A detection circuit and method for abnormal insertion of a power supply interface of a mobile substation

Abstract

The present invention relates to the technical field of mobile substation detection, and specifically, to a detection circuit and method for abnormal plugging of a mobile substation power supply interface. It includes a data monitoring module, a threshold adjustment module, a fault diagnosis module, and an alarm prompt module. The present invention detects the voltage, current, and temperature at the power supply interface of the mobile substation through the data monitoring module, establishes an initial determination criterion for abnormal plugging of the mobile substation power supply interface, uses the threshold adjustment module to establish a corresponding power-current-voltage model, and takes the change ratio after comparing the changed power with the initial power as the proportional coefficient to adjust the monitoring ranges of current, voltage, and temperature as well as the alarm threshold, so as to realize that when the load power of the mobile substation changes, the monitoring ranges of voltage and current and the alarm threshold can be adaptively adjusted, and thus the abnormal plugging of the power supply interface can be detected more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile substation detection, and more specifically, to a detection circuit and method for abnormal plugging of a power supply interface of a mobile substation. Background Art

[0002] The power supply interface of a mobile substation is a key component connecting the external power supply and the internal electrical equipment of the substation. Its main function is to achieve safe and efficient transmission of electric energy. The power supply interface of a mobile substation has pluggable interfaces and fixed interfaces. The pluggable interface is convenient for quick connection and disconnection of the mobile substation, and is widely used in occasions where the position of the mobile substation needs to be frequently moved or the power supply needs to be temporarily connected. The fixed interface is usually used for mobile substations in long-term fixed positions, with a more secure connection, but the installation and disassembly are relatively complex. Currently, when detecting abnormal plugging of the power supply interface of a mobile substation, the voltage and current at the interface are detected by sensors, and the abnormal threshold ranges of voltage and current are set based on the voltage and current when the mobile substation is working normally. When the detected voltage and current exceed the threshold ranges, an alarm signal is issued.

[0003] Since the load connected to the mobile substation changes during use, and its load power also changes. If the load power is too high, the voltage and current at the substation interface will also increase. If it exceeds the threshold range, an alarm will be triggered, resulting in waste of manpower and material resources. If the load power is too low, the voltage and current at the substation interface will also decrease. At this time, if an abnormality occurs, the time for the current and voltage to reach the alarm threshold will increase, and thus the alarm cannot be triggered in time, resulting in unnecessary losses. Therefore, in order to adaptively adjust the monitoring ranges of voltage and current and the alarm threshold according to the change of load power, so as to more accurately detect abnormal plugging of the power supply interface, we propose a detection circuit and method for abnormal plugging of a power supply interface of a mobile substation. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem that when the mobile substation is in use, the monitoring ranges of its voltage and current and the alarm threshold are all fixed. When the load power changes, the voltage and current at the power supply interface of the substation will also change. On the one hand, when the load power increases, the voltage and current at its power supply interface also increase, resulting in the voltage and current exceeding the monitoring range and the alarm threshold, and a false alarm is issued. On the other hand, when the load power decreases, the voltage and current at its power supply interface also decrease. If an abnormality occurs at this time, the time for the current and voltage to reach the alarm threshold will increase, and thus the alarm cannot be given in time, causing unnecessary losses. In order to be able to adaptively adjust the monitoring ranges of voltage and current and the alarm threshold according to the change of load power, so as to more accurately detect abnormal plugging of the power supply interface.

[0005] To achieve the above object, the present invention provides a detection circuit for abnormal plugging of a mobile substation power interface, including a data monitoring module, a threshold adjustment module, a fault diagnosis module, and an alarm prompt module;

[0006] The data monitoring module respectively detects the voltage, current, and temperature at the power interface of the mobile substation through a voltage divider resistor type voltage sensor, a Hall current sensor, and a temperature sensor. The analog signals of voltage, current, and temperature are converted into digital signals by an analog-to-digital converter. The average values, standard deviations, and powers of current, voltage, and temperature are calculated using statistical methods and used as the initial determination criteria for abnormal plugging of the mobile substation power interface. Then, the voltage, current, and temperature data are transmitted to the threshold adjustment module;

[0007] The threshold adjustment module determines the load type according to the voltage and current data transmitted by the data monitoring module, establishes a corresponding power-current-voltage model according to the load type. When it detects that the load power changes, it compares the changed power with the initial power to obtain the power change ratio, and adjusts the monitoring range and alarm threshold of current, voltage, and temperature of the initial determination criteria for abnormal plugging of the mobile substation power interface with this proportional coefficient, and establishes the determination criteria for abnormal plugging of the power interface under this load power. If the current, voltage, and temperature exceed the threshold range, an alarm signal is transmitted to the alarm prompt module. At the same time, the change amount of the current, voltage, and temperature exceeding the threshold range is transmitted to the fault diagnosis module;

[0008] The fault diagnosis module uses the change amounts of current, voltage, and temperature exceeding the threshold range as the input layer nodes and the abnormal plugging type of the mobile substation power interface as the output layer nodes to establish a recurrent neural network model. The model is trained using the collected historical data. According to this neural network model, the abnormal type of the power interface plugging is judged, and corresponding solutions are formulated. The abnormal type of the power interface plugging and the corresponding solutions are transmitted to the alarm prompt module;

[0009] When the alarm prompt module receives the alarm signal transmitted by the threshold adjustment module, it immediately triggers the alarm indicator light, and at the same time, it broadcasts the abnormal type of the power interface plugging and the corresponding solutions transmitted by the fault diagnosis module by voice to remind the staff to repair the mobile substation power interface.

[0010] Preferably, the data monitoring module includes an analog-to-digital conversion unit and an initial determination unit;

[0011] The analog-to-digital conversion unit uses an amplification and filtering circuit to amplify and filter the voltage collected by the sensor, and then the analog-to-digital converter converts the analog signal into a digital signal for subsequent accurate calculation;

[0012] The initial determination unit calculates the average values, standard deviations, and powers of current, voltage, and temperature using statistical methods, formulates the transformation ranges of current, voltage, and temperature, as well as the alarm thresholds, and uses them as the initial determination criteria for abnormal plugging of the power supply interface.

[0013] Preferably, the analog-to-digital conversion unit includes an amplification and filtering circuit, wherein the amplification and filtering circuit includes a triode Q1, a triode Q2, and an operational amplifier A;

[0014] The base of the triode Q1 is connected to one end of a resistor R1 and one end of a resistor R2. The other end of the resistor R1 is connected to the positive pole of the input voltage UI. The emitter of the triode Q1 is connected to the negative pole of the input voltage UI. The collector of the triode Q1 is connected to one end of a resistor R3 and one end of a resistor R4. The other end of the resistor R4 is connected to pin 2 of the operational amplifier A and one end of a resistor R6 and one end of a capacitor C1. Pin 3 of the operational amplifier A is connected to one end of a resistor R5, and the other end of the resistor R5 is grounded. Pin 1 of the operational amplifier A is connected to the base of the triode Q2 and one end of the resistor R6 and the other end of the capacitor C1. The collector of the triode Q2 is connected to one end of a resistor R7 and the positive pole of the output voltage UO. The resistor R7 is connected in parallel with the resistor R3 and the resistor R2, and is connected to the power supply VCC. The emitter of the triode Q2 is connected to the negative pole of the output voltage UO.

[0015] Preferably, the threshold adjustment module includes a power detection unit and a proportional adjustment unit;

[0016] The power detection unit draws a current-voltage curve based on voltage and current data, determines the load characteristics, and constructs a corresponding power-current-voltage model according to the characteristics and connection mode of the load using Ohm's law and the power formula. Substitute the voltage and current data into the model to obtain the power data at this time;

[0017] The proportional adjustment unit adopts a linear adjustment strategy, and uses the change ratio after comparing the changed power with the initial power as the proportional coefficient to adjust the monitoring ranges of current, voltage, and temperature, as well as the alarm thresholds.

[0018] Preferably, the power detection unit uses a polynomial fitting algorithm to draw a current-voltage curve and determines the load type as a resistive load, an inductive load, and a capacitive load according to the curve.

[0019] Preferably, when the proportional adjustment unit adjusts the monitoring ranges of current, voltage, and temperature, as well as the alarm thresholds, the proportional coefficient cannot exceed the safety margin ratio of the load. The calculation formula for the safety margin ratio is:

[0020]

[0021] Wherein, is the voltage safety margin ratio, is the maximum allowable voltage, is the rated voltage.

[0022] Preferably, the fault diagnosis module includes a model construction unit and a determination processing unit;

[0023] When constructing the recurrent neural network model, the model construction unit uses ReLU as the activation function for the hidden layer, uses the softmax activation function in the output layer, calculates the probability independently for each neuron, and uses the Adam optimization algorithm to propagate the gradient of the loss function back to each layer of the neural network through the backpropagation algorithm to update the weights and biases, so that the output of the model gradually approaches the true fault type;

[0024] The determination processing unit determines that the types of abnormal plugging of the power supply interface include short - circuit fault, overload fault, poor contact fault, poor heat dissipation fault, and power fluctuation fault according to the recurrent neural network model, and formulates corresponding solutions according to the fault types.

[0025] Preferably, when training the recurrent neural network model, the model construction unit calculates each neuron using the binary cross - entropy loss function, and then measures the difference between the probability distribution output by the model and the true fault type label.

[0026] Preferably, the alarm indicator lights of the alarm prompt module are divided into red indicator lights, yellow indicator lights, and green indicator lights according to the severity of the fault.

[0027] The second object of the present invention is to provide a method for detecting abnormal plugging of the power supply interface of a mobile substation, including the mobile substation power supply interface abnormal plugging detection circuit described in any one of the above, and including the following steps:

[0028] S1. Detect the voltage, current, and temperature at the power supply interface of the mobile substation through the data monitoring module, and establish an initial determination standard for abnormal plugging of the power supply interface of the mobile substation;

[0029] S2. Use the threshold adjustment module to establish a corresponding power - current - voltage model, and use the change ratio after comparing the changed power with the initial power as the proportionality coefficient to adjust the monitoring range and alarm threshold of current, voltage, and temperature;

[0030] S3. Establish a recurrent neural network model through the fault diagnosis module, judge the abnormal type of the power supply interface plugging according to the neural network model, and formulate corresponding solutions;

[0031] S4. When the alarm prompt module receives an alarm signal, it immediately triggers the alarm indicator light, and at the same time, voice - broadcasts the abnormal type and the corresponding solution.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] The abnormal detection circuit and method for the power interface of the mobile substation detect the voltage, current, and temperature at the power interface of the mobile substation through the data monitoring module, calculate the average value, standard deviation, and power of the current, voltage, and temperature using statistical methods, and use these as the initial determination criteria for abnormal insertion of the power interface of the mobile substation. The threshold adjustment module determines the load type, establishes a corresponding power-current-voltage model according to the load type, and adopts a linear adjustment strategy. Using the change ratio after comparing the changed power with the initial power as the proportionality coefficient, the monitoring ranges of current, voltage, and temperature, as well as the alarm threshold, are adjusted. When the load power of the mobile substation changes, it can adaptively adjust the monitoring ranges of voltage and current, as well as the alarm threshold, and thus can more accurately detect abnormal insertion of the power interface;

[0034] The fault diagnosis module establishes a recurrent neural network model, determines the abnormal type of the power interface insertion according to the change amount of the current, voltage, and temperature exceeding the threshold range, and formulates corresponding solutions. The alarm prompt module triggers the alarm indicator light, and at the same time, the abnormal type and the corresponding solutions are broadcasted by voice, reminding the staff to repair the power interface of the mobile substation and reducing unnecessary losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is the overall flow block diagram of the present invention;

[0036] Figure 2 is the overall detailed flow chart of the present invention;

[0037] Figure 3 is the flow chart of the fault diagnosis module of the present invention;

[0038] Figure 4 is the enlarged filter circuit diagram of the present invention;

[0039] Figure 5 is the method flow block diagram of the present invention.

[0040] The meanings of each label in the figure are as follows:

[0041] 100, data monitoring module; 110, analog-to-digital conversion unit; 120, initial determination unit; 200, threshold adjustment module; 210, power detection unit; 220, proportional adjustment unit; 300, fault diagnosis module; 310, model construction unit; 320, determination processing unit; 400, alarm prompt module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Currently, when a mobile substation is in use, due to the change in load power, the voltage and current at the substation interface change, resulting in abnormal plugging at the power interface of the mobile substation. Under the influence of the load power, the detected voltage and current cannot exceed the alarm threshold, and thus cannot alarm in time, causing unnecessary losses. In order to adaptively adjust the monitoring range of voltage and current and the alarm threshold according to the change in load power, so as to be able to more accurately detect abnormal plugging of the power interface.

[0044] Therefore, the present invention proposes to establish an initial determination criterion for abnormal plugging of the power interface of the mobile substation through a data monitoring module, use a threshold adjustment module to establish a corresponding power-current-voltage model, use the change ratio after comparing the changed power with the initial power as the proportionality coefficient to adjust the monitoring range of current, voltage and temperature and the alarm threshold, establish a recurrent neural network model through a fault diagnosis module, judge the abnormal type of the power interface plugging according to this neural network model, and formulate corresponding solutions. When the alarm prompt module receives an alarm signal, it immediately triggers the alarm indicator light and simultaneously broadcasts the abnormal type and the corresponding solution by voice.

[0045] Specifically as follows:

[0046] As Figure 1 shown, one of the purposes of the present invention is to provide a detection circuit for abnormal plugging of the power interface of a mobile substation, including a data monitoring module 100, a threshold adjustment module 200, a fault diagnosis module 300 and an alarm prompt module 400;

[0047] The data monitoring module 100 respectively detects the voltage, current and temperature at the power interface of the mobile substation through a voltage sensor of a voltage dividing resistor type, a Hall current sensor and a temperature sensor, converts the analog signals of the voltage, current and temperature into digital signals by an analog-to-digital converter, calculates the average value, standard deviation and power of the current, voltage and temperature by using statistical methods, and uses this as the initial determination criterion for abnormal plugging of the power interface of the mobile substation, and then transfers the voltage, current and temperature data to the threshold adjustment module 200;

[0048] The threshold adjustment module 200 determines the load type according to the voltage and current data transmitted by the data monitoring module 100, establishes a corresponding power-current-voltage model according to the load type. When it detects that the load power changes, it compares the changed power with the initial power to obtain the power change ratio, and adjusts the monitoring range and alarm threshold of current, voltage and temperature of the initial determination standard for abnormal plugging of the mobile substation power supply interface with this proportional coefficient, and establishes the determination standard for abnormal plugging of the power supply interface under this load power. If the current, voltage and temperature exceed the threshold range, it transmits an alarm signal to the alarm prompt module 400. At the same time, it transmits the change amount of the current, voltage and temperature exceeding the threshold range to the fault diagnosis module 300;

[0049] The fault diagnosis module 300 takes the change amount of the current, voltage and temperature exceeding the threshold range as the input layer nodes, takes the abnormal type of the mobile substation power supply interface plugging as the output layer nodes, establishes a recurrent neural network model, trains this model with the collected historical data, judges the abnormal type of the power supply interface plugging according to this neural network model, and formulates corresponding solutions, and transmits the abnormal type of the power supply interface plugging and the corresponding solutions to the alarm prompt module 400;

[0050] When the alarm prompt module 400 receives the alarm signal transmitted by the threshold adjustment module 200, it immediately triggers the alarm indicator light, and at the same time, it broadcasts the abnormal type of the power supply interface plugging and the corresponding solutions transmitted by the fault diagnosis module 300 by voice, reminding the staff to repair the mobile substation power supply interface.

[0051] As Figure 2 shown, among them, the data monitoring module 100 includes an analog-to-digital conversion unit 110 and an initial determination unit 120;

[0052] The analog-to-digital conversion unit 110 uses an amplification and filtering circuit to amplify and filter the voltage collected by the sensor, and then the analog-to-digital converter converts the analog signal into a digital signal for subsequent accurate calculation;

[0053] The initial determination unit 120 uses statistical methods to calculate the average value, standard deviation and power of current, voltage and temperature, and formulates the change range and alarm threshold of current, voltage and temperature, and makes them the initial determination standard for abnormal plugging of the power supply interface;

[0054] Taking current as an example, the formula for calculating its average value is:

[0055]

[0056] Among them, is the average current value, is the number, is the corresponding current value.

[0057] The formula for the variance of current is:

[0058]

[0059] Wherein, is the variance of the current, is the average value of the current, is the number, is the corresponding current value.

[0060] The formula for the standard deviation of current is:

[0061]

[0062] Wherein, is the standard deviation of the current, is the variance of the current.

[0063] For example, the following current, voltage, and temperature data are collected:

[0064] 1 2 3 4 5 6 7 8 9 10 Current 2 3 4 3 2.5 3.5 2 3 4 3 Voltage 220 218 222 219 221 220 221 219 221 218 Temperature 30 32 31 29 33 30 31 32 30 31

[0065] Then the average value of the current is:

[0066]

[0067] The variance of the current is:

[0068]

[0069] The standard deviation of the current is:

[0070]

[0071] For each data point , the anomaly coefficient is:

[0072]

[0073] For example, for , .

[0074] Then the corresponding current change range is:

[0075] Lower limit: , Upper limit: .

[0076] The alarm threshold is: the lower alarm threshold is 2.1A, and an alarm is triggered when the current is less than 2.1A; the upper alarm threshold is 3.9A, and an alarm is triggered when the current is greater than 3.9A.

[0077] Similarly, the change ranges of voltage and temperature and the alarm threshold are obtained, and the initial determination criteria for abnormal plugging of the power interface are established.

[0078] As Figure 4 shown, since the signals output by the current, voltage, and temperature sensors are usually weak analog signals, in order to more accurately reflect the current, voltage, and temperature data, among them, the analog-to-digital conversion unit 110 includes an amplification and filtering circuit, and the amplification and filtering circuit includes a triode Q1, a triode Q2, and an operational amplifier A;

[0079] The base of the triode Q1 is connected to one end of the resistor R1 and one end of the resistor R2. The other end of the resistor R1 is connected to the positive pole of the input voltage UI. The emitter of the triode Q1 is connected to the negative pole of the input voltage UI. The collector of the triode Q1 is connected to one end of the resistor R3 and one end of the resistor R4. The other end of the resistor R4 is connected to pin 2 of the operational amplifier A and one end of the resistor R6 and one end of the capacitor C1. Pin 3 of the operational amplifier A is connected to one end of the resistor R5. The other end of the resistor R5 is grounded. Pin 1 of the operational amplifier A is connected to the base of the triode Q2 and one end of the resistor R6 and the other end of the capacitor C1. The collector of the triode Q2 is connected to one end of the resistor R7 and the positive pole of the output voltage UO. The resistor R7 is connected in parallel with the resistor R3, and the resistor R2 is connected to the power supply VCC. The emitter of the triode Q2 is connected to the negative pole of the output voltage UO;

[0080] In this circuit, a first-stage amplification circuit is composed of the triode Q1 and the resistors R1, R2, R3, and R4. Among them, when there is a slight change in the input voltage UI, it will cause a change in the base current of the triode Q1. According to the current amplification effect of the triode Q1, the collector current of the triode Q1 will be amplified. Due to the increase in the collector current of the triode Q1, the voltage across the resistor R4 will increase, thereby realizing the first-stage amplification of the input voltage UI;

[0081] The voltage amplified by the first-stage amplification circuit is connected to the filtering circuit composed of the operational amplifier A and the resistors R5, R6, and C1. According to the virtual short and virtual open characteristics of the operational amplifier A, the voltages at the non-inverting input terminal 2 and the inverting input terminal 1 are approximately equal (virtual short), and the current flowing into the inverting input terminal 1 is approximately zero (virtual open). The input voltage passes through the low-pass filter network composed of the resistor R6 and the capacitor C1, and performs low-pass filtering on it;

[0082] The voltage after low-pass filtering by the filtering circuit is amplified a second time for the input voltage UI through the second-stage amplification circuit composed of the triode Q2 and the resistor R7. The principle is the same as that of the first-stage amplification circuit, and the output voltage UO is obtained.

[0083] The input voltage UI enters the first-stage amplification circuit for amplification to obtain an amplified voltage signal. This signal then enters the low-pass filter circuit. Since the capacitive reactance of the capacitor decreases as the frequency increases, high-frequency noise signals are more likely to be bypassed to the ground through the capacitor, thereby achieving the filtering of high-frequency noise and only allowing low-frequency useful signals to pass through. The filtered signal then enters the second-stage amplification circuit for further amplification to an appropriate amplitude for subsequent processing. Filtering first and then performing the next-stage amplification can effectively suppress the influence of high-frequency noise, improve the quality of the output signal of the entire circuit, and ensure the accuracy and reliability of detecting the current signal of the mobile substation power supply interface.

[0084] Among them, the threshold adjustment module 200 includes a power detection unit 210 and a proportional adjustment unit 220;

[0085] The power detection unit 210 draws a current-voltage curve based on voltage and current data, determines the load characteristics, and constructs a corresponding power-current-voltage model according to the load characteristics and connection method using Ohm's law and the power formula. Substitute the voltage and current data into this model to obtain the power data at this time;

[0086] The proportional adjustment unit 220 adopts a linear adjustment strategy, uses the change ratio after comparing the changed power with the initial power as the proportional coefficient, and adjusts the monitoring ranges of current, voltage, and temperature as well as the alarm threshold;

[0087] In order to be able to more accurately determine the load type, among them, the power detection unit 210 uses the polynomial fitting algorithm to draw the current-voltage curve and determines the load type as resistive load, inductive load, and capacitive load according to the curve;

[0088] Among them, polynomial fitting is a method of approximating given data points with a polynomial function. For the given number of data points , using degree polynomial , the least squares method is used to determine the coefficients of the polynomial . The goal of the least squares method is to minimize the sum of squared errors . Among them, is the value of the polynomial at ;

[0089] If the fitted curve is a straight line and passes through the origin (or is very close to the origin), then it is a resistive load;

[0090] For inductive loads, the current lags behind the voltage. On the current-voltage curve graph, the curve may not be a straight line. Especially in an AC circuit, the change in current lags behind the change in voltage. If it is observed that the peak of the current lags behind the peak of the voltage, or the fitted polynomial is not a simple linear function, and the rate of change of the current is not constant as the voltage changes (the curve is curved), then it is an inductive load;

[0091] For capacitive loads, the current leads the voltage. On the curve graph, similar to inductive loads, the curve is usually not a straight line. When the voltage starts to change, the capacitor will charge or discharge rapidly, causing the change in current to precede the change in voltage. If it is found that the peak of the current leads the peak of the voltage, or the fitted polynomial shows that the relationship between current and voltage does not conform to the linear law of resistive loads, and the current changes significantly at the initial stage of voltage change and then gradually stabilizes, this is a capacitive load.

[0092] In order to better define the monitoring ranges of current, voltage, and temperature as well as the alarm thresholds, among which, when the proportional adjustment unit (220) adjusts the monitoring ranges of current, voltage, and temperature as well as the alarm thresholds, the proportional coefficient cannot exceed the safety margin ratio of the load. The calculation formula for the safety margin ratio is:

[0093]

[0094] Among them, is the voltage safety margin ratio, is the maximum allowable voltage, is the rated voltage.

[0095] The safety margin ratio refers to a safety margin relative to the rated parameters reserved when designing the operating parameters of the load to ensure the safe and reliable operation of the load. For example, for a load with a rated current of 10A, considering possible voltage fluctuations, short-term overloads, etc., the maximum safe current it can actually withstand may be set to 12A. Then the safety margin ratio is ;

[0096] When adjusting the current monitoring range, it cannot exceed the maximum current allowed by the load safety margin. For example, the rated current of the load is , and the safety margin ratio is , then the maximum safe current is . When adjusting the current monitoring range according to factors such as load power, the upper limit value should be less than or equal to . If this value is exceeded, it may not be possible to accurately monitor when the load is close to or exceeds the safety limit, increasing the risk of load damage. The same applies to voltage and temperature;

[0097] If the proportionality coefficient for adjusting the monitoring range and alarm threshold exceeds the load safety margin ratio, it may lead to the failure to detect load overload in a timely manner. For example, for a motor load with a rated current of 10A and a safety margin ratio set at 20%, that is, the maximum safe current is 12A. If the adjustment of the current monitoring range and alarm threshold causes the alarm to sound only when the current reaches 13A, then the motor may continue to operate with a current exceeding the safe current, resulting in overheating of the winding, insulation damage, and even motor burnout. By controlling the proportionality coefficient within the safety margin ratio range, it can ensure that an alarm is issued before the load approaches the safety limit, and measures can be taken in a timely manner, such as reducing the load power or cutting off the power supply, thereby effectively preventing the load from being damaged due to overload.

[0098] As Figure 3 shown, among them, the fault diagnosis module 300 includes a model construction unit 310 and a determination and processing unit 320;

[0099] When constructing the recurrent neural network model, the model construction unit 310 uses ReLU as the activation function for the hidden layer, uses the softmax activation function in the output layer, calculates the probability independently for each neuron, and uses the Adam optimization algorithm to propagate the gradient of the loss function back to each layer of the neural network through the backpropagation algorithm to update the weights and biases, so that the output of the model gradually approaches the true fault type;

[0100] The determination and processing unit 320 determines that the types of abnormal plugging of the power interface include short - circuit faults, overload faults, poor contact faults, poor heat dissipation faults, and power fluctuation faults according to the recurrent neural network model, and formulates corresponding solutions according to the fault types;

[0101] In order to better measure the difference between the probability distribution output by this model and the true fault type label, among them, when the model construction unit (310) trains the recurrent neural network model, it uses the binary cross - entropy loss function to calculate each neuron separately, and then measures the difference between the probability distribution output by this model and the true fault type label;

[0102] For the principle of the binary cross - entropy loss function, for a binary classification problem, assuming the true label is , and the model prediction output is , then the binary cross - entropy loss function . The smaller the value of this function, the closer the predicted probability distribution of the model is to the true label ;

[0103] Assume that the output layer of the recurrent neural network has neurons, and each neuron corresponds to a fault type (simplified to binary classification here, such as normal and abnormal). For each neuron , the output of the neuron is converted into a probability distribution through the softmax function. Let the output of the neuron be , and the probability obtained after passing through the softmax function is . This probability represents the probability that the model predicts that the sample belongs to the th type of fault;

[0104] When there is a true fault type label (assuming is a one-hot encoded vector. For example, if the true fault type is the th fault, then the th element of is 1 and the rest are 0), for each neuron , the loss is calculated according to the binary cross-entropy loss function. If the prediction of the th neuron is regarded as a binary classification problem (i.e., belonging to the th type of fault or not), then the loss for this neuron, where is the th element of the true label vector;

[0105] For example, if the probability distribution output by the model and the true label , then for the first neuron: ;

[0106] For the second neuron: ;

[0107] And so on, the loss function value of each neuron can be obtained, and then the difference between the probability distribution output by the model and the true fault type label can be measured more accurately.

[0108] Among them, the alarm indicator lights of the alarm prompt module 400 are divided into red indicator lights, yellow indicator lights and green indicator lights according to the severity of the fault. By assigning alarm indicator lights of specific colors to faults of different severities, it is possible for the staff to quickly and intuitively judge the approximate urgency of the fault in the case of being at a distance or only glancing through the corner of the eye, etc., without having to carefully check the specific fault prompt information or analyze the relevant data, so that corresponding response actions can be taken quickly, precious fault handling time can be saved, and the impact of the fault on the operation of the mobile substation can be minimized.

[0109] Another object of the present invention is to provide a method for detecting abnormal plugging of a mobile substation power interface, including the mobile substation power interface abnormal plugging detection circuit according to any one of the above, as Figure 5 shown, and includes the following steps:

[0110] S1. Detect the voltage, current and temperature at the mobile substation power interface through the data monitoring module 100, and establish an initial determination criterion for abnormal plugging of the mobile substation power interface;

[0111] S2. Use the threshold adjustment module 200 to establish a corresponding power-current-voltage model, and use the change ratio after comparing the changed power with the initial power as the proportionality coefficient to adjust the monitoring ranges and alarm thresholds of current, voltage and temperature;

[0112] S3. Establish a recurrent neural network model through the fault diagnosis module 300, judge the abnormal type of the power interface plugging according to this neural network model, and formulate corresponding solutions;

[0113] S4. When the alarm prompt module 400 receives an alarm signal, immediately trigger the alarm indicator light, and at the same time, voice broadcast the abnormal type and the corresponding solution.

[0114] In summary, the working principle of this solution is as follows:

[0115] The mobile substation power interface abnormal plugging detection circuit and method detect the voltage, current and temperature at the mobile substation power interface through the data monitoring module 100, calculate the average value, standard deviation and power of the current, voltage and temperature by using statistical methods, and use these as the initial determination criterion for abnormal plugging of the mobile substation power interface. The threshold adjustment module 200 determines the load type, establishes a corresponding power-current-voltage model according to the load type, adopts a linear adjustment strategy, and uses the change ratio after comparing the changed power with the initial power as the proportionality coefficient to adjust the monitoring ranges and alarm thresholds of current, voltage and temperature, so as to be able to adaptively adjust the monitoring ranges and alarm thresholds of voltage and current when the load power of the mobile substation changes, and then be able to detect the abnormal plugging of the power interface more accurately. The fault diagnosis module 300 establishes a recurrent neural network model, judges the abnormal type of the power interface plugging according to the change amount of the current, voltage and temperature exceeding the threshold range, and formulates corresponding solutions. The alarm prompt module 400 triggers the alarm indicator light, and at the same time, voice broadcasts the abnormal type and the corresponding solution, reminding the staff to repair the mobile substation power interface and reducing unnecessary losses.

[0116] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A mobile substation power interface plug-in abnormality detection circuit, characterized in that: It comprises a data monitoring module (100), a threshold adjustment module (200), a fault diagnosis module (300) and an alarm prompt module (400); The data monitoring module (100) detects the voltage, current and temperature at the mobile substation power interface respectively through a voltage divider resistor voltage sensor, a Hall current sensor and a temperature sensor, converts the analog signals of the voltage, current and temperature into digital signals through an analog-to-digital converter, calculates the average value, standard deviation and power of the current, voltage and temperature using a statistical method, and uses this as an initial judgment standard for abnormal plugging of the mobile substation power interface, and then transmits the voltage, current and temperature data to the threshold adjustment module (200); The threshold adjustment module (200) determines the load type based on the voltage and current data transmitted by the data monitoring module (100), establishes a corresponding power-current-voltage model based on the load type, and when a change in load power is detected, compares the changed power with the initial power to obtain a power change ratio, adopts a linear adjustment strategy, uses the change ratio after comparing the changed power with the initial power as a proportionality coefficient, and uses the proportionality coefficient to adjust the monitoring range and alarm threshold of the current, voltage and temperature of the initial judgment standard for abnormal plugging of the power interface of the mobile substation, establishes a judgment standard for abnormal plugging of the power interface under this load power, and transmits an alarm signal to the alarm prompt module (400) if the current, voltage and temperature exceed the threshold range, and at the same time, transmits the change amount of the current, voltage and temperature exceeding the threshold range to the fault diagnosis module (300); The fault diagnosis module (300) uses the variation of current, voltage and temperature exceeding the threshold range as input layer nodes and the abnormal type of the mobile substation power interface plugging as output layer nodes, establishes a recurrent neural network model, trains the model using the collected historical data, determines the abnormal type of the power interface plugging according to the neural network model, formulates corresponding solutions, and transmits the abnormal type of the power interface plugging and the corresponding solutions to the alarm prompt module (400); When receiving the alarm signal transmitted by the threshold adjustment module (200), the alarm prompt module (400) immediately triggers the alarm indicator light and simultaneously voice broadcasts the abnormal type of power interface plugging transmitted by the fault diagnosis module (300) and the corresponding solution, thereby reminding the staff to repair the mobile substation power interface.

2. The mobile substation power interface plug-in abnormality detection circuit according to claim 1, characterized in that: The data monitoring module (100) comprises an analog-to-digital conversion unit (110) and an initial determination unit (120); The analog-to-digital conversion unit (110) uses an amplifying and filtering circuit to amplify and filter the voltage collected by the sensor, and then uses an analog-to-digital converter to convert the analog signal into a digital signal for subsequent accurate calculation; The initial determination unit (120) uses a statistical method to calculate the average value, standard deviation and power of current, voltage and temperature, and formulates the change range and alarm threshold of current, voltage and temperature, which are used as the initial determination standard of abnormal power interface plugging.

3. The mobile substation power interface plug-in abnormality detection circuit according to claim 2, characterized in that: The analog-to-digital conversion unit (110) comprises an amplifying and filtering circuit, wherein the amplifying and filtering circuit comprises a transistor Q1, a transistor Q2 and an operational amplifier A; The base of the transistor Q1 is connected to one end of the resistor R1 and one end of the resistor R2, the other end of the resistor R1 is connected to the positive electrode of the input voltage UI, the emitter of the transistor Q1 is connected to the negative electrode of the input voltage UI, the collector of the transistor Q1 is connected to one end of the resistor R3 and one end of the resistor R4, the other end of the resistor R4 is connected to pin 2 of the operational amplifier A and one end of the resistor R6 and one end of the capacitor C1, the pin 3 of the operational amplifier A is connected to one end of the resistor R5, the other end of the resistor R5 is grounded, the pin 1 of the operational amplifier A is connected to the base of the transistor Q2 and one end of the resistor R6 and the other end of the capacitor C1, the collector of the transistor Q2 is connected to one end of the resistor R7 and the positive electrode of the output voltage UO, the resistor R7 is connected in parallel to the resistor R3 and the resistor R2 to the power supply VCC, and the emitter of the transistor Q2 is connected to the negative electrode of the output voltage UO.

4. The mobile substation power interface plug-in abnormality detection circuit according to claim 1, characterized in that: The threshold adjustment module (200) comprises a power detection unit (210) and a ratio adjustment unit (220); The power detection unit (210) draws a current-voltage curve according to the voltage and current data to determine the load characteristics, and uses Ohm's law and a power formula to construct a corresponding power-current-voltage model according to the characteristics and connection mode of the load, and substitutes the voltage and current data into the model to obtain the power data at this time; The ratio adjustment unit (220) adopts a linear adjustment strategy, using the ratio of the changed power compared with the initial power as a ratio coefficient to adjust the monitoring range and alarm threshold of the current, voltage and temperature.

5. The mobile substation power interface plug-in abnormality detection circuit according to claim 4, characterized in that: The power detection unit (210) draws a current-voltage curve graph using a polynomial fitting algorithm, and determines the load type as a resistive load, an inductive load, or a capacitive load based on the curve graph.

6. The mobile substation power interface plug-in abnormality detection circuit according to claim 4, characterized in that: When the proportion adjustment unit (220) adjusts the monitoring range of current, voltage and temperature and the alarm threshold, the proportion coefficient cannot exceed the safety margin ratio of the load. The calculation formula of the safety margin ratio is: Among them, k V is the voltage safety margin ratio, V max is the maximum allowable voltage, V r is the rated voltage.

7. The mobile substation power interface plug-in abnormality detection circuit according to claim 1, characterized in that: The fault diagnosis module (300) comprises a model building unit (310) and a determination processing unit (320); When constructing a recurrent neural network model, the model construction unit (310) uses ReLU as an activation function of a hidden layer, uses a softmax activation function in an output layer, independently performs probability calculation on each neuron, and uses an Adam optimization algorithm to propagate the gradient of the loss function back to each layer of the neural network through a back propagation algorithm to update weights and biases, so that the output of the model gradually approaches the actual fault type; The determination processing unit (320) determines the type of abnormal connection of the power interface according to the recurrent neural network model, including short circuit fault, overload fault, poor contact fault, poor heat dissipation fault and power fluctuation fault, and formulates corresponding solutions according to the fault type.

8. The mobile substation power interface plug-in abnormality detection circuit according to claim 7, characterized in that: When training the recurrent neural network model, the model building unit (310) uses a binary cross entropy loss function to calculate each neuron separately, thereby measuring the difference between the probability distribution output by the model and the actual fault type label.

9. The mobile substation power interface plug-in abnormality detection circuit according to claim 1, characterized in that: The alarm indicator lights of the alarm prompt module (400) are divided into a red indicator light, a yellow indicator light and a green indicator light according to the severity of the fault.

10. A method for detecting abnormal plugging of a power interface of a mobile substation, comprising a circuit for detecting abnormal plugging of a power interface of a mobile substation as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Detecting the voltage, current and temperature at the power interface of the mobile substation through the data monitoring module (100), and establishing an initial judgment standard for abnormal plugging of the power interface of the mobile substation; S2, using the threshold adjustment module (200) to establish a corresponding power-current-voltage model, using the change ratio of the changed power compared with the initial power as a proportionality coefficient, and adjusting the monitoring range of the current, voltage and temperature and the alarm threshold; S3, establishing a recurrent neural network model through the fault diagnosis module (300), determining the abnormal type of the power interface plugging according to the neural network model, and formulating corresponding solutions; S4. When the alarm prompt module (400) receives the alarm signal, it immediately triggers the alarm indicator light and simultaneously announces the abnormality type and corresponding solution by voice.

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