External circuit breaker intelligent control method based on time delay neural feedback network

Abstract

The invention discloses an external circuit breaker intelligent control method based on a time delay neural feedback network, and the method comprises the following steps: collecting a state signal and a phase signal of an external circuit breaker intelligent control system in real time, carrying out the filtering processing, carrying out the nonlinear conversion of the signal through the time delay neural feedback network, and generating a preliminary control signal; optimizing the preliminary control signal by using an optimization objective function to minimize system error and control signal fluctuation so as to obtain an optimized control signal; and constructing a nonlinear oscillation model for simulating the dynamic response of the external circuit breaker and the intelligent control system, adjusting the oscillation behavior by controlling the input, generating a dynamic predicted value of the state based on the optimized control signal, and updating the control signal by adopting a correction formula according to the difference between the dynamic predicted value and the actual state. The invention has the advantages of strong dynamic adaptive capacity, long service life, good stability, timely response and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of control methods, and in particular relates to a control method for an external circuit breaker. Background Art

[0002] External circuit breakers are widely used in smart grids, industrial automation, power management, and smart energy dispatching, mainly for overload protection and short-circuit protection. In the case of dynamic changes in grid load, imbalanced power supply and demand, and frequent environmental disturbances, how to optimize the control strategy of external circuit breakers and improve system stability, response speed, and control accuracy has become an important direction of current intelligent control technology research.

[0003] However, most traditional control methods use linear control or fixed threshold strategies, which have the following problems: 1. It is unable to accurately describe the nonlinear dynamic characteristics of the circuit breaker system (such as load mutation, mechanical inertia delay), and is unable to adapt to the complex dynamic changes of the system state.

[0004] 2. Existing methods rely on simple feedback mechanisms, which have delayed responses when faced with sudden disturbances and can easily lead to overcompensation or control failure.

[0005] 3. Fixed threshold strategy Under disturbances such as power grid fluctuations and load changes, problems such as frequent switching of circuit breakers may occur, resulting in increased mechanical wear and reduced equipment life, affecting the safe and stable operation of the system. Summary of the invention

[0006] The present invention proposes an external circuit breaker intelligent control method based on a time-delay neural feedback network, the purpose of which is to solve the problems of poor dynamic adaptability, delayed response when facing sudden disturbances and frequent switching in existing methods.

[0007] The technical solution of the present invention is as follows: An external circuit breaker intelligent control method based on a time-delay neural feedback network comprises the following steps: Step S1, real-time acquisition of state signals and phase signals of the external circuit breaker intelligent control system, after filtering, using a time-delay neural feedback network to perform nonlinear conversion on the signals to generate a preliminary control signal; using an optimization objective function to optimize the preliminary control signal to minimize system errors and control signal fluctuations, and obtain an optimized control signal; Step S2, constructing a nonlinear oscillation model to simulate the dynamic response of the external circuit breaker and the intelligent control system, adjusting the oscillation behavior through the control input, generating a dynamic prediction value of the state based on the optimized control signal, and then updating the control signal using a correction formula according to the difference between the dynamic prediction value and the actual state; The corrected control signal is sent to the control system of the external circuit breaker of the electric energy meter to perform the switching operation.

[0008] As a further improvement of the external circuit breaker intelligent control method based on the time-delay neural feedback network: in step S1, the input of the time-delay neural feedback network model is a combination of the system state signal and the phase signal, and the connection weights and delay factors between neurons work together to determine the time-varying characteristics of the output signal.

[0009] As a further improvement of the external circuit breaker intelligent control method based on the time-delay neural feedback network: in step S1, the optimization objective function includes two weighted terms, one of which is based on the difference between the current state and the target state, and the other is based on the obtained preliminary control signal.

[0010] As a further improvement of the external circuit breaker intelligent control method based on the time-delay neural feedback network: in step S1, the optimization objective function is iteratively adjusted by the gradient descent method to solve the optimal control signal; the optimized control signal not only reflects the change in load demand, but also regulates the operating frequency of the circuit breaker and the stability of the intelligent control system.

[0011] As a further improvement of the external circuit breaker intelligent control method based on the time-delay neural feedback network: in step S1, the process of solving the optimal control signal is: first calculate the gradient of the optimization objective function, and dynamically adjust the control signal according to the direction and amplitude of the gradient; each iteration represents an update of the control signal.

[0012] As a further improvement of the external circuit breaker intelligent control method based on the time-delay neural feedback network: in step S2, a nonlinear oscillation model is constructed to simulate the dynamic response of the external circuit breaker and the intelligent control system, and is adjusted in combination with the intelligent control input; the nonlinear oscillation model is based on a nonlinear oscillation equation, which comprehensively considers the relationship between the acceleration, speed and position changes of the external circuit breaker under different operating conditions, and introduces the influence of external disturbances, so that the intelligent control system can more accurately predict the operating state and load response of the circuit breaker.

[0013] As a further improvement of the external circuit breaker intelligent control method based on the time-delay neural feedback network: in step S2, the control signal is corrected based on the deviation between the predicted state and the actual state as follows: first, the nonlinear oscillation model is solved to obtain the future state prediction value, which is used to estimate the future evolution trend of the system; then the future state prediction value is compared with the current measured state value to quantify the error between the current state of the system and the predicted trend; finally, the correction signal obtained based on the error is superimposed on the optimized control signal to generate a smoother and more stable corrected control signal, thereby significantly improving the control accuracy and response speed of the external circuit breaker.

[0014] Compared with the prior art, the present invention has the following positive effects: 1. The present invention adopts a time-delay neural feedback network, which can analyze the system state signal and phase signal in real time and perform nonlinear conversion on the input data, so that the control system has adaptive adjustment capabilities.

[0015] 2. The present invention constructs an optimization objective function with the goals of minimizing state error and optimizing control signal smoothness, which effectively reduces the system adjustment error, avoids excessive control signal fluctuations, reduces frequent operation of external circuit breakers, reduces mechanical losses, and improves equipment life and operational stability.

[0016] 3. The present invention introduces a nonlinear oscillation modeling method to simulate the dynamic response of an external circuit breaker and its controlled system. By establishing an oscillation model with inertia, damping and external disturbance terms, the system can accurately predict the future state and adjust the control signal to improve the system's adaptability to load fluctuations, sudden disturbances and environmental changes, avoid response lag, and reduce overcompensation and control failure.

[0017] 4. Based on the optimization of the control signal, the present invention further designs an intelligent correction mechanism, which dynamically adjusts according to the error between the predicted state value and the current state value, can effectively reduce the prediction error, improve the intelligent response capability of the external circuit breaker, and ensure the accuracy of the control signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0020] like Figure 1 , an external circuit breaker intelligent control method based on a time-delay neural feedback network, comprising the following steps: Step S1: collect the state signal and phase signal of the external circuit breaker intelligent control system in real time, and after filtering, use the time-delay neural feedback network to perform nonlinear conversion on the signal to generate a preliminary control signal. Use the optimization objective function to optimize the preliminary control signal to minimize the system error and control signal fluctuation, and obtain the optimized control signal.

[0021] The system status signal and phase signal are obtained by real-time acquisition through multiple sensors installed in the intelligent control system. The system status signal includes the load status of the external circuit breaker and the power grid status, which can be provided by smart meters, monitoring equipment or sensors, and reflects the operating parameters of each controlled object in the external circuit breaker intelligent control system. The phase signal is used to reflect the dynamic changes of the power grid, which is obtained through the phase measurement device and reflects the phase changes and angle information of each node in the power grid.

[0022] The collected signals are filtered to remove high-frequency noise and other interference components to ensure data accuracy. Specifically, a bandpass filter is used to filter the system status signal and phase signal, only receiving signals within the preset frequency band to remove low-frequency drift or high-frequency clutter. For power load signals, the filtered data can be used to calculate key information such as instantaneous power and power factor for use in subsequent control calculations.

[0023] A time-delay neural feedback network is designed to perform nonlinear transformation on the system state signal and phase signal through the time-delay neural feedback network to generate a preliminary control signal. The input of the time-delay neural feedback network model is a combination of the system state signal and the phase signal. The connection weights and delay factors between neurons work together to determine the time-varying characteristics of the output signal. The core calculation formula of the time-delay neural feedback network is: ; in, Delayed Neural Feedback Network The initial control signal output at all times; It is the first The connection weight of each neuron indicates the influence of the input signal; is the number of neurons; yes System status signals at all times (such as load status of external circuit breakers or grid status); yes Phase signal at each moment (used to reflect the dynamic changes of the power grid or system); It is The nonlinear adjustment factor of each neuron controls the nonlinear intensity of the response of the input signal to the output signal, which is obtained through experiments; It is The bias term of each neuron is used to simulate incomplete data or errors; It is The delay decay factor of each neuron is used to control the rate at which the input signal decays over time; It is The delay reference time point of each neuron is determined by the communication time. The initial control signal is extracted from the system state signal and phase signal through the delay neural feedback network. , providing a basis for the next step of optimization.

[0024] In the target optimization stage, in order to optimize the intelligent adjustment capability of the intelligent control system, an optimization objective function is designed to optimize the control signal to ensure that the control system can be adaptively adjusted in a dynamic environment, so that the error between the current state of the system and the target state is minimized, and the control signal remains stable to avoid the impact of violent fluctuations on the actuator (such as external circuit breakers). The expression of the optimization objective function is: ; in, It is the optimization objective function, which measures the error of the current control state; yes The target system state at the moment; is the maximum value of the system state, used for normalization; is the maximum amplitude of the preset control signal; , They are all weighting factors, which are used to adjust the weight of system state error and control signal amplitude.

[0025] The optimization objective function is iteratively adjusted through the gradient descent method to solve the optimal control signal. The specific process is as follows: First, the gradient of the optimization objective function is calculated, and the control signal is dynamically adjusted according to the direction and amplitude of the gradient; each iteration represents an update of the control signal. The optimized control signal can not only accurately reflect the changes in load demand, but also effectively adjust the operating frequency of the circuit breaker, while improving the stability of the intelligent control system. The update formula of the control signal is: ; in, is the optimized control signal; is the learning rate, which controls the step size of each iteration and is obtained through experiments; It is the gradient of the optimization objective function to the control signal, which indicates the relationship between the rate of change of the optimization objective function and the change of the control signal.

[0026] Step S2: construct a nonlinear oscillation model to simulate the dynamic response of the external circuit breaker and the intelligent control system, adjust the oscillation behavior through the control input, generate a dynamic prediction value of the state based on the optimized control signal, and then use a correction formula to update the control signal according to the difference between the dynamic prediction value and the actual state to ensure the accurate execution of the circuit breaker switching operation.

[0027] Specifically, in order to enhance the adaptive ability of the intelligent control system to dynamic behavior, a nonlinear oscillation model is constructed to simulate the dynamic response of the external circuit breaker and the intelligent control system, and is adjusted in combination with the intelligent control input. The nonlinear oscillation model is based on a nonlinear oscillation equation, which comprehensively considers the relationship between the acceleration, velocity and position changes of the external circuit breaker under different operating conditions, and introduces the influence of external disturbances, so that the intelligent control system can more accurately predict the operating state and load response of the circuit breaker. The equation of the nonlinear oscillation model is as follows: ; in, represents the predicted state acceleration, i.e. The second derivative of is the predicted rate of state change, i.e. The first derivative of ; is the predicted state value, that is, System status at the moment; is the forecast time step, which determines the time interval of load forecast; is the damping coefficient, which controls the resistance to oscillation and is obtained experimentally; is the stiffness coefficient, which represents the restoring force of the intelligent control system to the deviation; is the amplitude factor, which determines the amplitude of the oscillation; is the oscillation frequency, is the phase of the oscillation; It is the predicted external disturbance term, which is obtained by obtaining historical data through environmental monitoring and then predicted through machine learning, such as climate change, power grid fluctuations and other factors; It is the influence factor of the control input, which indicates the influence intensity of the optimized control signal on the state change, and is obtained through experiments.

[0028] The core purpose of the above predictions is to describe how the system state affects the control input Although the obtained control signal is not actually implemented, mathematical modeling can also be used to simulate the system response, so as to see the possible load fluctuations in the future in advance, and then make necessary adjustments before the control signal is implemented to avoid over-adjustment or delayed response.

[0029] In order to enhance the precise regulation capability of the intelligent control system for the external circuit breaker, the present invention corrects the control signal based on the deviation between the predicted state and the actual state. The specific method is as follows: firstly, the nonlinear oscillation model is solved to obtain the predicted value of the future state. , used to estimate the future evolution trend of the system; then the future state prediction value is compared with the current measured state value Compare and quantify the error between the current state of the system and the predicted trend; finally, superimpose the correction signal based on the error onto the optimized control signal A smoother and more stable corrected control signal is generated, thereby significantly improving the control accuracy and response speed of the external circuit breaker. The corrected control signal is updated using the following formula: ; in, It is the corrected control signal, used to adjust the action of the circuit breaker; is the state prediction value obtained by the nonlinear oscillation model; is the amplitude adjustment coefficient of the correction signal, which controls the strength of the correction signal and is obtained through experiments; is the delay factor, which controls the decay rate of the delay during the signal correction process and is obtained through experiments; It is a weighting factor for the state change rate, indicating the contribution of the state change rate to the correction signal, obtained through experiments; It is the influence index of the state change rate, which controls the response sensitivity of the state change rate in the correction signal and is obtained through experiments; It is the reference moment. Through the corrected control signal, the error in the state control process is minimized, ensuring the accurate execution of the circuit breaker switching operation.

[0030] The corrected control signal is sent to the control system of the external circuit breaker of the electric energy meter to execute the switching operation and complete the intelligent control of the external circuit breaker.

[0031] It should be noted that it is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. The scope of the present invention is defined by the claims rather than the above description.

Claims

1. An intelligent control method for an external circuit breaker based on a time-delay neural feedback network, characterized in that: The following steps are involved: Step S1, real-time acquisition of state signals and phase signals of the external circuit breaker intelligent control system, after filtering, using a time-delay neural feedback network to perform nonlinear conversion on the signals to generate a preliminary control signal; using an optimization objective function to optimize the preliminary control signal to minimize system errors and control signal fluctuations, and obtain an optimized control signal; Step S2, constructing a nonlinear oscillation model to simulate the dynamic response of the external circuit breaker and the intelligent control system, adjusting the oscillation behavior through the control input, generating a dynamic prediction value of the state based on the optimized control signal, and then updating the control signal using a correction formula according to the difference between the dynamic prediction value and the actual state; The corrected control signal is sent to the control system of the external circuit breaker of the electric energy meter to perform the switching operation.

2. The external circuit breaker intelligent control method based on the time-delay neural feedback network according to claim 1, characterized in that: In step S1, the input of the time-delay neural feedback network model is a combination of a system state signal and a phase signal, and the connection weights and delay factors between neurons work together to determine the time-varying characteristics of the output signal.

3. The external circuit breaker intelligent control method based on the time-delay neural feedback network according to claim 1, characterized in that: In step S1, the optimization objective function includes two weighted items, one of which is based on the difference between the current state and the target state, and the other is based on the obtained preliminary control signal.

4. The external circuit breaker intelligent control method based on time-delay neural feedback network according to claim 1, characterized in that: In step S1, the optimization objective function is iteratively adjusted by the gradient descent method to solve the optimal control signal; the optimized control signal not only reflects the change in load demand, but also regulates the operating frequency of the circuit breaker and the stability of the intelligent control system.

5. The external circuit breaker intelligent control method based on time-delay neural feedback network according to claim 4, characterized in that: In step S1, the process of solving the optimal control signal is: first calculate the gradient of the optimization objective function, and dynamically adjust the control signal according to the direction and amplitude of the gradient; each iteration represents an update of the control signal.

6. The external circuit breaker intelligent control method based on time-delay neural feedback network according to claim 1, characterized in that: In step S2, a nonlinear oscillation model is constructed to simulate the dynamic response of the external circuit breaker and the intelligent control system, and is adjusted in combination with the intelligent control input; the nonlinear oscillation model is based on a nonlinear oscillation equation, which comprehensively considers the relationship between the acceleration, speed and position changes of the external circuit breaker under different operating conditions, and introduces the influence of external disturbances, so that the intelligent control system can more accurately predict the operating state and load response of the circuit breaker.

7. The external circuit breaker intelligent control method based on time-delay neural feedback network according to claim 1, characterized in that: In step S2, the control signal is corrected based on the deviation between the predicted state and the actual state as follows: first, the nonlinear oscillation model is solved to obtain the future state prediction value, which is used to estimate the future evolution trend of the system; then, the future state prediction value is compared with the current measured state value to quantify the error between the current state of the system and the predicted trend; finally, the correction signal obtained based on the error is superimposed on the optimized control signal to generate a smoother and more stable corrected control signal, thereby significantly improving the control accuracy and response speed of the external circuit breaker.

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