Compensation type circuit breaker release based on T-S type fuzzy controller

Abstract

The invention discloses a compensation type circuit breaker release based on a T-S type fuzzy controller, and relates to the technical field of power electronics, and the compensation type circuit breaker release comprises an execution part, a compensation circuit and a fuzzy controller. Wherein the execution component is an electromagnet and is used for converting electromagnetic energy into mechanical energy so as to perform tripping operation of the circuit breaker; the compensation circuit is connected with the electromagnet and is used for adjusting the magnitude of compensation current according to the control signal so as to adjust the electromagnetic attraction force of the electromagnet; and the fuzzy controller is a T-S type fuzzy controller and is used for receiving a current signal of a circuit where the circuit breaker is located and outputting a control signal according to a preset fuzzy rule and a T-S model so as to control the working state of the compensation circuit, thereby preventing false operation and refusal operation of the release. According to the invention, the T-S type fuzzy controller is adopted to carry out current size control on the compensation circuit of the circuit breaker, so that false operation and refusal operation of the release can be effectively prevented, and the short-circuit protection function of the circuit breaker is prevented from losing efficacy.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology, and particularly to a compensated circuit breaker trip device based on a T-S type fuzzy controller. Background Art

[0002] The electromagnet is the actuating component of the short-circuit trip device and is an electromagnetic element that converts electromagnetic energy into mechanical energy. After a short circuit occurs in the circuit, the short-circuit current passes through the trip coil, and the coil generates an attraction force on the iron core, causing the iron core to pull the traction rod. The latch connected to the traction rod is unlocked, thereby disconnecting the circuit. The failure modes of short-circuit protection are misoperation and refusal to operate of the trip device.

[0003] The reasons for misoperation are: (1) The elastic force of the anti-force spring of the electromagnet decays. If the electromagnetic attraction force remains unchanged, at this time, the elastic force of the anti-force spring is insufficient, and the electromagnetic attraction force under the non-operating current overcomes the spring resistance and will pull the traction rod, resulting in misoperation of the trip device; (2) The electromagnetic attraction force is too large. The internal structure of the miniature circuit breaker is compact. When the current is large, the temperature rises, the iron core expands, resulting in an increase in the cross-sectional area and an increase in the electromagnetic attraction force. After overcoming the spring resistance, the traction rod is pulled, resulting in misoperation of the trip device.

[0004] The reasons for refusal to operate are: (1) The lap distance between the iron core of the electromagnet and the traction rod is too large. Under the fault current, when the electromagnetic attraction force moves the iron core to the maximum stroke, there is not enough large pulling force to pull the traction rod to operate. In most cases, the traction rod is fixed only by a thin spring, which is not firm; (2) The current loss increases. In addition to the coil resistance loss in the coil current, there are also hysteresis and eddy current losses of the magnetic conductor. In this way, the electromagnetic attraction force under the fault current is not enough to pull the traction rod to operate.

[0005] In the process of implementing the existing technology, the following problems exist: The use of a compensation circuit can effectively solve the problems of misoperation and refusal to operate of the trip device. When the elastic force of the anti-force spring inside the electromagnet decays, the compensation current decreases, the electromagnetic attraction force decreases, and the misoperation of the trip device is prevented; when the lap distance between the iron core of the ferromagnetic body and the traction rod is too large, or the current loss increases, the compensation current increases, the electromagnetic attraction force increases, and the refusal to operate of the trip device is avoided. However, how to control the magnitude of the compensation current is an urgent problem to be solved. Summary of the Invention

[0006] The purpose of the present invention is to provide a compensated circuit breaker trip device based on a T-S type fuzzy controller to solve the problems raised in the above background art.

[0007] To solve the above technical problems, the technical solution adopted by the present invention is: A compensated circuit breaker trip device based on a T-S type fuzzy controller, the compensated circuit breaker trip device includes an actuating component, a compensation circuit, and a fuzzy controller; Among them, the execution component is an electromagnet, which is used to convert electromagnetic energy into mechanical energy to perform the tripping operation of the circuit breaker; The compensation circuit is connected to the electromagnet and is used to adjust the magnitude of the compensation current according to the control signal to adjust the electromagnetic attraction of the electromagnet; The fuzzy controller is a T-S type fuzzy controller, which is used to receive the current signal of the line where the circuit breaker is located, and output a control signal according to the preset fuzzy rules and T-S model to control the working state of the compensation circuit, so as to prevent the tripping device from malfunctioning and failing to operate.

[0008] A further improvement of the technical solution of the present invention lies in that: the T-S type fuzzy controller is a single-input single-output system, and its control process includes the following steps: Monitor the current of the line where the circuit breaker is located in real time, and obtain a clear input quantity i through a current transformer. The clear input quantity i is the current value in the line where the circuit breaker is located; Input the obtained current data into the T-S type fuzzy controller for structure identification and parameter identification; According to the identification results, calculate and output a clear output quantity u through the preset fuzzy rules. The clear output quantity u is a compensation current value signal, and the calculated compensation current value signal is then output to the compensation circuit; The compensation circuit receives the compensation current value signal from the T-S type fuzzy controller, outputs an equivalent compensation current according to the compensation current value signal, and dynamically adjusts the electromagnetic attraction of the electromagnet by adjusting the magnitude of the compensation current, ensuring that the electromagnetic attraction decreases appropriately when the elastic force of the anti-rebound spring in the electromagnet decays, preventing the tripping device from malfunctioning, and when the lap distance between the iron core of the ferromagnetic body and the traction rod is too large or the current loss increases, the electromagnetic attraction can increase accordingly, avoiding the tripping device from failing to operate, so as to effectively ensure that the short-circuit protection function of the circuit breaker works normally and reliably.

[0009] A further improvement of the technical solution of the present invention lies in that: the structure identification and parameter identification specifically include: Perform structure identification on the data of the clear input quantity i input into the T-S type fuzzy controller to determine whether to adopt a zero-order or first-order linear model; After determining the form of the linear model, further perform parameter identification on the input current data according to the selected linear model to determine the parameters a and k in the model. For the zero-order model, the goal of parameter identification is to determine the constant k so that it can accurately reflect the output level of the system under steady-state conditions. For the first-order model, the constants a and k need to be determined simultaneously to describe the linear relationship between the input current and the output; According to the results of structure identification and parameter identification, establish a complete T-S model and output a clear output quantity u according to the model.

[0010] A further improvement of the technical solution of the present invention lies in that: the control rules of the T-S type fuzzy controller specifically include: Define the fuzzy set A, and determine the corresponding fuzzy rules according to the fuzzy set A to which the clear input quantity i belongs; In the 0th-order model, the fuzzy rule is "if the clear input quantity i belongs to A, then u = k", where k is a constant related to the set A. The 0th-order model is applicable to the case where the relationship between the system output and input is relatively simple, and the system is directly controlled through the constant output; In the 1st-order model, the fuzzy rule is "if the clear input quantity i belongs to A, then u = ai + k", where a and k are constants related to the set A. The 1st-order model can describe the linear relationship between the input and output, and is applicable to the case where the dynamic characteristics of the system are relatively complex, and the output is adjusted in the form of a linear function; Activate the corresponding fuzzy rules according to the membership degree of the clear input quantity i, and calculate the clear output quantity u according to the rules.

[0011] A further improvement of the technical solution of the present invention lies in that: the working mode of the compensation circuit specifically includes: When the elastic force of the anti-rebound spring in the electromagnet decays, the compensation current calculated according to the T-S model decreases, and the electromagnetic attraction decreases accordingly to prevent the release from malfunctioning; When the lap distance between the iron core of the ferromagnetic body in the electromagnet and the traction rod is too large, or the current loss increases, the compensation current calculated according to the T-S model increases, and the electromagnetic attraction increases accordingly to avoid the release from refusing to operate; The compensation circuit dynamically balances the electromagnetic attraction of the electromagnet and the elastic force of the anti-rebound spring by adjusting the magnitude of the compensation current; The compensation circuit outputs an equivalent compensation current according to the received compensation current value signal to accurately adjust the electromagnetic attraction of the electromagnet.

[0012] A further improvement of the technical solution of the present invention lies in that: the output of the T-S type fuzzy controller specifically includes: When the clear input quantity i is input into the T-S type fuzzy controller, the corresponding fuzzy rules are activated according to its membership degree, and the weight coefficient w of each rule is determined. Among them, each activated fuzzy rule corresponds to a weight coefficient w, and this weight coefficient reflects the degree to which the clear input quantity i belongs to the fuzzy set corresponding to this fuzzy rule; The T-S type fuzzy controller calculates the final output U by using the weighted summation method or the weighted average method; In the weighted summation method, the final output U is obtained by summing the product of the clear output quantity u of all activated fuzzy rules and their corresponding weight coefficients w. Its calculation formula is: ; The final output U is obtained by dividing the sum of the products of the crisp output values u of all activated fuzzy rules and their corresponding weight coefficients w by the sum of all weight coefficients. The calculation formula is as follows: , where u is the output of each rule and w is the corresponding weight coefficient.

[0013] A further improvement of the technical solution of the present invention lies in that the determination of the weight coefficients of each rule specifically includes: The minimum method is used to determine the weight coefficient, that is, the minimum membership degree of each fuzzy rule is taken as the weight coefficient; The product method is used to determine the weight coefficient, that is, the product of the membership degrees of each fuzzy rule is taken as the weight coefficient; According to the actual application scenario and control requirements, the weight coefficient is artificially determined through artificial experience or experimental data to optimize the control effect.

[0014] A further improvement of the technical solution of the present invention lies in that the compensation circuit can also adopt a compensation circuit based on a neural network, and its working mode includes: Receiving the compensation current value signal from the fuzzy controller, and adaptively adjusting the compensation current through a compensation model constructed based on a neural network. Among them, the training data of the neural network includes current signals and corresponding compensation current values under different working conditions to control the electromagnetic suction force of the electromagnet.

[0015] A further improvement of the technical solution of the present invention lies in that the construction process of the compensation model is as follows: Collect current signals and corresponding compensation current values under different working conditions, covering various situations such as normal working state, attenuation of the reaction spring elasticity, excessive lap distance between the iron core and the traction rod, and increased current loss. Normalize the collected data, map the current signal and the compensation current value to the same range, and integrate the normalized data, divide it into a training set and a test set, and then design a neural network structure, including an input layer, a hidden layer, and an output layer; Construct a compensation model based on the designed neural network structure, use the training set to input the neural network structure for training, randomly initialize the weights and biases of the neural network, perform forward propagation of the input current signal through the neural network, calculate the output compensation current value, and then use an independent test data set to verify the trained neural network, calculate the mean square error index on the test data, evaluate the performance of the neural network, and then optimize the neural network to finally obtain the constructed compensation model; Deploy the constructed compensation model, use the compensation circuit to receive the compensation current value signal from the T-S type fuzzy controller, input the input signal into the compensation model, calculate the final compensation current value through forward propagation, and according to the output of the compensation model, the compensation circuit outputs the corresponding compensation current to adjust the electromagnetic suction force of the electromagnet.

[0016] A further improvement of the technical solution of the present invention lies in that: the T-S type fuzzy controller further includes a fault diagnosis module, which is used to monitor the current signal of the line where the circuit breaker is located and the working state of the compensation circuit in real time. When an abnormal signal is detected, the fault diagnosis module issues an alarm signal and transmits the abnormal information to an external monitoring system for fault troubleshooting and handling; Among them, the specific working process of the fault diagnosis module is as follows: The fault diagnosis module continuously monitors the current signal of the line where the circuit breaker is located and the working state of the compensation circuit in real time, obtains the real-time value of the line current, and simultaneously monitors the current, voltage and electromagnetic suction data of the electromagnet in the compensation circuit; The collected signals and data are analyzed and processed in real time. Through preset threshold judgment, it is detected whether there is an abnormal signal. Among them, when the line current exceeds the set normal range threshold, or the parameters of the compensation circuit change in a way that does not conform to the normal working mode, it is identified as an abnormal signal; Once an abnormal signal is detected, the fault diagnosis module immediately triggers an alarm mechanism, issues an alarm signal to remind the on-site personnel to pay attention. At the same time, the abnormal information including the specific parameters, occurrence time and reason of the abnormal signal is transmitted to the external monitoring system through the communication interface. After receiving the abnormal information, the external monitoring system quickly locates the fault location and provides accurate fault information for the maintenance personnel, so as to carry out fault troubleshooting and handling in time, thereby minimizing the impact of the fault on the operation of the power system and ensuring the safe and stable operation of the power system.

[0017] Due to the adoption of the above technical solution, the technical progress achieved by the present invention compared with the prior art is that: the use of a compensation circuit can effectively solve the problems of misoperation and refusal to operate of the trip device, and the output of T-S type fuzzy inference can be directly used for the control of the compensation circuit, which can approximate any nonlinear system; the fuzzy controller is a single-input and single-output system. Through structure identification and parameter identification of the current data, a T-S model is established, and the working mode of the compensation circuit is determined by the T-S model: when the elastic force of the anti-reaction spring in the electromagnet decays, the compensation current decreases and the electromagnetic suction decreases to prevent the trip device from misoperating; when the lap distance between the iron core of the ferromagnetic body and the traction rod is too large, or the current loss increases, the compensation current increases and the electromagnetic suction increases to avoid the trip device from refusing to operate. Therefore, using a T-S type fuzzy controller to control the current magnitude of the compensation circuit of the circuit breaker can effectively prevent the trip device from misoperating and refusing to operate, and prevent the short-circuit protection function of the circuit breaker from failing. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0019] Figure 1 This is the working principle diagram of a compensation type circuit breaker trip device based on a T-S type fuzzy controller provided by the present invention; Figure 2 This is the relationship diagram between the clear input quantity i and the clear output quantity u of the single input-single output system of the T-S type fuzzy controller provided by the embodiment of the present invention; Figure 3 This is the position of the input quantity of the T-S type fuzzy controller provided by the embodiment of the present invention in the fuzzy subset mf1; Figure 4 This is the position of the input quantity of the T-S type fuzzy controller provided by the embodiment of the present invention in the fuzzy subset mf2; Figure 5 This is the position of the input quantity of the T-S type fuzzy controller provided by the embodiment of the present invention in the fuzzy subset mf3. Detailed implementation manners

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0021] Embodiment 1, as Figure 1 shown, the present invention provides a compensation type circuit breaker trip device based on a T-S type fuzzy controller. The compensation type circuit breaker trip device includes an execution component, a compensation circuit, and a fuzzy controller; Among them, the execution component is an electromagnet, which is used to convert electromagnetic energy into mechanical energy for the tripping operation of the circuit breaker; The compensation circuit is connected to the electromagnet and is used to adjust the magnitude of the compensation current according to the control signal to adjust the electromagnetic attraction of the electromagnet; The fuzzy controller is a T-S type fuzzy controller, which is used to receive the current signal of the circuit where the circuit breaker is located and output a control signal according to the preset fuzzy rules and the T-S model to control the working state of the compensation circuit, thereby preventing the trip device from malfunctioning and refusing to operate; The T-S type fuzzy controller is a single input-single output system, and its control process includes the following steps: The current of the circuit where the circuit breaker is located is monitored in real time. The clear input quantity i is obtained through a current transformer. The clear input quantity i is the current value in the circuit where the circuit breaker is located. The obtained current data is input into a T-S type fuzzy controller for structure identification and parameter identification. According to the identification results, the clear output quantity u is calculated through preset fuzzy rules. The clear output quantity u is a compensation current value signal. The calculated compensation current value signal is then output to a compensation circuit. The compensation circuit receives the compensation current value signal from the T-S type fuzzy controller and outputs an equivalent compensation current according to the compensation current value signal. By adjusting the magnitude of the compensation current, the electromagnetic suction force of the electromagnet is dynamically adjusted to ensure that the electromagnetic suction force decreases appropriately when the elastic force of the anti-rebound spring in the electromagnet decays, preventing the tripping device from malfunctioning. When the lap distance between the iron core of the ferromagnetic body and the traction rod is too large or the current loss increases, the electromagnetic suction force can increase correspondingly, avoiding the tripping device from failing to operate, thereby effectively ensuring that the short-circuit protection function of the circuit breaker works normally and reliably; The structure identification and parameter identification specifically include: The structure of the data of the clear input quantity i input into the T-S type fuzzy controller is identified to determine whether to adopt a zero-order or first-order linear model. Among them, the T-S type fuzzy controller first analyzes the characteristics of the input data to determine the form of the linear model that best describes the input-output relationship of the system. The core of the structure identification is to judge whether the system is more suitable for adopting a zero-order linear model (i.e., a constant model) or a first-order linear model (i.e., a linear function model) through a data-driven method. After determining the form of the linear model, the input current data is further subjected to parameter identification according to the selected linear model to determine the parameters a and k in the model. For the zero-order model, the goal of parameter identification is to determine the constant k so that it can accurately reflect the output level of the system under steady-state conditions. For the first-order model, the constants a and k need to be determined simultaneously to describe the linear relationship between the input current and the output. According to the results of the structure identification and parameter identification, a complete T-S model is established, and the clear output quantity u is output according to the model. Among them, the relationship between the clear input quantity i and the clear output quantity u is expressed in the form of a mathematical formula through the T-S model. Based on the established T-S model, the T-S type fuzzy controller can quickly and accurately output the corresponding clear output quantity u according to the real-time input clear input quantity i through fuzzy inference and calculation. The clear output quantity u, as the compensation current value, directly acts on the compensation circuit to guide it to adjust the magnitude of the compensation current, thereby realizing the dynamic adjustment of the electromagnetic suction force of the electromagnet and ensuring that the circuit breaker tripping device can work stably and reliably under various complex working conditions, effectively preventing misoperation and failure to operate; The control rules of the T-S type fuzzy controller specifically include: Define the fuzzy set A, and determine the corresponding fuzzy rules according to the fuzzy set A to which the clear input quantity i belongs. Among them, the fuzzy set is used to fuzzify the clear input quantity i. Each fuzzy set A corresponds to a membership function, which is used to describe the degree to which the clear input quantity i belongs to this fuzzy set. According to the value of the clear input quantity i, determine the fuzzy set A to which it belongs, and match the corresponding fuzzy rules. In the 0th-order model, the fuzzy rule is "if the clear input quantity i belongs to A, then u = k", where k is a constant related to the set A. The 0th-order model is applicable to the case where the relationship between the system output and input is relatively simple, and the system is directly controlled by the constant output. In the 1st-order model, the fuzzy rule is "if the clear input quantity i belongs to A, then u = ai + k", where a and k are constants related to the set A. The 1st-order model can describe the linear relationship between the input and output and is applicable to the case where the dynamic characteristics of the system are relatively complex, and the output is adjusted in the form of a linear function. Activate the corresponding fuzzy rules according to the membership degree of the clear input quantity i, and calculate the clear output quantity u according to the rules. Since the clear input quantity i may belong to multiple fuzzy sets at the same time, multiple fuzzy rules may be activated. Each activated fuzzy rule will calculate a local clear output quantity u according to its corresponding fuzzy rule form. In addition, according to the fuzzy rules, the system can be described by n control rules. When the specific input data is x, m control rules will be activated, and the final output U will be determined by the clear output quantities u of these m control rules; The working modes of the compensation circuit specifically include: When the elastic force of the reaction spring inside the electromagnet decays, the compensation current calculated according to the T-S model decreases, and the electromagnetic attraction force decreases accordingly to prevent the release from malfunctioning. Among them, when the elastic force of the reaction spring inside the electromagnet decays due to long-term use or environmental factors, the electromagnetic attraction force of the electromagnet will relatively increase under the same current. At this time, the value of the compensation current calculated according to the T-S model will decrease accordingly. By reducing the compensation current, the electromagnetic attraction force of the electromagnet also decreases, thus avoiding the malfunction of the release caused by excessive electromagnetic attraction force under the normal working current. This compensation mechanism can effectively extend the service life of the release and ensure its normal operation even when the reaction spring ages. When the lap distance between the iron core of the ferromagnetic body inside the electromagnet and the traction rod is too large, or the current loss increases, the compensation current calculated according to the T-S model increases, and the electromagnetic attraction force increases accordingly to avoid the release from failing to operate. Among them, when the lap distance between the iron core of the electromagnet and the traction rod increases due to mechanical wear or assembly error, or the current loss increases due to factors such as increased coil resistance, hysteresis, and eddy current loss, the electromagnetic attraction force of the electromagnet may not be sufficient to pull the traction rod to complete the tripping action. At this time, the value of the compensation current calculated according to the T-S model will increase accordingly. By increasing the compensation current, the electromagnetic attraction force of the electromagnet increases, thus ensuring that the traction rod can be reliably pulled under the fault current and avoiding the release from failing to operate. This compensation mechanism can effectively cope with the uncertainties in mechanical and electrical aspects and improve the reliability of the system. The compensation circuit dynamically balances the electromagnetic attraction force of the electromagnet and the elastic force of the reaction spring by adjusting the magnitude of the compensation current. Among them, the dynamic balance mechanism ensures that the release can maintain stable mechanical performance under various working conditions. In the normal working state, the compensation current is maintained at an appropriate level to cancel out the electromagnetic attraction force and the elastic force of the reaction spring, ensuring that the release does not malfunction. In the fault state, the compensation current can be quickly adjusted so that the electromagnetic attraction force is sufficient to overcome the elastic force of the reaction spring and complete the tripping action. The compensation circuit outputs an equivalent compensation current according to the received compensation current value signal to precisely adjust the electromagnetic attraction force of the electromagnet; The output of the T-S type fuzzy controller specifically includes: When the crisp input quantity i is input into the T-S type fuzzy controller, the corresponding fuzzy rules are activated according to its membership degree, and the weight coefficient w of each rule is determined. Among them, each activated fuzzy rule corresponds to a weight coefficient w, and this weight coefficient reflects the degree to which the crisp input quantity i belongs to the fuzzy set corresponding to this fuzzy rule. The T-S type fuzzy controller calculates the final output U using the weighted summation method or the weighted average method. In the weighted summation method, the final output U is obtained by summing the product of the crisp output quantity u of all activated fuzzy rules and their corresponding weight coefficients w. Its calculation formula is: , the final output U is obtained by dividing the sum of the products of the crisp output values u of all activated fuzzy rules and their corresponding weight coefficients w by the sum of all weight coefficients. Its calculation formula is: , where u is the output of each rule and w is the corresponding weight coefficient; The determination of the weight coefficients of each rule specifically includes: The minimum method is used to determine the weight coefficient, that is, the minimum membership degree of each fuzzy rule is taken as the weight coefficient. Among them, for multiple fuzzy rules activated by a crisp input quantity i, the weight coefficient w of each rule is set to the minimum membership degree value of its corresponding fuzzy set. By selecting the minimum membership degree, it is ensured that the activation degree of the rule will not be overestimated. The minimum method is applicable to scenarios with high requirements for system safety and reliability, and can effectively avoid over-control caused by excessive weights. The product method is used to determine the weight coefficient, that is, the product of the membership degrees of each fuzzy rule is taken as the weight coefficient. Among them, for multiple fuzzy rules activated by a crisp input quantity i, the weight coefficient w of each rule is set to the product of the membership degrees of its corresponding fuzzy sets. By comprehensively considering the membership degrees of all relevant fuzzy sets, a more accurate weight coefficient is obtained through product operation. The product method can better reflect the comprehensive membership relationship of the input quantity in multiple fuzzy sets and is applicable to scenarios that require precise control and comprehensive consideration of multiple factors. According to the actual application scenario and control requirements, the weight coefficient is determined manually through human experience or experimental data to optimize the control effect. Among them, according to the specific requirements and actual operating conditions of the system, the weight coefficient is manually adjusted to optimize the control effect, which requires combining the actual operating data, historical experience, and expert knowledge of the system, and determining the most suitable weight coefficient through experimental verification and adjustment. It is applicable to complex systems or scenarios with specific optimization goals and can effectively improve the control performance and adaptability of the system.

[0022] The technical solution of the present invention uses a T-S type fuzzy controller to implement a compensated circuit breaker trip unit. The basic principle of this technical solution is as follows: Using a compensation circuit can effectively solve the problems of misoperation and refusal to operate of the trip unit, and the output of T-S type fuzzy inference can be directly used to control the compensation circuit, which can approximate any nonlinear system. The fuzzy controller is a single-input and single-output system. By performing structure identification and parameter identification on the current data, a T-S model is established, and the working mode of the compensation circuit is determined by the T-S model: When the elastic force of the anti-reverse spring in the electromagnet decays, the compensation current decreases and the electromagnetic attraction decreases, preventing the trip unit from misoperating; When the lap distance between the iron core of the ferromagnetic body and the traction rod is too large, or the current loss increases, the compensation current increases and the electromagnetic attraction increases, avoiding the trip unit from refusing to operate. Using a T-S type fuzzy controller to control the magnitude of the current in the compensation circuit of the circuit breaker can effectively prevent the trip unit from misoperating and refusing to operate, and prevent the short-circuit protection function of the circuit breaker from failing; The compensation circuit can also adopt a neural network-based compensation circuit, and its working mode includes: Receiving the compensation current value signal from the fuzzy controller, and adaptively adjusting the compensation current through a compensation model constructed based on a neural network. Among them, the training data of the neural network includes current signals and corresponding compensation current values under different working conditions to control the electromagnetic suction of the electromagnet; In addition, the construction process of the compensation model is as follows: Collect current signals and corresponding compensation current values under different working conditions, covering various situations such as normal working state, attenuation of the reaction spring force, excessive lap distance between the iron core and the traction rod, and increased current loss. Normalize the collected data, map the current signal and the compensation current value to the same range, and integrate the normalized data. Divide it into a training set and a test set. Then design a neural network structure, including an input layer, a hidden layer, and an output layer. Among them, the number of neurons in the input layer is the same as the number of characteristics of the current signal. Design one or more hidden layers, each layer containing several neurons. The hidden layer uses a non-linear activation function. The number of neurons in the output layer is 1, representing the compensation current value, and uses a linear activation function to directly output the compensation current value. Build a compensation model based on the designed neural network structure. Use the training set to input the neural network structure for training. Randomly initialize the weights and biases of the neural network. Propagate the input current signal forward through the neural network, calculate the output compensation current value. Use the mean square error as the loss function to calculate the difference between the compensation current value output by the neural network and the actual compensation current value. Calculate the gradient of the loss function with respect to each weight through the backpropagation algorithm, and use the gradient descent method to update the weights to minimize the loss function. Repeat the steps of forward propagation, calculating the loss, and backpropagation until the loss function converges to a smaller value. Then use an independent test data set to verify the trained neural network, calculate the mean square error index on the test data, evaluate the performance of the neural network, and then optimize the neural network. Finally, obtain the constructed compensation model. Deploy the constructed compensation model. Use the compensation circuit to receive the compensation current value signal from the T-S type fuzzy controller, input the input signal into the compensation model, and calculate the final compensation current value through forward propagation. According to the output of the compensation model, the compensation circuit outputs the corresponding compensation current to adjust the electromagnetic suction of the electromagnet; The T-S type fuzzy controller also includes a fault diagnosis module, which is used to monitor the current signal of the line where the circuit breaker is located and the working state of the compensation circuit in real time. When an abnormal signal is detected, the fault diagnosis module issues an alarm signal and transmits the abnormal information to the external monitoring system for fault troubleshooting and handling; Among them, the specific working process of the fault diagnosis module is as follows: The fault diagnosis module continuously monitors the current signal of the line where the circuit breaker is located and the working state of the compensation circuit in real time, obtains the real-time value of the line current, and simultaneously monitors the current, voltage, and electromagnetic suction data of the electromagnet in the compensation circuit. It analyzes and processes the collected signals and data in real time, and detects whether there are abnormal signals through preset threshold judgments. Among them, when the line current exceeds the set normal range threshold, or the parameters of the compensation circuit change in a manner that does not conform to the normal working mode, it is identified as an abnormal signal. Once an abnormal signal is detected, the fault diagnosis module immediately triggers the alarm mechanism, emits an alarm signal to alert the on-site personnel, and at the same time, transmits the abnormal information including the specific parameters, occurrence time, and cause of the abnormal signal to the external monitoring system through the communication interface. After receiving the abnormal information, the external monitoring system quickly locates the fault location, provides accurate fault information for the maintenance personnel, so as to promptly conduct fault troubleshooting and handling, thereby minimizing the impact of the fault on the operation of the power system and ensuring the safe and stable operation of the power system.

[0023] Embodiment 2, as Figure 2 shown, on the basis of Embodiment 1, the present invention provides a technical solution: Preferably, a T-S type fuzzy controller is used to implement the compensating circuit breaker tripper. The compensation circuit of the circuit breaker tripper is a single-input single-output system, and multiple groups of data of the clear input quantity i and the clear output quantity u are measured, as Figure 2 shown. From Figure 2 it can be seen that these test data are two-segment linear functions, and in different intervals of the clear input quantity i, the slopes of the straight lines are not equal. According to Figure 2 the test data, the specific expression forms of the two functions can be obtained by fitting, and the coefficients a and k of the functions are determined. When a clear input quantity i is measured, the clear output quantity u can be obtained according to the two functions.

[0024] Embodiment 3, as Figure 3 、 Figure 4 shown, on the basis of Embodiments 1-2, the present invention provides a technical solution: Preferably, according to a large number of measured data of the input-output of a specific circuit breaker tripper system, three T-S type fuzzy rules describing it are obtained through identification, and the membership functions are mf1, mf2, and mf3 respectively, as Figure 3 、 Figure 4 shown. At this time, if the current i1 = 2 is measured in the system, the activated rules are obtained: Fuzzy set mf1, weight coefficient is 0.8; Fuzzy set mf2, weight coefficient is 0.4; Using the weighted average method, the final total output is u = mf1 * 0.8 + mf2 * 0.4; Embodiment 4, asFigure 3 , Figure 5 As shown in Figure 5 , on the basis of Embodiments 1-3, the present invention provides a technical solution: Preferably, according to a large number of measured input-output data of a specific circuit breaker trip unit system, three T-S type fuzzy rules describing it are obtained through identification, and the membership functions are mf1, mf2, and mf3 respectively, as Figure 3 , Figure 5 shown. At this time, if the current i2 = 8 is measured in the system, the activated rules are obtained: Fuzzy set mf1, with a weight coefficient of 0.1; Fuzzy set mf3, with a weight coefficient of 0.2; Using the weighted average method, the final total output is u = mf1 * 0.1 + mf3 * 0.2.

[0025] This application effectively solves the problems of misoperation and refusal to operate of the trip unit by adopting a compensation circuit, and the output of T-S type fuzzy inference is used for the control of the compensation circuit to prevent the short-circuit protection function of the circuit breaker from failing.

[0026] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

1. A compensating circuit breaker trip unit based on a T-S type fuzzy controller, characterized in that: The compensating circuit breaker release includes an actuator, a compensation circuit, and a fuzzy controller; Among them, the actuator is an electromagnet, which is used to convert electromagnetic energy into mechanical energy to perform the tripping operation of the circuit breaker; The compensation circuit is connected to the electromagnet and is used to adjust the magnitude of the compensation current according to the control signal to adjust the electromagnetic attraction of the electromagnet; The fuzzy controller is a T-S type fuzzy controller, which is used to receive the current signal of the line where the circuit breaker is located, and output a control signal according to the preset fuzzy rules and T-S model to control the working state of the compensation circuit, so as to prevent the release from malfunctioning and refusing to operate.

2. The compensated circuit breaker trip device based on the T-S type fuzzy controller according to claim 1, characterized in that: The T-S type fuzzy controller is a single-input single-output system, and its control process includes the following steps: Monitor the current of the line where the circuit breaker is located in real time, and obtain the clear input quantity i through the current transformer. The clear input quantity i is the current value in the line where the circuit breaker is located; Input the obtained current data into the T-S type fuzzy controller for structure identification and parameter identification; According to the identification results, calculate and output the clear output quantity u through the preset fuzzy rules. The clear output quantity u is the compensation current value signal, and the calculated compensation current value signal is then output to the compensation circuit; The compensation circuit receives the compensation current value signal from the T-S type fuzzy controller, outputs an equivalent compensation current according to the compensation current value signal, and dynamically adjusts the electromagnetic attraction of the electromagnet by adjusting the magnitude of the compensation current.

3. The compensating circuit breaker release based on the T-S type fuzzy controller according to claim 2, characterized in that: The structure identification and parameter identification specifically include: Perform structure identification on the data of the clear input quantity i input into the T-S type fuzzy controller to determine whether to adopt a zero-order or first-order linear model; After determining the form of the linear model, further perform parameter identification on the input current data according to the selected linear model to determine the parameters a and k in the model. For the zero-order model, the goal of parameter identification is to determine the constant k. For the first-order model, the constants a and k need to be determined simultaneously; According to the results of structure identification and parameter identification, establish a complete T-S model, and output the clear output quantity u according to the model.

4. The compensating circuit breaker release based on the T-S type fuzzy controller according to claim 3, characterized in that: The control rules of the T-S type fuzzy controller specifically include: Define the fuzzy set A, and determine the corresponding fuzzy rules according to the fuzzy set A to which the clear input quantity i belongs; In the zero-order model, the fuzzy rule is "if the clear input quantity i belongs to A, then u = k", where k is a constant related to the set A; In the first-order model, the fuzzy rule is "if the clear input quantity i belongs to A, then u = ai + k", where a and k are constants related to the set A; Activate the corresponding fuzzy rules according to the membership degree of the clear input quantity i, and calculate the clear output quantity u according to the rules.

5. The compensating circuit breaker release based on the T-S type fuzzy controller according to claim 2, characterized in that: The working mode of the compensation circuit specifically includes: When the elastic force of the anti-rebound spring in the electromagnet decays, the compensation current calculated according to the T-S model decreases, and the electromagnetic attraction decreases accordingly to prevent the release from malfunctioning; When the lap distance between the iron core of the ferromagnetic body in the electromagnet and the traction rod is too large, or the current loss increases, the compensation current calculated according to the T-S model increases, and the electromagnetic attraction increases accordingly to avoid the release from refusing to operate; The compensation circuit dynamically balances the electromagnetic attraction of the electromagnet and the elastic force of the reaction spring by adjusting the magnitude of the compensation current; The compensation circuit outputs an equivalent compensation current according to the received compensation current value signal to precisely adjust the electromagnetic attraction of the electromagnet.

6. The compensated circuit breaker release based on the T-S type fuzzy controller according to claim 5, characterized in that: The output of the T-S fuzzy controller specifically includes: When the crisp input quantity i is input into the T-S fuzzy controller, the corresponding fuzzy rules are activated according to its membership degree, and the weight coefficient w of each rule is determined. Among them, each activated fuzzy rule corresponds to a weight coefficient w; The T-S fuzzy controller calculates the final output U using the weighted summation method or the weighted average method; In the weighted summation method, the final output U is obtained by summing the products of the crisp output values u of all activated fuzzy rules and their corresponding weight coefficients w. Its calculation formula is: ; The final output U is obtained by dividing the sum of the products of the crisp output values u of all activated fuzzy rules and their corresponding weight coefficients w by the sum of all weight coefficients. Its calculation formula is: , where u is the output of each rule and w is the corresponding weight coefficient.

7. The compensating circuit breaker trip device based on the T-S type fuzzy controller according to claim 6, characterized in that: The determination of the weight coefficients of each rule specifically includes: The minimum method is used to determine the weight coefficient, that is, the minimum membership degree of each fuzzy rule is taken as the weight coefficient; The product method is used to determine the weight coefficient, that is, the product of the membership degrees of each fuzzy rule is taken as the weight coefficient; The weight coefficient is determined manually according to the actual application scenario and control requirements.

8. The compensating circuit breaker trip device based on the T-S type fuzzy controller according to claim 1, characterized in that: The compensation circuit can also adopt a compensation circuit based on a neural network, and its working mode includes: Receiving the compensation current value signal from the fuzzy controller, and adaptively adjusting the compensation current through a compensation model constructed based on a neural network. Among them, the training data of the neural network includes current signals and corresponding compensation current values under different working conditions to control the electromagnetic attraction of the electromagnet.

9. The compensating circuit breaker trip device based on the T-S type fuzzy controller according to claim 8, characterized in that: The construction process of the compensation model is as follows: Collect current signals and corresponding compensation current values under different working conditions, covering various situations such as normal working state, attenuation of the elastic force of the reaction spring, excessive lap distance between the iron core and the traction rod, and increased current loss. Normalize the collected data, and integrate the normalized data, divide it into a training set and a test set, and then design a neural network structure, including an input layer, a hidden layer, and an output layer; Construct a compensation model based on the designed neural network structure, use the training set to input the neural network structure for training, randomly initialize the weights and biases of the neural network, perform forward propagation of the input current signal through the neural network, calculate the output compensation current value, and then use an independent test data set to verify the trained neural network, calculate the mean square error index on the test data, evaluate the performance of the neural network, and then optimize the neural network to finally obtain the constructed compensation model; Deploy the constructed compensation model, use the compensation circuit to receive the compensation current value signal from the T-S fuzzy controller, input the input signal into the compensation model, calculate the final compensation current value through forward propagation, and according to the output of the compensation model, the compensation circuit outputs the corresponding compensation current to adjust the electromagnetic attraction of the electromagnet.

10. The compensated circuit breaker trip device based on the T-S type fuzzy controller according to claim 1, characterized in that: The T-S fuzzy controller also includes a fault diagnosis module for real-time monitoring of the current signal of the line where the circuit breaker is located and the working state of the compensation circuit. When an abnormal signal is detected, the fault diagnosis module issues an alarm signal and transmits the abnormal information to an external monitoring system for fault troubleshooting and handling; Among them, the specific working process of the fault diagnosis module is: The fault diagnosis module continuously monitors the current signal of the line where the circuit breaker is located and the working state of the compensation circuit in real time, obtains the real-time value of the line current, and simultaneously monitors the current, voltage and electromagnetic suction data of the electromagnet in the compensation circuit; The collected signals and data are analyzed and processed in real time. Through preset threshold judgment, it is detected whether there are abnormal signals. Among them, when the line current exceeds the set normal range threshold, or the parameters of the compensation circuit change in a way that does not conform to the normal working mode, it is identified as an abnormal signal; Once an abnormal signal is detected, the fault diagnosis module immediately triggers the alarm mechanism and issues an alarm signal to remind the on-site personnel to pay attention.

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