An intelligent power distribution safety system

Abstract

The embodiment of the present invention provides an intelligent power distribution safety system, which relates to the technical field of power distribution systems; including: obtaining the status data of the distribution nodes through a collector; converting the status data into a time domain signal using a wavelet transform algorithm; processing the time domain signal using a high smoothing algorithm to obtain a frequency domain signal with N mutation points; using a signal-to-noise ratio detection algorithm to process the frequency domain signal with mutation points, and determining the safety status of the distribution node by comparing it with a preset threshold. Through the above-mentioned intelligent power distribution safety system, the distribution nodes can be accurately monitored, the safety status can be accurately judged using advanced algorithms, and potential electrical hazards can be effectively warned in advance, which greatly reduces the accident rate, ensures the stable operation of the distribution system, and reduces the risk of power outages, equipment damage and personal injury. It can also help operation and maintenance personnel to accurately locate fault points, significantly improve operation and maintenance efficiency and reduce costs.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution systems, and in particular to an intelligent power distribution safety system and an implementation method thereof. Background Art

[0002] The section of the power system from the step-down distribution substation (high-voltage distribution substation) to the user end is called the distribution system. The distribution system is a power network system composed of a variety of distribution equipment (or components) and distribution facilities that transforms voltage and directly distributes electric energy to end users.

[0003] Although the current power distribution system monitors the safety of the power distribution lines and has corresponding safety measures, electrical safety accidents still occur frequently and further improvement is still needed. Therefore, there is a need for an intelligent power distribution safety system that can at least partially solve the above problems. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide an intelligent power distribution safety system that overcomes the above problems or at least partially solves the above problems, including: An intelligent power distribution safety system, comprising: Obtain status data of power distribution nodes through the collector; Using wavelet transform algorithm, the state data is converted into time domain signal; Using a high smoothing algorithm to process the time domain signal, obtaining a frequency domain signal having N mutation points; The frequency domain signal with the mutation point is processed by using a signal-to-noise ratio detection algorithm, and the safety state of the distribution node is determined by comparing it with a preset threshold.

[0005] Optionally, obtaining the status data of the power distribution node through the collector includes: The first temperature state data is acquired through a preset temperature sensor, and the electrical parameter data is acquired through an electrical parameter sensor.

[0006] Optionally, the converting the state data into a time domain signal by using a wavelet transform algorithm includes: The time domain signal is subjected to convolution operation processing by using a wavelet algorithm to obtain a time domain signal corresponding to the state data.

[0007] Optionally, the using a high smoothing algorithm to process the time domain signal to obtain a frequency domain signal having N mutation points includes: By combining the wavelet basis function with the smooth Gaussian function of low-pass nature, the frequency domain function expression is obtained; The time domain signal is processed by the frequency domain function expression to obtain a frequency domain signal with N mutation points.

[0008] Optionally, the frequency domain function expression is obtained by combining the wavelet basis function with a smooth Gaussian function with a low-pass property, comprising: The convolution operation is performed on the smooth Gaussian function algorithm with low-pass properties through the wavelet signal function; The time domain signal is filtered and denoised using the first-order derivative of the smoothed Gaussian function as a wavelet basis function to form a frequency domain function expression for processing the time domain signal.

[0009] Optionally, the signal-to-noise ratio detection algorithm is used to process the frequency domain signal having the mutation point, and the safety state of the distribution node is determined by comparing it with a preset threshold value; The signal-to-noise ratio of the frequency domain signal having the N mutation points is summed by a signal-to-noise ratio detection algorithm, and the average value of the summed value is calculated to obtain a ratio of the signal to the noise floor; The ratio of the noise floor is compared with a preset threshold value, and when the base noise is greater than the threshold value, the safety state of the power distribution node is determined.

[0010] Optionally, the acquiring the first temperature state data through a preset temperature sensor includes: Acquire a pseudo-color image or brightness image of infrared thermal imaging of a power distribution node through a thermal imaging sensor; Convert the chromaticity value of the pseudo-color image or the brightness value of the brightness image into a corresponding temperature data value; or, The temperature data value of the power distribution node is obtained through the temperature sensor.

[0011] The embodiments of the present invention have the following advantages: In the embodiment of the present invention, through the above-mentioned intelligent power distribution safety system, the power distribution nodes can be accurately monitored, the advanced algorithm can be used to accurately judge the safety status, and potential electrical hazards can be effectively warned in advance, which greatly reduces the accident rate, ensures the stable operation of the power distribution system, and reduces the risk of power outages, equipment damage and personal injury. It can also help operation and maintenance personnel to accurately locate the fault point, significantly improve operation and maintenance efficiency and reduce costs. At the same time, it is beneficial for the power department to optimize resource allocation according to the status of each node and improve the overall economic and operational benefits of the power distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0013] Figure 1 It is a flowchart of the implementation steps of an intelligent power distribution safety system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] Reference Figure 1 , shows a flowchart of the implementation steps of an intelligent power distribution safety system provided by an embodiment of the present invention, which may specifically include the following steps: S1. Obtain the status data of the power distribution node through the collector; S2, using wavelet transform algorithm to convert the state data into time domain signal; S3, using a high smoothing algorithm to process the time domain signal to obtain a frequency domain signal with N mutation points; S4. Process the frequency domain signal with the mutation point by using a signal-to-noise ratio detection algorithm, and determine the safety status of the distribution node by comparing it with a preset threshold.

[0016] In this embodiment, the above-mentioned intelligent power distribution safety system obtains the status data of the power distribution node through the collector, and sets up information collection points at the key nodes of the power distribution network, which can capture relevant operating status information in real time and directly. Compared with the traditional monitoring method that may be relatively extensive, it can understand the specific situation of each node in more detail, without missing any subtle abnormalities, and lay the foundation for subsequent accurate judgment of the safety status. For example, traditional monitoring may simply detect whether the voltage and current exceed a certain wide range, while the status data collected by this system from the power distribution node may cover richer dimensions, including the temperature of each node, partial discharge conditions and other parameters that may affect electrical safety, which can better capture potential hidden dangers.

[0017] Furthermore, the state data is converted into time domain signals using the wavelet transform algorithm. Wavelet transform has the characteristics of multi-resolution analysis. It can decompose the complex state data of distribution nodes containing various frequency components into different scales and frequency bands for observation. It is like using a microscope with finely adjusted focal length to observe objects. It can clearly separate signals with different characteristics that were originally mixed together, making it easier to analyze abnormal features in the signals more accurately later, thus avoiding the omission of safety hazards in traditional distribution systems due to blurred signal features.

[0018] Then, the high smoothing algorithm is used to process the time domain signal to obtain a frequency domain signal with N mutation points. The signals of the power distribution system are often subject to various interferences during operation, such as electromagnetic interference, which can cause glitches and fluctuations in the signal, which is not conducive to accurately judging the true safety status. The high smoothing algorithm can effectively remove the irregular fluctuations caused by these interference noises, highlight the truly meaningful mutation points, and make it more reliable to judge the safety status through the mutation points in the subsequent process. For example, a signal that originally looked messy due to interference can be highlighted after being processed by the high smoothing algorithm, instead of being covered by interference noise.

[0019] Finally, the signal-to-noise ratio detection algorithm is used to process the frequency domain signal with mutation points, and the safety status of the distribution node is determined by comparing it with the preset threshold. In the actual power distribution environment, there is always effective information and noise in the signal. The signal-to-noise ratio detection algorithm can accurately measure the proportional relationship between the effective component and the noise component in the signal. When compared with the preset threshold, it can clearly define whether the current state of the distribution node is in a safe range or a critical state where a safety accident is about to occur. Through this precise quantitative comparison, safety can be judged more scientifically, changing the previous situation where it may be inaccurate based on experience or simple threshold judgment, so as to discover and prevent electrical safety accidents in advance.

[0020] In a specific implementation, the state data of the distribution node is obtained through the collector, including: obtaining first temperature state data through a preset temperature sensor, and obtaining electrical parameter data through an electrical parameter sensor. By obtaining the temperature of the distribution node, it is possible to determine whether the line temperature is abnormal.

[0021] As an example, the use of a wavelet transform algorithm to convert the state data into a time domain signal includes: The time domain signal is subjected to convolution operation processing by wavelet algorithm to obtain a time domain signal corresponding to the state data, wherein the time domain function is: In the formula, f (t) is the state data, φ(t) is the wavelet basis function, a is the scale factor, b is the displacement factor, and t is the time parameter; among them, the scale factor a is the scale transformation of the wavelet, which means "stretching" or "compressing" the wavelet. The scale can be regarded as a frequency parameter, that is, the frequency 1 / a. The larger the scale factor a is, the lower the corresponding frequency is, and vice versa, the smaller a is, the higher the corresponding frequency is. The displacement factor b is the delayed or accelerated wavelet. Mathematically, delaying a function f(t) is expressed as f(tk); a is the scale factor, which determines the size of the time-frequency window and its position in the frequency domain; b is the displacement factor, which determines the position of the time-frequency window in the time domain. b can also be regarded as the time parameter t.

[0022] It should be noted that the sensor signal is processed by convolution operation using the wavelet algorithm to obtain the time domain signal representation of the sensor signal. With higher frequency signals, it often appears as a sudden change waveform, and the wavelet transform has a unique advantage in detecting sudden change waveforms. Specifically, by applying the wavelet transform, sudden change points with higher frequency components can be identified in electrical sensor parameters such as current and voltage, and these sudden change points may indicate areas where faults exist.

[0023] In one embodiment of the present invention, the step of processing the time domain signal by using a high smoothing algorithm to obtain a frequency domain signal having N mutation points comprises: By combining the wavelet basis function with the smooth Gaussian function of low-pass nature, the frequency domain function expression is obtained; The time domain signal is processed by the frequency domain function expression to obtain a frequency domain signal with N mutation points. The frequency domain function expression is: In the formula, a is the scale factor, w is the frequency domain parameter, generally used as a constant, F(w) is the frequency domain representation of the signal, Ψ*(aw) is the frequency domain part of the wavelet function, e is a natural constant, j is an imaginary unit, and t is a time variable.

[0024] When the temperature of some points in the power distribution node is higher than the normal state, the signal detected by the sensor will change. By detecting the mutation point of the signal, it can be determined whether the corresponding node has a fault.

[0025] In one embodiment of the present invention, the frequency domain function expression is obtained by combining the wavelet basis function with a smooth Gaussian function with a low-pass property, including: The convolution operation is performed on the low-pass smooth Gaussian function algorithm through the wavelet signal function. Among them, the smoothed Gaussian function is: , In the formula, φ(t, a) is the wavelet basis function, a is the scale factor, t is the time variable, and e is the natural constant; According to the first-order derivative of the smoothed Gaussian function as a wavelet basis function, the time domain signal is filtered and denoised to form a frequency domain function expression for processing the time domain signal; Among them, the function combining the wavelet time domain expression and the smoothed Gaussian function is: After function derivation calculation, the frequency domain function expression is obtained: In the formula, * represents the convolution operation, and φ1(t) is used to transform the wavelet signal f(t), which is equivalent to smoothing f(t) by the smoothing function θ(t) and then taking the first-order derivative with respect to t (time parameter).

[0026] After the convolution processing, it is further filtered by combining with a smoothing function with low-pass properties. The specific processing process is to use the first-order derivative of the smoothing function as the wavelet basis function. Assuming any smoothing function is θ(t), the following formula is obtained: ; ; It is equivalent to combining the above time domain function with the smoothing function through further calculation and transformation; In the formula, * represents the convolution operation, that is, here φ 1 (t) is used as the transformation of the wavelet signal x(t), which is equivalent to smoothing x(t) by the smoothing function θ(t) and then taking the first-order derivative with respect to t (time parameter). The corresponding φ 2 (t) finds the second-order derivative. Therefore, for a fixed value of a, the inflection point of x (t)*θ(t) is the function W φ1 The extreme value of x(a,t) is also the function W φ2 The zero crossing of x(a,t) can thus detect the change in the signal.

[0027] In one embodiment of the present invention, the signal-to-noise ratio detection algorithm is used to process the frequency domain signal having the mutation point, and the safety state of the distribution node is determined by comparing it with a preset threshold value; The signal-to-noise ratio of the frequency domain signal with N mutation points is summed by the following formula, and the average value of the summed value is calculated to obtain the ratio of the signal to the noise floor; In the formula, k is the position corresponding to the maximum value, and N is the number of points from the maximum value position. is the base noise, l is the number of signal frames; The base noise value (ratio of the noise floor) is compared with a preset threshold value, and when the base noise is greater than the threshold value, the safety state of the power distribution node is determined.

[0028] In one embodiment of the present invention, the obtaining of the first temperature status data through a preset temperature sensor includes: obtaining a pseudo-color image or a brightness image of an infrared thermal imaging of a distribution node through a thermal imaging sensor; converting the chromaticity value of the pseudo-color image or the brightness value of the brightness image into a corresponding temperature data value; or obtaining the temperature data value of the distribution node through a temperature sensor.

[0029] By applying wavelet transform, mutation points with higher frequency components can be identified in thermal imaging images, which may indicate areas where faults exist. In thermal imaging images, when the temperature of the distribution node changes, such as when an overload occurs in the line causing the line to overheat, a higher temperature change will occur, resulting in an increase in the corresponding frequency components. Through wavelet transform, the high-frequency components can be separated from the low-frequency components, and then the mutation points can be detected. In this application, the respective advantages of electrical parameter sensors (such as current and voltage sensors) and thermal imaging can be combined to improve the accuracy and reliability of fault diagnosis and enhance the fault monitoring capabilities of each node in the distribution system.

[0030] It should be noted that thermal imaging can monitor temperature more comprehensively because it is not limited by the temperature sensing of a sensor at a specific point, making it suitable for temperature sensing over a wide range.

[0031] The beneficial effects of this application include: By precisely monitoring the status of distribution nodes and using a variety of advanced algorithms to accurately determine the safety status, potential electrical safety hazards can be discovered in time before an accident occurs, such as early warning of local overheating of the line and the initial signs of insulation aging. Appropriate maintenance and repair measures can then be taken to effectively reduce the frequency of electrical safety accidents, ensure the stable and reliable operation of the distribution system, and avoid consequences such as power outages, equipment damage, and possible injuries to personnel caused by electrical accidents.

[0032] (2) Since the real-time safety status of each distribution node can be accurately known, operation and maintenance personnel do not need to conduct large-scale inspections as in the past or rely on experience to speculate where problems may occur. Instead, they can carry out targeted maintenance and inspection of nodes with safety hazards based on the accurate information fed back by the system, which greatly shortens the time for troubleshooting, improves the operation and maintenance efficiency of the entire distribution system, and reduces operation and maintenance costs.

[0033] (III) Accurate safety status monitoring can enable the power sector to better understand the health of various parts of the distribution system. For some areas that have been in a good safety state for a long time, resource allocation can be appropriately optimized to avoid excessive redundant safety protection investment. For weak links that are prone to safety problems, corresponding security resources can be reasonably increased, making the resource allocation of the entire distribution system more scientific and reasonable, and improving the overall economic and operational benefits.

[0034] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0035] It should be understood by those skilled in the art that the embodiments of the present invention can be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0036] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0037] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0038] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0039] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0040] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0041] The above is a detailed introduction to an intelligent power distribution safety system provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An intelligent power distribution safety system, characterized in that: include: Obtain status data of power distribution nodes through the collector; Using wavelet transform algorithm, the state data is converted into time domain signal; Using a high smoothing algorithm to process the time domain signal, obtaining a frequency domain signal having N mutation points; The frequency domain signal with the mutation point is processed by using a signal-to-noise ratio detection algorithm, and the safety state of the distribution node is determined by comparing it with a preset threshold.

2. The system according to claim 1, characterized in that The acquiring of the status data of the power distribution node through the collector includes: The first temperature state data is acquired through a preset temperature sensor, and the electrical parameter data is acquired through an electrical parameter sensor.

3. The system according to claim 1, characterized in that The method of converting the state data into a time domain signal by using a wavelet transform algorithm comprises: The time domain signal is subjected to convolution operation processing by using a wavelet algorithm to obtain a time domain signal corresponding to the state data.

4. The system according to claim 1, characterized in that The method of processing the time domain signal by using a high smoothing algorithm to obtain a frequency domain signal having N mutation points comprises: By combining the wavelet basis function with the smooth Gaussian function of low-pass nature, the frequency domain function expression is obtained; The time domain signal is processed by the frequency domain function expression to obtain a frequency domain signal with N mutation points.

5. The system according to claim 4, characterized in that The frequency domain function expression is obtained by combining the wavelet basis function with the smooth Gaussian function with low-pass properties, including: The convolution operation is performed on the smooth Gaussian function algorithm with low-pass properties through the wavelet signal function; The time domain signal is filtered and denoised using the first-order derivative of the smoothed Gaussian function as a wavelet basis function to form a frequency domain function expression for processing the time domain signal.

6. The system according to claim 1, characterized in that The signal-to-noise ratio detection algorithm is used to process the frequency domain signal having the mutation point, and the safety state of the distribution node is determined by comparing it with a preset threshold value; The signal-to-noise ratio of the frequency domain signal having the N mutation points is summed by a signal-to-noise ratio detection algorithm, and the average value of the summed value is calculated to obtain a ratio of the signal to the noise floor; The ratio of the noise floor is compared with a preset threshold value, and when the base noise is greater than the threshold value, the safety state of the power distribution node is determined.

7. The system according to claim 2, characterized in that The obtaining of first temperature state data by a preset temperature sensor includes: Acquire a pseudo-color image or brightness image of infrared thermal imaging of a power distribution node through a thermal imaging sensor; Convert the chromaticity value of the pseudo-color image or the brightness value of the brightness image into a corresponding temperature data value; or, The temperature data value of the power distribution node is obtained through the temperature sensor.

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