Update optimization system for static synchronous reactive power compensation device

Abstract

The invention relates to the technical field of static synchronous reactive power compensation devices, and discloses an updating and optimizing system for a static synchronous reactive power compensation device, a first module is provided with a plurality of contaminant detection points on the static synchronous reactive power compensation device, and a plurality of updating and optimizing contaminant data chains are constructed according to contaminant data; the second module constructs a related contaminant data set according to the related contaminant data, and calculates a sub-update optimization influence coefficient; the third module is used for analyzing and calculating the residual related miscellaneous data and constructing a sub-update optimization influence coefficient set; a fourth module sorts the sub-update optimization influence coefficient sets, determines a comprehensive update optimization influence coefficient set, and calculates comprehensive update optimization influence coefficients; and the fifth module judges whether updating optimization is carried out or not based on the comprehensive updating optimization influence coefficient, updating optimization can be accurately carried out on the static synchronous reactive power compensation device, the operation environment of equipment is effectively improved, high reliability is achieved, and the long-term stable operation requirement of the equipment can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of static synchronous reactive power compensation devices, and in particular to an updating and optimizing system for static synchronous reactive power compensation devices. Background Art

[0002] Static synchronous VAR compensator is a parallel type reactive compensation FACTS device, which can generate or absorb reactive power, and has the characteristics of continuously adjustable capacitive or inductive reactive current output, and is not affected by the voltage at the system connection point. Static synchronous VAR compensator has become an indispensable and important part of the modern power system with its advanced technical principles, excellent performance characteristics, wide application fields and significant social benefits. With the continuous advancement of technology and the continuous growth of power market demand, it will play a greater role in improving the power quality, stability and economic benefits of the power grid.

[0003] However, traditional static synchronous reactive power compensation devices may encounter some problems during operation: With the development of the power system, the requirements for the stability and reliability of the power grid are getting higher and higher, especially in the case of long-distance power transmission, new energy grid connection and large industrial load fluctuations, the role of static synchronous reactive power compensation devices is particularly important. However, with the passage of time and technological progress, the original equipment may not be able to meet the current operating needs, or the performance may deteriorate due to long-term operation, which requires updating and optimization. However, for the update and optimization of static synchronous reactive power compensation devices, relevant staff are required to make judgments based on work experience, which is prone to judgment errors and the problem of premature or untimely updating of static synchronous reactive power compensation devices. Summary of the invention

[0004] The embodiment of the present invention provides an update optimization system for a static synchronous reactive power compensation device, which can accurately update and optimize the static synchronous reactive power compensation device, effectively improve the operating environment of the equipment, has high reliability, and can meet the long-term stable operation requirements of the equipment.

[0005] In order to achieve the above object, the present invention provides an updating and optimizing system for a static synchronous reactive power compensation device, comprising:

[0006] The first module is used to determine a static synchronous reactive power compensation device, set a plurality of pollution detection points on the static synchronous reactive power compensation device, obtain pollution data of each pollution detection point, and construct a plurality of updated and optimized pollution data chains according to all the pollution data;

[0007] The second module is used to construct a related pollution data set according to the related pollution data on each update optimization pollution data chain, analyze the related pollution data set, and calculate the corresponding sub-update optimization influence coefficient based on the analysis result;

[0008] The third module is used to analyze and calculate the remaining relevant junk data on the update optimization junk data chain, determine the corresponding sub-update optimization influence coefficient, and construct a sub-update optimization influence coefficient set according to all the sub-update optimization influence coefficients;

[0009] The fourth module is used to sort the sub-update optimization influence coefficient sets, determine the comprehensive update optimization influence coefficient set based on the sorting result, and calculate the comprehensive update optimization influence coefficient of the static synchronous reactive power compensation device according to the comprehensive update optimization influence coefficient set;

[0010] The fifth module is used to determine whether to update and optimize the static synchronous reactive power compensation device based on the comprehensive update optimization influence coefficient.

[0011] Furthermore, the first module is used for:

[0012] The first module is used to output the noise data entropy value corresponding to each noise data based on the pre-trained data entropy value model;

[0013] The first module is used to obtain a preset entropy value of the dirty data, and generate a chain mark for all the dirty data that is greater than or equal to the preset entropy value of the dirty data;

[0014] The first module is used to generate a downlink mark for all the contamination data that is less than the preset contamination data entropy value;

[0015] The first module is used to determine the maximum and minimum entropy values ​​of the contaminated data according to the entropy values ​​of the contaminated data carrying the downlink mark, and calculate the difference between the maximum and minimum entropy values ​​of the contaminated data;

[0016] The first module is used to obtain a preset difference in the entropy value of the pre-set dirty data, and if the entropy value of the dirty data is less than the preset difference in the entropy value of the dirty data, then construct the updated and optimized dirty data chain according to all the dirty data;

[0017] The first module is used to construct the updated and optimized dirty data chain according to the dirty data carrying the chain mark if the difference in the dirty data entropy value is greater than or equal to the preset dirty data entropy value difference.

[0018] Furthermore, the first module is used for:

[0019] The first module is used to collect a training data set, wherein the training data set includes a plurality of samples, and each sample has a plurality of features;

[0020] The first module is used to calculate the entropy value of the feature of each sample to evaluate the information content of the feature;

[0021] The first module is used to sort the features according to the calculated entropy values ​​and select a predetermined number of high entropy features as inputs to the model;

[0022] The first module is used to train an initial data entropy value model using selected features and corresponding sample labels;

[0023] The first module is used to evaluate the performance of the initial model by a cross-validation method;

[0024] The first module is used to output the final trained data entropy value model if the performance of the initial model reaches a preset standard.

[0025] Furthermore, the second calculation module is used for:

[0026] The second calculation module is used to determine a contamination data range corresponding to the relevant contamination data set, wherein the contamination data range includes a first preset contamination data value and a second preset contamination data value;

[0027] The second calculation module is used for dividing the contamination data in the related contamination data set that is less than or equal to the first preset contamination data value into a first data sequence;

[0028] The second calculation module is used to divide the contamination data in the related contamination data set that is greater than the first preset contamination data value and less than the second preset contamination data value into a second data sequence;

[0029] The second calculation module is used to divide the contamination data in the related contamination data set that is greater than or equal to the second preset contamination data value into a third data sequence.

[0030] Furthermore, the second calculation module is used for:

[0031] The second calculation module is used to calculate a first average value and a first standard deviation of the first data series, and calculate a first numerical processing range of the first data series according to the first average value and the first standard deviation;

[0032] The second calculation module is used to calculate the first numerical processing range of the first data sequence according to the following formula:

[0033] k(k1,k2)=(b1×f1)±(b2×f2);

[0034] Wherein, k(k1, k2) is the first numerical processing range, k1 is the left boundary value, k2 is the right boundary value, b1 is the calculation coefficient corresponding to the first average value, f1 is the first average value, b2 is the calculation coefficient corresponding to the first standard deviation, and f2 is the first standard deviation;

[0035] The second calculation module is used to calculate a second mean value and a second standard deviation of the second data series, and calculate a second numerical processing range of the second data series according to the second mean value and the second standard deviation;

[0036] The second calculation module is used to calculate a third mean value and a third standard deviation of the third data series, and calculate a third value processing range of the third data series according to the third mean value and the third standard deviation;

[0037] The second calculation module is used to compare the contamination data in each data sequence with the corresponding numerical processing range, and if the contamination data is within the corresponding numerical processing range, generate an internal correlation code for the contamination data; if the contamination data is not within the corresponding numerical processing range, generate an external correlation code for the contamination data;

[0038] The second calculation module is used to calculate the sub-update optimization influence coefficient according to the internal correlation code.

[0039] Furthermore, the second calculation module is used for:

[0040] The second calculation module is used to generate a first factor for the first data sequence, generate a second factor for the second data sequence, and generate a third factor for the third data sequence;

[0041] The second calculation module is used to calculate the sub-update optimization influence coefficient according to the following formula:

[0042] w=p1×m1+p2×m2+p3×m3;

[0043] Among them, w is the sub-update optimization influence coefficient, p1 is the first factor, m1 is the number of internal correlation codes in the first data sequence, p2 is the second factor, m2 is the number of internal correlation codes in the second data sequence, p3 is the third factor, and m3 is the number of internal correlation codes in the third data sequence.

[0044] Furthermore, the fourth module is used for:

[0045] The fourth module is used to determine the median and variance of the sub-update optimization influence coefficient set;

[0046] The fourth module is used to extract the sub-update optimization influence coefficients greater than the median in the sub-update optimization influence coefficient set to construct a first coefficient set;

[0047] The fourth module is used to extract the sub-update optimization influence coefficients greater than the variance in the sub-update optimization influence coefficient set to construct a second coefficient set;

[0048] The fourth module is used to determine whether there is an intersection between the first coefficient set and the second coefficient set;

[0049] The fourth module is used to construct the comprehensive update optimization influence coefficient set according to the intersection value if yes;

[0050] The fourth module is used to, if not, perform non-repetitive fusion of the first coefficient set and the second coefficient set to construct the comprehensive update optimization influence coefficient set, wherein the non-repetitive fusion is to retain the non-repetitive sub-update optimization influence coefficients in the first coefficient set and the second coefficient set, retain one repeated sub-update optimization influence coefficient in the first coefficient set and the second coefficient set, and delete the remaining repeated sub-update optimization influence coefficients.

[0051] Furthermore, the fourth module is used for:

[0052] The fourth module is used to calculate the comprehensive update optimization influence coefficient of the static synchronous reactive power compensation device according to the following formula;

[0053]

[0054] Where R is the comprehensive update optimization influence coefficient of the static synchronous reactive compensation device, n is the number of sub-update optimization influence coefficients in the comprehensive update optimization influence coefficient set, Δt i The weight corresponding to the influence coefficient of the optimization for the i-th sub-update, t i Optimize the influence coefficient for the i-th sub-update, t min Optimize the influence coefficient for the smallest sub-update, t max Optimize the influence coefficient for maximum sub-update, For all The maximum value in .

[0055] Furthermore, the fifth module is used for:

[0056] The fifth module is used to obtain a preset comprehensive update optimization influence coefficient;

[0057] The fifth module is used for determining not to update and optimize the static synchronous reactive power compensation device when the comprehensive update optimization influence coefficient is less than the preset comprehensive update optimization influence coefficient;

[0058] The fifth module is used to determine whether to update and optimize the static synchronous reactive power compensation device when the comprehensive update optimization influence coefficient is greater than or equal to the preset comprehensive update optimization influence coefficient.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The first module sets multiple pollution detection points on the static synchronous reactive power compensation device, and constructs multiple update optimization pollution data chains according to the pollution data; the second module constructs a relevant pollution data set according to the relevant pollution data, and calculates the sub-update optimization influence coefficient; the third module analyzes and calculates the remaining relevant pollution data, and constructs a sub-update optimization influence coefficient set; the fourth module sorts the sub-update optimization influence coefficient set, determines the comprehensive update optimization influence coefficient set, and calculates the comprehensive update optimization influence coefficient; the fifth module determines whether to perform update optimization based on the comprehensive update optimization influence coefficient, and can accurately update and optimize the static synchronous reactive power compensation device, effectively improve the operating environment of the equipment, has high reliability, and can meet the long-term stable operation requirements of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0062] Figure 1 A structural schematic diagram of an updating and optimizing system for a static synchronous reactive power compensation device in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0063] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0064] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0065] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0066] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0067] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0068] like Figure 1 As shown, an embodiment of the present invention discloses an updating and optimizing system for a static synchronous reactive power compensation device, comprising:

[0069] The first module is used to determine a static synchronous reactive power compensation device, set a plurality of pollution detection points on the static synchronous reactive power compensation device, obtain pollution data of each pollution detection point, and construct a plurality of updated and optimized pollution data chains according to all the pollution data;

[0070] The second module is used to construct a related pollution data set according to the related pollution data on each update optimization pollution data chain, analyze the related pollution data set, and calculate the corresponding sub-update optimization influence coefficient based on the analysis result;

[0071] The third module is used to analyze and calculate the remaining relevant junk data on the update optimization junk data chain, determine the corresponding sub-update optimization influence coefficient, and construct a sub-update optimization influence coefficient set according to all the sub-update optimization influence coefficients;

[0072] The fourth module is used to sort the sub-update optimization influence coefficient sets, determine the comprehensive update optimization influence coefficient set based on the sorting result, and calculate the comprehensive update optimization influence coefficient of the static synchronous reactive power compensation device according to the comprehensive update optimization influence coefficient set;

[0073] The fifth module is used to determine whether to update and optimize the static synchronous reactive power compensation device based on the comprehensive update optimization influence coefficient.

[0074] In this embodiment, the debris data includes dust accumulation, corrosion degree, fluid degradation degree, wear degree, pollutant deposition amount, etc.

[0075] The beneficial effect of the above technical solution is that the present invention can accurately update and optimize the static synchronous reactive power compensation device, effectively improve the operating environment of the equipment, has high reliability, and can meet the long-term stable operation requirements of the equipment.

[0076] In some embodiments of the present application, the first module is used to:

[0077] The first module is used to output the noise data entropy value corresponding to each noise data based on the pre-trained data entropy value model;

[0078] The first module is used to obtain a preset entropy value of the dirty data, and generate a chain mark for all the dirty data that is greater than or equal to the preset entropy value of the dirty data;

[0079] The first module is used to generate a downlink mark for all the contamination data that is less than the preset contamination data entropy value;

[0080] The first module is used to determine the maximum and minimum entropy values ​​of the contaminated data according to the entropy values ​​of the contaminated data carrying the downlink mark, and calculate the difference between the maximum and minimum entropy values ​​of the contaminated data;

[0081] The first module is used to obtain a preset difference in the entropy value of the pre-set dirty data, and if the entropy value of the dirty data is less than the preset difference in the entropy value of the dirty data, then construct the updated and optimized dirty data chain according to all the dirty data;

[0082] The first module is used to construct the updated and optimized dirty data chain according to the dirty data carrying the chain mark if the difference in the dirty data entropy value is greater than or equal to the preset dirty data entropy value difference.

[0083] The beneficial effect of the above technical solution is that the present invention can lay a foundation for the update optimization judgment of the static synchronous reactive power compensation device by constructing an update optimization data chain.

[0084] In some embodiments of the present application, the first module is used to:

[0085] The first module is used to collect a training data set, wherein the training data set includes a plurality of samples, and each sample has a plurality of features;

[0086] The first module is used to calculate the entropy value of the feature of each sample to evaluate the information content of the feature;

[0087] The first module is used to sort the features according to the calculated entropy values ​​and select a predetermined number of high entropy features as inputs to the model;

[0088] The first module is used to train an initial data entropy value model using selected features and corresponding sample labels;

[0089] The first module is used to evaluate the performance of the initial model by a cross-validation method;

[0090] The first module is used to output the final trained data entropy value model if the performance of the initial model reaches a preset standard.

[0091] In this embodiment, the entropy value of the feature of each sample is calculated according to the following formula:

[0092]

[0093] Among them, E(X) is the entropy value of feature X, p(xi) is the probability of the i-th value in feature X, and n is the number of different values ​​in feature X.

[0094] The beneficial effect of the above technical solution is that the present invention can accurately output the data entropy value of each piece of contaminated data by training the data entropy value model, thereby avoiding errors and causing subsequent judgment errors.

[0095] In some embodiments of the present application, the second computing module is used to:

[0096] The second calculation module is used to determine a contamination data range corresponding to the relevant contamination data set, wherein the contamination data range includes a first preset contamination data value and a second preset contamination data value;

[0097] The second calculation module is used for dividing the contamination data in the related contamination data set that is less than or equal to the first preset contamination data value into a first data sequence;

[0098] The second calculation module is used to divide the contamination data in the related contamination data set that is greater than the first preset contamination data value and less than the second preset contamination data value into a second data sequence;

[0099] The second calculation module is used to divide the contamination data in the related contamination data set that is greater than or equal to the second preset contamination data value into a third data sequence.

[0100] In this embodiment, the related dirty data refers to the same type of dirty data.

[0101] In this embodiment, the range of the contamination data corresponding to each contamination data is different, which can be set according to actual conditions, and the first preset contamination data value is smaller than the second preset contamination data value.

[0102] The beneficial effect of the above technical solution is that the present invention divides the contaminated data into a first data sequence, a second data sequence and a third data sequence according to the contaminated data range, which can provide reliable data support for the calculation of the sub-update optimization influence coefficient.

[0103] In some embodiments of the present application, the second computing module is used to:

[0104] The second calculation module is used to calculate a first average value and a first standard deviation of the first data series, and calculate a first numerical processing range of the first data series according to the first average value and the first standard deviation;

[0105] The second calculation module is used to calculate the first numerical processing range of the first data sequence according to the following formula:

[0106] k(k1,k2)=(b1×f1)±(b2×f2);

[0107] Wherein, k(k1, k2) is the first numerical processing range, k1 is the left boundary value, k2 is the right boundary value, b1 is the calculation coefficient corresponding to the first average value, f1 is the first average value, b2 is the calculation coefficient corresponding to the first standard deviation, and f2 is the first standard deviation;

[0108] The second calculation module is used to calculate a second mean value and a second standard deviation of the second data series, and calculate a second numerical processing range of the second data series according to the second mean value and the second standard deviation;

[0109] The second calculation module is used to calculate a third mean value and a third standard deviation of the third data series, and calculate a third value processing range of the third data series according to the third mean value and the third standard deviation;

[0110] The second calculation module is used to compare the contamination data in each data sequence with the corresponding numerical processing range, and if the contamination data is within the corresponding numerical processing range, generate an internal correlation code for the contamination data; if the contamination data is not within the corresponding numerical processing range, generate an external correlation code for the contamination data;

[0111] The second calculation module is used to calculate the sub-update optimization influence coefficient according to the internal correlation code.

[0112] In this embodiment, the calculation process of the second numerical processing range and the third numerical processing range is consistent with that of the first numerical processing range, and in order to save space, they are not repeatedly limited.

[0113] The beneficial effect of the above technical solution is that the present invention calculates the sub-update optimization influence coefficient according to the internal correlation coding, which can ensure the comprehensiveness and accuracy of the calculation of the sub-update optimization influence coefficient and avoid errors caused by manual participation.

[0114] In some embodiments of the present application, the second computing module is used to:

[0115] The second calculation module is used to generate a first factor for the first data sequence, generate a second factor for the second data sequence, and generate a third factor for the third data sequence;

[0116] The second calculation module is used to calculate the sub-update optimization influence coefficient according to the following formula:

[0117] w=p1×m1+p2×m2+p3×m3;

[0118] Among them, w is the sub-update optimization influence coefficient, p1 is the first factor, m1 is the number of internal correlation codes in the first data sequence, p2 is the second factor, m2 is the number of internal correlation codes in the second data sequence, p3 is the third factor, and m3 is the number of internal correlation codes in the third data sequence.

[0119] In this embodiment, the first factor is 0.2, the second factor is 0.4, and the third factor is 0.8.

[0120] In some embodiments of the present application, the fourth module is used to:

[0121] The fourth module is used to determine the median and variance of the sub-update optimization influence coefficient set;

[0122] The fourth module is used to extract the sub-update optimization influence coefficients greater than the median in the sub-update optimization influence coefficient set to construct a first coefficient set;

[0123] The fourth module is used to extract the sub-update optimization influence coefficients greater than the variance in the sub-update optimization influence coefficient set to construct a second coefficient set;

[0124] The fourth module is used to determine whether there is an intersection between the first coefficient set and the second coefficient set;

[0125] The fourth module is used to construct the comprehensive update optimization influence coefficient set according to the intersection value if yes;

[0126] The fourth module is used to, if not, perform non-repetitive fusion of the first coefficient set and the second coefficient set to construct the comprehensive update optimization influence coefficient set, wherein the non-repetitive fusion is to retain the non-repetitive sub-update optimization influence coefficients in the first coefficient set and the second coefficient set, retain one repeated sub-update optimization influence coefficient in the first coefficient set and the second coefficient set, and delete the remaining repeated sub-update optimization influence coefficients.

[0127] In this embodiment, if the sub-update optimization influence coefficient in the first coefficient set is 4, 4, 5, 7, 8, 9, and the sub-update optimization influence coefficient in the second coefficient set is 5, 7, 8, 9, there is an intersection, and the comprehensive update optimization influence coefficient set is 4, 4, 5, 7, 8, 9, 5, 7, 8, 9. If the sub-update optimization influence coefficient in the first coefficient set is 3, 3, 5, 7, and the sub-update optimization influence coefficient in the second coefficient set is 8, 8, 9, there is no intersection, and the comprehensive update optimization influence coefficient set is 3, 5, 7, 8, 9. The above is shown by way of example and is not specifically limited.

[0128] The beneficial effect of the above technical solution is: the present invention constructs a comprehensive update optimization influence coefficient set, and then calculates the comprehensive update optimization influence coefficient based on the comprehensive update optimization influence coefficient set, which can provide a basis for judging update optimization instead of judging based on the work experience of the staff, thus avoiding subjectivity.

[0129] In some embodiments of the present application, the fifth module is used to:

[0130] The fifth module is used to obtain a preset comprehensive update optimization influence coefficient;

[0131] The fifth module is used for determining not to update and optimize the static synchronous reactive power compensation device when the comprehensive update optimization influence coefficient is less than the preset comprehensive update optimization influence coefficient;

[0132] The fifth module is used to determine whether to update and optimize the static synchronous reactive power compensation device when the comprehensive update optimization influence coefficient is greater than or equal to the preset comprehensive update optimization influence coefficient.

[0133] The beneficial effect of the above technical solution is: the present invention determines whether to update and optimize the static synchronous reactive compensation device based on the comprehensive update optimization influence coefficient, which can not only ensure the timely update and optimization of the static synchronous reactive compensation device, but also avoid the problem of premature update and optimization, thereby wasting devices.

[0134] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0135] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0136] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An updating and optimizing system for a static synchronous reactive power compensation device, characterized in that: include: The first module is used to determine a static synchronous reactive power compensation device, set a plurality of pollution detection points on the static synchronous reactive power compensation device, obtain pollution data of each pollution detection point, and construct a plurality of updated and optimized pollution data chains according to all the pollution data; The second module is used to construct a related pollution data set according to the related pollution data on each update optimization pollution data chain, analyze the related pollution data set, and calculate the corresponding sub-update optimization influence coefficient based on the analysis result; The third module is used to analyze and calculate the remaining relevant junk data on the update optimization junk data chain, determine the corresponding sub-update optimization influence coefficient, and construct a sub-update optimization influence coefficient set according to all the sub-update optimization influence coefficients; The fourth module is used to sort the sub-update optimization influence coefficient sets, determine the comprehensive update optimization influence coefficient set based on the sorting result, and calculate the comprehensive update optimization influence coefficient of the static synchronous reactive power compensation device according to the comprehensive update optimization influence coefficient set; The fifth module is used to determine whether to update and optimize the static synchronous reactive power compensation device based on the comprehensive update optimization influence coefficient.

2. The updating and optimizing system for static synchronous reactive power compensation device according to claim 1, characterized in that: The first module is used for: The first module is used to output the contaminated data entropy value corresponding to each contaminated data based on the pre-trained data entropy value model; The first module is used to obtain a preset entropy value of the dirty data, and generate a chain mark for all the dirty data that is greater than or equal to the preset entropy value of the dirty data; The first module is used to generate a downlink mark for all the contaminated data that is less than the preset contaminated data entropy value; The first module is used to determine the maximum and minimum entropy values ​​of the contaminated data according to the entropy values ​​of the contaminated data carrying the downlink mark, and calculate the difference between the maximum and minimum entropy values ​​of the contaminated data; The first module is used to obtain a preset difference in the entropy value of the pre-set dirty data, and if the entropy value of the dirty data is less than the preset difference in the entropy value of the dirty data, then construct the updated and optimized dirty data chain according to all the dirty data; The first module is used to construct the updated and optimized dirty data chain according to the dirty data carrying the chain mark if the difference in the entropy value of the dirty data is greater than or equal to the preset difference in the entropy value of the dirty data.

3. The updating and optimizing system for static synchronous reactive power compensation device according to claim 2, characterized in that: The first module is used for: The first module is used to collect a training data set, wherein the training data set includes a plurality of samples, and each sample has a plurality of features; The first module is used to calculate the entropy value of the feature of each sample to evaluate the information content of the feature; The first module is used to sort the features according to the calculated entropy values ​​and select a predetermined number of high entropy features as inputs to the model; The first module is used to train an initial data entropy value model using selected features and corresponding sample labels; The first module is used to evaluate the performance of the initial model by a cross-validation method; The first module is used to output the final trained data entropy value model if the performance of the initial model reaches the preset standard.

4. The updating and optimizing system for static synchronous reactive power compensation device according to claim 1, characterized in that: The second calculation module is used for: The second calculation module is used to determine a contamination data range corresponding to the relevant contamination data set, wherein the contamination data range includes a first preset contamination data value and a second preset contamination data value; The second calculation module is used for dividing the contamination data in the related contamination data set that is less than or equal to the first preset contamination data value into a first data sequence; The second calculation module is used to divide the contamination data in the related contamination data set that is greater than the first preset contamination data value and less than the second preset contamination data value into a second data sequence; The second calculation module is used to divide the contamination data in the related contamination data set that is greater than or equal to the second preset contamination data value into a third data sequence.

5. The updating and optimizing system for static synchronous reactive power compensation device according to claim 4, characterized in that: The second calculation module is used for: The second calculation module is used to calculate a first average value and a first standard deviation of the first data sequence, and calculate a first numerical processing range of the first data sequence according to the first average value and the first standard deviation; The second calculation module is used to calculate the first numerical processing range of the first data sequence according to the following formula: k(k1,k2)=(b1×f1)±(b2×f2); Wherein, k(k1, k2) is the first numerical processing range, k1 is the left boundary value, k2 is the right boundary value, b1 is the calculation coefficient corresponding to the first average value, f1 is the first average value, b2 is the calculation coefficient corresponding to the first standard deviation, and f2 is the first standard deviation; The second calculation module is used to calculate a second mean value and a second standard deviation of the second data series, and calculate a second numerical processing range of the second data series according to the second mean value and the second standard deviation; The second calculation module is used to calculate a third mean value and a third standard deviation of the third data series, and calculate a third value processing range of the third data series according to the third mean value and the third standard deviation; The second calculation module is used to compare the contamination data in each data sequence with the corresponding numerical processing range, and if the contamination data is within the corresponding numerical processing range, generate an internal correlation code for the contamination data; if the contamination data is not within the corresponding numerical processing range, generate an external correlation code for the contamination data; The second calculation module is used to calculate the sub-update optimization influence coefficient according to the internal correlation code.

6. The updating and optimizing system for static synchronous reactive power compensation device according to claim 5, characterized in that: The second calculation module is used for: The second calculation module is used to generate a first factor for the first data sequence, generate a second factor for the second data sequence, and generate a third factor for the third data sequence; The second calculation module is used to calculate the sub-update optimization influence coefficient according to the following formula: w=p1×m1+p2×m2+p3×m3; Among them, w is the sub-update optimization influence coefficient, p1 is the first factor, m1 is the number of internal correlation codes in the first data sequence, p2 is the second factor, m2 is the number of internal correlation codes in the second data sequence, p3 is the third factor, and m3 is the number of internal correlation codes in the third data sequence.

7. The updating and optimizing system for static synchronous reactive power compensation device according to claim 1, characterized in that: The fourth module is used for: The fourth module is used to determine the median and variance of the sub-update optimization influence coefficient set; The fourth module is used to extract the sub-update optimization influence coefficients greater than the median in the sub-update optimization influence coefficient set to construct a first coefficient set; The fourth module is used to extract the sub-update optimization influence coefficients greater than the variance in the sub-update optimization influence coefficient set to construct a second coefficient set; The fourth module is used to determine whether there is an intersection between the first coefficient set and the second coefficient set; The fourth module is used to construct the comprehensive update optimization influence coefficient set according to the intersection value if yes; The fourth module is used to, if not, perform non-repetitive fusion of the first coefficient set and the second coefficient set to construct the comprehensive update optimization influence coefficient set, wherein the non-repetitive fusion is to retain the non-repetitive sub-update optimization influence coefficients in the first coefficient set and the second coefficient set, retain one repeated sub-update optimization influence coefficient in the first coefficient set and the second coefficient set, and delete the remaining repeated sub-update optimization influence coefficients.

8. The updating and optimizing system for static synchronous reactive power compensation device according to claim 7, characterized in that: The fourth module is used for: The fourth module is used to calculate the comprehensive update optimization influence coefficient of the static synchronous reactive power compensation device according to the following formula; Where R is the comprehensive update optimization influence coefficient of the static synchronous reactive compensation device, n is the number of sub-update optimization influence coefficients in the comprehensive update optimization influence coefficient set, Δt i The weight corresponding to the influence coefficient of the optimization for the i-th sub-update, t i Optimize the influence coefficient for the i-th sub-update, t min Optimize the influence coefficient for the smallest sub-update, t max Optimize the influence coefficient for maximum sub-update, For all The maximum value in .

9. The updating and optimizing system for static synchronous reactive power compensation device according to claim 1, characterized in that: The fifth module is used for: The fifth module is used to obtain a preset comprehensive update optimization influence coefficient; The fifth module is used for determining not to update and optimize the static synchronous reactive power compensation device when the comprehensive update optimization influence coefficient is less than the preset comprehensive update optimization influence coefficient; The fifth module is used to determine whether to update and optimize the static synchronous reactive power compensation device when the comprehensive update optimization influence coefficient is greater than or equal to the preset comprehensive update optimization influence coefficient.

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