Power quality index dynamic weight method and device considering index scene characteristics

Abstract

This invention discloses a dynamic weighting method and apparatus for power quality indicators that takes into account the characteristics of indicator scenarios. The method includes: determining an indicator system; the indicator system includes categories under the evaluation target and indicators under each category; inviting experts to score the indicators and categories using a 1-9 scale to obtain indicator scores and category scores; based on the indicator scores and category scores, considering the basic probability allocation by experts, obtaining pairwise comparison weights for each indicator and pairwise comparison weights for each category, and integrating all weights to obtain an unweighted supermatrix and a weighted matrix respectively; obtaining a weighted supermatrix based on the unweighted supermatrix and the weighted matrix; obtaining a constant weight matrix containing constant weight vectors for each indicator based on the weighted supermatrix; constructing a weighting function for the time series data of the indicators based on the constant weight matrix; the weighting function includes a constraint function reflecting the degree of indicator degradation; and applying weighting to the constant weight matrix based on the weighting function.

Description

Technical Field

[0001] This invention relates to the field of power quality index evaluation, and in particular to a dynamic weighting method and apparatus for power quality indexes that takes into account the characteristics of index scenarios. Background Technology

[0002] With the large-scale connection of nonlinear, impulsive, and asymmetrical loads to the distribution network, the power quality problems of the distribution network are gradually aggravated. This not only affects the safe and stable operation of the distribution network, but also shortens the normal service life of internal electrical equipment, causing high economic losses to the distribution network and power users.

[0003] Currently, scholars both domestically and internationally have conducted extensive research on comprehensive power quality evaluation methods. Some scholars have used the projection pursuit method to transform multiple indicators into a single projected indicator problem, establishing a power quality assessment model based on projection pursuit and pecking order graph methods. However, the evaluation process is subject to uncertainty. Other scholars have introduced grey relational analysis to improve the TOPSIS method, solving the ranking ambiguity problem caused by equal relative proximity in comprehensive power quality evaluation. However, the model does not quantify power quality levels, cannot classify the power quality level of monitoring points, and lacks detailed power quality grading assessment results. Still other scholars have used coarse hierarchical analysis to aggregate the opinions of multiple experts and obtain the overall power quality assessment level through extension analysis. However, this method does not fully consider the state changes of scenario indicators and different governance needs, failing to provide real-time targeted decision-making for power quality governance. In summary, existing assessment methods mostly adopt fixed weights and offline assessment modes, which are difficult to meet the needs of real-time dynamic power quality monitoring. Furthermore, they ignore the differences in power quality characteristics and actual governance needs in different scenario demonstration areas, leading to a disconnect between assessment results and engineering applications, and failing to provide accurate and timely decision support for multi-entity collaborative governance of power sources, grids, and loads.

[0004] Therefore, a new technical solution is urgently needed to address the technical problem of how to dynamically change the weights of power quality assessment indicators based on the scenario characteristics and correlations among them. Summary of the Invention

[0005] This invention provides a method and apparatus for dynamic weighting of power quality indicators that takes into account the characteristics of indicator scenarios, in order to solve the technical problem of how to dynamically weight indicators based on the scenario characteristics and correlations between power quality assessment indicators.

[0006] To achieve the above objectives, the present invention provides a dynamic weighting method for power quality indicators that takes into account the characteristics of indicator scenarios, comprising:

[0007] Determine the indicator system; the indicator system includes categories under the evaluation objectives and indicators under each category; invite experts to score the indicators and categories using the 1-9 scale to obtain indicator scores and category scores;

[0008] Based on the indicator scores and category scores, the basic probability allocation by experts is used to obtain the pairwise comparison weights of each indicator and the pairwise comparison weights of each category. All weights are integrated to obtain the unweighted hypermatrix and the weighted matrix respectively. The weighted hypermatrix is ​​obtained from the unweighted hypermatrix and the weighted matrix. The constant weight matrix containing the constant weight vectors of each indicator is obtained from the weighted hypermatrix.

[0009] Construct a variable weight function for the time series data of the indicators based on the constant weight matrix; the variable weight function includes a constraint function that reflects the degree of deterioration of the indicators; and apply the variable weight function to the constant weight matrix.

[0010] Preferably, the pairwise comparison weights of each indicator and each category, obtained by considering the expert's basic probability allocation based on indicator scores and category scores, include:

[0011] The fuzziness of expert ratings is obtained by combining indicator scores or category scores with the Gamma function; the basic probability assignment of each expert is obtained based on the fuzziness; the degree of divergence of the basic probability assignment of each expert is quantified based on the Jousselme distance function to obtain the credibility of the basic probability assignment of each expert; the basic probability assignments of each expert are fused based on the credibility to obtain the fused basic probability assignment; the weights of each indicator in pairwise comparisons or the weights of each category in pairwise comparisons are obtained based on the fused basic probability assignment.

[0012] Preferably, the fuzziness of expert scores obtained by combining indicator scores or category scores with the Gamma function includes:

[0013] For both indicator scores and category scores:

[0014] ;

[0015] in, Indicates ambiguity; Represents fractions; For the Gamma function, ; f is the shape parameter of the Gamma function, reflecting the degree of ambiguity in expert opinions; x is the independent variable of the Gamma function; The highest evaluation scale is 9 in the 1-9 scale method; This is a scale parameter that reflects the confidence level of the expert's judgment.

[0016] Preferably, the basic probability allocation for each expert based on ambiguity includes:

[0017] Based on scores The possible values ​​of and the basic probability assignments include:

[0018] ;

[0019] in, Indicates the first Experts on the topic Basic support level; It is a subset of the overall identification framework;

[0020] Overall Recognition Framework include:

[0021] ;

[0022] Among them, the event This indicates that experts believe the indicators or categories Comparison indicators or categories Important; event This indicates that experts believe the indicators or categories Comparison indicators or categories Important; event This indicates that experts believe the indicators or categories and indicators or categories Equally important.

[0023] Preferably, the confidence level of each expert's basic probability assignment is obtained by quantifying the divergence of the basic probability assignments based on the Jousselme distance function, including:

[0024] The Jousselme distance function includes:

[0025] ;

[0026] ;

[0027] ;

[0028] ;

[0029] in, and The evidence body is the basic probability allocation of the experts; For the first The expert and the Jousselme distance of basic support for the proposition from the experts; Indicating evidence and The inner product; This indicates the number of events in the overall identification framework, which is 3; and Each is a piece of evidence and Jiao Yuan, and These are the corresponding indices; Denote the base of a set;

[0030] evidence and The similarity measure is :

[0031] ;

[0032] Then the evidence body support and credibility :

[0033] ;

[0034] ;

[0035] in, This represents the total number of experts.

[0036] Preferably, the basic probability allocations of each expert are merged based on their credibility to obtain a merged basic probability allocation; the weights for pairwise comparisons of each indicator or the weights for pairwise comparisons of each category are obtained based on the merged basic probability allocation, including:

[0037] Fusion basic probability allocation include:

[0038] ;

[0039] in, Indicates the first One expert; Indicates the first The credibility of the experts;

[0040] Based on basic probability assignment This yields the weights for pairwise comparisons of each indicator or the weights for pairwise comparisons of each category:

[0041] ;

[0042] in, To be based on indicators or categories Indicators or categories in pairwise evaluation events for evaluation basis The weight, The total number of indicators or categories in a single pairwise evaluation event; It is the natural base.

[0043] Preferably, integrating all weights yields the following unweighted supermatrix and weighted matrix:

[0044] By integrating the weights of all pairwise comparisons of each indicator, an unweighted supermatrix is ​​obtained. :

[0045] ;

[0046] Among them, in the unweighted supermatrix In the matrix, each element is a submatrix, and the first element is the first submatrix. I Line number J Column elements Corresponding to the I The category for the first J The influence relationship of each category, including the first I Category and J The weights for pairwise comparisons of indicators within a category are assigned as follows: if there is no correlation between two indicators, i.e. no comparison is needed, the corresponding position is assigned a value of 0. Indicates the total number of categories;

[0047] The weights of all categories compared pairwise are combined to obtain a weighted matrix. :

[0048] ;

[0049] in, Represents the weighted matrix No. Line number Column elements;

[0050] Preferably, the weighted supermatrix is ​​obtained from the unweighted supermatrix and the weighted supermatrix; the constant weight matrix containing the constant weight vectors of each index is obtained from the weighted supermatrix, including:

[0051] Weighting the unweighted hypermatrix using the weighted matrix yields the weighted hypermatrix. :

[0052] ;

[0053] Weighted hypermatrix Continue multiplying by itself until the values ​​in each row tend to be the same, to obtain a constant weight matrix containing the constant weight vectors of each index. :

[0054] .

[0055] Preferably, a variable weighting function is constructed based on the constant weight matrix for the time series data of the indicators; the variable weighting function includes a constraint function reflecting the degree of indicator deterioration; the variable weighting of the constant weight matrix based on the variable weighting function includes:

[0056] The variable weighting functions include:

[0057] ;

[0058] in, For time series data of indicators; For indicator time series data State-dependent weighting function; The penalty coefficient of the weighting function is used to adjust the penalty intensity of the weights in each time period and the dynamic response of the optimized weighting result as the index deteriorates. constant weight matrix Elements in; For constraint functions;

[0059] constraint functions include:

[0060] ;

[0061] in, and These represent the upper limit of the maximum level limit and the lower limit of the minimum level limit for the indicator, respectively.

[0062] Using variable weight function Hadamard product with constant weight matrix Perform state transformation:

[0063] ;

[0064] in, For indicator time series data The result of state transformation; As an indicator In indicator time series data The result of state transformation.

[0065] The present invention also provides a dynamic weighting device for power quality indicators that takes into account the characteristics of indicator scenarios, for use in the method of the present invention, the device comprising a first module, a second module, a third module and a fourth module;

[0066] The first module is used to determine the indicator system; the indicator system includes categories under the evaluation objectives and indicators under each category; experts are invited to score the indicators and categories using the 1-9 scale to obtain indicator scores and category scores.

[0067] The second module is used to obtain the pairwise comparison weights of each indicator and the pairwise comparison weights of each category based on the indicator score and category score, and to integrate all weights to obtain the unweighted supermatrix and the weighted matrix respectively.

[0068] The third module is used to obtain the weighted supermatrix from the unweighted supermatrix and the weighted supermatrix; and to obtain the constant weight matrix containing the constant weight vectors of each index from the weighted supermatrix.

[0069] The fourth module is used to construct a variable weight function for the time series data of the indicators based on the constant weight matrix; the variable weight function includes a constraint function that reflects the degree of indicator deterioration; and the constant weight matrix is ​​weighted according to the variable weight function.

[0070] The present invention has the following beneficial effects:

[0071] The present invention provides a dynamic weighting method for power quality indicators that considers the characteristics of different scenarios. This method comprehensively assigns constant weights to power quality indicators for the scenario being evaluated. Furthermore, based on the scenario characteristics and correlations between power quality evaluation indicators, it constructs indicator state constraint functions and a dynamic weighting model to dynamically adjust the comprehensive constant weights of the indicators. This significantly enhances the real-time targeted governance capability of microgrid power quality. The method universally guarantees the dynamic characteristics of the time-series weighting of indicators, enhancing the real-time targeted governance capability of distribution network management equipment and demonstrating good scalability.

[0072] The dynamic weighting device for power quality indicators that takes into account the characteristics of the indicator scenarios of the present invention, when used in the method of the present invention, has the same beneficial effects as the method of the present invention.

[0073] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0074] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0075] Figure 1 This is a flowchart illustrating a preferred embodiment of the present invention.

[0076] Figure 2 This is a schematic diagram illustrating the correlation between indicators in a preferred embodiment of the present invention.

[0077] Figure 3 This is a topological schematic diagram of a preferred embodiment of the present invention.

[0078] Figure 4 This is a schematic diagram illustrating the subjective weighting results of various methods in a preferred embodiment of the present invention.

[0079] Figure 5 This is a schematic diagram of standardized data from a single-factor perturbation sampling sequence according to a preferred embodiment of the present invention.

[0080] Figure 6 This is a schematic diagram of the index weighting results under a single main factor perturbation according to a preferred embodiment of the present invention. Detailed Implementation

[0081] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0082] See Figure 1 In a preferred embodiment of the present invention, a dynamic weighting method for power quality indicators that takes into account the characteristics of indicator scenarios is provided, comprising:

[0083] S1. Determine the indicator system; the indicator system includes categories under the evaluation objectives and indicators under each category; invite experts to score the indicators and categories using the 1-9 scale to obtain indicator scores and category scores.

[0084] In a preferred embodiment of the present invention, the evaluation target is used for a comprehensive assessment of the overall power quality of the regional distribution network; the categories under the evaluation target include three dimensions: voltage index, frequency index, and waveform quality; the voltage index category includes three indicators: three-phase voltage imbalance T1, voltage fluctuation T2, and voltage deviation T3; the frequency index category includes one indicator: frequency deviation T4; and the waveform quality category includes two indicators: total harmonic distortion rate T5 and interharmonic content rate T6.

[0085] In a preferred embodiment of the present invention, there is a preset correlation between the indicators; see [link to previous embodiment]. Figure 2 In the comprehensive power quality evaluation index system of this invention, the specific indicators include:

[0086] There are correlations between three-phase voltage imbalance and voltage deviation and total harmonic distortion (THD); there is a correlation between voltage fluctuation and voltage deviation; there are correlations between voltage deviation and three-phase voltage imbalance, voltage fluctuation and frequency deviation; there is a correlation between frequency deviation and voltage deviation; there are correlations between THD and three-phase voltage imbalance and interharmonic content; there is a correlation between interharmonic content and THD.

[0087] In a preferred embodiment of the present invention, when experts are invited to score the indicators using the 1-9 scale, the comparison indicators and the indicators being compared are first determined based on the correlation between the indicators. The comparison indicators are used as the basis for expert decision-making. Experts are then invited to score the relative importance of the indicators being compared using the 1-9 scale. The 1-9 scale and its meaning are shown in Table 1.

[0088] Table 1. Scales 1-9 and their meanings

[0089] ;

[0090] S2. Based on the indicator scores and category scores, the basic probability allocation of experts is used to obtain the pairwise comparison weights of each indicator and the pairwise comparison weights of each category. All weights are then integrated to obtain the unweighted supermatrix and the weighted matrix, respectively.

[0091] In a preferred embodiment of the present invention, the pairwise comparison weights of each indicator and the pairwise comparison weights of each category are obtained by considering the basic probability allocation of experts based on the indicator scores and category scores, including:

[0092] The fuzziness of expert ratings is obtained by combining indicator scores or category scores with the Gamma function; the basic probability assignment of each expert is obtained based on the fuzziness; the degree of divergence of the basic probability assignment of each expert is quantified based on the Jousselme distance function to obtain the credibility of the basic probability assignment of each expert; the basic probability assignments of each expert are fused based on the credibility to obtain the fused basic probability assignment; the weights of each indicator in pairwise comparisons or the weights of each category in pairwise comparisons are obtained based on the fused basic probability assignment.

[0093] In a preferred embodiment of the present invention, the fuzziness of the expert score obtained by combining the index score or category score with the Gamma function includes:

[0094] The scores are based on experts' intuitive judgments of the importance of each power quality indicator, and therefore have a degree of ambiguity. This applies to both indicator scores and category scores.

[0095] ;

[0096] in, Indicates ambiguity; Represents fractions; For the Gamma function, ; f The shape parameter of the Gamma function reflects the degree of ambiguity in expert opinions; is the independent variable of the Gamma function; The highest evaluation scale is 9 in the 1-9 scale method; The scale parameter reflects the confidence level of the expert's judgment; in this embodiment, it is set to 1.

[0097] The ambiguity of expert scores obtained based on the Gamma function can overcome the limitations of traditional fixed ambiguity assignment that relies on human experience and lacks objective basis. This effectively quantifies the hesitation of experts when scoring, making the integrated opinions of multiple experts more convincing and providing a precise uncertainty quantification basis for the construction of subsequent expert evidence.

[0098] In a preferred embodiment of the present invention, obtaining the basic probability allocation for each expert based on the ambiguity includes:

[0099] Transforming expert intuition into probability distributions with quantified uncertainty, based on scores. The possible values ​​of and the basic probability assignments include:

[0100] ;

[0101] in, Indicates the first Experts on the topic Basic support level; It is a subset of the overall identification framework;

[0102] Overall Recognition Framework include:

[0103] ;

[0104] Among them, the event This indicates that experts believe the indicators or categories Comparison indicators or categories Important; event This indicates that experts believe the indicators or categories Comparison indicators or categories Important; event This indicates that experts believe the indicators or categories and indicators or categories Equally important.

[0105] Based on the ambiguity of expert ratings, this method directly maps the uncertainty of subjective expert ratings into the probabilistic framework of evidence theory, solving the problem that traditional BPA assignments rely on empirical settings and ensuring that the probability allocation of each event can objectively reflect the effective opinions of experts.

[0106] In a preferred embodiment of the present invention, the confidence level of the basic probability assignments of each expert is obtained by quantifying the divergence of the basic probability assignments based on the Jousselme distance function, including:

[0107] The Jousselme distance function is introduced to calculate the distance between pieces of evidence, quantifying the degree of disagreement among expert opinions. The Jousselme distance function includes:

[0108] ;

[0109] ;

[0110] ;

[0111] ;

[0112] in, and The evidence body is the basic probability allocation of the experts; For the first The expert and the Jousselme distance of basic support for the proposition from the experts; Indicating evidence and The inner product; This indicates the number of events in the overall identification framework, which is 3; and Each is a piece of evidence and Jiao Yuan, and These are the corresponding indices; Denote the base of a set;

[0113] evidence and The similarity measure is :

[0114] ;

[0115] Then the evidence body support and credibility :

[0116] ;

[0117] ;

[0118] in, This represents the total number of experts.

[0119] By employing the Jousselme distance function, which adapts to the power set space structure of evidence theory, we can accurately quantify the degree of disagreement among multi-expert BPA evidence bodies. This overcomes the problem of large quantification biases caused by traditional Euclidean distance and other methods, and can effectively identify abnormal expert opinions with low consistency and high bias.

[0120] In a preferred embodiment of the present invention, the basic probability allocations of each expert are fused according to their credibility to obtain a fused basic probability allocation; the weights for pairwise comparisons of each indicator or the weights for pairwise comparisons of each category are obtained based on the fused basic probability allocation, including:

[0121] Fusion basic probability allocation include:

[0122] ;

[0123] in, Indicates the first One expert; Indicates the first The credibility of the experts;

[0124] Based on basic probability assignment This yields the weights for pairwise comparisons of each indicator or the weights for pairwise comparisons of each category:

[0125] ;

[0126] in, To be based on indicators or categories Indicators or categories in pairwise evaluation events for evaluation basis The weight, The total number of indicators or categories in a single pairwise evaluation event; It is the natural base.

[0127] Evidence fusion based on the credibility of expert evidence solves the paradox of high conflicting evidence caused by traditional DS synthesis rules. By adaptively amplifying the contribution of highly reliable and consistent expert evidence through credibility fusion rules, the impact of low-credibility anomalous evidence is reduced, which greatly improves the robustness of the fused BPA results and can objectively reflect the consistent engineering judgment of multiple experts.

[0128] By directly converting the consensus reached by multiple experts into indicator weights, the traditional ANP method, which relies on the judgment of a single expert and is susceptible to subjective bias, is solved. This improves the scientificity and objectivity of the weighting results and provides a reliable weighting basis for multi-dimensional comprehensive evaluation of power quality.

[0129] In a preferred embodiment of the present invention, integrating all weights to obtain an unweighted supermatrix and a weighted matrix respectively includes:

[0130] By integrating the weights of all pairwise comparisons of each indicator, an unweighted supermatrix is ​​obtained. :

[0131] ;

[0132] Among them, in the unweighted supermatrix In the matrix, each element is a submatrix, and the first element is the first submatrix. I Line number J Column elements Corresponding to the I The category for the first J The influence relationship of each category, including the first I Category and J The weights for pairwise comparisons of indicators within a category are assigned as follows: if there is no correlation between two indicators, i.e. no comparison is needed, the corresponding position is assigned a value of 0. Indicates the total number of categories;

[0133] The weights of all categories compared pairwise are combined to obtain a weighted matrix. :

[0134] ;

[0135] in, Represents the weighted matrix No. Line number The elements of the column.

[0136] Compared with the traditional ANP method, this invention quantifies the subjective ambiguity of expert ratings through the Gamma function and accurately measures the degree of disagreement and credibility of multi-expert evidence based on the Jousselme distance, thereby achieving adaptive weighted fusion of highly conflicting expert opinions. This solves the problems of traditional ANP's inability to quantify subjective uncertainty and the susceptibility of multi-expert equal-weight fusion to interference from abnormal opinions. The calculated index weights are scientific and effective.

[0137] S3. Obtain the weighted supermatrix from the unweighted and weighted supermatrixes; obtain the constant weight matrix containing the constant weight vectors of each index from the weighted supermatrix. S3 specifically includes:

[0138] Weighting the unweighted hypermatrix using the weighted matrix yields the weighted hypermatrix. :

[0139] ;

[0140] Weighted hypermatrix Continue multiplying by itself until the values ​​in each row tend to be the same, to obtain a constant weight matrix containing the constant weight vectors of each index. :

[0141] .

[0142] S4. Construct a weighting function for the time series data of the indicators based on the constant weight matrix; the weighting function includes a constraint function reflecting the degree of indicator deterioration; apply the weighting function to the constant weight matrix. S4 specifically includes:

[0143] Considering the varying power quality requirements across different scenarios, the more critical the indicator in a given scenario, the higher the sensitivity of the indicator weighting result should be to changes in its parameters. Therefore, the indicator state variables need to be linked to the power quality requirements. Based on the constant weight matrix... It can be seen that,

[0144] Subjective weighting is the result of experts comparing the importance of indicators in a given scenario based on differences in power quality requirements. Therefore, in this embodiment, the subjective weighting result is used as the penalty factor, the indicator data as the variable, and the degree of degradation of the indicator data as the constraint function. The variable weighting function is constructed as follows:

[0145] ;

[0146] in, For time series data of indicators; For indicator time series data State-dependent weighting function; The penalty coefficient of the weighting function is used to adjust the penalty intensity of the weights in each time period and the dynamic response of the optimized weighting result as the index deteriorates. constant weight matrix For elements in the evaluation scenario, the more important the indicator is, the higher the responsiveness of its state weight should be to changes in the indicator. For constraint functions;

[0147] constraint functions include:

[0148] ;

[0149] in, and These represent the upper limit of the maximum level limit and the lower limit of the minimum level limit for the indicator, respectively.

[0150] Using variable weight function Hadamard product with constant weight matrix Perform state transformation:

[0151] ;

[0152] in, For indicator time series data The result of state transformation; As an indicator In indicator time series data The result of state transformation.

[0153] As can be seen from the above state-weighted changes, the same indicator is assigned a dynamic weight related to the degree of change of the indicator under different time series. The higher the importance and the degree of deterioration of the indicator, the higher its state weight, the greater its impact on the evaluation results, and the better it can reflect the priority of power quality indicator governance within the time period.

[0154] The present invention provides a dynamic weighting method for power quality indicators that considers the characteristics of different scenarios. This method comprehensively assigns constant weights to power quality indicators for the scenario being evaluated. Furthermore, based on the scenario characteristics and correlations between power quality evaluation indicators, it constructs indicator state constraint functions and a dynamic weighting model to dynamically adjust the comprehensive constant weights of the indicators. This significantly enhances the real-time targeted governance capability of microgrid power quality. The method universally guarantees the dynamic characteristics of the time-series weighting of indicators, enhancing the real-time targeted governance capability of distribution network management equipment and demonstrating good scalability.

[0155] In a preferred embodiment of the present invention, a dynamic weighting device for power quality indicators that takes into account the characteristics of indicator scenarios is also provided for use in the method of the present invention. The device includes a first module, a second module, a third module and a fourth module.

[0156] The first module is used to determine the indicator system; the indicator system includes categories under the evaluation objectives and indicators under each category; experts are invited to score the indicators and categories using the 1-9 scale to obtain indicator scores and category scores.

[0157] The second module is used to obtain the pairwise comparison weights of each indicator and the pairwise comparison weights of each category based on the indicator score and category score, and to integrate all weights to obtain the unweighted supermatrix and the weighted matrix respectively.

[0158] The third module is used to obtain the weighted supermatrix from the unweighted supermatrix and the weighted supermatrix; and to obtain the constant weight matrix containing the constant weight vectors of each index from the weighted supermatrix.

[0159] The fourth module is used to construct a variable weight function for the time series data of the indicators based on the constant weight matrix; the variable weight function includes a constraint function that reflects the degree of indicator deterioration; and the constant weight matrix is ​​weighted according to the variable weight function.

[0160] The dynamic weighting device for power quality indicators that takes into account the characteristics of the indicator scenarios of the present invention, when used in the method of the present invention, has the same beneficial effects as the method of the present invention.

[0161] Verification section:

[0162] To verify the feasibility of the method of this invention, relevant evaluation data were obtained through the power quality monitoring system of a real-world platform. The topology of the example is shown below. Figure 3 As shown, DG1 to DG n This refers to distributed generation, PCC, PCC1 to PCC n All represent grid connection points; the effectiveness and superiority of the method of the present invention are verified through simulation analysis. Table 2 shows the power quality evaluation index levels in the embodiments.

[0163] Table 2. Levels of various power quality assessment indicators

[0164] ;

[0165] In this embodiment, the traditional AHP (Analytic Hierarchy Process) and ANP (Analog-Network Analysis) methods are used to subjectively assign weights to the evaluation scenario, and the results are compared with the weighting results of the method of this invention. The subjective weighting results of each method are as follows: Figure 4As shown, the weight allocation of the method in this invention is roughly the same as that of the traditional ANP method, but it differs significantly from the traditional AHP method. This is because the traditional AHP method does not consider the correlation between indicators. Furthermore, the traditional ANP method has limitations when dealing with multiple expert opinions; a particular expert's strong preference for an indicator can significantly influence the weighting results. This invention utilizes DS evidence theory to optimize the comprehensive decision matrix calculation method in the traditional ANP method, enhancing and weakening expert intuitive opinions to a certain extent. Simulation results show that this invention not only inherits the traditional ANP method's consideration of the correlation between evaluation indicators but also effectively identifies radical and conservative opinions based on expert intuition, thereby assigning more scientific and rigorous subjective weights to the indicators.

[0166] Furthermore, to verify the real-time response capability of the method of this invention to the indicator status, a sampling sequence of measured data of single-cause disturbance of power quality was first extracted. The data was then standardized with reference to the upper limit of the indicator for Level III to facilitate subsequent simulation analysis. Figure 5 As shown in the figure, the overall power quality is relatively stable before sampling point 7 during this sampling period. At this time, the weights of each power quality indicator are determined by the relative importance of the indicator to the scenario and the initial state of the indicator. The relative importance of each indicator is ranked as follows: T1>T3>T5>T2>T6>T4. The dynamic weights of the time-series data indicators are calculated using the method of this invention, and the results of the weighted calculations are as follows: Figure 6 As shown.

[0167] Depend on Figure 5 and Figure 6 Analysis shows that the overall power quality began to fluctuate at 42s, primarily due to T3, whose weight rapidly increased with changes in the indicator's state. At 72s, T3 returned to near its initial state, and the relative rate of change of the indicator data was similar to that of T5; however, its weight change rate was higher than that of T5. This indicates that the higher the relative importance of an indicator, the more sensitive its weight assignment is to data changes. Furthermore, the weight change curve at T4 shows that when indicators with lower relative importance deteriorate rapidly, their weighting also responds quickly.

[0168] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A dynamic weight method of power quality index considering index scene characteristics, characterized in that, include: Determine the indicator system; the indicator system includes categories under the evaluation objectives and indicators under each category; invite experts to score the indicators and categories using the 1-9 scale to obtain indicator scores and category scores; The evaluation objectives are categorized into three dimensions: voltage indicators, frequency indicators, and waveform quality. The voltage indicator category includes three-phase voltage imbalance, voltage fluctuation, and voltage deviation; the frequency indicator category includes frequency deviation; and the waveform quality category includes total harmonic distortion and interharmonic content. Based on the indicator scores and category scores, the basic probability allocation by experts is used to obtain the pairwise comparison weights of each indicator and the pairwise comparison weights of each category. All weights are then integrated to obtain the unweighted hypermatrix and the weighted matrix, respectively. The weighted hypermatrix is ​​then obtained based on the unweighted hypermatrix and the weighted matrix. Based on the weighted hypermatrix, a constant weight matrix containing the constant weight vectors of each index is obtained; Based on indicator scores and category scores, and considering the expert's basic probability allocation, the pairwise comparison weights for each indicator and each category include: The fuzziness of expert ratings is obtained by combining indicator scores or category scores with the Gamma function; the basic probability assignment of each expert is obtained based on the fuzziness; the divergence of the basic probability assignment of each expert is quantified based on the Jousselme distance function to obtain the credibility of the basic probability assignment of each expert; the basic probability assignments of each expert are fused based on the credibility to obtain the fused basic probability assignment; the weights of each indicator in pairwise comparisons or the weights of each category in pairwise comparisons are obtained based on the fused basic probability assignment. Construct a variable weight function for the time series data of the indicator based on the constant weight matrix; the variable weight function includes a constraint function reflecting the degree of indicator degradation; perform variable weighting on the constant weight matrix according to the variable weight function, including: The weighting function includes: ； wherein, is the index time series data; is the index time series data is the state variable function of the index; is the penalty coefficient of the variable function, used to adjust the weight penalty degree of each period and the dynamic response of the optimization variable function with the deterioration degree of the index; is the element in the constant weight matrix ; is the constraint function; is the natural base; The constraint function comprises: ； wherein and are the upper limit of the maximum rating limit and the lower limit of the minimum rating, respectively. Using variable weight functions Hadamard product of constant weight matrices State variable weighting: ； in, For indicator time series data The result of state transformation; As an indicator In indicator time series data The result of state-weighted transformation; N Indicates the total number of categories; w b As an indicator b The constant power.

2. The dynamic weight method of power quality index considering index scene characteristics according to claim 1, characterized in that, The fuzziness of expert scores, derived from combining indicator scores or category scores with the Gamma function, includes: For both indicator scores and category scores: ； in, Indicates ambiguity; Represents fractions; For the Gamma function, ; f is the shape parameter of the Gamma function, reflecting the degree of ambiguity in expert opinions; x is the independent variable of the Gamma function; The highest evaluation scale is 9 in the 1-9 scale method; This is a scale parameter that reflects the confidence level of the expert's judgment.

3. The dynamic weight method of power quality index considering index scene characteristics according to claim 2, characterized in that, The basic probability allocation for each expert is obtained based on the aforementioned ambiguity, including: Depending on the value of the score The basic probability assignment includes: ； wherein, represents the bit expert's basic support for the proposition; is a subset of the population identified framework;​ The overall identification framework comprises: ； Among them, the event This indicates that experts believe the indicators or categories Comparison indicators or categories Important; event This indicates that experts believe the indicators or categories Comparison indicators or categories Important; event This indicates that experts believe the indicators or categories and indicators or categories Equally important.

4. The dynamic weighting method for power quality indicators taking into account the characteristics of indicator scenarios as described in claim 3, characterized in that, Based on the Jousselme distance function, the divergence of the basic probability assignments of each expert is quantified, and the reliability of each expert's basic probability assignments is obtained, including: The Jousselme distance function includes: ； ； ； ； in, and The evidence body is the basic probability allocation of the experts; For the first The expert and the Jousselme distance of basic support for the proposition from the experts; Indicating evidence and The inner product; This indicates the number of events in the overall identification framework, which is 3; and Each is a piece of evidence and Jiao Yuan, and These are the corresponding indices; Denote the base of a set; evidence and The similarity measure is : ； Then the evidence body support and credibility : ； ； in, This represents the total number of experts.

5. The dynamic weighting method for power quality indicators taking into account the characteristics of indicator scenarios as described in claim 4, characterized in that, Based on the aforementioned credibility, the basic probability allocations of each expert are fused to obtain a fused basic probability allocation; based on the fused basic probability allocation, the weights for pairwise comparisons of each indicator or the weights for pairwise comparisons of each category are obtained, including: The fusion basic probability allocation include: ； in, Indicates the first One expert; Indicates the first The credibility of the experts; Based on the aforementioned basic probability allocation This yields the weights for pairwise comparisons of each indicator or the weights for pairwise comparisons of each category: ； in, To be based on indicators or categories Indicators or categories in pairwise evaluation events for evaluation basis The weight, The total number of indicators or categories in a single pairwise evaluation event; It is the natural base.

6. The dynamic weighting method for power quality indicators taking into account the characteristics of indicator scenarios according to claim 5, characterized in that, Each weight is integrated to obtain the unweighted hypermatrix and the weighted matrix, respectively: By integrating the weights of all pairwise comparisons of each indicator, an unweighted supermatrix is ​​obtained. : ； Among them, in the unweighted supermatrix In the matrix, each element is a submatrix, and the first element is the first submatrix. I Line number J Column elements Corresponding to the I The category for the first J The influence relationship of each category, including the first I Category and J The weights for pairwise comparisons of indicators within a category are assigned as follows: if there is no correlation between two indicators, i.e. no comparison is needed, the corresponding position is assigned a value of 0. Indicates the total number of categories; The weights of all categories compared pairwise are combined to obtain a weighted matrix. : ； in, Represents the weighted matrix No. Line number The elements of the column.

7. The dynamic weighting method for power quality indicators taking into account the characteristics of indicator scenarios as described in claim 6, characterized in that, The weighted hypermatrix is ​​obtained from the unweighted hypermatrix and the weighted hypermatrix; The constant weight matrix containing the constant weight vectors of each index, obtained from the weighted hypermatrix, includes: The unweighted hypermatrix is ​​weighted according to the weighting matrix to obtain the weighted hypermatrix. : ； The weighted hypermatrix Continue multiplying by itself until the values ​​in each row tend to be the same, to obtain a constant weight matrix containing the constant weight vectors of each index. : 。 8. A dynamic weighting device for power quality indicators that takes into account the characteristics of indicator scenarios, used in the method described in any one of claims 1 to 7, characterized in that, The device includes a first module, a second module, a third module, and a fourth module; The first module is used to determine the indicator system; the indicator system includes categories under the evaluation objective and indicators under each category; experts are invited to score the indicators and categories using the 1-9 scale to obtain indicator scores and category scores; The second module is used to obtain the pairwise comparison weights of each indicator and the pairwise comparison weights of each category based on the indicator score and category score, and integrate all weights to obtain the unweighted supermatrix and the weighted matrix respectively. The third module is used to obtain a weighted supermatrix based on the unweighted supermatrix and the weighted matrix; and to obtain a constant weight matrix containing the constant weight vectors of each index based on the weighted supermatrix. The fourth module is used to construct a variable weight function for the time series data of the indicators based on the constant weight matrix; the variable weight function includes a constraint function that reflects the degree of indicator deterioration; and the constant weight matrix is ​​weighted according to the variable weight function.

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