A method and system for evaluating the compensation capacity of power quality governance equipment under different power grid scenarios

Abstract

The present invention discloses a method and system for evaluating the compensation capacity of power quality management equipment in different power grid scenarios. The method comprises the following steps: grading key adaptability indicators in the power grid to construct a typical power grid scenario set covering different types of adaptability indicators; establishing a comprehensive evaluation index system for power quality improvement solutions, including technical evaluation indicators, economic evaluation indicators, and adaptability evaluation indicators for power quality improvement solutions; obtaining an evaluation matrix based on the entropy weight analysis-AHP method, and determining indicator weights based on the DEMATEL method to synthesize the final evaluation results. The evaluation method of the present invention is applied to distribution network power quality improvement devices to clarify the response characteristics of different power grid disturbances to the equipment, effectively identify the advantages and disadvantages of the converter equipment, and thus realize a quantitative performance evaluation system for power quality improvement devices.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply quality improvement device evaluation, and in particular to a method and system for evaluating the compensation capability of power quality management equipment in different power grid scenarios. Background Art

[0002] In recent years, a large number of power quality improvement devices have been put into use. Operational experience has demonstrated that these devices, without changing the grid's structure, improve power quality, operational stability, and control flexibility, effectively reducing user energy consumption. This is fully consistent with energy conservation and emission reduction policies and is of great significance to the safe and stable operation of the power grid. However, non-ideal grid operating conditions, such as voltage deviation, frequency deviation, three-phase voltage imbalance, voltage fluctuation, voltage harmonics, and grid impedance variations, can directly impact the compensation performance of power quality improvement devices.

[0003] The research and development of power quality improvement devices, both domestically and internationally, suffers from a common problem, primarily a failure to consider the diverse scenarios in which these devices connect to the power grid. Due to a lack of sufficient experiments, testing methods, and detection technologies, in-depth research on the operational characteristics of power quality improvement devices in different grid scenarios is impossible, hindering the proper evaluation of the quality of distribution network power quality improvement devices. Furthermore, the lack of unified specifications and standards for the manufacture of power quality improvement devices, coupled with a lack of appropriate testing methods and technologies, makes it difficult to distinguish between good and bad devices. This results in a wide range of quality in the current market. Summary of the Invention

[0004] The purpose of the present invention is to propose a compensation capacity evaluation method and system for power quality management equipment under different power grid scenarios, use the entropy weight analysis-AHP-DEMATEL method for quantitative processing, evaluate the level of equipment compensation performance under different scenarios, and thus identify the performance of power quality improvement devices.

[0005] A method for evaluating the compensation capability of power quality management equipment under different power grid scenarios, characterized by comprising the following steps:

[0006] Step 1: Establish a set of typical adaptability scenarios: Based on the national standard GB / T12326-2008, the limits of voltage deviation, frequency deviation, voltage harmonics, and voltage imbalance in key grid adaptability indicators are divided into levels I, II, III, and IV. This is used to construct a set of typical grid scenarios S covering different types of adaptability indicators.

[0007] Step 2: Measurement and quantification of compensation performance indicators under various adaptation scenarios: The scenarios in the typical power grid scenario set S are numbered as S i, determine the performance evaluation indicators for evaluating the distribution network power supply quality improvement device, including technical evaluation indicators V i , Economic evaluation index C i , Adaptability evaluation index E i ,Compensation performance tests are conducted on the target in various scenarios, and three performance indicator factor sets V, C, and E are obtained with the indicators in different scenarios as factors;

[0008] Step 3. Comprehensive performance evaluation of distribution network power supply quality improvement device based on entropy weight analysis-AHP-DEMATEL method: define the index set C and evaluation set E, use entropy weight analysis method to obtain the entropy weight V of each performance index; use hierarchical analysis method to evaluate the technical evaluation index V i , Economic evaluation index C i , Adaptability evaluation index E i Compare the importance of each pair to obtain the judgment matrix H; use the DEMATEL method to process the judgment matrix H to obtain the comprehensive influence matrix T, and then obtain the centrality M of the evaluation index based on the comprehensive influence matrix T. j , combined with the evaluation index center M j And the entropy weight V is used to obtain the comprehensive evaluation weight w, thereby synthesizing the evaluation result f.

[0009] Furthermore, the compensation performance indicators under each adaptation scenario are measured and quantified, and the steps include:

[0010] Calculate and evaluate the technical evaluation indicators V of the performance of distribution network power supply quality improvement devices: voltage deviation index V1, voltage frequency deviation index V2, voltage distortion rate V3, voltage sag V4, voltage flicker V5, and three-phase unbalance V6;

[0011] Calculate and evaluate the economic evaluation index C of the performance of the distribution network power supply quality improvement device: equipment cost C1, annual operation and maintenance cost C2;

[0012] Calculate and evaluate the adaptability evaluation index E of the performance of the distribution network power supply quality improvement device: reactive power compensation benefit E1, harmonic compensation benefit E2, and unbalance compensation benefit E3;

[0013] Quantify the first-level evaluation indicators and obtain the indicator set V, C, and E.

[0014] Furthermore, the voltage deviation index V1 is the ratio of the difference between the effective voltage and the rated voltage to the rated voltage:

[0015]

[0016] The voltage frequency deviation index V2 refers to the difference between two adjacent extreme values on the voltage RMS curve, expressed as a percentage of the system nominal voltage:

[0017]

[0018] Where U max with U min They are adjacent extreme values of the effective value of the grid connection point voltage;

[0019] Voltage distortion rate V3:

[0020]

[0021] Where U n is the effective value of the nth harmonic voltage; U f is the effective value of the fundamental voltage;

[0022] The voltage sag V4 indicator uses the SARFI indicator, which is the frequency of voltage sags within a specific period:

[0023]

[0024] Where N is the number of voltage sags with a residual voltage less than X% within a certain period of time; T1 is the total detection time; T2 is the indicator calculation cycle time;

[0025] Voltage flicker V5:

[0026]

[0027] Where p 0.1 , p1, p3, p 10 , p 50 The P values are respectively when the instantaneous flicker visual sensitivity S(t) exceeds 0.1%, 1%, 3%, 10%, and 50% of the time ratio. k value; S(t) is the instantaneous flicker visual sensitivity, which refers to a series of values of the instantaneous value of flicker intensity changing with time; P k It is the proportion of a certain instantaneous visual sensitivity S(t) value in the entire detection time period.

[0028] Three-phase unbalance V6:

[0029]

[0030]

[0031] Where U A 、U B 、U C are the RMS values of the phase voltages of each phase respectively;

[0032] Among them, equipment cost C1:

[0033] C1=F0+F1+F2

[0034] Where F0 is the purchase price; F1 is the relevant taxes and fees; F2 is the transportation costs, loading and unloading costs, installation costs and professional service fees attributable to the asset before the fixed asset reaches the intended usable condition;

[0035] Annual operation and maintenance cost C2:

[0036] C2=E1+E2+E3+E4

[0037] In the formula, E1 is fuel and power cost; E2 is maintenance cost; E3 is administrative cost; E4 is other expenditure cost;

[0038] Among them, the reactive power compensation benefit E1 is mainly the saved network loss fee:

[0039]

[0040] Where ΔP 网损 is the active power saved before and after reactive power compensation, σ is the proportion of reactive power compensation device operation time, and λ is the real-time electricity price;

[0041] The harmonic compensation benefit E2 is mainly the saved transformer loss and line loss costs:

[0042]

[0043] Where, γ s，2 is the proportion of additional transformer losses caused by harmonics, ΔP tm is the load loss of the distribution transformer, k l，2 is the line loss rate reduced by harmonic compensation, ΔP l is line loss;

[0044] The unbalanced compensation benefit E3 is mainly the saved distribution transformer loss and line loss costs:

[0045]

[0046] Where, γ s,3 k is the proportion of additional transformer loss caused by imbalance, l,3 To reduce line loss rate.

[0047] Furthermore, the quantitative first-level evaluation indicators are obtained to obtain the indicator set V, C, and E, which are specifically:

[0048] 1) Determine the technical evaluation index V

[0049] For indicators V1 to V6, use the hierarchical analysis method to obtain the weight vector ω:

[0050] ω=[ω1,ω2,ω3,ω4,ω5,ω6]

[0051] The evaluation index V is:

[0052] V=ω1V1+ω2V2+ω3V3+ω4V4+ω5V5+ω6V6

[0053] 2) Determine the economic evaluation index C and adaptability evaluation index E

[0054] Determine the compensation effect evaluation indexes C and E based on the type of power supply quality improvement device:

[0055] C=λ1C1+λ2C2

[0056] E=λ1E1+λ2E2+λ3E3

[0057] Where, λ1, λ2, and λ3 are mainly set according to the type of power quality improvement device.

[0058] Furthermore, the comprehensive performance evaluation of the distribution network power supply quality improvement device based on the entropy weight analysis-AHP-DEMATEL method includes the following steps:

[0059] 1) Define the indicator set C and evaluation set E:

[0060] C={C1,C2,…,C i ,…C n}

[0061] E={E1,E2,…,E j ,…E m}

[0062] Where C i is the next level indicator of indicator C, and E is the evaluation set, which is divided according to the specific situation of the indicator;

[0063] 2) Quantify the evaluation set E and quantify the indicators in the evaluation set into values between 0 and 1:

[0064] E * ={E * 1,E * 2,…,E * j ,…E * m}

[0065] 3) Standardize the original data of each evaluation indicator. Assuming there are n indicator sets and m evaluation sets, the original data matrix Y is:

[0066]

[0067] 4) After standardization, the new evaluation matrix F is obtained:

[0068]

[0069] in

[0070]

[0071] 5) Calculate the entropy weight V of each evaluation indicator. The entropy weight of the jth evaluation indicator is defined as:

[0072]

[0073] Among them H j It represents the entropy value of the jth evaluation index, and the calculation formula is:

[0074]

[0075] 6) Use the hierarchical analysis method to obtain the n×n direct influence matrix H:

[0076]

[0077] Where a ij (i, j = 1, 2, ..., n) is the importance between indicators i and j;

[0078] 7) Use the row sum and column sum maximum method to normalize the direct impact matrix H and normalize the data to the interval (0, 1), thereby improving the comparability between data indicators:

[0079]

[0080] 8) Obtain the normative influence matrix N:

[0081]

[0082] 9) The normalized direct impact matrix can be multiplied by itself continuously to represent the indirect impact between factors. After continuous multiplication, the values of all elements in the matrix will approach 0. Based on this, the comprehensive impact matrix T is obtained:

[0083] T=N(IN) -1

[0084] Where I is the identity matrix;

[0085] 10) According to the comprehensive influence matrix T, the measurement criteria of the importance of the four measurement elements, namely influence, influence, centrality and cause, in the system can be obtained, thereby determining the comprehensive evaluation weight w:

[0086]

[0087] Where D i is the indicator influence; C i is the degree of influence of the indicator; M iis the indicator centrality; R i is the indicator cause degree; ij is the element in the comprehensive influence matrix T;

[0088] Get the comprehensive evaluation weight w:

[0089]

[0090] Where M j represents the centrality of the jth evaluation index, V j represents the entropy weight of the jth evaluation indicator;

[0091] 11) Synthetic evaluation results:

[0092] f=wF

[0093] Where F is the evaluation matrix and w is the comprehensive evaluation weight.

[0094] A compensation capability evaluation system for power quality management equipment under different power grid scenarios includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the compensation capability evaluation method for power quality management equipment under different power grid scenarios as described above is implemented.

[0095] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the compensation capability evaluation method of power quality management equipment under different power grid scenarios as described above.

[0096] The present invention has the following characteristics:

[0097] (1) The entropy weight analysis method and DEMATEL method are used to quantify the performance evaluation indicators of the distribution network power supply quality improvement device and evaluate the level of equipment compensation performance in different scenarios;

[0098] (2) The importance of the three key performance indicators is compared pairwise using the hierarchical analysis method to determine the weight coefficient of each indicator, making it more reasonable, more in line with objective reality and easier to express quantitatively, thereby improving the accuracy of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 This is a flowchart of one embodiment of a method for evaluating the compensation capability of power quality management equipment in different power grid scenarios according to the present invention. DETAILED DESCRIPTION

[0100] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0101] Figure 1 The evaluation process of the present invention is shown, which mainly includes the classification of grid adaptability indicators, the establishment of a set of typical grid scenarios considering grid adaptability, the actual measurement of various performance indicators of the target compensation equipment under different typical scenarios, the establishment of a judgment set of performance indicators for each scenario, the use of entropy weight analysis and DEMATEL methods to determine the weight of performance indicators for comprehensive evaluation results, and the comprehensive evaluation results of equipment compensation performance. The process involved in the present invention is as follows:

[0102] Step 1: Establish a set of typical adaptability scenarios. Key grid adaptability indicators are classified based on the limits for voltage deviation, frequency deviation, voltage harmonics, and voltage imbalance in the national standard GB / T12326-2008 (as shown in Table 1). This is used to construct a set of typical grid scenarios S covering different types of adaptability indicators. The graded adaptability indicators include voltage deviation, frequency deviation, voltage harmonics, and voltage imbalance.

[0103] Step 2: Measure and quantify the compensation performance indicators in each adaptation scenario. The specific process is as follows:

[0104] (1) Determine the secondary indicators under the three primary indicators (technical evaluation index V, economic evaluation index C, and adaptability evaluation index E) for evaluating the performance of distribution network power supply quality improvement devices and calculate them.

[0105] 1) Technical evaluation index V

[0106] The technical evaluation index V for the connection of power quality improvement devices is mainly divided into the voltage deviation index V1 at the grid connection point, the voltage frequency deviation index V2, the voltage distortion rate V3, the voltage sag V4, the voltage flicker V5, and the three-phase imbalance V6;

[0107] The voltage deviation index V1 is the ratio of the difference between the effective voltage and the rated voltage to the rated voltage:

[0108]

[0109] The voltage frequency deviation index V2 refers to the difference between two adjacent extreme values on the voltage RMS curve, expressed as a percentage of the system nominal voltage:

[0110]

[0111] Where Umax with U min They are respectively the adjacent extreme values of the effective value of the grid connection point voltage.

[0112] Voltage distortion rate V3:

[0113]

[0114] Where U n is the effective value of the nth harmonic voltage; U f is the effective value of the fundamental voltage.

[0115] The voltage sag V4 indicator uses the SARFI indicator, which is the frequency of voltage sags within a specific period:

[0116]

[0117] Where N is the number of voltage sags with a residual voltage less than X% within a certain period of time; T1 is the total detection time; and T2 is the indicator calculation cycle time.

[0118] Voltage flicker V5:

[0119]

[0120] Where p 0.1 , p1, p3, p 10 , p 50 The P values are respectively when the instantaneous flicker visual sensitivity S(t) exceeds 0.1%, 1%, 3%, 10%, and 50% of the time ratio. k value; S(t) is the instantaneous flicker visual sensitivity, which refers to a series of values of the instantaneous value of flicker intensity changing with time; P k It is the proportion of a certain instantaneous visual sensitivity S(t) value in the entire detection time period.

[0121] Three-phase unbalance V6:

[0122]

[0123]

[0124] Where U A 、U B 、U C are the RMS values of the phase voltages of each phase.

[0125] 2) Economic evaluation index C

[0126] The economic evaluation index C for the access of power quality improvement equipment is mainly divided into equipment cost C1 and annual operation and maintenance cost C2.

[0127] Equipment cost C1:

[0128] C1=F0+F1+F2

[0129] Where F0 is the purchase price; F1 is the relevant taxes and fees; and F2 is the transportation costs, loading and unloading costs, installation costs, and professional service fees incurred attributable to the asset before the fixed asset reaches the intended usable state.

[0130] Annual operation and maintenance cost C2:

[0131] C2=E1+E2+E3+E4

[0132] In the formula, E1 is fuel and power cost; E2 is maintenance cost; E3 is administrative cost; and E4 is other expenditure.

[0133] 3) Adaptability evaluation index E

[0134] The adaptability evaluation index E for the access of power supply quality improvement devices is mainly divided into reactive power compensation benefit E1, harmonic compensation benefit E2, and unbalanced compensation benefit E3.

[0135] The reactive power compensation benefit E1 mainly comes from the saved network loss fee:

[0136]

[0137] Where ΔP 网损 is the active power saved before and after reactive power compensation, σ is the proportion of reactive power compensation device operation time, and λ is the real-time electricity price.

[0138] The harmonic compensation benefit E2 is mainly the saved transformer loss and line loss costs:

[0139]

[0140] Where, γ s，2 is the proportion of additional transformer losses caused by harmonics, ΔP tm is the load loss of the distribution transformer, k l，2 is the line loss rate reduced by harmonic compensation, ΔP l For line loss.

[0141] The unbalanced compensation benefit E3 is mainly the saved distribution transformer loss and line loss costs:

[0142]

[0143] Where, γ s,3 k is the proportion of additional transformer loss caused by imbalance, l,3 To reduce line loss rate;

[0144] (2) Quantify the first-level evaluation indicators and obtain the indicator set V, C, E

[0145] 1) Technical evaluation index V

[0146] For indicators V1 to V6, use the hierarchical analysis method to obtain the weight vector ω:

[0147] ω=[ω1,ω2,ω3,ω4,ω5,ω6]

[0148] The evaluation index V is:

[0149] V=ω1V1+ω2V2+ω3V3+ω4V4+ω5V5+ω6V6

[0150] 2) Economic evaluation index C and adaptability evaluation index E

[0151] Determine the compensation effect evaluation indexes C and E based on the type of power supply quality improvement device:

[0152] C=λ1C1+λ2C2

[0153] E=λ1E1+λ2E2+λ3E3

[0154] Wherein, λ1, λ2, and λ3 are mainly set according to the type of power quality improvement device.

[0155] Step 3: Comprehensive performance evaluation of distribution network power supply quality improvement devices based on entropy weight analysis-AHP-DEMATEL method

[0156] (1) Define the indicator set C and the evaluation set E.

[0157] C={C1,C2,…,C i ,…C n}

[0158] E={E1,E2,…,E j ,…E m}

[0159] Where C i It is the next level indicator of indicator C. E is the evaluation set, such as "bad", "average", "good", etc., which is divided according to the specific situation of the indicator.

[0160] (2) Quantify the evaluation set E and quantify the indicators in the evaluation set into values between 0 and 1.

[0161] E * ={E * 1,E * 2,…,E * j ,…E * m}

[0162] (3) Entropy weight analysis method

[0163] The original data of each evaluation indicator are standardized. Assuming there are n indicator sets and m evaluation sets, the original data matrix Y is:

[0164]

[0165] (4) After standardization, the new evaluation matrix F is obtained:

[0166]

[0167] in

[0168]

[0169] (5) Calculate the entropy weight V of each evaluation indicator. The entropy weight of the jth evaluation indicator is defined as:

[0170]

[0171] Among them H j It represents the entropy value of the jth evaluation index, and the calculation formula is:

[0172]

[0173] (6) DEMATEL method

[0174] According to the importance of each indicator, the hierarchical analysis method is used to score each indicator, on this basis, the n×n direct influence matrix H can be obtained:

[0175]

[0176] Where a ij (i, j = 1, 2, ..., n) is the importance between indicators i and j.

[0177] (7) The direct influence matrix H is normalized using the row sum and column sum maximum method to normalize the data to the interval (0, 1), thereby improving the comparability between data indicators:

[0178]

[0179] (8) Obtain the normative influence matrix N:

[0180]

[0181] (9) The normalized direct impact matrix can be multiplied by itself continuously to represent the indirect impact between factors. After continuous multiplication, the values of all elements in the matrix will approach 0. Based on this, the comprehensive impact matrix T can be obtained:

[0182] T=N(IN)-1

[0183] Where I is the identity matrix.

[0184] (10) Based on the comprehensive influence matrix T, the measurement criteria for the importance of the four measurement elements, namely, influence, influence, centrality, and causality, in the system can be obtained, thereby determining the comprehensive evaluation weight. Among them, influence and influence are used to measure the connection and influence between various indicator items. The measurement criteria for the four measurement elements are as follows:

[0185]

[0186] Where D i is the indicator influence; C i is the degree of influence of the indicator; M i is the indicator centrality; R i is the indicator cause degree; ij is the element in the comprehensive influence matrix T.

[0187] Calculate the comprehensive evaluation weight w:

[0188]

[0189] Where M j represents the centrality of the jth evaluation index, V j represents the entropy weight of the jth evaluation indicator;

[0190] (11) Final synthetic evaluation results:

[0191] f=wF

[0192] Where F is the evaluation matrix and w is the comprehensive evaluation weight.

[0193] The technical solution disclosed in this application is described in detail below using a specific application scenario: Taking a 380V power grid in a certain region as an example, the key adaptability indicators of the power grid (voltage deviation, frequency deviation, voltage harmonics and voltage imbalance) are first classified, and a typical power grid scenario set S covering different types of adaptability indicators is constructed, as shown in Table 1.

[0194] The performance evaluation indicators for distribution network power supply quality improvement devices include: 1. Technical evaluation indicators (voltage deviation index V1, voltage frequency deviation index V2, voltage distortion rate V3, voltage sag V4, voltage flicker V5, and three-phase imbalance V6); 2. Economic evaluation indicator C (equipment cost C1, annual operation and maintenance cost C2); and 3. Adaptability evaluation indicator E (reactive power compensation benefit E1, harmonic compensation benefit E2, and imbalance compensation benefit E3). Three distribution network power supply quality improvement devices were selected and connected to the power grid. The actual values corresponding to the distribution network power supply quality evaluation indicators were measured. After quantification, the primary indicator set V, C, and E were obtained, as shown in Table 2.

[0195] Table 1 Typical power grid scenario set

[0196]

[0197]

[0198] Table 2

[0199]

[0200] According to the corresponding formula in the above technical solution disclosed in this application, the evaluation matrix F of the three power quality management devices is calculated based on the entropy weight analysis method:

[0201]

[0202] According to the corresponding formula in the above technical solution disclosed in this application, the comprehensive evaluation weight w of the three power quality control devices is calculated based on the DEMATEL method:

[0203] w=[0.47 0.14 0.38]

[0204] According to the corresponding formula in the above technical solution disclosed in this application, the evaluation result f is calculated:

[0205] f = [0.1777 0.2141 0.192]

[0206] The results show that the compensation capability of the second type of power quality control equipment is the best.

[0207] The present invention proposes a compensation capability evaluation method for power quality management equipment under different power grid scenarios, which can effectively identify the advantages and disadvantages of power conversion equipment and more comprehensively evaluate the compensation capability of equipment under different power grid environments.

[0208] Another aspect of the present invention provides a compensation capability evaluation system for power quality management equipment in different power grid scenarios, comprising: a computer-readable storage medium and a processor;

[0209] The computer-readable storage medium is used to store executable instructions;

[0210] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the compensation capability evaluation method of the power quality management equipment under different power grid scenarios described in the first aspect.

[0211] On the other hand, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for evaluating the compensation capability of power quality management equipment under different power grid scenarios described in the first aspect is implemented.

[0212] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0213] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0214] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0215] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0216] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the compensation capability of power quality management equipment in different power grid scenarios, characterized by: The following steps are involved: Step 1: Establish a set of typical adaptability scenarios: Based on the national standard GB / T12326-2008, the limits for voltage deviation, frequency deviation, voltage harmonics, and voltage imbalance, which are key adaptability indicators of the power grid, are divided into levels I, II, III, and IV. This is used to construct a set of typical power grid scenarios S covering different types of adaptability indicators. Step 2: Measurement and quantification of compensation performance indicators under various adaptation scenarios: The scenarios in the typical power grid scenario set S are numbered as S i , determine the performance evaluation indicators for evaluating the distribution network power supply quality improvement device, including technical evaluation indicators V i , Economic evaluation index C i , Adaptability evaluation index E i ,Compensation performance tests are conducted on the target in various scenarios, and three performance indicator factor sets V, C, and E are obtained with the indicators in different scenarios as factors; Step 3. Comprehensive performance evaluation of distribution network power supply quality improvement devices based on entropy weight analysis-AHP-DEMATEL method: define the indicator set C and evaluation set E, and use the entropy weight analysis method to obtain the entropy weight V of each performance indicator; Based on the hierarchical analysis method, the technical evaluation index V i , Economic evaluation index C i , Adaptability evaluation index E i Compare the importance of each pair to obtain the judgment matrix H; use the DEMATEL method to process the judgment matrix H to obtain the comprehensive influence matrix T, and then obtain the centrality M of the evaluation index based on the comprehensive influence matrix T. j , combined with the evaluation index center M j And entropy weight V to get comprehensive evaluation weight , thus synthesizing the evaluation results ; The comprehensive performance evaluation of the distribution network power supply quality improvement device based on the entropy weight analysis-AHP-DEMATEL method includes the following steps: 1) Define the indicator set C and evaluation set E: ； ； Where C i is the next level indicator of indicator C, and E is the evaluation set, which is divided according to the specific situation of the indicator; 2) Quantify the evaluation set E and quantify the indicators in the evaluation set into values between 0 and 1: ； 3) Standardize the original data of each evaluation indicator. Assuming there are n indicator sets and m evaluation sets, the original data matrix Y is: ； 4) After standardization, we get the new evaluation matrix F: ； in ； 5) Calculate the entropy weight V of each evaluation indicator. The entropy weight of the j-th evaluation indicator is defined as: ； Among them H j It represents the entropy value of the jth evaluation index, and the calculation formula is: ； 6) Use the hierarchical analysis method to obtain the n×n direct influence matrix H: ； Where, is the importance between indicators i and j; 7) Use the row sum and column sum maximum method to normalize the direct impact matrix H and normalize the data to the interval (0, 1) to improve the comparability between data indicators: ； 8) Obtain the normative influence matrix N: ； 9) The normalized direct impact matrix can be multiplied by itself continuously to represent the indirect impact between factors. After continuous multiplication, the values of all elements in the matrix will approach 0. Based on this, the comprehensive impact matrix T is obtained: ； Where I is the identity matrix; 10) According to the comprehensive influence matrix T, the measurement criteria of the importance of the four measurement elements of influence, influence, centrality and cause in the system can be obtained, so as to determine the comprehensive evaluation weight : ； Where D i is the indicator influence; C i is the degree of influence of the indicator; M i is the indicator centrality; R i is the indicator cause degree; ij is the element in the comprehensive influence matrix T; Get comprehensive evaluation weight : ； Where M j represents the centrality of the jth evaluation index, V j represents the entropy weight of the jth evaluation indicator; 11) Synthetic evaluation results: ； Where, is the evaluation matrix, is the comprehensive evaluation weight.

2. The method for evaluating the compensation capability of power quality management equipment under different power grid scenarios according to claim 1 is characterized in that: The steps of measuring and quantifying the compensation performance indicators in each adaptation scenario include: Calculate and evaluate the technical evaluation indicators V of the performance of distribution network power supply quality improvement devices: voltage deviation index V1, voltage frequency deviation index V2, voltage distortion rate V3, voltage sag V4, voltage flicker V5, and three-phase unbalance V6; Calculate and evaluate the economic evaluation index C of the performance of the distribution network power supply quality improvement device: equipment cost C1, annual operation and maintenance cost C2; Calculate and evaluate the adaptability evaluation index E of the performance of the distribution network power supply quality improvement device: reactive power compensation benefit E1, harmonic compensation benefit E2, and unbalance compensation benefit E3; Quantify the first-level evaluation indicators and obtain the indicator set V, C, and E.

3. The method for evaluating the compensation capability of power quality management equipment under different power grid scenarios according to claim 2, It is characterized by: Among them, the voltage deviation index V1 is the ratio of the difference between the effective value of the voltage and the rated voltage to the rated voltage: ； The voltage frequency deviation index V2 refers to the difference between two adjacent extreme values on the voltage RMS curve, expressed as a percentage of the system nominal voltage: ； In the formula and They are the adjacent extreme values of the effective value of the grid connection point voltage; Voltage distortion rate V3: ； Where, is the effective value of the nth harmonic voltage; is the effective value of the fundamental voltage; The voltage sag V4 indicator uses the SARFI indicator, which is the frequency of voltage sags within a specific period: ； Where N is the number of voltage sags with a residual voltage less than X% within a certain period of time; T1 is the total detection time; T2 is the indicator calculation cycle time; Voltage flicker V5: ； Where, , , , , Instantaneous flicker visual sensitivity More than 0.1%, 1%, 3%, 10%, 50% of the time value; The instantaneous flicker visual sensitivity refers to a series of values of the instantaneous value of flicker intensity that changes with time; For a certain instantaneous visual acuity The proportion of the value in the entire detection time period; Three-phase unbalance V6: ； ； Where, 、 、 are the RMS values of the phase voltages respectively; Among them, equipment cost C1: ； Where, for the purchase price; For relevant taxes and fees; Transportation costs, loading and unloading costs, installation costs and professional service fees attributable to the fixed asset incurred before the fixed asset reaches the intended usable condition; Annual operation and maintenance cost C2: ； Where, Fuel and power costs; For maintenance costs; For administrative expenses; For other expenses; Among them, the reactive power compensation benefit E1 is mainly the saved network loss fee: ； Where, is the active power saved before and after reactive power compensation, is the proportion of reactive power compensation device operation time, is the real-time electricity price; The harmonic compensation benefit E2 is mainly the saved transformer loss and line loss costs: ； Where, is the proportion of additional transformer losses caused by harmonics, is the load loss of the distribution transformer, The line loss rate reduced by harmonic compensation is is line loss; The unbalanced compensation benefit E3 is mainly the saved distribution transformer loss and line loss costs: ； Where, The proportion of additional transformer loss caused by imbalance, To reduce line loss rate.

4. The method for evaluating the compensation capability of power quality management equipment under different power grid scenarios according to claim 3 is characterized in that: The quantitative first-level evaluation indicators are obtained by obtaining the indicator set V, C, and E, which are specifically: 1) Determine the technical evaluation index V For indicators V1~V6, use the hierarchical analysis method to obtain the weight vector : ； The evaluation index V is: ； 2) Determine the economic evaluation index C and adaptability evaluation index E Determine the compensation effect evaluation indexes C and E based on the type of power supply quality improvement device: ； ； Where, , , , which is mainly set according to the type of power supply quality improvement device.

5. A compensation capability evaluation system for power quality management equipment in different power grid scenarios, characterized by: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for evaluating the compensation capability of power quality management equipment under different power grid scenarios as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the compensation capability evaluation method of power quality management equipment under different power grid scenarios as described in any one of claims 1 to 3.

Images