Auxiliary switch function rapid detection device and detection method

Abstract

The invention discloses an auxiliary switch function rapid detection device and a detection method, relates to the technical field of auxiliary switch detection, and aims to improve safe and stable operation of isolation switches and circuit breakers in transformer substations and power distribution rooms. The device comprises a main control unit, a signal generation module, a signal acquisition module, a comparison and judgment module, a communication module, a display module and a power supply module, and can efficiently and accurately detect the performance of the auxiliary switch and timely discover faults such as poor contact and signal transmission errors. By simulating operation conditions of opening, closing and the like, the normal work of the auxiliary switch under various functions is ensured, so that equipment damage or safety accidents are avoided. In addition, the method comprises the steps of equipment connection, initialization, action simulation, data acquisition, electrical performance test, abnormality judgment and the like, and the detection efficiency and accuracy are further improved. According to the invention, manual detection time and errors are reduced, and operation safety of power equipment is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of auxiliary switch detection, and in particular to a rapid detection device and method for an auxiliary switch function. Background Art

[0002] In the power system, disconnectors and circuit breakers are important devices to ensure the safe operation of the circuit, while auxiliary switches are used to reflect the opening and closing status of these devices and provide necessary signals for protection, control and signal circuits. However, the existing auxiliary switch function detection method has many shortcomings and needs to be improved urgently, as shown below:

[0003] The detection process is cumbersome: Traditional auxiliary switch function detection methods usually rely on manual on-site operation. The detection personnel need to go to the equipment site to perform manual operation and observe the contact status and signal changes of the auxiliary switch. This method is not only time-consuming and labor-intensive, but also leads to low detection efficiency.

[0004] Safety risks: In some complex environments or high-voltage equipment areas, inspectors face greater safety risks. For example, after the maintenance of disconnectors and circuit breakers, workers often need to climb or use lifting equipment for inspection, which increases operational risks and may cause safety accidents.

[0005] Environmental limitations: Although existing electrical testing technologies and equipment can provide high-precision testing in theory, they often have high requirements for the testing environment and require professional personnel to operate, which makes on-site application inconvenient and limits widespread practical use.

[0006] Detection accuracy: Many existing detection devices are highly complex and require high operating skills, which may lead to non-standard operation in actual application and affect the accuracy of the test results. At the same time, manual observation is easily affected by subjective factors, which affects the objectivity of the test results.

[0007] High cost: For large-scale inspection of disconnectors and circuit breakers, traditional inspection methods consume a lot of manpower and financial resources, resulting in relatively high maintenance costs, which creates a great economic pressure on the daily management and maintenance of the power system. Summary of the invention

[0008] In view of the shortcomings of the prior art, the present invention provides a device and method for quickly detecting the function of an auxiliary switch, which are mainly used in substations and distribution rooms to ensure the safe and stable operation of disconnectors and circuit breakers. The device can efficiently and accurately detect the performance of the auxiliary switch, and promptly discover faults such as poor contact and signal transmission errors, thereby improving the detection efficiency and reducing the time and error of manual detection. By simulating different operating conditions, the device ensures the normal functions of the auxiliary switch in opening, closing, signal control and interlocking protection, thereby avoiding equipment damage or safety accidents caused by faults.

[0009] The technical solution adopted by the present invention is:

[0010] A quick detection device for auxiliary switch function, comprising a detection device box, a main control unit installed in the detection device box, a signal generation module, a signal acquisition module, a comparison and judgment module, a communication module, a display module, and a power supply module;

[0011] Among them, the main control unit is electrically connected to the signal generating module, the signal acquisition module, the comparison and judgment module, the communication module, the display module, and the power module for signal processing and control; the signal generating module is connected to the signal acquisition module, the signal generating module receives instructions from the main control unit, generates corresponding action signals and transmits them to the auxiliary switch; the interface of the signal acquisition module passes through the detection device box and is connected to the contacts of the auxiliary switch to monitor the on-off state of the contacts, and transmits the data to the main control unit after collecting the signal; the comparison and judgment module obtains data from the signal acquisition module and feeds back the judgment result to the main control unit; the main control unit is connected to the external system through the communication module for data transmission and remote control; the display module obtains data from the main control unit and displays it; the interface of the power module passes through the detection device box and is connected to the main power supply, which provides the required power for the main control unit, the signal generating module, the signal acquisition module, the communication module, and the display module.

[0012] Furthermore, the main control unit adopts a high-performance microcontroller or programmable logic controller PLC; the signal generating module is a signal generator, which generates an electrical signal for simulating the action of the main contacts of the isolating switch and the circuit breaker; the signal acquisition module includes a sensor and / or a data acquisition circuit connected to each contact of the auxiliary switch, as well as an electrical isolation chip and a high-resolution A / D converter; the power supply module uses the mains power to step down, rectify, filter and stabilize the voltage through a transformer to obtain the required DC working voltage, and is equipped with a rechargeable battery as a backup power supply.

[0013] Furthermore, an alarm unit electrically connected to the main control unit and the power module is installed in the detection device housing. The alarm unit is an audible and visual alarm, which is controlled by the main control unit and triggers an alarm when an abnormal situation occurs.

[0014] A method for quickly detecting the function of an auxiliary switch is provided. The method is based on the above-mentioned device for quickly detecting the function of an auxiliary switch and is characterized in that it comprises the following detection steps:

[0015] Step 1, equipment connection: connect the interface of the signal acquisition module to the contact of the auxiliary switch through the connecting cable; the interface of the power module is connected to the mains through the power cord;

[0016] Step 2, initialization phase: the main control unit loads preset parameters, including withstand voltage value, action time standard, stroke standard, and communication rate; the main control unit communicates with the external system through the communication module to synchronize historical data and standard parameters;

[0017] Step 3, action simulation: the main control unit sends instructions to the signal generation module to simulate the disconnector or circuit breaker opening and closing action signal to change the state of the auxiliary switch;

[0018] Step 4, data acquisition: The data acquisition circuit of the signal acquisition module detects the on / off state and voltage value of the auxiliary switch contact, and obtains digital data through a high-precision A / D converter;

[0019] Step 5, electrical performance test: The main control unit calculates the on-resistance, and the main control unit cooperates with the signal generation module to apply a known current I to the contact when the auxiliary switch contact is closed. test At the same time, the main control unit cooperates with the signal acquisition module to measure the voltage difference V across the auxiliary switch test , calculate the on-resistance R according to Ohm's law con ;

[0020] Step 6, determine the abnormality: the main control unit turns on the resistor R con The calculation result is compared with the preset standard range. If it exceeds the range, it is judged that the contact is abnormal.

[0021] Furthermore, the step 1 also includes installing a sensor by connecting cables, and installing the sensor at each contact position of the auxiliary switch; the step 4 also includes the sensor detecting the travel change of the auxiliary switch during the action process, and the data acquisition circuit of the signal acquisition module measuring the time from the auxiliary switch receiving the instruction to completing the action;

[0022] The auxiliary switch function rapid detection method also includes the following mechanical performance detection steps:

[0023] Step 7, travel detection: Use sensors to collect travel data L measured , and the standard travel range [L min , L max ]Compare; if L measured If it is not within this range, it is judged as malfunction;

[0024] Step 8, action time detection: record the time T from issuing the operation command to the auxiliary switch completing the action action , and the standard time range [T min , T max ]Compare; if it exceeds the range, the action is unqualified;

[0025] Step 9, Mechanical life test: Repeat the above operation several times N cycles , record the cumulative number of actions of the auxiliary switch; when the contact is irreversibly damaged or the travel and time parameters seriously deviate from the standard, it is recorded as the end of life and the life parameters are counted;

[0026] Step 10, intelligent data processing and judgment: The main control unit processes the signal data based on the fuzzy logic algorithm, filters and recognizes the signal with noise interference; and uses the historical data set to train the model, which includes normal and abnormal state data, so as to automatically identify whether the working mode of the auxiliary switch is abnormal; the comparison and judgment module matches the current collected signal with the standard pattern library, and outputs an abnormal signal if the difference exceeds the threshold;

[0027] Step 11, result display and storage: the main control unit transmits the detected parameters and analysis results to the display module for visualization; at the same time, the data is saved to the local memory or sent to the external system through the communication module for archiving and subsequent analysis.

[0028] Further, in step 10, the steps of the fuzzy logic algorithm are as follows:

[0029] Step 10.1, fuzzification: converting characteristic parameters into fuzzy language variables, wherein the characteristic parameters include: on-resistance value, action time deviation, stroke deviation, and noise amplitude;

[0030] For the formation deviation ΔL, it is defined as three fuzzy sets:

[0031] Small deviation S: membership function μs(ΔL); medium deviation M: membership function μm(ΔL); large deviation L: membership function μl(ΔL);

[0032] The membership function is calculated using trigonometric functions, and the calculation formula is as follows:

[0033]

[0034] Where [a, b, c] are the three parameters of the triangular membership function, corresponding to the characteristic points of the fuzzy set definition range;

[0035] Step 10.2, build a fuzzy rule base: Based on historical data, define several fuzzy rules, such as:

[0036] If the stroke deviation is small, the on-resistance is small, and the action time is normal, the status is normal; if the stroke deviation is large, or the on-resistance is large, the status is abnormal;

[0037] Fuzzy rules exist in the form of If-Then, with the antecedent being a fuzzy condition and the consequent being a fuzzy conclusion;

[0038] Step 10.3, fuzzy reasoning: synthesize the membership of the input features using fuzzy logic operations, which include finding the minimum and maximum values;

[0039] Step 10.4, defuzzification: convert the fuzzy reasoning results back to precise values;

[0040] Step 10.5, judgment: make a normal or abnormal judgment based on the fuzzy reasoning result.

[0041] Furthermore, in step 10.3, the fuzzy reasoning process adopts Mamdani fuzzy reasoning;

[0042] For AND condition:

[0043] μ 前件合成 (ΔL,R con )=min(μs(ΔL),μs(R con ))

[0044] For the OR condition:

[0045] μ 前件合成 (ΔL,R con )=max(μm(ΔL),μl(R con ))

[0046] Where, ΔL is the travel deviation value; R con is the on-resistance; μs(), μm(), μl() are fuzzy membership functions, outputting fuzziness in the range of [0, 1]; AND and OR are operations to implement the basic rules of fuzzy logic operations; μs(R con ) represents the on-resistance R con Membership in small deviations; μm(R con ) represents the on-resistance R con Membership in medium deviation; μl(R con ) represents the on-resistance R con Membership in large deviations;

[0047] Finally, the membership distribution of multiple fuzzy conclusions is obtained.

[0048] Furthermore, in the defuzzification process of step 10.4, the centroid method is used for defuzzification, and the calculation formula is as follows:

[0049]

[0050] In the formula, y defuzzified is the quantitative result after defuzzification; z represents the variable in the defuzzification process; μ 状态 (z) represents the membership function of state z;

[0051] If y defuzzified If the value is greater than γ, it is considered to be basically normal; if it is lower than this value, it is considered to be abnormal; γ is the health score = 70.

[0052] Further, in step 10, filtering and pattern recognition of the signal with noise interference includes the following steps:

[0053] Denoising:

[0054] Use weighted average filtering to denoise the signal:

[0055]

[0056] In the formula, S filtered represents the signal after denoising, that is, the value of the filtered signal; S i is the i-th sampling value; ω i is the weight factor, which is usually set to decrease, that is,

[0057] Normalization:

[0058] Map signal feature values ​​to the range [0, 1]:

[0059]

[0060] Where, X norm is the normalized signal characteristic value; X is the original signal value; X min is the minimum value of the signal; X max is the maximum value of the signal;

[0061] Feature extraction:

[0062] Mean calculation of time domain features:

[0063]

[0064] Where μ is the signal average value; X i is the signal value of the i-th sampling point; N is the total number of sampling points;

[0065] Variance calculation of time domain features:

[0066]

[0067] In the formula, σ2 is the variance of the signal, which indicates the discreteness or volatility of the signal value;

[0068] The frequency components of the frequency domain features are extracted by performing fast Fourier transform FFT on the signal:

[0069]

[0070] Where X(f) is the frequency domain representation of the signal; f is the frequency; t is the time; x(t) is the signal value at time t; N is the total number of sampling points; e -j2πft / N is the complex exponential function; e is the base of natural logarithm; j is the imaginary unit;

[0071] Wavelet transform of time-frequency characteristics, by decomposing the time-frequency information of the signal:

[0072]

[0073] Where W(a,b) is the wavelet coefficient; a is the scale factor; b is the time displacement; x(t) is the signal value at time t; is the complex conjugate of the wavelet function; t is the time.

[0074] Furthermore, in step 10, the model is trained using the historical data set, where the model structure is as follows:

[0075] The model uses a fully connected feedforward neural network model, which contains L hidden layers, each with several neurons;

[0076] Its input layer is: input feature vector x = [x1, x2, ..., x n ], n is the feature dimension;

[0077] Its hidden layer is: the output h of the lth layer (l) Expressed as:

[0078] h (l) =σ(W (l) h (l-1) +b (l) )

[0079] In the formula, h (0) = x(input layer output); h (l) is the output vector of the lth layer; W (l) and b (l) are the weight matrix and bias vector of the lth layer respectively; h (l-1) is the output of the l-1th layer, which is the input of the lth layer; σ() is the activation function;

[0080] The output layer is: output a probability distribution or score value; if there is 1 neuron in the output layer for binary classification, the output is:

[0081] y=σ(W (L) h (L-1) +b (L) )

[0082] Where y is the output of the model; W (L) is the weight matrix of the output layer; L represents the last layer of the network;

[0083] h (L-1) is the output of the l-1th layer, which is passed to the output layer as input; b (L) is the bias value of the output layer;

[0084] If σ is a Sigmoid function, then y∈(0,1) represents the abnormal probability;

[0085] The model training process is as follows:

[0086] Loss function:

[0087]

[0088] Where L is the loss value; y i is the actual label; y i Model predicted value; N is the number of samples; It means to sum all samples from 1 to N;

[0089] The model output is: classification probability P 正常 and P 异常 ; Abnormal types include: poor contact and slow movement.

[0090] The beneficial effects of the present invention are:

[0091] 1. Portability and flexibility: The auxiliary switch function rapid detection device adopts a box structure as a whole, which makes the detection device compact and lightweight, and easy to carry on site. The detection personnel can operate directly at the installation site of the disconnector and circuit breaker without the need for large-scale testing equipment, which improves work efficiency. The convenient connection interface design of the power module and signal acquisition module makes the connection with different types of auxiliary switches fast and reliable, reducing the difficulty of operation.

[0092] 2. High-precision detection: The main control unit cooperates with the signal generation module and the signal acquisition module to generate and collect high-precision signals, ensuring the accuracy of the detection. The main control unit cooperates with the signal generation module to generate high-precision analog opening and closing signals, which can cover different working conditions and comprehensively evaluate the response performance of the auxiliary switch. The signal acquisition module is equipped with a data acquisition circuit that can collect signals at high speed and high resolution, so that the detection can capture subtle signal changes, thereby accurately measuring the action time and other important parameters of the auxiliary switch, improving the reliability of the data.

[0093] 3. Intelligent data analysis: The main control unit cooperates with the comparison and judgment module and the communication module to perform real-time data processing and analysis, which reduces the data transmission and processing time and makes the detection process more efficient. The auxiliary switch function rapid detection device can quickly identify and judge the functional status of the auxiliary switch and issue an alarm in time. Intelligent fault diagnosis and prediction provides predictive maintenance capabilities by analyzing historical data, which can effectively identify potential faults, reduce the risk of faults, and ensure the continuous operation of the power system.

[0094] 4. Improve work efficiency and safety: The detection device significantly improves the detection efficiency and accuracy of auxiliary switches, reduces the possibility of human error, and enhances the safety and reliability of the power system. This efficient and intelligent detection device can better cope with the high requirements of modern power systems for equipment detection and promote the healthy operation of power equipment.

[0095] 5. Reduce maintenance costs: The auxiliary switch function rapid detection device can predict possible failures in advance, carry out repairs in advance, reduce downtime and maintenance costs caused by equipment failure, and provide a more economical solution for power operations.

[0096] In summary, the auxiliary switch function rapid detection device and detection method thereof have important practical value and play a positive role in improving the management and service level of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0098] Figure 1 It is a schematic diagram of the overall structure of the auxiliary switch function rapid detection device of the present invention;

[0099] Figure 2 It is a connection diagram between the internal electrical components and the auxiliary switch function of the auxiliary switch function rapid detection device of the present invention;

[0100] Figure 3 is a flow chart of the auxiliary switch function rapid detection method of the present invention;

[0101] Figure 4 is a flow chart of the fuzzy logic algorithm of the present invention;

[0102] In the figure: 1. Detection device housing; 2. Interface of power module; 3. Interface of signal acquisition module. DETAILED DESCRIPTION

[0103] To make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them.

[0104] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely represents some embodiments of the present invention. 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. It should be noted that the embodiments of the present invention and the features and technical solutions in the embodiments can be combined with each other without conflict. It should be noted that similar numbers and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0105] Example 1

[0106] In view of the auxiliary switch detection problem, this embodiment provides an auxiliary switch function rapid detection device; Figure 1 As shown, the auxiliary switch function rapid detection device includes a detection device box 1, and a main control unit, a signal generation module, a signal acquisition module, a comparison and judgment module, a communication module, a display module, and a power module are installed inside the detection device box 1. Among them, the interface 3 of the signal acquisition module passes through the detection device box 1; the interface 2 of the power module passes through the detection device box 1, and a switch for switching the power module is provided.

[0107] like Figure 2 As shown, the connection relationship and function of each electrical component of the auxiliary switch function rapid detection device are as follows:

[0108] Among them, the main control unit is electrically connected to the signal generation module, signal acquisition module, comparison and judgment module, communication module, display module, and power module to perform signal processing and control; the main control unit adopts a high-performance microcontroller or programmable logic controller PLC; the main control unit is responsible for the core functions such as control, data processing and analysis of the entire detection device. It can coordinate and schedule each module according to the preset program and is the core of the device.

[0109] The signal generating module is electrically connected to the main control unit and the signal acquisition module; the signal generating module is a signal generator, which generates electrical signals for simulating the action of the main contacts of the disconnector and the circuit breaker; it generates electrical signals simulating the action of the main contacts of the disconnector and the circuit breaker according to the instructions of the control unit, such as voltage pulse signals, and the parameters such as the amplitude, frequency and waveform of these signals can be set and adjusted according to the working characteristics of the actual disconnector and the circuit breaker to accurately simulate the opening and closing actions of the main contacts under various working conditions, thereby driving the auxiliary switch to act; the signal generating module receives instructions from the main control unit, generates corresponding action signals and transmits them to the auxiliary switch.

[0110] The signal acquisition module is electrically connected to the main control unit and the signal generation module. At the same time, it is connected to the contacts of the auxiliary switch through the detection device box 1 through the interface 3 of the signal acquisition module to monitor the on-off state of the contacts, such as the normally open contact is closed, the normally closed contact is disconnected, etc., and transmits the data to the main control unit after collecting the signal. When the signal acquisition module performs electrical performance testing on the auxiliary switch, the data acquisition circuit is used; when the mechanical performance and life testing of the auxiliary switch is performed, it also includes sensors installed on each contact of the auxiliary switch; in addition, the signal acquisition module also includes an electrical isolation chip and a high-resolution A / D converter. The electrical isolation chip isolates the acquisition circuit from the auxiliary switch circuit to prevent interference and ensure the accuracy and stability of the collected signal.

[0111] The comparison and judgment module is electrically connected to the main control unit and the signal acquisition module. It obtains data from the signal acquisition module and feeds back the judgment result to the main control unit. The comparison and judgment module is used to cooperate with the main control unit to compare the collected signal with the preset correct signal mode to determine whether the auxiliary switch function is normal.

[0112] The communication module is electrically connected to the main control unit, and the main control unit is connected to the external system through the communication module for data transmission and remote control; the communication module has a variety of communication interfaces, such as RS485, Ethernet interface, etc.; the communication module facilitates data transmission and remote control, and the communication rate can meet the real-time data transmission requirements, such as the RS485 baud rate can be set to 9600bps and above.

[0113] The display module is electrically connected to the main control unit, and uses a liquid crystal display LCD or a light-emitting diode display LED to intuitively display the detected parameters, status information, and test results. The display screen size can be selected according to actual needs, such as the common 5-inch, 7-inch, 10-inch, etc., which can clearly display key data.

[0114] The interface 2 of the power module passes through the detection device box 1 to connect to the mains, which provides the required power for the main control unit, signal generation module, signal acquisition module, communication module, and display module. The power module uses the mains to step down, rectify, filter and stabilize the voltage through a transformer to obtain the required DC working voltage, and is equipped with a rechargeable battery as a backup power supply. The power input voltage range can adapt to a wider range, such as AC 85V-265V, and the output voltage can meet the needs of each module of the device, such as providing different voltage levels such as +5V, +12V, +24V, and has overcurrent, overvoltage, undervoltage and other protection functions to ensure that the device can work normally under power fluctuations. In the power circuit, electromagnetic compatibility is fully considered, and filtering, shielding and other measures are taken to reduce the interference of the power supply to other units.

[0115] In addition, an alarm unit electrically connected to the main control unit and the power module is installed in the detection device housing 1. The alarm unit is an audible and visual alarm, which is controlled by the main control unit and triggers an alarm when an abnormal situation occurs.

[0116] Through the collaborative work of the above modules, the auxiliary switch function rapid detection device can comprehensively, quickly and accurately detect the various functions of the auxiliary switch, timely discover potential faults, and ensure the normal operation of the disconnector and circuit breaker system. Different actual application scenarios and specific needs may adjust and optimize the technical solution of the detection device.

[0117] Example 2

[0118] Based on the auxiliary switch function rapid detection device proposed in Example 1, this embodiment provides an auxiliary switch function rapid detection method, which includes electrical performance detection, mechanical performance detection, and mechanical life test of the auxiliary switch;

[0119] like Figure 3 As shown, the specific test steps for electrical performance testing are as follows:

[0120] Step 1, equipment connection: connect the interface 3 of the signal acquisition module to the contact of the auxiliary switch through the connecting cable; connect the interface 2 of the power module to the mains through the power cord.

[0121] Step 2, initialization phase: the main control unit loads preset parameters, which include withstand voltage value, action time standard, stroke standard, and communication rate; the main control unit communicates with the external system through the communication module to synchronize historical data and standard parameters.

[0122] Step 3, action simulation: the main control unit sends instructions to the signal generation module to simulate the disconnector or circuit breaker opening and closing action signals to change the state of the auxiliary switch.

[0123] Step 4, data acquisition: The data acquisition circuit of the signal acquisition module detects the on / off state and voltage value of the auxiliary switch contacts, and obtains digital data through a high-precision A / D converter.

[0124] Step 5, electrical performance test: The main control unit calculates the on-resistance, and the main control unit cooperates with the signal generation module to apply a known current I to the contact when the auxiliary switch contact is closed. test At the same time, the main control unit cooperates with the signal acquisition module to measure the voltage difference V across the auxiliary switch test , calculate the on-resistance R according to Ohm's law con ;

[0125] Step 6, determine the abnormality: the main control unit turns on the resistor R con The calculation result is compared with the preset standard range. If it exceeds the range, it is judged that the contact is abnormal.

[0126] On this basis, if it is necessary to test the mechanical properties and mechanical life of the auxiliary switch at the same time, step 1 also includes installing the sensor by connecting the cable, and installing the sensor at each contact position of the auxiliary switch; step 4 also includes the sensor detecting the travel change of the auxiliary switch during the action process, and the data acquisition circuit of the signal acquisition module measures the time from the auxiliary switch receiving the instruction to completing the action.

[0127] The mechanical properties testing steps are as follows:

[0128] Step 7, travel detection: Use sensors to collect travel data L measured , and the standard travel range [L min , L max ]Compare; if L measured If it is not within this range, it is judged as malfunction;

[0129] Step 8, action time detection: record the time T from issuing the operation command to the auxiliary switch completing the action action , and the standard time range [T min , T max ]Compare; if it exceeds the range, the action is unqualified;

[0130] Step 9, Mechanical life test: Repeat the above operation several times N cycles , record the cumulative number of actions of the auxiliary switch; when the contact is irreversibly damaged or the travel and time parameters seriously deviate from the standard, it is recorded as the end of life and the life parameters are counted;

[0131] Step 10, intelligent data processing and judgment: The main control unit processes signal data based on fuzzy logic algorithms, filters and patterns signals with noise interference; and uses historical data sets to train models. The historical data sets include normal and abnormal state data, so as to automatically identify whether the working mode of the auxiliary switch is abnormal; the comparison and judgment module matches the current collected signal with the standard pattern library, and outputs an abnormal signal if the difference exceeds the threshold.

[0132] In step 10, the steps of the fuzzy logic algorithm are as follows:

[0133] Fuzzification: converting characteristic parameters into fuzzy language variables, where the characteristic parameters include: on-resistance value, action time deviation, stroke deviation, and noise amplitude;

[0134] For the formation deviation ΔL, it is defined as three fuzzy sets:

[0135] Small deviation S: membership function μs(ΔL); medium deviation M: membership function μm(ΔL); large deviation L: membership function μl(ΔL);

[0136] The membership function is calculated using trigonometric functions, and the calculation formula is as follows:

[0137]

[0138] Where [a, b, c] are the three parameters of the triangular membership function, corresponding to the characteristic points of the fuzzy set definition range;

[0139] Step 10.2, build a fuzzy rule base: Based on historical data, define several fuzzy rules, such as:

[0140] If the stroke deviation is small, the on-resistance is small, and the action time is normal, the status is normal; if the stroke deviation is large, or the on-resistance is large, the status is abnormal;

[0141] Fuzzy rules exist in the form of If-Then, with the antecedent being a fuzzy condition and the consequent being a fuzzy conclusion;

[0142] Step 10.3, fuzzy reasoning: synthesize the membership of the input features using fuzzy logic operations, which include finding the minimum and maximum values;

[0143] The fuzzy reasoning process uses Mamdani fuzzy reasoning;

[0144] For AND condition:

[0145] μ 前件合成 (ΔL,R con )=min(μs(ΔL),μs(R con ))

[0146] For the OR condition:

[0147] μ 前件合成 (ΔL,R con )=max(μm(ΔL),μl(R con ))

[0148] Where, ΔL is the travel deviation value; R con is the on-resistance; μs(), μm(), μl() are fuzzy membership functions, outputting fuzziness in the range of [0, 1]; AND and OR are operations to implement the basic rules of fuzzy logic operations; μs(R con ) represents the on-resistance R con Membership in small deviations; μm(R con ) represents the on-resistance R con Membership in medium deviation; μl(R con ) represents the on-resistance R con Membership in large deviations;

[0149] Finally, the membership distribution of multiple fuzzy conclusions is obtained.

[0150] Step 10.4, defuzzification: convert the fuzzy reasoning results back to precise values;

[0151] In the defuzzification process, the centroid method is used for defuzzification, and the calculation formula is as follows:

[0152]

[0153] In the formula, y defuzzified is the quantitative result after defuzzification; z represents the variable in the defuzzification process; μ 状态 (z) represents the membership function of state z;

[0154] If y defuzzified If the value is greater than γ, it is considered to be basically normal; if it is lower than this value, it is considered to be abnormal; γ is the health score = 70.

[0155] Step 10.5, judgment: make a normal or abnormal judgment based on the fuzzy reasoning result.

[0156] In step 10, filtering and pattern recognition of the signal with noise interference includes the following steps:

[0157] Denoising:

[0158] Use weighted average filtering to denoise the signal:

[0159]

[0160] In the formula, S filteredrepresents the signal after denoising, that is, the value of the filtered signal; S i is the i-th sampling value; ω i is the weight factor, which is usually set to decrease, that is,

[0161] Normalization:

[0162] Map signal feature values ​​to the range [0, 1]:

[0163]

[0164] Where, X norm is the normalized signal characteristic value; X is the original signal value; X min is the minimum value of the signal; X max is the maximum value of the signal;

[0165] Feature extraction:

[0166] Mean calculation of time domain features:

[0167]

[0168] Where μ is the signal average value; X i is the signal value of the i-th sampling point; N is the total number of sampling points;

[0169] Variance calculation of time domain features:

[0170]

[0171] In the formula, σ 2 is the variance of the signal, which indicates the discreteness or volatility of the signal value;

[0172] The frequency components of the frequency domain features are extracted by performing fast Fourier transform FFT on the signal:

[0173]

[0174] Where X(f) is the frequency domain representation of the signal; f is the frequency; t is the time; x(t) is the signal value at time t; N is the total number of sampling points; e -j2πft / N is the complex exponential function; e is the base of natural logarithm; j is the imaginary unit;

[0175] Wavelet transform of time-frequency characteristics, by decomposing the time-frequency information of the signal:

[0176]

[0177] Where W(a,b) is the wavelet coefficient; a is the scale factor; b is the time displacement; x(t) is the signal value at time t; is the complex conjugate of the wavelet function; t is the time.

[0178] In step 10, the model is trained using the historical data set, where the model structure is as follows:

[0179] The model uses a fully connected feedforward neural network model, which contains L hidden layers, each with several neurons;

[0180] Its input layer is: input feature vector x = [x1, x2, ..., x n ], n is the feature dimension;

[0181] Its hidden layer is: the output h of the lth layer (l) Expressed as:

[0182] h (l) =σ(W (l) h (l-1) +b (l) )

[0183] In the formula, h (0) = x(input layer output); h (l) is the output vector of the lth layer; W (l) and b (l) are the weight matrix and bias vector of the lth layer respectively; h (l-1) is the output of the l-1th layer, which is the input of the lth layer; σ() is the activation function;

[0184] The output layer is: output a probability distribution or score value; if there is 1 neuron in the output layer for binary classification, the output is:

[0185] y=σ(W (L) h (L-1) +b (L) )

[0186] Where y is the output of the model; W (L) is the weight matrix of the output layer; L represents the last layer of the network;

[0187] h (L-1) is the output of the l-1th layer, which is passed to the output layer as input; b (L) is the bias value of the output layer

[0188] If σ is a Sigmoid function, then y∈(0,1) represents the abnormal probability;

[0189] The model training process is as follows:

[0190] Loss function:

[0191]

[0192] Where L is the loss value; yi is the actual label; y i Model predicted value; N is the number of samples; It means to sum all samples from 1 to N;

[0193] The model output is: classification probability P 正常 and P 异常 ; Abnormal types include: poor contact and slow movement.

[0194] Step 11, result display and storage: the main control unit transmits the detected parameters and analysis results to the display module for visualization; at the same time, the data is saved to the local memory or sent to the external system through the communication module for archiving and subsequent analysis.

[0195] In summary, the auxiliary switch function rapid detection method is based on the auxiliary switch function rapid detection device. Through advanced microprocessors and intelligent algorithms, it realizes real-time processing and analysis of signal data, significantly improving the detection efficiency. The data processing unit can monitor the key parameters of the auxiliary switch in real time, such as the opening and closing state, action time and contact resistance of the contacts, and compare them with the standard parameters to quickly determine whether its function is normal or not. When the parameters are abnormal, the system will issue an alarm and display the fault information. In addition, the detection device also has the function of intelligent fault diagnosis and prediction. By analyzing historical detection data and using deep learning algorithms, the system can establish a fault model to predict the type and time of possible faults. This function provides support for preventive maintenance of power systems, reduces operational risks, and improves the reliability of equipment.

[0196] At the same time, it should be pointed out that the above description is only a typical embodiment of the present invention and does not limit the present invention in any form. Any changes made to the above embodiments based on the ideas and technical essence of the present invention are within the protection scope of the present invention.

Claims

1. A quick detection device for auxiliary switch function, characterized in that: The auxiliary switch function rapid detection device comprises a detection device box, a main control unit installed in the detection device box, a signal generation module, a signal acquisition module, a comparison and judgment module, a communication module, a display module, and a power supply module; Among them, the main control unit is electrically connected to the signal generating module, the signal acquisition module, the comparison and judgment module, the communication module, the display module, and the power module for signal processing and control; the signal generating module is connected to the signal acquisition module, the signal generating module receives instructions from the main control unit, generates corresponding action signals and transmits them to the auxiliary switch; the interface of the signal acquisition module passes through the detection device box and is connected to the contacts of the auxiliary switch to monitor the on-off state of the contacts, and transmits the data to the main control unit after collecting the signal; the comparison and judgment module obtains data from the signal acquisition module and feeds back the judgment result to the main control unit; the main control unit is connected to the external system through the communication module for data transmission and remote control; the display module obtains data from the main control unit and displays it; the interface of the power module passes through the detection device box and is connected to the main power supply, which provides the required power for the main control unit, the signal generating module, the signal acquisition module, the communication module, and the display module.

2. The auxiliary switch function rapid detection device according to claim 1, characterized in that: The main control unit adopts a high-performance microcontroller or programmable logic controller PLC; the signal generating module is a signal generator, which generates an electrical signal for simulating the action of the main contacts of the isolating switch and the circuit breaker; the signal acquisition module includes a sensor and / or a data acquisition circuit connected to each contact of the auxiliary switch, as well as an electrical isolation chip and a high-resolution A / D converter; the power supply module uses the mains power to step down, rectify, filter and stabilize the voltage through a transformer to obtain the required DC working voltage, and is equipped with a rechargeable battery as a backup power supply.

3. The auxiliary switch function rapid detection device according to claim 1, characterized in that: An alarm unit electrically connected to the main control unit and the power module is also installed in the detection device box. The alarm unit is an audible and visual alarm, which is controlled by the main control unit and triggers an alarm when an abnormal situation occurs.

4. A method for quickly detecting the function of an auxiliary switch, the method for quickly detecting the function of an auxiliary switch is based on the device for quickly detecting the function of an auxiliary switch according to any one of claims 1 to 3, characterized in that: The detection steps include the following: Step 1, equipment connection: connect the interface of the signal acquisition module to the contact of the auxiliary switch through the connecting cable; the interface of the power module is connected to the mains through the power cord; Step 2, initialization phase: the main control unit loads preset parameters, including withstand voltage value, action time standard, stroke standard, and communication rate; the main control unit communicates with the external system through the communication module to synchronize historical data and standard parameters; Step 3, action simulation: the main control unit sends instructions to the signal generation module to simulate the disconnector or circuit breaker opening and closing action signal to change the state of the auxiliary switch; Step 4, data acquisition: The data acquisition circuit of the signal acquisition module detects the on / off state and voltage value of the auxiliary switch contact, and obtains digital data through a high-precision A / D converter; Step 5, electrical performance test: The main control unit calculates the on-resistance, and the main control unit cooperates with the signal generation module to apply a known current I to the contact when the auxiliary switch contact is closed. test At the same time, the main control unit cooperates with the signal acquisition module to measure the voltage difference V across the auxiliary switch test , calculate the on-resistance R according to Ohm's law con ; Step 6, determine the abnormality: the main control unit turns on the resistor R con The calculation result is compared with the preset standard range. If it exceeds the range, it is judged that the contact is abnormal.

5. The auxiliary switch function rapid detection method according to claim 4, characterized in that: The step 1 also includes installing a sensor by connecting cables, and installing the sensor at each contact position of the auxiliary switch; the step 4 also includes the sensor detecting the travel change of the auxiliary switch during the action process, and the data acquisition circuit of the signal acquisition module measuring the time from the auxiliary switch receiving the instruction to completing the action; The auxiliary switch function rapid detection method also includes the following mechanical performance detection steps: Step 7, travel detection: Use sensors to collect travel data L measured , and the standard travel range [L min , L max ]Compare; if L measured If it is not within this range, it is judged as malfunction; Step 8, action time detection: record the time T from issuing the operation command to the auxiliary switch completing the action action , and the standard time range [T min , T max ]Compare; if it exceeds the range, the action is unqualified; Step 9, Mechanical life test: Repeat the above operation several times N cycles , record the cumulative number of actions of the auxiliary switch; when the contact is irreversibly damaged or the travel and time parameters seriously deviate from the standard, it is recorded as the end of life and the life parameters are counted; Step 10, intelligent data processing and judgment: The main control unit processes the signal data based on the fuzzy logic algorithm, filters and recognizes the signal with noise interference; and uses the historical data set to train the model, which includes normal and abnormal state data, so as to automatically identify whether the working mode of the auxiliary switch is abnormal; the comparison and judgment module matches the current collected signal with the standard pattern library, and outputs an abnormal signal if the difference exceeds the threshold; Step 11, result display and storage: the main control unit transmits the detected parameters and analysis results to the display module for visualization; at the same time, the data is saved to the local memory or sent to the external system through the communication module for archiving and subsequent analysis.

6. The auxiliary switch function rapid detection method according to claim 5, characterized in that: In step 10, the steps of the fuzzy logic algorithm are as follows: Step 10.1, fuzzification: converting characteristic parameters into fuzzy language variables, wherein the characteristic parameters include: on-resistance value, action time deviation, stroke deviation, and noise amplitude; For the formation deviation ΔL, it is defined as three fuzzy sets: Small deviation S: membership function μs(ΔL); medium deviation M: membership function μm(ΔL); large deviation L: membership function μl(ΔL); The membership function is calculated using trigonometric functions, and the calculation formula is as follows: Where [a, b, c] are the three parameters of the triangular membership function, corresponding to the characteristic points of the fuzzy set definition range; Step 10.2, build a fuzzy rule base: Based on historical data, define several fuzzy rules, such as: If the stroke deviation is small, the on-resistance is small, and the action time is normal, the status is normal; if the stroke deviation is large, or the on-resistance is large, the status is abnormal; Fuzzy rules exist in the form of If-Then, with the antecedent being a fuzzy condition and the consequent being a fuzzy conclusion; Step 10.3, fuzzy reasoning: synthesize the membership of the input features using fuzzy logic operations, which include finding the minimum and maximum values; Step 10.4, defuzzification: convert the fuzzy reasoning results back to precise values; Step 10.5, judgment: make a normal or abnormal judgment based on the fuzzy reasoning result.

7. The auxiliary switch function rapid detection method according to claim 6, characterized in that: Step 10.3: The fuzzy reasoning process uses Mamdani fuzzy reasoning; For AND condition: m 前件合成 (ΔL,R con )=min(μs(ΔL),μs(R con )) For the OR condition: m 前件合成 (ΔL,R con )=max(μm(ΔL),μl(R con )) Where, ΔL is the travel deviation value; R con is the on-resistance; μs(), μm(), μl() are fuzzy membership functions, outputting fuzziness in the range of [0, 1]; AND and OR are operations to implement the basic rules of fuzzy logic operations; μs(R con ) represents the on-resistance R con Membership in small deviations; μm(R con ) represents the on-resistance R con Membership in medium deviation; μl(R con ) represents the on-resistance R con Membership in large deviations; Finally, the membership distribution of multiple fuzzy conclusions is obtained.

8. The auxiliary switch function rapid detection method according to claim 6, characterized in that: In the defuzzification process of step 10.4, the centroid method is used for defuzzification, and the calculation formula is as follows: In the formula, y defuzzified is the quantitative result after defuzzification; z represents the variable in the defuzzification process; μ 状态 (z) represents the membership function of state z; If y defuzzified If the value is greater than γ, it is considered to be basically normal; if it is lower than this value, it is considered to be abnormal; γ is the health score = 70.

9. The auxiliary switch function rapid detection method according to claim 5, characterized in that: In step 10, filtering and pattern recognition of the signal with noise interference includes the following steps: Denoising: Use weighted average filtering to denoise the signal: In the formula, S filtered represents the signal after denoising, that is, the value of the filtered signal; S i is the i-th sampling value; ω i is the weight factor, which is usually set to decrease, that is, Normalization: Map signal feature values ​​to the range [0, 1]: Where, X norm is the normalized signal characteristic value; X is the original signal value; X min is the minimum value of the signal; X max is the maximum value of the signal; Feature extraction: Mean calculation of time domain features: Where μ is the signal average value; X i is the signal value of the i-th sampling point; N is the total number of sampling points; Variance calculation of time domain features: In the formula, σ 2 is the variance of the signal, which indicates the discreteness or volatility of the signal value; The frequency component of the frequency domain feature is extracted by performing fast Fourier transform FFT on the signal: Where X(f) is the frequency domain representation of the signal; f is the frequency; t is the time; x(t) is the signal value at time t; N is the total number of sampling points; e -j2πft / N is the complex exponential function; e is the base of the natural logarithm; j is the imaginary unit; Wavelet transform of time-frequency characteristics, by decomposing the time-frequency information of the signal: Where W(a,b) is the wavelet coefficient; a is the scale factor; b is the time displacement; x(t) is the signal value at time t; is the complex conjugate of the wavelet function; t is the time.

10. The auxiliary switch function rapid detection method according to claim 5, characterized in that: In step 10, the model is trained using the historical data set, where the model structure is as follows: The model uses a fully connected feedforward neural network model, which contains L hidden layers, each with several neurons; Its input layer is: input feature vector x = [x1, x2, ..., x n ], n is the feature dimension; Its hidden layer is: the output h of the lth layer (l) Expressed as: h (l) =σ(W (l) h (l-1) +b (l) ) In the formula, h (0) = x(input layer output); h (l) is the output vector of the lth layer; W (l) and b (l) are the weight matrix and bias vector of the lth layer respectively; h (l-1) is the output of the l-1th layer, which is the input of the lth layer; σ() is the activation function; The output layer is: output a probability distribution or score value; if there is 1 neuron in the output layer for binary classification, the output is: y=σ(W (L) h (L-1) +b (L) ) Where y is the output of the model; W (L) is the weight matrix of the output layer; L represents the last layer of the network; h (L-1) is the output of the l-1th layer, which is passed to the output layer as input; b (L) is the bias value of the output layer; If σ is a Sigmoid function, then y∈(0,1) represents the abnormal probability; The model training process is as follows: Loss function: Where L is the loss value; y i is the actual label; y i Model predicted value; N is the number of samples; It means to sum all samples from 1 to N; The model output is: classification probability P 正常 and P 异常 ; Abnormal types include: poor contact and slow movement.

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