High-speed railway electric energy quality evaluation system and method based on combined empowerment

Abstract

The invention relates to the technical field of electric energy quality management, in particular to a high-speed railway electric energy quality evaluation system and method based on combined weighting, the system combines an analytic hierarchy process (AHP) to perform subjective weighting and an entropy weight method to perform objective weighting, and performs consistency check by means of a Kendall coefficient to judge the matching degree of subjective and objective weights. A calculation strategy of the combined weight is formulated according to different conditions; the weights specifically comprise subjective weights based on an analytic hierarchy process, objective weights based on an entropy weight method and combined weights. Through feasibility analysis of the evaluation method, the result shows that the influence of expert experience and objective data can be effectively balanced by adopting a combined empowerment mode, and the method can be suitable for different power systems and operating environments. An example analysis result shows that the method can effectively evaluate the electric energy quality level in the high-speed railway electrification system, and provides quantitative evaluation on the electric energy quality of each monitoring point.

Description

Technical Field

[0001] The invention relates to the field of power quality management technology, specifically to a high-speed railway power quality assessment system and method based on combined weighting. Background Technology

[0002] As the primary form of modern railway transportation, electrified railways place increasingly higher demands on power quality. Power quality issues not only affect the normal operation of railways but also the safety, stability, and operational efficiency of equipment. Therefore, accurate power quality assessment is crucial for ensuring the reliable operation of high-speed railways. Especially in the power systems of high-speed railway lines, power quality problems can trigger a series of chain reactions, such as equipment failures and train delays, thereby impacting transportation efficiency and service quality. Currently, research on power quality assessment focuses primarily on traditional power systems, particularly distribution networks and industrial power systems. However, high-speed railways, as a special type of power load, exhibit more complex power quality issues, and existing assessment methods and technologies often fall short of fully meeting their requirements. Traditional methods typically rely on single evaluation indicators, failing to comprehensively consider multiple aspects of power quality; furthermore, some methods may be overly subjective or inaccurate in the objective weighting process, leading to insufficient reliability and accuracy in the assessment results. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a high-speed railway power quality assessment system and method based on combined weighting. The system combines the Analytic Hierarchy Process (AHP) for subjective weighting, the entropy weight method for objective weighting, and the Kendall coefficient for consistency testing to determine the degree of fit between subjective and objective weights. Furthermore, it develops a calculation strategy for combined weights for different situations.

[0004] To achieve the above objectives, the following technical solution is provided:

[0005] The high-speed railway power quality assessment system based on combined weighting is characterized by combining subjective weighting using the analytic hierarchy process (AHP) and objective weighting using the entropy weight method. It also uses the Kendall coefficient for consistency testing to determine the degree of fit between subjective and objective weights and formulates a calculation strategy for combined weights for different situations. Specifically, it includes subjective weights based on the analytic hierarchy process, objective weights based on the entropy weight method, and combined weights.

[0006] Preferably, in subjective weighting based on the Analytic Hierarchy Process (AHP), AHP can decompose complex decision problems into objective, criterion, and indicator layers, helping decision-makers clearly understand the relationships and hierarchical structure among various factors. In power quality assessment, AHP can help clarify multiple power quality indicators and their interactions, making the assessment process transparent. Through hierarchical analysis, each indicator in power quality assessment can be meticulously decomposed, ensuring that indicators of different dimensions are fully considered. Specifically, this includes:

[0007] (1) Constructing a hierarchical structure model; a three-level hierarchical structure model was constructed. The first level is the target level, which takes power quality assessment as the target and represents the overall level of power quality of the assessment object. The second level is the criterion level, which is the main index category of power quality assessment, including voltage quality, harmonic quality, frequency quality, etc. The third level is the indicator level, which is the specific power quality assessment indicator, including voltage fluctuation, three-phase voltage imbalance, voltage deviation, total harmonic distortion rate, harmonic content, frequency deviation.

[0008] (2) Construct the judgment matrix; invite experts to compare each criterion and indicator at the same level pairwise, and construct the judgment matrix using the 9-point scale method; based on their professional knowledge, experts give a score for the relative importance between each pair of elements to form the judgment matrix:

[0009] (3)

[0010] in, The importance of element i relative to element j is represented by a number from 1 to 9, where 1 indicates that both are equally important, 3 indicates that one is slightly more important, 5 indicates that one is significantly important, 7 indicates that one is extremely important, and 9 indicates that one is absolutely important. If the importance of element i and element j is equal, then... The value equals 1; if element i is more important than element j, then >1; If element j is more important than element i, then <1;

[0011] (3) Calculate the weight vector by judging the matrix ;

[0012] (4)

[0013] In the formula, It is the largest eigenvalue of the pairwise judgment matrix A. These are the corresponding feature vectors, reflecting the relative importance of each factor, which are the weight vectors we are looking for.

[0014] (4) Consistency check; The consistency of the judgment matrix provided by the experts is checked; The consistency ratio (CR) of the judgment matrix can be calculated using the following formula:

[0015] (5)

[0016] in, As a consistency indicator, the calculation formula is:

[0017] (6)

[0018] in, It is the largest eigenvalue of the pairwise judgment matrix A, n is the dimension of the matrix, i.e. the number of comparison criteria or indicators, and RI is the random consistency index, which depends on the dimension of the matrix.

[0019] if If the consistency of the matrix is ​​acceptable, then the consistency of the matrix is ​​acceptable.

[0020] if If so, the judgment matrix needs to be readjusted.

[0021] Preferably, in objective weighting based on the entropy weighting method, for a certain indicator, the entropy value can be used to determine the degree of dispersion of the indicator. The smaller the entropy value, the greater the degree of dispersion of the indicator, and the greater the weight of the indicator in the comprehensive evaluation; specifically including:

[0022] (1) Solving for information entropy For a certain indicator Information entropy is :

[0023] (7)

[0024] (8)

[0025] in, Let be the value of the i-th sample (object) on the j-th indicator, and m be the number of objects being evaluated. It is the weight of each indicator among all evaluation objects; if the information entropy of a certain indicator... The smaller the value, the greater the degree of variation in the indicator value and the greater the amount of information it provides. It can be considered that the indicator plays a greater role in the comprehensive evaluation.

[0026] (2) Calculate objective weights

[0027] (9).

[0028] Preferably, in the combined weights, there are k weight variables. ,…, Each weight has n observed index weight values, meaning each variable is n-dimensional. First, the Kendall correlation test is used to perform a consistency test. If the test passes, it indicates that the subjective and objective weights are synergistic, and then the combined weight is calculated using formula (12). If the test fails, it indicates that they are not synergistic, meaning that the differences between the subjective and objective weights are relatively large, and then the CRITIC method is used to calculate the combined weight. The specific steps are as follows:

[0029] set up for exist The rank (i.e., order) in the text. Let represent the rank of the j-th indicator in the i-th weighting method. It indicates the relative importance ranking of indicators under different weighting methods and is used to test the consistency between subjective and objective weights. Hypothesis testing question:

[0030] K variables are uncorrelated K variables are related;

[0031] If the null hypothesis is true, then the rank of each row should be roughly the same; however, if the alternative hypothesis is true, the rank of each row should differ significantly. A test statistic can be constructed as follows:

[0032] (10)

[0033] (11)

[0034] Kendall's co-correlation coefficient can be expressed as:

[0035] (12)

[0036] Can check zero distribution The test result is accepted. Otherwise, accept ;

[0037] If the test passes, let the total weight be... If the consistency test passes, it means that the weights calculated by each method are not significantly different, and we can use formula (13) to calculate the total weight:

[0038] (13)

[0039] If the test fails, it indicates that the weights calculated by the different methods differ significantly. The CRITIC method should then be used to calculate their respective weights. Thus, the combined weights are obtained. As shown in equations (14)-(16):

[0040] (14)

[0041] (15)

[0042] (16)

[0043] in, This represents the correlation coefficient between weight i and weight j. The standard deviation of weight j is represented by Represents the information content measurement index. This represents the weight allocation coefficients obtained by applying the weights calculated using the CRITIC method to each method.

[0044] The high-speed railway power quality assessment method based on combined weighting is characterized by the following steps:

[0045] Step 1: Obtain power quality data through power quality monitoring equipment;

[0046] Step 2: Obtain the original power quality assessment matrix from the actual power quality measurement data. , ,in, Let be the value of the i-th sample (object) on the j-th index. The matrix contains m measurement points and n power quality indices.

[0047] Step 3: Analyze the original power quality assessment matrix. Standardization is performed to ensure that all indicators are compared under the same dimension, thus providing data preprocessing for the subsequent weight calculation of the analytic hierarchy process and the entropy weight method.

[0048] Step 4: Calculate subjective weights using the Analytic Hierarchy Process (AHP): First, construct a three-tiered hierarchical model with power quality assessment as the target layer, power quality categories as the criterion layer, and power quality indicators as the indicator layer. Then, invite experts to construct a discriminant matrix and use the discriminant matrix to calculate the weight vector. , It consists of the subjective weights of various power quality indicators;

[0049] Step 5: Calculate objective weights using the entropy weight method: First, calculate the information entropy of all power quality indicators. The objective weights of each indicator are calculated using the information entropy of power quality indicators. ;

[0050] Step 6: Combine the obtained subjective and objective weights into a weight matrix. ;

[0051] Step 7: Perform a consistency test using the Kendall correlation test. If the test passes, calculate the average weight to obtain the combined weight. If the test fails, use the CRITIC method to determine the combined weights. ;

[0052] Step 8: Calculate the score for each measuring point:

[0053] (17)

[0054] Step 9: Calculate the scoring level range by dividing the power quality index into levels according to relevant national standards, following the steps above.

[0055] Step 10: Compare the score calculated from the measured data with the standard score to obtain a comprehensive evaluation result of the power quality at different measuring points.

[0056] The beneficial effects of this invention are as follows:

[0057] This invention proposes a high-speed railway power quality assessment system and method based on combined weighting, integrating the Analytic Hierarchy Process (AHP), the CRITIC method, and the entropy method. By comprehensively considering subjective and objective weights, it achieves accurate power quality assessment. In the method's construction, the differences and correlations among various indicators in power quality assessment were considered, resulting in a complete indicator system. Subjective weights are calculated using the AHP method, objective weights are calculated using the entropy weight method, and the weight allocation is optimized using the CRITIC method, further improving the accuracy and objectivity of the assessment results. Feasibility analysis of this assessment method shows that the combined weighting approach can effectively balance the influence of expert experience and objective data, and is applicable to different power systems and operating environments. Case study results demonstrate that this method can effectively assess the power quality level in high-speed railway electrification systems, providing a quantitative evaluation of the power quality at various monitoring points, and offering important references for subsequent power quality optimization and improvement. Attached Figure Description

[0058] Figure 1 This is a diagram of the power quality assessment index system in this invention;

[0059] Figure 2 This is a diagram of the hierarchical structure model in this invention;

[0060] Figure 3 This is a flowchart of the power quality comprehensive assessment based on combined weighting in this invention.

[0061] Figure 4 This is a main wiring diagram of a joint-construction facility in an embodiment of the present invention;

[0062] Figure 5This is a graph showing the measured data of measurement point 1 in an embodiment of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0064] The following detailed description of the construction process of the gene editing system of the present invention, with reference to the accompanying drawings, and further illustrates the usage method and experimental results of the gene editing system of the present invention through the examples.

[0065] Example 1

[0066] With the rapid development of high-speed railways, especially under the background of frequent operation and long-term, high-load operation of high-speed trains, the impact of power quality issues on system stability, equipment safety, and operational efficiency has become increasingly prominent. High-speed railway electrification systems typically rely on overhead contact lines for power supply, and experience large load fluctuations, high power demands, and high transport density, placing far higher demands on power quality than traditional power systems. First, voltage fluctuations, three-phase voltage imbalance, voltage deviation, and voltage flicker can lead to power loss in the train traction system, and even prevent normal starting or shutdown. Second, frequency deviation can affect the train dispatching and control system, causing deviations in train schedules. Finally, harmonic pollution can cause overheating and damage to electrical equipment, and in severe cases, may lead to system shutdown. Therefore, this invention establishes a power quality assessment index system from three dimensions: voltage quality, harmonic quality, and frequency quality. Voltage quality considers voltage fluctuations, three-phase voltage imbalance, and voltage deviation in the traction system; harmonic quality includes total harmonic distortion (THD) and harmonic content; and frequency quality considers the frequency deviation of the traction system. The power quality assessment index system is as follows: Figure 1 As shown.

[0067] According to relevant national standards, power quality indicators are divided into five levels, and the values ​​of individual indicators in each power quality level are shown in Table 1.

[0068] Table 1. Classification of Power Quality Indicators

[0069] index Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Voltage fluctuation / % 0.5 1 1.5 2 2 Three-phase voltage imbalance / % 0.5 1 1.5 2 2 Voltage deviation / % 1.2 3 4.5 7 7 Total harmonic distortion of voltage / % 1 2 3 5 5 Odd-order voltage harmonics / % 0.4 0.8 1.2 1.6 1.6 Even-order voltage harmonics / % 0.2 0.4 0.6 0.8 0.8 Frequency deviation / Hz 0.05 0.1 0.15 0.2 0.2

[0070] Example 2: Calculation of Weights for Various Power Quality Indicators

[0071] The original power quality assessment matrix is ​​obtained from the power quality assessment index system. :

[0072] (1)

[0073] in, Let be the value of the i-th sample (object) on the j-th indicator, m be the number of objects being evaluated, and n be the number of evaluation indicators;

[0074] To ensure all indicators are compared on the same scale, the original power quality assessment matrix is... The data for each indicator in this paper are standardized. Standardization of the original data typically uses range standardization (min-max standardization), also known as 0-1 normalization. Since the seven indicators selected in this invention are all inverse indicators, smaller indicator values ​​are better. Data of various indicators Reverse standardization is performed to obtain .

[0075] (2)

[0076] 1. Subjective weights based on the analytic hierarchy process

[0077] The Analytic Hierarchy Process (AHP) can decompose complex decision problems into goal, criterion, and indicator layers, helping decision-makers clearly understand the relationships and hierarchical structure between various factors. In power quality assessment, AHP can help clarify multiple power quality indicators and their interactions, making the assessment process transparent. Through the analytic hierarchy process, each indicator in power quality assessment can be decomposed in detail, ensuring that indicators of different dimensions are fully considered.

[0078] (1) Constructing a hierarchical structure model

[0079] This invention constructs a three-level hierarchical model. The first level is the target layer, which aims at power quality assessment and represents the overall level of power quality of the assessed object. The second level is the criterion layer, which contains the main categories of power quality assessment indicators, including voltage quality, harmonic quality, and frequency quality. The third level is the indicator layer, which contains specific power quality assessment indicators, including voltage fluctuation, three-phase voltage imbalance, voltage deviation, total harmonic distortion, harmonic content, and frequency deviation. The hierarchical model is as follows: Figure 2 As shown.

[0080] (2) Construct the judgment matrix

[0081] Experts were invited to conduct pairwise comparisons of various criteria and indicators at the same level, constructing a judgment matrix using a 9-point scale. Based on their expertise, experts assigned a score to the relative importance of each pair of elements. The resulting judgment matrix is ​​as follows:

[0082] (3)

[0083] Among them, 1 indicates that both are equally important, 3 indicates that one is slightly more important, 5 indicates that one is obviously important, 7 indicates that one is extremely important, and 9 indicates that one is absolutely important. Let be the importance of element i relative to element j, represented by numbers 1 to 9. If element i and element j have equal importance, then... The value equals 1; if element i is more important than element j, then >1; If element j is more important than element i, then <1.

[0084] (3) Calculate the weight vector by judging the matrix

[0085] (4)

[0086] In the formula, It is the largest eigenvalue of the pairwise judgment matrix A. These are the corresponding feature vectors, reflecting the relative importance of each factor, which are the weight vectors we are looking for.

[0087] (4) Consistency check

[0088] Perform a consistency check on the judgment matrix provided by the experts; the consistency ratio (CR) of the judgment matrix can be calculated using the following formula:

[0089] (5)

[0090] in, As a consistency indicator, the calculation formula is:

[0091] (6)

[0092] in, To determine the largest eigenvalue of a matrix, n is the dimension of the matrix (i.e., the number of comparison criteria or indicators), and RI is the random consistency index, which depends on the dimension of the matrix.

[0093] if If so, then the consistency of the matrix can be judged as acceptable.

[0094] if If so, the judgment matrix needs to be readjusted.

[0095] 2. Objective weights based on the entropy weight method

[0096] Entropy weighting is also an algorithm used for assigning weights. Unlike subjective evaluation methods such as the analytic hierarchy process (AHP), entropy weighting assigns weights based on the inherent attributes of the data. Entropy is a thermodynamic unit; in mathematics, information entropy represents the expected amount of information contained in an event. By definition, for a given indicator, the entropy value can be used to determine its degree of dispersion. The smaller the entropy value, the greater the dispersion of the indicator, and the greater its influence (weight) on the overall evaluation. ] .

[0097] (1) Solving for information entropy

[0098] For a certain indicator Information entropy is :

[0099] (7)

[0100] (8)

[0101] in, It is the weight of each indicator among all evaluation objects; if the information entropy of a certain indicator... The smaller the value, the greater the degree of variation in the indicator value and the greater the amount of information it provides. It can be considered that the indicator plays a greater role in the comprehensive evaluation.

[0102] (2) Calculate objective weights

[0103] (9)

[0104] 3. Combined weights

[0105] Suppose there are k weight variables ,…, Each weight has n observed index weight values, meaning each variable is n-dimensional. First, a consistency test is performed using the Kendall correlation test. If the test passes, it indicates that the subjective and objective weights are synergistic, and the combined weight is calculated using formula (12). If the test fails, it indicates that there is no synergy, meaning the differences between the subjective and objective weights are relatively large, and the combined weight is calculated using the CRITIC method. The specific steps are as follows:

[0106] set up for exist The rank (i.e., order) in the hypothesis testing problem:

[0107] K variables are uncorrelated K variables are related;

[0108] If the null hypothesis is true, then the rank of each row should be roughly the same; however, if the alternative hypothesis is true, the rank of each row should differ significantly. A test statistic can be constructed as follows:

[0109] (10)

[0110] (11)

[0111] Kendall's co-correlation coefficient can be expressed as:

[0112] (12)

[0113] Can check zero distribution The test result is accepted. Otherwise, accept

[0114] If the test passes, let the total weight be... If the consistency test passes, it means that the weights calculated by each method are not significantly different, and we can use formula (13) to calculate the total weight:

[0115] (13)

[0116] If the test fails, it indicates that the weights calculated by the different methods differ significantly. The CRITIC method should then be used to calculate their respective weights. Thus, the combined weights are obtained. As shown in equations (14)-(16):

[0117] (14)

[0118] (15)

[0119] (16)

[0120] in This represents the correlation coefficient between weight i and weight j. The standard deviation of weight j is used as a measure of information content. express.

[0121] 4. Power quality comprehensive assessment model based on combined weighting

[0122] In the preceding text, this invention elaborated on the weighting method, including using the analytic hierarchy process (AHP) to determine subjective weights, the entropy weight method to calculate objective weights, and the Kendall coefficient for consistency testing to determine the degree of fit between subjective and objective weights. A combined weight calculation strategy was also developed for different situations. Next, the specific process of high-speed railway power quality assessment will be detailed to construct a complete framework for the high-speed railway power quality assessment system, further clarifying each step from power quality data collection to the final assessment result and its inherent logical connections. The assessment flowchart is as follows: Figure 3 As shown, it includes the following steps:

[0123] Step 1: Obtain power quality data through power quality monitoring equipment.

[0124] Step 2: Obtain the original power quality assessment matrix from the actual power quality measurement data. , The matrix contains m measurement points and n power quality indicators.

[0125] Step 3: Analyze the original power quality assessment matrix. Standardization is performed to ensure that all indicators are compared under the same dimension, thus providing data preprocessing for the subsequent weight calculations of the analytic hierarchy process and the entropy weight method.

[0126] Step 4: Calculate subjective weights using the Analytic Hierarchy Process (AHP): First, construct a three-tiered hierarchical model with power quality assessment as the target layer, power quality categories as the criterion layer, and power quality indicators as the indicator layer. Then, invite experts to construct a discriminant matrix, and use the discriminant matrix to calculate the weight vector. , It consists of subjective weights for each power quality indicator.

[0127] Step 5: Calculate the objective weights using the entropy weight method: First, calculate the information entropy of all power quality indicators. The objective weights of each indicator are calculated using the information entropy of power quality indicators. .

[0128] Step 6: Combine the obtained subjective and objective weights into a weight matrix.

[0129] Step 7: Perform a consistency test using the Kendall correlation test. If the test passes, calculate the average weight to obtain the combined weight. If the test fails, use the CRITIC method to determine the combined weights. .

[0130] Step 8: Calculate the score for each measuring point.

[0131] (17)

[0132] Step 9: Calculate the scoring range by dividing the power quality index into grades according to relevant national standards, following the steps above.

[0133] Step 10: Compare the score calculated from the measured data with the standard score to obtain a comprehensive evaluation result of the power quality at different measuring points.

[0134] Example 3 Case Study Analysis

[0135] The measured data for this invention comes from five measuring points at a joint construction site of a high-speed railway. The locations and voltage levels of the measuring points are as follows: Figure 4 As shown in the figure. The measurement time was 1 day, with a measurement interval of 1 minute, resulting in 1440 data points. Statistical calculations were performed on the actual measurement data from the five measuring points to obtain the measured data for seven power quality indicators: voltage fluctuation, three-phase voltage imbalance, voltage deviation, total harmonic distortion rate, harmonic content, and frequency deviation. The 95% probability value of the measured data was used as the evaluation data, as shown in Table 2. The measured data shows that the 35th and 38th voltage harmonics have relatively higher contents than other voltage harmonics at the five measuring points. Therefore, odd voltage harmonics are represented by the 35th voltage harmonic, and even voltage harmonics are represented by the 38th voltage harmonic. Taking measuring point one as an example, the actual measured data graph is shown below. Figure 5 As shown.

[0136] Table 2. Power quality index assessment data for each measuring point in the joint-construction facility.

[0137] measuring point Voltage fluctuation / % Three-phase voltage imbalance / % Voltage deviation / % Total harmonic distortion of voltage / % Odd-order voltage harmonic content / % Even-order voltage harmonic content / % Frequency deviation Measurement point 1 0.075 4.54 0.056 1.46 0.22 0.08 0.001 Measurement point 2 0.057 5 0.053 4.6 1.28 0.35 0.001 Measurement point three 0.01 1 0.01 8.57 1.28 0.27 0.002 Measurement point four 0.05 1 0.01 7.1 4.18 0.94 0.002 Measurement point five 0.05 1 0.01 4.5 2 0.8 0.002

[0138] The specific evaluation process is as follows:

[0139] (1) Power quality data were obtained through power quality monitoring equipment, as shown in Table 2;

[0140] (2) The power quality assessment matrix is ​​composed of power quality assessment data from five measurement points. :

[0141]

[0142] (3) Standardization processing according to formula (2) Later obtained

[0143]

[0144] (4) Construct the discriminant matrix according to equation (3).

[0145]

[0146] (5) Calculate the subjective and objective weight vectors according to equations (4)-(9). ,

[0147] , ;

[0148] (6) A weight matrix composed of subjective and objective weights. ;

[0149] (7) After performing a consistency test using the Kendall correlation test, the test failed. The CRITIC method was then used to calculate the combined weights. According to equations (14)-(16), we get ;

[0150] (8) The scores for each measuring point obtained from equation (17) are shown in Table 3:

[0151] Table 3 Scoring for each measuring point

[0152] measuring point Measurement point 1 Measurement point 2 Measurement point three Measurement point four Measurement point five score 0.4998 0.4272 0.3176 0.1738 0.2645

[0153] (9) The scoring standard level range is obtained according to the power quality index values ​​divided by relevant national standards. The scoring standard level ranges are [0, 0.2031], [0.2031, 0.3744], [0.3744, 0.5497], and [0.5497, 0.7172]. The scores of the measuring points in the four ranges represent unqualified, qualified, good, and excellent.

[0154] (10) The actual measured scores were compared with the standard scores, and the final power quality assessment results of each measuring point in the joint construction site are shown in Table 4:

[0155] Table 4 Evaluation results of each measuring point

[0156] measuring point Measurement point 1 Measurement point 2 Measurement point three Measurement point four Measurement point five Evaluation results good good qualified Unqualified qualified

[0157] As shown in Table 4, the relative quality of the power at the five measuring points in the joint facility is ranked as follows: Measuring Point 1 > Measuring Point 2 > Measuring Point 3 > Measuring Point 5 > Measuring Point 4. Measuring Point 1 has the best power quality, while Measuring Point 4 has the worst power quality.

[0158] Based on the power quality evaluation data from various monitoring points in the joint-construction facility, the frequency quality of monitoring points 1 and 2 is significantly better than that of other monitoring points, hence their scores are significantly higher. The three-phase voltage imbalance, total harmonic distortion (THD), voltage fluctuation, and voltage deviation are similar between monitoring points 1 and 2. However, because monitoring point 1 performs relatively better in terms of voltage harmonic content, its power quality is slightly better than that of monitoring point 2. Comparing the power quality evaluation data from monitoring points 3, 4, and 5, it is clear that the power quality of monitoring point 4 is relatively poor. These evaluation results are consistent with the results obtained using combined weighted scoring, indicating that the method used in this paper can effectively assess the relative strengths and weaknesses of power quality among different monitoring points, providing a basis and direction for subsequent power quality management of this joint-construction facility.

[0159] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A high-speed railway power quality assessment system based on combined weighting, characterized in that, The system combines subjective weighting using the analytic hierarchy process (AHP) and objective weighting using the entropy weighting method. It also uses the Kendall coefficient for consistency testing to determine the degree of fit between subjective and objective weights and develops a combined weighting calculation strategy for different situations. Specifically, it includes subjective weights based on the AHP, objective weights based on the entropy weighting method, and combined weights.

2. The high-speed railway power quality assessment system based on combined weighting as described in claim 1, characterized in that, In subjective weighting based on the Analytic Hierarchy Process (AHP), AHP can decompose complex decision-making problems into objective, criterion, and indicator layers, helping decision-makers clearly understand the relationships and hierarchical structure among various factors. In power quality assessment, AHP can help clarify multiple power quality indicators and their interactions, making the assessment process transparent. Through AHP, the various indicators in power quality assessment can be meticulously decomposed, ensuring that indicators from different dimensions are fully considered. Specifically, this includes: (1) Constructing a hierarchical structure model; a three-level hierarchical structure model was constructed. The first level is the target level, which takes power quality assessment as the target and represents the overall level of power quality of the assessment object. The second level is the criterion level, which is the main index category of power quality assessment, including voltage quality, harmonic quality and frequency quality. The third level is the index level, which is the specific power quality assessment index, including voltage fluctuation, three-phase voltage imbalance, voltage deviation, total harmonic distortion rate, harmonic content and frequency deviation. (2) Construct the judgment matrix; invite experts to compare each criterion and indicator at the same level pairwise, and construct the judgment matrix using the 9-point scale method; based on their professional knowledge, experts give a score for the relative importance between each pair of elements to form the judgment matrix: （3）； in, The importance of element i relative to element j is represented by a number from 1 to 9, where 1 indicates that both are equally important, 3 indicates that one is slightly more important, 5 indicates that one is significantly important, 7 indicates that one is extremely important, and 9 indicates that one is absolutely important. If the importance of element i and element j is equal, then... The value equals 1; if element i is more important than element j, then >1; If element j is more important than element i, then <1; (3) Calculate the weight vector by judging the matrix ; （4）； In the formula, It is the largest eigenvalue of the pairwise judgment matrix A. These are the corresponding feature vectors, reflecting the relative importance of each factor, which are the weight vectors we are looking for. (4) Consistency check; The consistency of the judgment matrix provided by the experts is checked; The consistency ratio (CR) of the judgment matrix can be calculated using the following formula: (5)； in, As a consistency indicator, the calculation formula is: (6)； in, It is the largest eigenvalue of the pairwise judgment matrix A, n is the dimension of the matrix, i.e. the number of comparison criteria or indicators, and RI is the random consistency index, which depends on the dimension of the matrix. if If the consistency of the matrix is ​​acceptable, then the consistency of the matrix is ​​acceptable. if If so, the judgment matrix needs to be readjusted.

3. The high-speed railway power quality assessment system based on combined weighting according to claim 2, characterized in that, In objective weighting based on entropy weighting, for a given indicator, the entropy value can be used to determine the degree of dispersion of that indicator. The smaller the entropy value, the greater the dispersion of the indicator, and the greater the weight of that indicator in the overall evaluation. Specifically, this includes: (1) Solving for information entropy For a certain indicator Information entropy is : (7)； (8)； in, Let be the value of the i-th sample (object) on the j-th indicator, and m be the number of objects being evaluated. It is the weight of each indicator among all evaluation objects; if the information entropy of a certain indicator... The smaller the value, the greater the degree of variation in the indicator value and the greater the amount of information it provides. It can be considered that the indicator plays a greater role in the comprehensive evaluation. (2) Calculate objective weights , (9)。 4. The high-speed railway power quality assessment system based on combined weighting according to claim 3, characterized in that, In combined weights, assume there are k weight variables. ,…, Each weight has n observed index weight values, meaning each variable is n-dimensional. First, the Kendall correlation test is used to perform a consistency test. If the test passes, it indicates that the subjective and objective weights are synergistic, and then the combined weight is calculated using formula (12). If the test fails, it indicates that they are not synergistic, meaning that the differences between the subjective and objective weights are relatively large, and then the CRITIC method is used to calculate the combined weight. The specific steps are as follows: set up for exist rank in The rank of the j-th indicator in the i-th weighting method represents the relative importance ranking of the indicators under different weighting methods, and is used to test the consistency between subjective and objective weights. Hypothesis testing problem: K variables are uncorrelated; K variables are related; If the null hypothesis is true, then the rank of each row should be roughly the same; however, if the alternative hypothesis is true, the rank of each row should differ significantly. A test statistic can be constructed as follows: （10）； (11)； Kendall's co-correlation coefficient can be expressed as: (12)； Can check zero distribution The test result is accepted. Otherwise, accept ; If the test passes, let the total weight be... If the consistency test passes, it means that the weights calculated by each method are not significantly different, and we can use formula (13) to calculate the total weight: (13)； If the test fails, it indicates that the weights calculated by the different methods differ significantly. The CRITIC method should then be used to calculate their respective weights. Thus, the combined weights are obtained. As shown in equations (14)-(16): (14)； (15)； (16)； in, This represents the correlation coefficient between weight i and weight j. The standard deviation of weight j is represented by Represents the information content measurement index. This represents the weight allocation coefficients obtained by applying the weights calculated using the CRITIC method to each method.

5. A method for assessing power quality in high-speed railways based on combined weighting, characterized in that, Includes the following steps: Step 1: Obtain power quality data through power quality monitoring equipment; Step 2: Obtain the original power quality assessment matrix from the actual power quality measurement data. , ,in, Let be the value of the i-th sample on the j-th index. The matrix contains m measurement points and n power quality indicators. Step 3: Analyze the original power quality assessment matrix. Standardization is performed to ensure that all indicators are compared under the same dimension, thus providing data preprocessing for the subsequent weight calculation of the analytic hierarchy process and the entropy weight method. Step 4: Calculate subjective weights using the Analytic Hierarchy Process (AHP): First, construct a three-tiered hierarchical model with power quality assessment as the target layer, power quality categories as the criterion layer, and power quality indicators as the indicator layer. Then, invite experts to construct a discriminant matrix and use the discriminant matrix to calculate the weight vector. , It consists of the subjective weights of various power quality indicators; Step 5: Calculate objective weights using the entropy weight method: First, calculate the information entropy of all power quality indicators. The objective weights of each indicator are calculated using the information entropy of power quality indicators. ; Step 6: Combine the obtained subjective and objective weights into a weight matrix. ; Step 7: Perform a consistency test using the Kendall correlation test. If the test passes, calculate the average weight to obtain the combined weight. If the test fails, use the CRITIC method to determine the combined weights. ; Step 8: Calculate the score for each measuring point: (17)； Step 9: Calculate the scoring level range by dividing the power quality index into levels according to relevant national standards, following the steps above. Step 10: Compare the score calculated from the measured data with the standard score to obtain a comprehensive evaluation result of the power quality at different measuring points.

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