High-loss transformer area treatment system based on big data internet of things and HPLC (High Performance Liquid Chromatography)

Abstract

The invention discloses a high-loss transformer area treatment system based on a big data internet of things and HPLC, and belongs to the technical field of high-loss transformer area treatment. The system comprises a position detection module which is used for obtaining the arrangement position of each power line link in a transformer area; the power line carrier communication module is used for collecting current and voltage of a power line; the line loss calculation module is used for randomly generating a distance range value I and calculating power line loss in the distance range value I of each power line link; and the treatment module is used for comparing the power line loss within the distance range value I of each power line link, and outputting a first high-loss transformer area treatment scheme if the power line loss difference exceeds a threshold value. According to the method, whether the transformer area high loss exists or not is determined by comparing the line loss data of different geographic positions and comparing the line loss data of the same geographic position at different times, and the reason causing the transformer area high loss and the position of the transformer area high loss can be rapidly determined.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-loss substation area management, and specifically is a high-loss substation area management system based on big data Internet of Things and HPLC. Background Art

[0002] The line loss rate refers to the percentage of (power supply - power sales) / power supply in the substation. If the line loss rate of the substation exceeds 10%, the substation is identified as a high-loss substation. To address these problems, it is necessary to analyze the causes of high losses in the substation and adopt effective methods to deal with the problems.

[0003] In the prior art, a metering data statistics method is adopted, that is, the timed meter reading of the area total meter and each household meter is realized through the electricity consumption collection system, and then the meter reading data is compared with the area total meter data to determine the existing line loss; however, the collection results of the area line loss are relatively rough, and it is difficult to accurately and timely judge the line loss related information, which is not convenient for the management of high-loss areas.

[0004] To this end, Chinese patent document CN112615428A records a line loss analysis and management system and method, which summarizes a quasi-real-time line loss analysis system, an intelligent substation topology analysis system, an electricity theft analysis system and an intelligent meter abnormality perception system. Among them, after the quasi-real-time line loss analysis system collects the electric energy information, it conducts preliminary intelligent analysis and judgment to determine the type of line loss. According to the type of judgment, the fault information is pushed to the corresponding professional intelligent substation topology analysis system, electricity theft analysis system, and intelligent meter abnormality perception system to classify the fault and provide corresponding solutions. Finally, the work order is formed by the work order flow push system and pushed to the corresponding operation and maintenance terminal. At this time, the pushed information includes not only the fault information, but also the reference fault solution, thereby realizing efficient fault operation and maintenance of line loss.

[0005] The above technical solution can basically determine line loss anomalies more accurately and timely, but it is difficult to determine the location of the line loss anomaly and the possible causes of the line loss anomaly, resulting in the efficiency of high-loss substation management cannot be guaranteed, causing economic losses to power supply companies. Summary of the invention

[0006] The purpose of the present invention is to provide a high-loss area management system based on big data Internet of Things and HPLC to solve the problem that it is difficult to determine the location of abnormal line loss and the possible causes of abnormal line loss.

[0007] In order to achieve the above-mentioned purpose, the technical scheme of the present invention is as follows: a high-loss area management system based on big data Internet of Things and HPLC, including a position detection module, a power line acquisition module, a line loss calculation module and a management module;

[0008] The location detection module is used to obtain the layout location of each power line link in the substation area;

[0009] The power line carrier communication module is used to collect the current and voltage of the power line;

[0010] The line loss calculation module is used to randomly generate a distance range value I, and randomly select at least two power line links according to the layout position of the power line link, select a detection starting point A with the same distance from the transformer on each power line link, extract the current and voltage of the power line within the distance range according to the detection starting point A and the distance range value I, and calculate the power line loss within the distance range value I of each power line link;

[0011] The management module is used to compare the power line losses within the distance range value I of each power line link. If the difference in power line losses exceeds a threshold, the first high-loss area management plan is output.

[0012] Furthermore, the line loss calculation module is also used to randomly generate a detection interval, randomly select the same power line link according to the layout position of the power line link, and calculate the power line loss within the distance range value I of multiple power line links according to the detection starting point A, the distance range value I and the detection interval according to the detection interval;

[0013] The management module is also used to compare the power line losses within a plurality of power line link distance range values ​​I. If the difference in power line losses exceeds a threshold, a management plan for the second highest loss area is output.

[0014] Furthermore, the first high-loss substation management plan includes whether there is electricity theft and the location where the electricity theft occurs; the second high-loss substation management plan includes whether there is a metering device failure, line damage or line abnormality, and the location where the metering device failure, line damage or line abnormality occurs.

[0015] Furthermore, the power line carrier communication module is also used to send a verification packet along the selected power line link when the management module outputs the first high-loss area management plan;

[0016] The governance module is also used to obtain the detection end point B according to the detection starting point A and the distance range value I, obtain a verification package at the detection end point B, and obtain whether the verification package has completely passed the detection end point B. If the verification package has not completely passed the detection end point B, the location of the electricity theft is obtained based on the direction of the detection verification package.

[0017] Further, the detection range value is 10% of the total length of the shortest power line link among the selected power line links.

[0018] Furthermore, it also includes a device prediction module, which is used to predict the aging degree of the metering devices and power lines on each power line link in the substation area, and whether the aging degree of the metering devices and power lines within the distance range value I matches the line loss calculated by the line loss calculation module. The management module is also used to output a management plan for the second high-loss substation, specifically a metering device failure, line damage or line abnormality, based on whether the aging degree of the metering devices and power lines within the distance range value I matches the line loss calculated by the line loss calculation module.

[0019] Further, the detection starting point A is randomly generated when the line loss calculation module randomly generates the distance range value I.

[0020] Furthermore, the line loss calculation module calculates the line loss rate within the distance range value I by the following formula:

[0021] Line loss rate = [(P A -P B ) / P A ]×100%

[0022] Where: P A To detect the power of starting point A, P B Detect the power at end point B.

[0023] The above scheme has the following beneficial effects:

[0024] 1. The present invention obtains the location of the power cable layout in the substation through the existing Internet of Things big data collection method, and determines whether there is high loss in the substation by comparing the line loss data of different geographical locations with the same distance from the transformer and within the same length range, and whether there is electricity theft due to the high speed of the substation; by comparing the line loss data of the same power cable at the same geographical location with the same distance from the transformer and within the same length range at different times, it determines whether there is high loss, whether there is a fault or abnormality such as a metering device failure or line damage. Compared with the existing technology, it can not only determine whether there is high loss in the substation, but also determine the specific cause of the high loss, and based on the detected position, it can determine the location where the line loss abnormality exists.

[0025] 2. The present invention monitors the whereabouts of the verification package by sending the verification package based on HPLC. When the verification package does not reach the detection end point completely, it can determine whether there is an abnormality in the power cable along the path of the verification package; and determine the abnormal position of the power cable in combination with the whereabouts of the verification package, so as to further accurately determine the abnormal position of the power cable.

[0026] 3. The present invention detects electricity theft by comparing line loss data at different geographical locations to identify abnormal line loss conditions. If the line loss at a certain geographical location is much higher than that at similar locations, it means that electricity theft may exist in the power cable.

[0027] For metering device failure and line damage: By comparing line loss data at different times in the same geographical location, metering device failure or line damage can be quickly discovered. If the line loss rate at a certain location changes significantly in a short period of time, it may mean that the metering device is faulty and there is a problem with the line.

[0028] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a structural schematic diagram of an embodiment of the high-loss area management system based on big data Internet of Things and HPLC of the present invention. DETAILED DESCRIPTION

[0030] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0031] In the description of the present invention, it is necessary to understand that the terms "longitudinal", "lateral", "vertical", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "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 invention 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 invention.

[0032] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0033] The following is further described in detail through specific implementation methods:

[0034] The reference numerals in the drawings of the specification include: position detection module 1, power line acquisition module 2, line loss calculation module 3, management module 4, and device prediction module 5.

[0035] Example

[0036] As attached Figure 1As shown: A high-loss area management system based on big data Internet of Things and HPLC, including a position detection module 1, a power line acquisition module 2, a line loss calculation module 3 and a management module 4; the position detection module 1, the power line acquisition module 2, the line loss calculation module 3 and the management module 4 are signal-connected in sequence.

[0037] The position detection module 1 is used to obtain the layout position of each power line link in the substation area; wherein the layout position of the power line link can be obtained by the following method:

[0038] 1: The power company or grid operation department responsible for the substation usually has detailed information about the power lines in the substation, including the location of each power line link.

[0039] 2: The power company may provide relevant information about the power lines in the substation area on its official website or other public platforms. You can search for relevant regional information or substation layout maps on its website.

[0040] 3: Conduct on-site investigation.

[0041] 4: Apply to relevant government departments to obtain authorization for the location of power line layout in the substation area.

[0042] 5: Use aerial photography, remote sensing technology, GIS (Geographic Information System) and other technologies to obtain the layout location information of power lines. These technologies can provide high-resolution images and geographic data for accurate positioning of power lines.

[0043] When the power cable laying position changes, the position detection module 1 should also update the power line link laying position to obtain the latest and accurate power line link laying position in the substation area.

[0044] The power line carrier communication module is used to collect the current and voltage of the power line; the current and voltage collected from the power line specifically include the total current and voltage of each power cable link in the substation area, and the current and voltage between each metering device on the power cable link.

[0045] The line loss calculation module 3 is used to randomly generate a distance range value I, and randomly select at least two power line links according to the layout position of the power line link, select a detection starting point A with the same distance transformer on each power line link, extract the current and voltage of the power line within the distance range according to the detection starting point A and the distance range value I, and calculate the power line loss within the distance range value I of each power line link.

[0046] In this embodiment, it is set to select two power line links for detection by default, the detection starting point A is usually at a certain metering device, and the detection range value I is 10% of the total length of the shortest power line link in the selected power line links; the detection starting point A is randomly generated when the line loss calculation module 3 randomly generates the distance range value I.

[0047] The management module 4 is used to compare the power line losses within the distance range value I of each power line link. If the difference in power line losses exceeds a threshold, a first high-loss area management plan is output.

[0048] The line loss calculation module 3 is also used to randomly generate detection intervals, randomly select the same power line link according to the layout position of the power line link, and calculate the power line loss within the distance range value I of multiple power line links according to the detection starting point A, the distance range value I and the detection interval according to the detection interval.

[0049] The management module 4 is also used to compare the power line losses within a plurality of power line link distance range values ​​I. If the difference in power line losses exceeds a threshold, a second high-loss area management plan is output.

[0050] The above-mentioned first high-loss substation management plan includes whether there is electricity theft and the location where the electricity theft occurs; the second high-loss substation management plan includes whether there is a metering device failure, line damage or line abnormality, and the location where the metering device failure, line damage or line abnormality occurs.

[0051] In order to further accurately determine the location of the electricity theft, the power line carrier communication module is also used to send a verification package along the selected power line link when the management module 4 outputs the first high-loss area management plan.

[0052] The governance module 4 is also used to obtain the detection end point B according to the detection starting point A and the distance range value I, obtain a verification package at the detection end point B, and obtain whether the verification package has completely passed the detection end point B. If the verification package has not completely passed the detection end point B, the location of the electricity theft is obtained based on the direction of the detection verification package.

[0053] The line loss calculation module 3 calculates the line loss rate within the distance range value I by the following formula:

[0054] Line loss rate = [(P A -P B ) / P A ]×100%

[0055] Where: P A To detect the power of starting point A, P B Detect the power at end point B.

[0056] The degree of aging of power lines can directly affect the resistance and insulation performance of the line. Severely aged cables or wires may cause increased resistance, thereby increasing line losses. In addition, aging insulation materials may cause insulation performance to deteriorate, increase leakage current, and further increase line losses.

[0057] Therefore, this embodiment also includes a device prediction module 5, which is used to predict the aging degree of the metering devices and power lines on each power line link in the substation area, and whether the aging degree of the metering devices and power lines within the distance range value I matches the line loss calculated by the line loss calculation module 3. The management module 4 is also used to output the second high-loss substation management plan, specifically metering device failure, line damage or line abnormality, based on whether the aging degree of the metering devices and power lines within the distance range value I matches the line loss calculated by the line loss calculation module 3.

[0058] Specifically, the device prediction module 5 is an equipment aging prediction model, and its construction method is as follows:

[0059] 1. Data collection:

[0060] Collect data related to power line aging. This may include historical operating data of the power line, temperature, humidity, current, voltage, characteristics of cable insulation materials, etc. Ensure the quality and integrity of the data.

[0061] 2. Data preprocessing:

[0062] Clean the data to handle missing values ​​and outliers. Standardize or normalize the data to ensure that different features are at the same scale.

[0063] 3. Feature Engineering:

[0064] According to the problem background and domain knowledge, appropriate features are selected and feature extraction, transformation and selection are performed. These features should be able to capture the potential patterns of power line aging.

[0065] 4. Data division:

[0066] Split the dataset into training and testing sets, often using cross-validation or holdout techniques, to evaluate model performance.

[0067] 5. Model selection:

[0068] Choose a machine learning or deep learning model that fits the problem. For power line aging prediction, you can consider using regression models, time series models, or deep learning models such as recurrent neural networks (RNNs) or convolutional neural networks (CNNs).

[0069] 6. Model training:

[0070] Train the model using the training set. Depending on the model chosen, you may need to adjust hyperparameters to improve performance. During training, monitor the performance metrics of the model and use techniques such as regularization to prevent overfitting.

[0071] 7. Model evaluation:

[0072] Use the test set to evaluate the performance of the model. Common performance indicators include mean square error (MSE), root mean square error (RMSE), coefficient of determination (R-squared), etc. Based on the evaluation results, the model can be further optimized.

[0073] 8. Model deployment:

[0074] After the model is verified and obtains satisfactory performance, it is deployed in the actual environment for real-time or periodic prediction of power line aging degree.

[0075] The above is only an embodiment of the present invention, and the common knowledge such as the known specific structure and / or characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A high-loss area management system based on big data Internet of Things and HPLC, characterized by: It includes location detection module, power line acquisition module, line loss calculation module and management module; The location detection module is used to obtain the layout location of each power line link in the substation area; The power line carrier communication module is used to collect the current and voltage of the power line; The line loss calculation module is used to randomly generate a distance range value I, and randomly select at least two power line links according to the layout position of the power line link, select a detection starting point A with the same distance from the transformer on each power line link, extract the current and voltage of the power line within the distance range according to the detection starting point A and the distance range value I, and calculate the power line loss within the distance range value I of each power line link; The management module is used to compare the power line losses within the distance range value I of each power line link. If the difference in power line losses exceeds a threshold, the first high-loss area management plan is output.

2. The high-loss area management system based on big data Internet of Things and HPLC according to claim 1 is characterized by: The line loss calculation module is also used to randomly generate a detection interval, randomly select the same power line link according to the layout position of the power line link, and calculate the power line loss within the distance range value I of multiple power line links according to the detection starting point A, the distance range value I and the detection interval according to the detection interval; The management module is also used to compare the power line losses within a plurality of power line link distance range values ​​I. If the difference in power line losses exceeds a threshold, a management plan for the second highest loss area is output.

3. The high-loss area management system based on big data Internet of Things and HPLC according to claim 2 is characterized by: The first high-loss area governance plan includes whether there is electricity theft and where the theft occurs; The second high-loss substation management plan includes whether there is a metering device failure, line damage or line abnormality, and the location of the metering device failure, line damage or line abnormality.

4. The high-loss area management system based on big data Internet of Things and HPLC according to claim 3 is characterized by: The power line carrier communication module is also used to send a verification packet along the selected power line link when the management module outputs the first high-loss area management plan; The governance module is also used to obtain the detection end point B according to the detection starting point A and the distance range value I, obtain a verification package at the detection end point B, and obtain whether the verification package has completely passed the detection end point B. If the verification package has not completely passed the detection end point B, the location of the electricity theft is obtained based on the direction of the detection verification package.

5. The high-loss area management system based on big data Internet of Things and HPLC according to claim 4 is characterized by: The detection range value is 10% of the total length of the shortest power line link among the selected power line links.

6. The high-loss area management system based on big data Internet of Things and HPLC according to claim 5 is characterized by: It also includes a device prediction module, which is used to predict the aging degree of the metering devices and power lines on each power line link in the substation area, and whether the aging degree of the metering devices and power lines within the distance range value I matches the line loss calculated by the line loss calculation module. The management module is also used to output the second high-loss substation management plan, specifically metering device failure, line damage or line abnormality, based on whether the aging degree of the metering devices and power lines within the distance range value I matches the line loss calculated by the line loss calculation module.

7. The high-loss area management system based on big data Internet of Things and HPLC according to claim 6 is characterized by: The detection starting point A is randomly generated when the line loss calculation module randomly generates a distance range value I.

8. The high-loss area management system based on big data Internet of Things and HPLC according to claim 7 is characterized by: The line loss calculation module calculates the line loss rate within the distance range I using the following formula: Line loss rate = [(P A -P B ) / P A ]×100% Where: P A To detect the power of starting point A, P B Detect the power at end point B.

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