity coefficient. Here, the first-order sensitivity is expressed as the variance value of a single factor to the total variance value D, and the global sensitivity coefficient is expressed as the sum of all the ratios related to the factor;

[0089] The sensitivity coefficient is defined as:

[0090]

[0091] Among them, S i For x i The first-order sensitivity coefficient, indicating x i The degree of influence on f(x); S ij is the second-order sensitivity coefficient, indicating that the two parameters x i and x j The cross-effect on f(x) is that if S ij exists and is not zero, indicating that there is coupling in the influence on f(x), x i 、x j The effect on f(x) cannot be considered as x i 、x j The sum of individual effects; S Ti represents the total impact of factor i on the input variance, expressed as follows:

[0092]

[0093] S ～i represents all S excluding the i-th factor 1Λk The sum of , and the sensitivity satisfies:

[0094]

[0095] S405, according to step S404, the definitions of the two sensitivity coefficients are obtained, and the probability method is used to estimate to obtain the first-order sensitivity coefficient estimation value and the global sensitivity coefficient estimation value;

[0096] In practical applications, it is impossible to obtain a specific analytical function f(x), but the input variables and output results of the system are relatively easy to obtain. In this case, Monte Carlo is used to calculate the integral of the above formula. For two independent groups of samples a and b with a sampling size of N:

[0097]

[0098] Formula (6) and the estimated value are:

[0099]

[0100] Among them, xis The input variable x i The sth sampling value; x (～i)s Divide x i The sth sampling value of other input variables except .

[0101] Get x i The estimated global sensitivity coefficient of and the first-order sensitivity coefficient estimate

[0102]

[0103] S5. According to the first-order sensitivity coefficient and the global sensitivity coefficient of the input random variable obtained in step S4, the corresponding additional loss value obtained in step S1 is analyzed to obtain the degree of loss influence. If the harmonic factor is the most sensitive, it is controlled by an active filter or a passive filter; if the three-phase unbalance factor is the most sensitive, it is controlled by on-load capacity regulation and voltage regulation distribution transformer; if the voltage deviation factor is the most sensitive, it is controlled by reactive power compensation.

[0104] Compare the first-order sensitivity coefficients and global sensitivity coefficients of the three input variables of harmonic distortion rate, three-phase unbalance and voltage deviation calculated in step S4, analyze the influence of the three variables on the additional power loss, and calculate and analyze the power quality factors with the greatest impact among the three variables of harmonic distortion rate, three-phase unbalance and voltage deviation, and focus on their treatment.

[0105] In view of the sensitivity problem of additional loss under composite power quality disturbance, the additional loss model has three parameters (used to characterize three-phase unbalance, harmonics, and voltage deviation). For example, in the above formula, S 1 It represents the first-order sensitivity of the three-phase unbalance parameter, reflecting the impact of the three-phase unbalance changes on the additional loss.

[0106] Accordingly, S 1,2 is a second-order global sensitivity coefficient, which reflects the impact of three-phase imbalance and harmonic changes on additional losses. By analogy, the total sensitivity S 1,2,3 The value is the sum of the sensitivity values ​​of each order of the parameter (three-phase imbalance), which not only reflects the effect of the three-phase imbalance when it changes alone, but also includes the interaction between the three-phase imbalance and harmonics and voltage deviation. Therefore, when the first-order sensitivity and total sensitivity values ​​of the three-phase imbalance differ greatly, it can be considered that there is an obvious interaction between the three-phase imbalance and the harmonics and voltage deviation.

[0107] In still another embodiment of the present invention, a distribution network loss sensitivity analysis system based on power quality is provided, which can be used to implement the above-mentioned distribution network loss sensitivity analysis method based on power quality. Specifically, the distribution network loss sensitivity analysis system based on power quality includes module, module, module, module and module.

[0108] Among them, the calculation module calculates the power quality sensitivity index and the corresponding additional loss value corresponding to the distribution network branch according to the basic parameters of the distribution network;

[0109] The preprocessing module determines the sampling sample N according to the power quality sensitivity index and the corresponding additional loss value obtained by the calculation module, and preprocesses the sampling sample N as the original sample;

[0110] The module is used to sample the original samples preprocessed by the preprocessing module according to the range of the power quality sensitivity index to obtain the sample X (a) and sample X (b) ;

[0111] Coefficient module, based on the sample X obtained by using the module (a) and sample X (b) Calculate the first-order sensitivity coefficient estimate and the global sensitivity coefficient estimate of the input random variables;

[0112] The analysis module analyzes the factors most sensitive to the loss based on the first-order sensitivity coefficient estimate and the global sensitivity coefficient estimate of the input random variable obtained by the coefficient module, and manages the actual distribution network based on the factors most sensitive to the loss.

[0113] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0114] The calculation of the first-order sensitivity coefficient and the global sensitivity coefficient requires additional loss values. After obtaining the first-order sensitivity, there is no need to compare the additional loss values ​​in the analysis, because the first-order sensitivity itself contains the characteristics of additional loss values.

[0115] The analysis mainly focuses on these two aspects.

[0116] Firstly, we use data to quantify which power quality factors are more sensitive to the additional losses in the distribution network, so as to provide a reference for the management of the distribution network in the future.

[0117] Secondly, the interactive impact of various power quality factors on the additional losses of the distribution network is quantified in the form of data, indicating which factors have more obvious coupling effects.

[0118] See also Figure 2 and Figure 3 , which respectively represent the convergence of three-phase unbalance, harmonics and voltage deviation to the distribution transformer FSC (first-order sensitivity) and TSC (global sensitivity).

[0119] The loss of distribution transformer is most affected by harmonics, followed by three-phase imbalance, and the voltage deviation has the least impact. From the above table, we can see that the sum of FSC (first-order sensitivity) of three-phase imbalance, harmonics and voltage deviation is equal to 0.9881, which is very close to 1. At the same time, for each indicator TSC (global sensitivity), its TSC is slightly greater than FSC, indicating that the interaction of various factors of power quality has little effect on distribution transformers, and the probability distribution of distribution transformers is mainly determined by the input variables alone.

[0120] See also Figure 4 The loss of S11 distribution transformer is most affected by harmonics, followed by three-phase imbalance, and the voltage deviation has the least impact; the sum of FSC (first-order sensitivity) of three-phase imbalance, harmonics and voltage deviation is equal to 0.9881, which is very close to 1. At the same time, for each indicator TSC (global sensitivity), its TSC is slightly larger than FSC, indicating that the interaction of various factors of power quality has little effect on distribution transformer, and the probability distribution of distribution transformer is mainly determined by the input variables alone. Therefore, the evaluation of transformer internal parameters below is based on FSC as the main research indicator.

[0121] The global sensitivity analysis method based on the Sobol index method can quantitatively describe the influence of the model input on the output, and analyze the joint influence of multiple parameters on the model output and the joint effect between parameters in the entire parameter space, which can be applied to the analysis and research of complex systems. In recent years, the global sensitivity analysis method based on the Sobol index method has been widely used in the sensitivity analysis of basin hydrological model parameters, which provides an opportunity for the method to be applied to the power system network loss analysis, thus providing a crucial method for quantifying the influence of various power quality sensitivity indicators on the distribution network loss.

[0122] In summary, the present invention provides a distribution network loss sensitivity analysis method and system based on power quality, which solves the problem of quantifying the loss added value caused by each power quality disturbance, and quantifies the influence of each disturbance factor on the loss, while considering the interaction effect between multiple factors, thereby providing a reference for the loss reduction problem of the distribution network.

[0123] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0124] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0127] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A distribution network loss sensitivity analysis method based on power quality, characterized in that: The following steps are involved: S1. Calculate the power quality sensitivity index and the corresponding additional loss value of the distribution network branch according to the basic parameters of the distribution network. The power quality sensitivity index includes harmonic distortion rate, three-phase imbalance and voltage deviation, and the variance of the additional loss value. for: in, is the variance of additional loss caused by factor i, is the variance of the additional loss caused by factor ij, is the variance of additional loss caused by the combined effect of factors 1…k, There are k factors in the space; S2. Determine the sampling sample according to the power quality sensitivity index and the corresponding additional loss value obtained in step S1. , and the sample Perform preprocessing as the original sample; S3. According to the range of the power quality sensitivity index, data sampling is performed on the original sample pre-processed in step S2 to obtain a sampled sample. and sampling samples ; S4. Sampling samples obtained according to step S3 and sampling samples Calculate the first-order sensitivity coefficient estimate and global sensitivity coefficient estimate of the input random variables, use the Sobol index method to perform sensitivity analysis, and solve the first-order sensitivity coefficient estimate and global sensitivity coefficient estimate of the three variables of harmonic distortion rate, three-phase unbalance and voltage deviation. and the first-order sensitivity coefficient estimate for: in, represents the additional loss variance of the two groups of samples, represents the additional loss variance of the i-th factor of the two groups of samples, Represents the additional loss variance of the non-i-th factor of the two groups of samples; S5. Analyze the factors that are most sensitive to the loss based on the first-order sensitivity coefficient estimate and the global sensitivity coefficient estimate of the input random variable obtained in step S4, and manage the actual distribution network based on the factors that are most sensitive to the loss.

2. The power quality-based distribution network loss sensitivity analysis method according to claim 1 is characterized in that: In step S2, the sample is removed by pre-processing Error value in .

3. The method for analyzing the loss sensitivity of distribution network based on power quality according to claim 2 is characterized in that: Sampling samples for: in, , are the samples obtained by sampling, For Contains the sampling results of the i-th factor, is the sampling result including the removal of the i-th factor, Indicated in Contains the sampling results of the i-th factor, Contains the sampling results after removing the i-th factor.

4. The method for analyzing the loss sensitivity of distribution network based on power quality according to claim 1, characterized in that: In step S3, the range of the power quality sensitivity index is (0, 1).

5. The method for analyzing the loss sensitivity of distribution network based on power quality according to claim 1, characterized in that: In step S5, the actual distribution network is managed as follows: If the harmonic factor is the most sensitive, it can be controlled through active filters or passive filters; if the three-phase imbalance factor is the most sensitive, it can be controlled through on-load capacity regulation and voltage regulation distribution transformer; if the voltage deviation factor is the most sensitive, it can be controlled through reactive power compensation.

6. A distribution network loss sensitivity analysis system based on power quality, characterized in that: include: The calculation module calculates the power quality sensitivity index and the corresponding additional loss value of the distribution network branch according to the basic parameters of the distribution network. The power quality sensitivity index includes harmonic distortion rate, three-phase imbalance and voltage deviation, and the variance of the additional loss value. for: in, is the variance of additional loss caused by factor i, is the variance of the additional loss caused by factor ij, is the variance of additional loss caused by the combined effect of factors 1…k, There are k factors in the space; The preprocessing module determines the sampling sample according to the power quality sensitivity index and the corresponding additional loss value obtained by the calculation module , and the sample Perform preprocessing as the original sample; The module is used to sample the original samples preprocessed by the preprocessing module according to the range of the power quality sensitivity index to obtain the sampled samples. and sampling samples ; Coefficient module, based on the sample samples obtained by using the module and sampling samples Calculate the first-order sensitivity coefficient estimate and global sensitivity coefficient estimate of the input random variables, use the Sobol index method to perform sensitivity analysis, and solve the first-order sensitivity coefficient estimate and global sensitivity coefficient estimate of the three variables of harmonic distortion rate, three-phase unbalance and voltage deviation. and the first-order sensitivity coefficient estimate for: in, represents the additional loss variance of the two groups of samples, represents the additional loss variance of the i-th factor of the two groups of samples, Represents the additional loss variance of the non-i-th factor of the two groups of samples; The analysis module analyzes the factors most sensitive to the loss based on the first-order sensitivity coefficient estimate and the global sensitivity coefficient estimate of the input random variable obtained by the coefficient module, and manages the actual distribution network based on the factors most sensitive to the loss.

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