Method for monitoring humidity in 35kV box-type transformer

Abstract

The invention relates to the technical field of humidity monitoring, and discloses a humidity monitoring method for the interior of a 35kV box-type transformer, and the method comprises the steps: building a plurality of wind power distribution models based on wind speed data; selecting a corresponding wind power distribution model according to the real-time environment parameters, and setting a humidity threshold value of each monitoring point according to the selected wind power distribution model; collecting a first-level humidity value of each humidity monitoring point according to the sampling time node; correcting the first-level humidity value of each humidity monitoring point based on the environmental data, and generating a second-level humidity value according to a correction result; generating a total evaluation value of the internal humidity of the transformer based on the secondary humidity value and the humidity threshold value of each humidity monitoring point; and generating a control instruction based on the total evaluation value of the internal humidity of the transformer. The humidity monitoring accuracy is improved, intelligent monitoring and early warning are realized, the distribution of monitoring resources is optimized, and a visual humidity management tool is provided through a digital twin model.

Description

Technical Field

[0001] The invention relates to the technical field of humidity monitoring, and in particular to a method for monitoring humidity inside a 35kV box-type transformer. Background Art

[0002] Currently, with the development of smart grid and automated monitoring technology, real-time monitoring of environmental parameters of power equipment such as transformers has become an industry trend. Advances in humidity monitoring technology make it possible to more accurately track and control humidity levels inside transformers, thereby preventing potential failures and extending equipment life.

[0003] 35kV box-type transformer is a common power equipment, widely used in urban distribution network and industrial power supply system. Monitoring of internal humidity is essential to ensure the safe operation of transformer. Excessive humidity may cause aging, corrosion and short circuit of insulation materials, thus affecting the performance and life of transformer. Therefore, effective monitoring and control of internal humidity of box-type transformer is an important measure to ensure the stability of power system. Summary of the invention

[0004] The purpose of the present invention is to accurately monitor the humidity changes inside the transformer box by establishing a wind distribution model and a humidity change model, and to detect potential humidity anomalies in advance through an early warning mechanism combining multiple thresholds with change rate judgment, thereby effectively ensuring the safe operation of the box-type transformer.

[0005] In order to achieve the above object, the present invention provides a method for monitoring humidity inside a 35kV box-type transformer, comprising:

[0006] Establish multiple wind distribution models based on wind speed data and wind speed data, select the corresponding wind distribution model according to the real-time environmental parameters, and set the humidity threshold of each monitoring point according to the selected wind distribution model;

[0007] The transformer internal humidity sampling period is set according to the real-time environmental parameters, and multiple sampling time nodes are generated, wherein the sampling time node is the starting point of the sampling period;

[0008] The primary humidity value of each humidity monitoring point is collected according to the sampling time node, and the primary humidity value of each humidity monitoring point is corrected according to the real-time environmental parameters, and the secondary humidity value is generated according to the correction result;

[0009] Based on the secondary humidity value and humidity threshold of each humidity monitoring point, a total evaluation value of the internal humidity of the transformer is generated, and a control instruction is generated according to the total evaluation value of the internal humidity of the transformer.

[0010] In some embodiments of the present invention, the establishing of multiple wind force distribution models includes:

[0011] Obtain historical data of wind speed and wind direction entering the box-type transformer;

[0012] The historical data of wind speed entering the box-type transformer is divided into grade intervals to generate a wind speed grade data set W, where W = [W1, W2…Wi…Wn];

[0013] Based on the historical wind direction data, the wind direction interval data set AC entering the box-type transformer is established, AC = [AC1, AC2…ACi…ACm]

[0014] Where Wi represents the i-th wind speed level, ACi represents the i-th wind direction interval, n represents the number of wind speed levels, and m represents the number of wind direction intervals;

[0015] Input wind speed level Wi and wind direction range ACi inside the box-type transformer, and collect wind speed level at each monitoring point inside the box-type transformer;

[0016] Generate a wind distribution model Mi when the input wind speed level is Wi and the wind direction interval is ACi.

[0017] In some embodiments of the present invention, the collecting of the primary humidity value of each humidity monitoring point includes:

[0018] Select the corresponding wind distribution model according to the real-time environmental parameters, and set the humidity threshold of each monitoring point according to the selected wind distribution model;

[0019] Monitor the real-time data of humidity changes at the current monitoring point, and divide the monitored real-time data of humidity changes into intervals;

[0020] Evaluate the humidity of each interval and generate the first-level humidity value set H of the current sampling period, H = [H1, H2…Hi…Hn];

[0021] Wherein, Hi represents the humidity evaluation value of the i-th monitoring point in the current sampling period; n represents the total number of monitoring points.

[0022] In some embodiments of the present invention, the generating of the secondary humidity value includes:

[0023] Obtain the historical environmental data of the external environment of the box-type transformer and the historical humidity data of each monitoring point inside the box-type transformer;

[0024] Evaluate the external historical environmental data of the box-type transformer to obtain the external environmental score S, and obtain the evaluation data set H of each monitoring point inside the box-type transformer under the wind distribution model Mi;

[0025] Calculate the variance D(H) of the evaluation data set H at each monitoring point inside the box-type transformer;

[0026] Based on the variance D(H) of the evaluation data set H, a first humidity compensation data set C is generated, where C = [C1, C2...Ci...Cn];

[0027] Wherein, Ci represents the first humidity compensation value of the i-th monitoring point, and n represents the number of monitoring points;

[0028] The first humidity compensation data set C is the compensation value of the internal humidity monitoring data under the wind distribution model Mi when the external environment score is S.

[0029] Based on the first humidity compensation data set C, the primary humidity value set H is corrected, and a second humidity real-time change data set D is generated according to the correction result, where D = [D1, D2...Di...Dn];

[0030] Wherein, Di represents the second real-time temperature data value of the i-th monitoring point, and n represents the number of monitoring points.

[0031] In some embodiments of the present invention, the step of selecting and setting the transformer internal humidity sampling period according to the real-time environmental parameters includes:

[0032] Based on historical data, set the threshold B of humidity change, B = [B1, B2, B3];

[0033] Wherein, B1 represents the first threshold value of humidity change, B2 represents the second threshold value of humidity change, and B3 represents the third threshold value of humidity change;

[0034] Count the number of humidity changes exceeding the threshold on a certain date, and based on the statistical results, generate the humidity change index k for the current date, k = n1 + 2*(n2-n1) + 3*(n3-n2-n1);

[0035] Where n1 represents the number of humidity changes on the current date that exceed the first threshold, n2 represents the number of humidity changes on the current date that exceed the second threshold, and n3 represents the number of humidity changes on the current date that exceed the third threshold;

[0036] Based on the humidity change index k of the current date, generate the humidity change index dataset K of the current year, K = [k1, k2…ki…kn];

[0037] Among them, ki represents the humidity change index value of the i-th date, and n represents the number of dates;

[0038] The levels are divided based on the value of ki and the length of the sampling period is adjusted.

[0039] In some embodiments of the present invention, adjusting the duration of the sampling period includes:

[0040] Based on the humidity change index value ki of the i-th date, the level is divided, the average value μ and standard deviation σ of ki are obtained, and multiple types of monitoring periods are generated;

[0041] If ki∈(0,μ-σ), it is the first type of monitoring period, and the monitoring frequency is set to a*(1+b);

[0042] If ki∈(μ-σ,μ+σ), it is the second type of monitoring period, and the monitoring frequency is set to a;

[0043] If ki∈(μ+σ,∞), it is the third type of detection period, and the monitoring frequency is set to a*(1-b);

[0044] Among them, a is the preset sampling period length, and b is the proportional coefficient.

[0045] In some embodiments of the present invention, generating a total evaluation value of the internal humidity of the transformer based on the secondary humidity value and the humidity threshold of each humidity monitoring point includes:

[0046] Obtain the second humidity real-time change data set D of each humidity monitoring point at the current sampling time node, where D = [D1, D2…Di…Dn];

[0047] And determine the humidity threshold A of each monitoring point according to the selected wind distribution model, A=[A1,A2…Ai…An];

[0048] Among them, Ai represents the humidity threshold of the i-th monitoring point, and n represents the number of monitoring points;

[0049] Combined with the humidity threshold A of each monitoring point and the second humidity real-time change data set D of each humidity monitoring point at the current sampling time node, an evaluation value E of each humidity monitoring point at the current sampling time node is generated, and a transformer control strategy is generated based on the evaluation value of each humidity monitoring point at the current sampling time node.

[0050] In some embodiments of the present invention, the generating of the transformer control strategy includes:

[0051] Calculate the difference F between the second humidity real-time change data set D of each humidity monitoring point at the current sampling time node and the humidity threshold value A of each monitoring point, F = [F1, F2...Fi...Fn];

[0052] Fi = Di-Ai;

[0053] Fi represents the difference between the second humidity real-time change data set Di of each humidity monitoring point at the current sampling time node and the humidity threshold Ai of each monitoring point;

[0054] If Fi is a positive number, the current humidity monitoring point is marked as 1;

[0055] If Fi is a negative number, the current humidity monitoring point is marked as 0;

[0056] Count the sum of the marks of each humidity monitoring point at the current sampling time node to generate the evaluation value E of each humidity monitoring point at the current sampling time node;

[0057] Based on the evaluation value E of each humidity monitoring point at the current sampling time node; generate the proportion V of each humidity monitoring point exceeding the threshold;

[0058] V = E / n;

[0059] Where n represents the total number of monitoring points;

[0060] Generate a transformer control strategy based on the value of V.

[0061] In some embodiments of the present invention, the step of generating a transformer control strategy based on the value of V includes:

[0062] Generate a multi-stage transformer control strategy based on the value of V;

[0063] If V>v1, a primary transformer control strategy is generated;

[0064] If V>v2, then generate the secondary transformer control strategy;

[0065] If V>v3; then generate a three-level transformer control strategy;

[0066] Among them, v1, v2, and v3 are constants set according to the internal historical data of the box-type transformer.

[0067] In some embodiments of the present invention, the multi-stage transformer control strategy includes:

[0068] Primary transformer control strategy: controls the monitoring cycle and generates alarm instructions;

[0069] Secondary transformer control strategy: control monitoring cycle and generate dehumidification instructions;

[0070] Three-stage transformer control strategy: generating shutdown commands.

[0071] Compared with the prior art, the humidity monitoring method inside a 35kV box-type transformer according to the embodiment of the present invention has the following beneficial effects:

[0072] By establishing a wind distribution model through the box opening parameters and box structure parameters, we can accurately grasp the impact of external wind on the humidity distribution in the box, determine the reasonable layout of humidity monitoring points under different wind speed conditions, and thus improve the accuracy of humidity monitoring.

[0073] By constructing a wind distribution model, important basic data is provided for subsequent humidity monitoring, equipment operation status evaluation, etc.

[0074] By obtaining the humidity evaluation value of each monitoring point, the humidity distribution inside the transformer can be fully reflected, which helps operation and maintenance personnel to promptly discover monitoring points with abnormal humidity so that targeted measures can be taken, such as adjusting the operating parameters of the dehumidification equipment or conducting key inspections on specific areas, thereby ensuring the normal operation of the transformer.

[0075] The humidity change index k for the current date is generated by counting the number of humidity changes exceeding the threshold on a certain date. The exceeding of different thresholds is comprehensively considered and calculated according to the importance of different thresholds, which can more comprehensively and accurately reflect the overall degree of humidity change on that day.

[0076] By generating evaluation values ​​for each humidity monitoring point, a quantitative assessment of the humidity inside the transformer from multiple monitoring points to the whole is achieved; this helps operation and maintenance personnel to accurately understand the overall status of the humidity inside the transformer

[0077] By monitoring humidity changes in real time and dynamically adjusting control strategies based on monitoring data, precise humidity monitoring and targeted control strategies can effectively maintain the humidity inside the transformer within an appropriate range, thereby ensuring the insulation performance of the transformer and improving the reliability and stability of the entire power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 The present invention discloses a flow chart of a humidity monitoring method inside a 35kV box-type transformer. DETAILED DESCRIPTION

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] like Figure 1 As shown, the embodiment of the present invention discloses a humidity monitoring method inside a 35kV box-type transformer, comprising:

[0084] Establish multiple wind distribution models based on wind speed data and wind speed data, select the corresponding wind distribution model according to the real-time environmental parameters, and set the humidity threshold of each monitoring point according to the selected wind distribution model;

[0085] The transformer internal humidity sampling period is set according to the real-time environmental parameters, and multiple sampling time nodes are generated, wherein the sampling time node is the starting point of the sampling period;

[0086] The primary humidity value of each humidity monitoring point is collected according to the sampling time node, and the primary humidity value of each humidity monitoring point is corrected according to the real-time environmental parameters, and the secondary humidity value is generated according to the correction result;

[0087] Based on the secondary humidity value and humidity threshold of each humidity monitoring point, a total evaluation value of the internal humidity of the transformer is generated, and a control instruction is generated according to the total evaluation value of the internal humidity of the transformer.

[0088] In this embodiment, the wind distribution model is created:

[0089] Input parameters include the size of the box opening, the box structure (such as material thermal conductivity, gap size and position). Use fluid mechanics software (such as ANSYS Fluent) to perform CFD (Computational Fluid Dynamics) simulation to generate a wind speed distribution map; consider the impact of changes in wind speed and direction on airflow.

[0090] Combined with the wind distribution model, the effect of air flow on the humidity inside the box is considered.

[0091] The model needs to consider the relationship between temperature, humidity and material hygroscopicity, and use a suitable physical model to describe the diffusion process of water molecules.

[0092] Match the real-time collected wind speed data with the pre-established wind speed level data set. For example, if the wind speed level data set is divided into 0-2m / s as W1 level, 2-5m / s as W2 level, 5-10m / s as W3 level, etc., when the real-time wind speed is 3m / s, it is matched to W2 level.

[0093] Taking into account the fluctuation of wind speed, if the wind speed is near the boundary of adjacent levels (such as around 2m / s), the weighted average method can be used to determine a more appropriate level.

[0094] The quantified wind direction data is matched with the established wind direction interval data set. For example, if the wind direction is northeast by east, and the wind direction interval data set contains the interval northeast by east (such as AC3), the wind direction interval is determined.

[0095] If the wind direction is at the boundary of two intervals, the interval to which it belongs is determined based on the historical trend of the wind direction or the set priority rules.

[0096] Select wind distribution model

[0097] According to the matched wind speed level and wind direction range, a corresponding model is selected from the established multiple wind distribution models. For example, if the wind speed level is W2 and the wind direction range is AC3, the corresponding Mij (i represents the wind speed level number, j represents the wind direction range number) wind distribution model is selected.

[0098] When establishing a wind distribution model, a set of initial humidity thresholds are pre-set for each monitoring point under each wind distribution model. These initial thresholds can be determined based on the design parameters of the transformer, operating experience, and relevant standards. For example, for a certain monitoring point under a specific wind distribution model, the low threshold of relative humidity is set to 30% and the high threshold is set to 70%.

[0099] Dynamic Adjustment

[0100] The initial humidity threshold is dynamically adjusted according to the changes in real-time environmental parameters and the operating status of the transformer. If the temperature rises, the saturated humidity of the air will increase, and the upper limit of the humidity threshold can be appropriately increased; conversely, if the temperature drops, the upper limit of the humidity threshold can be appropriately lowered.

[0101] Consider the load inside the transformer. If the load is high, the transformer will generate more heat, which may affect the humidity distribution. At this time, the humidity threshold needs to be fine-tuned. For example, when the load increases by 20%, the upper and lower limits of the humidity threshold can be adjusted by ±5% respectively.

[0102] Special situation handling

[0103] When encountering extreme weather conditions (such as high humidity after heavy rain or snowstorm) or abnormal conditions inside the transformer (such as local leakage), the humidity threshold is specially adjusted according to the pre-set rules. For example, for a period of time after a heavy rain, the upper limit of the humidity threshold is raised to 80%, and the humidity changes are closely monitored. Once the humidity shows a downward trend and approaches the normal threshold range, it is gradually restored to the normal threshold setting.

[0104] Embodiment 1:

[0105] The establishment of multiple wind distribution models includes:

[0106] Obtain historical data of wind speed and wind direction entering the box-type transformer;

[0107] The historical data of wind speed entering the box-type transformer is divided into grade intervals to generate a wind speed grade data set W, where W = [W1, W2…Wi…Wn];

[0108] Based on the historical wind direction data, the wind direction interval data set AC entering the box-type transformer is established, AC = [AC1, AC2…ACi…ACm]

[0109] Where Wi represents the i-th wind speed level, ACi represents the i-th wind direction interval, n represents the number of wind speed levels, and m represents the number of wind direction intervals;

[0110] Input wind speed level Wi and wind direction range ACi inside the box-type transformer, and collect wind speed level at each monitoring point inside the box-type transformer;

[0111] Generate a wind distribution model Mi when the input wind speed level is Wi and the wind direction interval is ACi.

[0112] In this embodiment, the historical data of wind speed and wind direction are obtained by installing an anemometer and a wind vane at the entrance of the box-type transformer, so that the historical data of wind speed and wind direction entering the box-type transformer can be obtained. For example, we continuously collect data for one year, and these data are collected every 10 minutes, and a large number of numerical records of wind speed and wind direction are obtained.

[0113] Wind speed level interval division: divide the wind speed historical data into level intervals according to actual needs; for example, divide the wind speed into 5 levels:

[0114] 0-2m / s is W1 (breeze level); 2-5m / s is W2 (light wind level); 5-10m / s is W3 (moderate wind level); 10-15m / s is W4 (strong wind level);

[0115] Above 15m / s is W5 (strong wind level); thus, a wind speed level data set W = [W1, W2, W3, W4, W5] is generated;

[0116] Wind direction interval data set establishment. For the historical wind direction data, we establish the wind direction interval data set AC; for example, taking 45° as an interval, the 360° wind direction is divided into 8 intervals:

[0117] 0-45° is AC1; 45-90° is AC2; 90-135° is AC3; 135-180° is AC4; 180-225° is AC5; 225-270° is AC6; 270-315° is AC7; 315-360° is AC8;

[0118] So AC=[AC1,AC2,AC3,AC4,AC5,AC6,AC7,AC8];

[0119] Internal wind speed collection, when the internal wind speed level of the box-type transformer is W3 (medium wind level, 5-10m / s) and the wind direction range is AC3 (90-135°), we collect wind speed levels at various monitoring points inside the box-type transformer (for example, monitoring points are set at key locations such as the surroundings, top and bottom of the transformer). Assuming we have 10 monitoring points, the collected wind speed levels may vary due to the influence of the internal structure of the box-type transformer.

[0120] Wind force distribution model generation, based on the wind speed level data collected at each monitoring point, we can generate a wind force distribution model M3 with an input wind speed level of W3 and a wind direction interval of AC3. This model can be represented by a matrix or function, for example, a 10×10 matrix, where the elements in the matrix represent the wind speed relationship or energy transfer relationship between each monitoring point, etc. It reflects the wind force distribution inside the box-type transformer under this specific wind speed and wind direction input condition.

[0121] Embodiment 2:

[0122] The collecting of the primary humidity values ​​of each humidity monitoring point includes:

[0123] Select the corresponding wind distribution model according to the real-time environmental parameters, and set the humidity threshold of each monitoring point according to the selected wind distribution model;

[0124] Monitor the real-time data of humidity changes at the current monitoring point, and divide the monitored real-time data of humidity changes into intervals;

[0125] Evaluate the humidity of each interval and generate the first-level humidity value set H of the current sampling period, H = [H1, H2…Hi…Hn];

[0126] Wherein, Hi represents the humidity evaluation value of the i-th monitoring point in the current sampling period; n represents the total number of monitoring points.

[0127] In this embodiment, the wind distribution model is selected

[0128] In a large industrial plant, there are multiple devices running inside, and humidity monitoring points are distributed in different areas of the plant. Real-time environmental parameters include the current wind speed (5m / s), wind direction (120°), etc. Based on these real-time environmental parameters, we select the previously established wind distribution model M3 (the wind distribution model corresponding to the range of wind speed of 5-10m / s and wind direction of 90-135°).

[0129] Humidity threshold setting

[0130] Based on the selected wind distribution model M3, we set the humidity threshold of each humidity monitoring point. For example, for monitoring point 1 located in the corner of the factory, since the wind has a relatively small impact on it, we set its humidity threshold to be normal if the humidity change is within ±3%, and if it exceeds this range, it needs to be paid special attention; for monitoring point 2 located in the center of the factory and close to the vent, since the wind may bring more water vapor exchange, we set its humidity threshold to be normal if the humidity change is within ±5%.

[0131] Humidity data monitoring

[0132] A humidity sensor is installed at each humidity monitoring point, and the real-time data of humidity change is monitored with a sampling period of every 10 minutes. For example, the real-time data of humidity change at monitoring point 1 in a sampling period starts from 50%, rises to 52%, and then drops to 49%, etc.

[0133] Interval division and humidity evaluation

[0134] For monitoring point 1, we divide the humidity change range into intervals. For example, 48%-52% is divided into the normal interval, and less than 48% or greater than 52% is divided into the abnormal interval. Humidity evaluation is performed based on the distribution of the real-time humidity change data monitored in these intervals. If most of the data is in the normal interval, then its humidity evaluation value H1 is higher, assuming it is 80 (the full score is 100); if more data falls in the abnormal interval, the humidity evaluation value will be lower.

[0135] For all n monitoring points, we operate in this way. For example, the humidity evaluation value of monitoring point 2 is H2, the humidity evaluation value of monitoring point 3 is H3, and so on. Finally, the first-level humidity value set H = [H1, H2, H3...Hi...Hn] of the current sampling period is generated.

[0136] Embodiment 3:

[0137] The generating of the secondary humidity value includes:

[0138] Obtain the historical environmental data of the external environment of the box-type transformer and the historical humidity data of each monitoring point inside the box-type transformer;

[0139] Evaluate the external historical environmental data of the box-type transformer to obtain the external environmental score S, and obtain the evaluation data set H of each monitoring point inside the box-type transformer under the wind distribution model Mi;

[0140] Calculate the variance D(H) of the evaluation data set H at each monitoring point inside the box-type transformer;

[0141] Based on the variance D(H) of the evaluation data set H, a first humidity compensation data set C is generated, where C = [C1, C2...Ci...Cn];

[0142] Wherein, Ci represents the first humidity compensation value of the i-th monitoring point, and n represents the number of monitoring points;

[0143] The first humidity compensation data set C is the compensation value of the internal humidity monitoring data under the wind distribution model Mi when the external environment score is S.

[0144] Based on the first humidity compensation data set C, the primary humidity value set H is corrected, and a second humidity real-time change data set D is generated according to the correction result, where D = [D1, D2...Di...Dn];

[0145] Wherein, Di represents the second real-time temperature data value of the i-th monitoring point, and n represents the number of monitoring points.

[0146] Through the above steps, the humidity data set D' more accurately reflects the actual humidity changes inside the box, improving the reliability and accuracy of the monitoring system. This correction process makes full use of historical data and model predictions, effectively reducing the measurement errors caused by environmental factors.

[0147] In this embodiment, the external historical environmental data of the box-type transformer is obtained from the weather station, including the temperature, wind speed, rainfall and other data of the past year, and these data are recorded at one point per hour. At the same time, we obtain the historical humidity data of the past year from the humidity sensors installed at various monitoring points (assuming there are 5 monitoring points) inside the box-type transformer.

[0148] External environment score

[0149] Evaluate the external historical environmental data of the box-type transformer. For example, if the external temperature is relatively stable (fluctuation within ±5°C), the wind speed is low (average wind speed is less than 3m / s) and the rainfall is small (monthly rainfall is less than 50mm), the external environment score S=80 (full score 100) is given. If the external environment fluctuates greatly, the score will be reduced accordingly.

[0150] Internal evaluation dataset acquisition

[0151] According to the current wind distribution model Mi (assuming it is the M3 model mentioned above), obtain the evaluation data set H of each monitoring point inside the box-type transformer. For example, under the M3 model, for 5 monitoring points, the evaluation data set H = [H1 = 70, H2 = 75, H3 = 80, H4 = 65, H5 = 72] obtained by the previous humidity evaluation method.

[0152] Based on the variance D(H) of the evaluation data set H, the first humidity compensation data set C is generated. Assuming that we have an empirically established functional relationship, when D(H) = 21.84, the first humidity compensation data set C generated for the five monitoring points is [C1 = 2, C2 = 3, C3 = 1, C4 = 4, C5 = 2].

[0153] The humidity data set is corrected, and the primary humidity value set H is corrected based on the first humidity compensation data set C. For monitoring point 1, the corrected humidity evaluation value D1 = H1 + C1 = 70 + 2 = 72; for monitoring point 2, D2 = H2 + C2 = 75 + 3 = 78; and so on.

[0154] The second real-time humidity change data set D = [D1 = 72, D2 = 78, D3 = 81, D4 = 69, D5 = 74] is generated based on the correction result. This data set D more accurately reflects the actual humidity changes inside the box, and effectively improves the reliability and accuracy of the humidity monitoring system by considering factors such as the external environment score, the variance of the internal humidity data, and the humidity compensation value.

[0155] Embodiment 4:

[0156] The step of selecting and setting the transformer internal humidity sampling period according to the real-time environmental parameters includes:

[0157] Based on historical data, set the threshold B of humidity change, B = [B1, B2, B3];

[0158] Wherein, B1 represents the first threshold value of humidity change, B2 represents the second threshold value of humidity change, and B3 represents the third threshold value of humidity change;

[0159] Count the number of humidity changes exceeding the threshold on a certain date, and based on the statistical results, generate the humidity change index k for the current date, k = n1 + 2*(n2-n1) + 3*(n3-n2-n1);

[0160] Where n1 represents the number of humidity changes on the current date that exceed the first threshold, n2 represents the number of humidity changes on the current date that exceed the second threshold, and n3 represents the number of humidity changes on the current date that exceed the third threshold;

[0161] Based on the humidity change index k of the current date, generate the humidity change index dataset K of the current year, K = [k1, k2…ki…kn];

[0162] Among them, ki represents the humidity change index value of the i-th date, and n represents the number of dates;

[0163] The levels are divided based on the value of ki and the length of the sampling period is adjusted.

[0164] In this embodiment, the humidity change threshold setting stage

[0165] The humidity change threshold B is set based on historical data. Assume that we have analyzed the humidity data inside the box-type transformer for the past five years and divided the humidity change threshold into three levels.

[0166] B1 (the first humidity change threshold) is set to 5%, that is, when the humidity change exceeds 5% within a sampling period, it is considered to have reached the first threshold.

[0167] B2 (the second humidity change threshold) is set to 10%, indicating that the second threshold is reached when the humidity change exceeds 10%.

[0168] B3 (the third humidity change threshold) is set to 15%. If the humidity change exceeds 15%, the third threshold is reached.

[0169] Data statistics and indicator calculation

[0170] Count the number of humidity changes that exceed the threshold in one day. Assume that the humidity sensor collects data once an hour during the day, for a total of 24 times.

[0171] By analyzing the data, it is found that the number of times n1 when the humidity changes exceeds the first threshold (5%) is 5 times. The number of times n2 when the humidity changes exceeds the second threshold (10%) is 3 times (here n2 includes some of the cases in n1, because exceeding the second threshold must exceed the first threshold). The number of times n3 when the humidity changes exceeds the third threshold (15%) is 1 time (also n3 includes some of the cases in n2 and n1).

[0172] Calculate the humidity change index k on the current date according to the formula:

[0173] k = n1 + 2*(n2 - n1) + 3*(n3 - n2 - n1)

[0174] Substitute the values:

[0175] k = 5 + 2*(3 - 5) + 3*(1 - 3 - 5)

[0176] k = 5 + 2*(-2) + 3*(-7)

[0177] k = 5 - 4 - 21 = -20

[0178] Dataset generation

[0179] Calculate for each day of the whole year according to the above method to obtain the dataset K of the humidity change degree index for the current year. Assume that October 1st is the i-th day, and the calculated ki = -20. As the index values of each day of the whole year are calculated, finally K = [k1, k2... ki... kn], where n is 365;

[0180] Grade division and sampling period adjustment

[0181] Divide the value of ki into grades. For example, we divide the value of ki into three grades:

[0182] When ki ≥ 0, it is considered that the humidity change degree is high, and the sampling period is shortened from the original 1 hour to 30 minutes to monitor the humidity change more timely.

[0183] When -10 < ki < 0, it is considered that the humidity change degree is medium, and the sampling period remains unchanged at 1 hour.

[0184] When ki ≤ -10, it is considered that the humidity change degree is low, and the sampling period is extended to 2 hours, which can reduce unnecessary data collection and also monitor the humidity change within an acceptable accuracy range.

[0185] Example 5:

[0186] The duration of adjusting the sampling period includes:

[0187] Based on the humidity change degree index value ki of the i-th date, conduct grade division, obtain the average value μ and standard deviation σ of ki, and generate multiple types of monitoring time periods;

[0188] If ki ∈ (0, μ - σ), it is the first type of monitoring time period, and the monitoring frequency is set to a*(1 + b);

[0189] If ki ∈ (μ - σ, μ + σ), it is the second type of monitoring time period, and the monitoring frequency is set to a;

[0190] If ki∈(μ+σ,∞), it is the third type of detection period, and the monitoring frequency is set to a*(1-b);

[0191] Among them, a is the preset sampling period length, and b is the proportional coefficient.

[0192] Embodiment 6:

[0193] The method of generating a total evaluation value of the internal humidity of the transformer based on the secondary humidity value and the humidity threshold of each humidity monitoring point includes:

[0194] Obtain the second humidity real-time change data set D of each humidity monitoring point at the current sampling time node, where D = [D1, D2…Di…Dn];

[0195] And determine the humidity threshold A of each monitoring point according to the selected wind distribution model, A=[A1,A2…Ai…An];

[0196] Among them, Ai represents the humidity threshold of the i-th monitoring point, and n represents the number of monitoring points;

[0197] Combined with the humidity threshold A of each monitoring point and the second humidity real-time change data set D of each humidity monitoring point at the current sampling time node, an evaluation value E of each humidity monitoring point at the current sampling time node is generated, and a transformer control strategy is generated based on the evaluation value of each humidity monitoring point at the current sampling time node.

[0198] In this embodiment:

[0199] Assuming that the current sampling time node is 10:00 am on October 28, 2024, we obtain the second humidity real-time change data set D of each humidity monitoring point. For example, there are 5 humidity monitoring points inside a box-type transformer, then D = [D1 = 60, D2 = 65, D3 = 58, D4 = 62, D5 = 55], where the value represents the humidity value of each monitoring point (assuming the unit is %).

[0200] The humidity threshold A of each monitoring point is determined according to the selected wind distribution model (assuming it is the M3 model mentioned above). Due to the influence of factors such as the location of the monitoring point and its relationship with the vent, for these five monitoring points:

[0201] A1 (humidity threshold of monitoring point 1) is set to a humidity value of 58-62% as normal, with an evaluation value of 80 (full score 100). 5 points will be deducted for every 1% deviation outside this range.

[0202] A2 (humidity threshold of monitoring point 2) is set to a humidity value of 60-65% as normal, the evaluation value is 85, and 3 points will be deducted for every 1% deviation outside the range.

[0203] A3 (humidity threshold of monitoring point 3) is set to a humidity value of 55-60% as normal, with an evaluation value of 90. 4 points will be deducted for every 1% deviation outside the range.

[0204] A4 (humidity threshold of monitoring point 4) is set to a humidity value of 59-63% as normal, with an evaluation value of 82. 2 points will be deducted for every 1% deviation outside the range.

[0205] A4 (humidity threshold of monitoring point 5) is set to a humidity value of 53-58% as normal, with an evaluation value of 88. 3 points will be deducted for every 1% deviation outside the range.

[0206] For monitoring point 1, D1=60. According to the evaluation standard of A1, the humidity value is within the normal range, so E1=80.

[0207] D2=65 at monitoring point 2, the normal range in A2 is 60-65%, and the humidity value is just on the boundary, so E2=85.

[0208] D3=58 at monitoring point 3, the normal range in A3 is 55-60%, the humidity value is within the normal range, and E3=90.

[0209] The D4 of monitoring point 4 is 62, the normal range of A4 is 59-63%, the humidity value is within the normal range, and E4 is 82.

[0210] The D5 of monitoring point 5 is 55, the normal range of A5 is 53-58%, the humidity value is within the normal range, and E5 is 88.

[0211] Embodiment 7:

[0212] The generating of the transformer control strategy includes:

[0213] Calculate the difference F between the second humidity real-time change data set D of each humidity monitoring point at the current sampling time node and the humidity threshold value A of each monitoring point, F = [F1, F2...Fi...Fn];

[0214] Fi = Di-Ai;

[0215] Fi represents the difference between the second humidity real-time change data set Di of each humidity monitoring point at the current sampling time node and the humidity threshold Ai of each monitoring point;

[0216] If Fi is a positive number, the current humidity monitoring point is marked as 1;

[0217] If Fi is a negative number, the current humidity monitoring point is marked as 0;

[0218] Count the sum of the marks of each humidity monitoring point at the current sampling time node to generate the evaluation value E of each humidity monitoring point at the current sampling time node;

[0219] Based on the evaluation value E of each humidity monitoring point at the current sampling time node; generate the proportion V of each humidity monitoring point exceeding the threshold;

[0220] V = E / n;

[0221] Where n represents the total number of monitoring points;

[0222] Generate a transformer control strategy based on the value of V.

[0223] Embodiment 8:

[0224] The transformer control strategy is generated based on the value of V, including:

[0225] Generate a multi-stage transformer control strategy based on the value of V;

[0226] If V>v1, a primary transformer control strategy is generated;

[0227] If V>v2, then generate the secondary transformer control strategy;

[0228] If V>v3, a three-level transformer control strategy is generated;

[0229] Among them, v1, v2, and v3 are constants set according to the internal historical data of the box-type transformer.

[0230] Embodiment 9:

[0231] The multi-stage transformer control strategy includes:

[0232] Primary transformer control strategy: controls the monitoring cycle and generates alarm instructions;

[0233] Secondary transformer control strategy: control monitoring cycle and generate dehumidification instructions;

[0234] Three-stage transformer control strategy: generating shutdown commands.

[0235] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A humidity monitoring method inside a 35kV box-type transformer, characterized in that: include: Establish multiple wind distribution models based on wind speed data and wind speed data, select the corresponding wind distribution model according to the real-time environmental parameters, and set the humidity threshold of each monitoring point according to the selected wind distribution model; The transformer internal humidity sampling period is set according to the real-time environmental parameters, and multiple sampling time nodes are generated, wherein the sampling time node is the starting point of the sampling period; The primary humidity value of each humidity monitoring point is collected according to the sampling time node, and the primary humidity value of each humidity monitoring point is corrected according to the real-time environmental parameters, and the secondary humidity value is generated according to the correction result; Based on the secondary humidity value and humidity threshold of each humidity monitoring point, a total evaluation value of the internal humidity of the transformer is generated, and a control instruction is generated according to the total evaluation value of the internal humidity of the transformer.

2. The humidity monitoring method inside the 35kV box-type transformer according to claim 1, characterized in that: The establishment of multiple wind distribution models includes: Obtain historical data of wind speed and wind direction entering the box-type transformer; The historical data of wind speed entering the box-type transformer are divided into grade intervals to generate a wind speed grade data set W, where W = [W1, W2…W i …W n ]; Based on the historical wind direction data, the wind direction interval data set AC entering the box-type transformer is established, AC = [AC1, AC2…AC i …AC m ] Where Wi represents the i-th wind speed level, AC i represents the i-th wind direction interval, n represents the number of wind speed levels, and m represents the number of wind direction intervals; The internal input wind speed level of the box-type transformer is W i , the wind direction interval is AC i Internal wind, collect wind speed levels at various monitoring points inside the box-type transformer; Generate input wind speed level W i , the wind direction interval is AC i Wind force distribution model M under the condition i .

3. The humidity monitoring method inside the 35kV box-type transformer according to claim 1 is characterized in that: The collecting of the primary humidity values ​​of each humidity monitoring point includes: Select the corresponding wind distribution model according to the real-time environmental parameters, and set the humidity threshold of each monitoring point according to the selected wind distribution model; Monitor the real-time data of humidity changes at the current monitoring point, and divide the monitored real-time data of humidity changes into intervals; Evaluate the humidity of each interval and generate the first-level humidity value set H of the current sampling period, H = [H1, H2…Hi…Hn]; Wherein, Hi represents the humidity evaluation value of the i-th monitoring point in the current sampling period; n represents the total number of monitoring points.

4. The humidity monitoring method inside the 35kV box-type transformer according to claim 1 is characterized in that: The generating of the secondary humidity value includes: Obtain the historical environmental data of the external environment of the box-type transformer and the historical humidity data of each monitoring point inside the box-type transformer; Evaluate the external historical environmental data of the box-type transformer, obtain the external environment score S, and obtain the wind distribution model M i Evaluation data set H of each monitoring point inside the lower box transformer; Calculate the variance D(H) of the evaluation data set H at each monitoring point inside the box-type transformer; Based on the variance D(H) of the evaluation data set H, a first humidity compensation data set C is generated, where C = [C1, C2...Ci...Cn]; Wherein, Ci represents the first humidity compensation value of the i-th monitoring point, and n represents the number of monitoring points; When the first humidity compensation data set C is the external environment score S, the wind distribution model M i The compensation value for the internal humidity monitoring data is as follows; Based on the first humidity compensation data set C, the primary humidity value set H is corrected, and a second humidity real-time change data set D is generated according to the correction result, where D = [D1, D2...Di...Dn]; Wherein, Di represents the second real-time temperature data value of the i-th monitoring point, and n represents the number of monitoring points.

5. The humidity monitoring method inside the 35kV box-type transformer according to claim 4, characterized in that: The step of selecting and setting the transformer internal humidity sampling period according to the real-time environmental parameters includes: Based on historical data, set the threshold B of humidity change, B = [B1, B2, B3]; Wherein, B1 represents the first threshold value of humidity change, B2 represents the second threshold value of humidity change, and B3 represents the third threshold value of humidity change; Count the number of humidity changes exceeding the threshold on a certain date, and based on the statistical results, generate the humidity change index k for the current date, k = n1 + 2*(n2-n1) + 3*(n3-n2-n1); Where n1 represents the number of humidity changes on the current date that exceed the first threshold, n2 represents the number of humidity changes on the current date that exceed the second threshold, and n3 represents the number of humidity changes on the current date that exceed the third threshold; Based on the humidity change index k of the current date, generate the humidity change index dataset K of the current year, K = [k1, k2…k i …k n ]; Among them, k i represents the humidity change index value of the i-th date, and n represents the number of dates; Based on k i The value is divided into levels and the length of the sampling period is adjusted.

6. The humidity monitoring method inside the 35kV box-type transformer according to claim 5, characterized in that: The adjusting the duration of the sampling period includes: The humidity change index value k based on the i-th date i Perform level division and obtain k i The mean value μ and standard deviation σ of the , generate multiple types of monitoring periods; If k i ∈(0,μ-σ), then it is the first type of monitoring period, and the monitoring frequency is set to a*(1+b); If k i ∈(μ-σ,μ+σ), then it is the second type of monitoring period, and the monitoring frequency is set to a; If k i ∈(μ+σ,∞), then it is the third type of detection period, and the monitoring frequency is set to a*(1-b); Among them, a is the preset sampling period length, and b is the proportional coefficient.

7. The humidity monitoring method inside the 35kV box-type transformer according to claim 4, characterized in that: The method of generating a total evaluation value of the internal humidity of the transformer based on the secondary humidity value and the humidity threshold of each humidity monitoring point includes: Obtain the second humidity real-time change data set D of each humidity monitoring point at the current sampling time node, where D = [D1, D2…Di…Dn]; And determine the humidity threshold A of each monitoring point according to the selected wind distribution model, A=[A1,A2…Ai…An]; Among them, Ai represents the humidity threshold of the i-th monitoring point, and n represents the number of monitoring points; Combined with the humidity threshold A of each monitoring point and the second humidity real-time change data set D of each humidity monitoring point at the current sampling time node, an evaluation value E of each humidity monitoring point at the current sampling time node is generated, and a transformer control strategy is generated based on the evaluation value of each humidity monitoring point at the current sampling time node.

8. The humidity monitoring method inside the 35kV box-type transformer according to claim 5, characterized in that: The generating of the transformer control strategy includes: Calculate the difference F between the second humidity real-time change data set D of each humidity monitoring point at the current sampling time node and the humidity threshold value A of each monitoring point, F = [F1, F2...Fi...Fn]; Fi = Di-Ai; Fi represents the difference between the second humidity real-time change data set Di of each humidity monitoring point at the current sampling time node and the humidity threshold Ai of each monitoring point; If Fi is a positive number, the current humidity monitoring point is marked as 1; If Fi is a negative number, the current humidity monitoring point is marked as 0; Count the sum of the marks of each humidity monitoring point at the current sampling time node to generate the evaluation value E of each humidity monitoring point at the current sampling time node; Based on the evaluation value E of each humidity monitoring point at the current sampling time node; generate the proportion V of each humidity monitoring point exceeding the threshold; V = E / n; Where n represents the total number of monitoring points; Generate a transformer control strategy based on the value of V.

9. The humidity monitoring method inside the 35kV box-type transformer according to claim 8, characterized in that: The transformer control strategy is generated based on the value of V, including: Generate a multi-stage transformer control strategy based on the value of V; If V>v1, a primary transformer control strategy is generated; If V>v2, then generate the secondary transformer control strategy; If V>v3, a three-level transformer control strategy is generated; Among them, v1, v2, and v3 are constants set according to the internal historical data of the box-type transformer.

10. The humidity monitoring method inside the 35kV box-type transformer according to claim 9, characterized in that: The multi-stage transformer control strategy includes: Primary transformer control strategy: controls the monitoring cycle and generates alarm instructions; Secondary transformer control strategy: control monitoring cycle and generate dehumidification instructions; Three-stage transformer control strategy: generating shutdown commands.

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