Intelligent control device for complementary circuit breaker based on fuzzy control

Abstract

The present invention discloses a complementary circuit breaker intelligent control device based on fuzzy control, which relates to the field of power electronics technology and includes: a switchable solid current transformer and a hollow current transformer, each connected to a fuzzy controller through a signal acquisition circuit. The complementary circuit breaker intelligent control device based on fuzzy control dynamically fuses the current magnitude and change rate through a fuzzy controller, and combines the complementary characteristics of solid and hollow current transformers to achieve high-precision measurement over the entire range from rated current to short-circuit current. The solid transformer maintains linearity using an iron core structure under low current, and the hollow transformer avoids saturation under high current through a coreless design. The fuzzy rule base and the membership function are collaboratively optimized to accurately predict current trends and adaptively switch transformers, overcoming the hysteresis and misjudgment problems of traditional fixed threshold switching, reducing measurement errors, and increasing response speed to milliseconds, thereby enhancing the protection reliability of the circuit breaker under transient conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of power electronics, and in particular to a complementary circuit breaker intelligent control device based on fuzzy control. Background Art

[0002] Smart circuit breakers play a critical role in power systems, relying on real-time and accurate detection of line currents to trigger appropriate protective actions. However, current transformers used in existing technologies have significant limitations: solid-core current transformers offer excellent linearity in low-current scenarios, but suffer from inaccurate measurements at high currents due to core saturation. While hollow-core current transformers can accommodate a wide range of high current measurements, they introduce significant errors due to weak low-current signals. A single transformer cannot accommodate measurement requirements across the entire current range, potentially leading to circuit breaker protection failures due to measurement deviations under extreme operating conditions such as overload or short circuit, posing a serious threat to the safety and reliability of the power system. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention provides a complementary circuit breaker intelligent control device based on fuzzy control, which solves the problem of how to dynamically judge the current state and realize intelligent switching control of the two transformers, thereby ensuring high-precision measurement in the full range from rated current to short-circuit current.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: A complementary circuit breaker intelligent control device based on fuzzy control, comprising:

[0005] The switchable solid current transformer and hollow current transformer are respectively connected to the fuzzy controller through the signal acquisition circuit;

[0006] The fuzzy controller includes a dual-input-single-output structure, wherein the input port receives the logarithmic normalized value i of the current and the logarithmic normalized value di of the current change rate, and the output port generates a control signal y;

[0007] An intelligent circuit breaker control unit is connected to the output port of the fuzzy controller and switches the solid current transformer or the hollow current transformer to the circuit according to the control signal y;

[0008] Among them, the fuzzy controller dynamically judges the current state through fuzzy reasoning logic, switches to a solid current transformer when the logarithmic normalized value i of the current represents a small current or the logarithmic normalized value di of the current change rate tends to decrease, and switches to a hollow current transformer when the logarithmic normalized value i of the current represents a large current or the logarithmic normalized value di of the current change rate tends to increase, thereby realizing linear measurement of the full current range.

[0009] Preferably, the domain of the input quantity of the fuzzy controller, that is, the logarithmic normalized value i of the current is [0, 100], which is divided into three fuzzy subsets of small current SD, medium current MD, and large current LD, and each subset uses a triangular membership function to cover the domain.

[0010] Preferably, the domain of the input quantity, i.e., the logarithmic normalized value di of the current change rate is [-50, 50], which is divided into five fuzzy subsets of current change: negative large NB, negative small NS, exactly O, positive small PS, and positive large PB, and each subset uses a trapezoidal membership function to cover the domain.

[0011] Preferably, the domain of the control signal y is [-1, 1], which is divided into two fuzzy subsets, solid S and hollow H, which correspond to the operation instructions of switching to the solid or hollow current transformer respectively, and each subset uses a Gaussian membership function to cover the domain.

[0012] Preferably, the fuzzy controller has the following built-in fuzzy rules:

[0013] When the logarithmically normalized value i of the current belongs to the small SD of the current, the output control signal y belongs to the solid fuzzy subset S;

[0014] When the logarithm-normalized value i of the current belongs to the large current LD, the output control signal y belongs to the hollow fuzzy subset H;

[0015] When the logarithmic normalized value i of the current belongs to the current MD and the logarithmic normalized value di of the current change rate belongs to the current change large negative fuzzy subset NB or the current change small negative fuzzy subset NS, the output control signal y belongs to the solid fuzzy subset S;

[0016] When the logarithmic normalized value i of the current belongs to the current MD and the logarithmic normalized value di of the current change rate belongs to the current change positive small fuzzy subset PS or the current change positive large fuzzy subset PB, the output control signal y belongs to the hollow fuzzy subset H.

[0017] Preferably, the fuzzy rules are constructed by 30 fuzzy control rules, each rule corresponds to a fuzzy implication relation Ri, where i=1, 2, ..., 30; the union of all fuzzy implication relations Ri constitutes the total implication relation R, and the fuzzy quantity U is derived and outputted through the synthesis operation formula;

[0018] The synthesis operation formula is:

[0019] , where R: total implication relation, Ri: fuzzy implication relation, i: logarithmic normalized value of current;

[0020] The synthesis operation formula of the output fuzzy quantity U is: , where A: input fuzzy quantity, R: total implication relation, U: output fuzzy quantity, represents the fuzzy set of control signal y, which is used to decide the switching of solid or hollow current transformer; : Composition operator, which represents the reasoning operation between the input fuzzy quantity A and the total implication relation R, including the composition rule;

[0021] Among them, A: input fuzzy quantity, which contains the fuzzy set of two input variables, namely:

[0022] i: Logarithmic normalized value of current, obtained by logarithmic transformation and linear normalization of the real-time current signal.

[0023] di: Logarithmic normalized value of the current change rate, obtained by calculating the logarithmic derivative and normalizing the current change rate.

[0024] The composition rules include the maximum-minimum composition rule and the maximum-product composition rule;

[0025] The maximum-minimum composition rule includes: U(y)=max x [min(A(x), R(x, y))], where U(y) represents the membership of the output fuzzy quantity at the control signal y. The larger the value of U(y), the more credible the action corresponding to the control signal y is under the current input. x represents the joint domain of the input variable, i, the logarithmic normalized value of the current and di, the logarithmic normalized value of the current change rate. A(x) represents the membership of the input fuzzy quantity at x. R(x, y) represents the strength of the association between the input variable x and the output control signal y in the total implication relationship. The calculation logic is: for each input x, take the minimum value of A(x) and R(x, y), and then take the maximum value among all x as U(y).

[0026] The calculation logic is: multiply A(x) by R(x, y), and then take the maximum value among all x as U(y);

[0027] After the output fuzzy quantity is processed by the maximum membership average method, the exact value y∈[-1,1] is obtained, which means: if y<0, switch to the solid current transformer; if y>0, switch to the hollow current transformer; if y=0, maintain the current state.

[0028] Preferably, the fuzzy controller uses a maximum membership average method to clarify the output fuzzy quantity, including: extracting the median of the interval corresponding to the maximum membership value in the output fuzzy quantity as the final control signal y.

[0029] Preferably, the solid current transformer adopts an iron core structure, and the number of turns of its secondary winding is fixed; the hollow current transformer adopts a coreless coil structure, and the secondary output signal is adjusted to a sampleable range through a dynamic gain amplifier.

[0030] Preferably, the intelligent circuit breaker control unit includes a microprocessor module, which drives a relay switching circuit according to a control signal y, and the relay switching circuit is connected in series with the secondary output terminals of the solid current transformer and the hollow current transformer.

[0031] Preferably, the input quantities of the fuzzy controller: the logarithmic normalized value i of the current and the logarithmic normalized value di of the current change rate are generated by:

[0032] The original current signal is logarithmically transformed and mapped to the preset domain through linear normalization. The conversion formula is:

[0033] , ;

[0034] Where, i: the logarithmic normalized value of the current, I is the real-time current value, I min and I max are the preset minimum and maximum current thresholds, respectively; k is the normalization coefficient; dt is the time derivative, used to quantify the instantaneous trend of current change; d(log(I)) represents the change of the natural logarithm of current I over time; d(log(I)) / dt represents the rate of change of the logarithm of the current, reflecting the relative change trend of the current;

[0035] Among them, dt quantifies the real-time change trend of the current into a numerical value di through differential operation, helping the fuzzy controller to predict the current state. Among them, the real-time change trend of the current includes accelerated growth or decelerated decay.

[0036] If di>0, it means the current is increasing;

[0037] If di<0, it means the current is decreasing.

[0038] The time differential is used to quantify the instantaneous trend of current change. Combined with logarithmic transformation and normalization, the actual current signal is converted into a dynamic input di that can be processed by the fuzzy controller, ultimately realizing the intelligent switching of the transformer.

[0039] By dynamically capturing current change trends, the system's response to transient conditions such as overload and short circuit is enhanced, solving the problem of switching lag in traditional transformers.

[0040] The present invention provides a complementary circuit breaker intelligent control device based on fuzzy control. It has the following beneficial effects:

[0041] (1) This complementary circuit breaker intelligent control device based on fuzzy control dynamically integrates current magnitude and rate of change through a fuzzy controller, combining the complementary characteristics of solid and hollow current transformers to achieve high-precision measurement across the entire current range, from rated current to short-circuit current. Solid transformers maintain linearity at low currents through their iron core structure, while hollow transformers avoid saturation at high currents through their coreless design. The fuzzy rule base and membership function are collaboratively optimized to accurately predict current trends and adaptively switch transformers, overcoming the hysteresis and misjudgment issues of traditional fixed threshold switching. This reduces measurement errors and increases response speed to milliseconds, significantly enhancing the circuit breaker's protection reliability under transient conditions such as overload and short circuit.

[0042] (2) This fuzzy-control-based complementary circuit breaker intelligent control device maintains stable operation in complex scenarios such as noise interference and mechanical delays through dynamic gain adjustment, redundant switching of buffer circuits, and anti-oscillation logic design. The signal preprocessing mechanism compresses current signals with a large dynamic range. Combined with closed-loop gain calibration and historical state memory functions, it effectively suppresses signal distortion and the risk of frequent switching, extending equipment life. This solution has a simple hardware structure, and the fuzzy control algorithm can be embedded and deployed. It is compatible with existing circuit breaker systems and is suitable for multiple scenarios such as smart grids and industrial power distribution. It combines high reliability, low cost, and easy maintainability, promoting the upgrade of power protection equipment to intelligent and adaptive systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the overall framework of the present invention;

[0044] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] See also Figure 1 and Figure 2 The present invention provides a technical solution: a complementary circuit breaker intelligent control device based on fuzzy control, comprising:

[0047] The switchable solid-core and hollow-core current transformers are each connected to a fuzzy controller via a signal acquisition circuit. The implementation of the switchable solid and hollow-core current transformers is achieved through the collaborative operation of the signal acquisition circuit and the fuzzy controller. The solid-core current transformer uses an iron core structure with a fixed number of turns on its secondary winding, directly outputting an analog signal proportional to the primary current, making it suitable for low-current scenarios. The hollow-core current transformer uses a coreless coil structure, and the secondary output signal is adjusted by a dynamic gain amplifier to ensure that the signal amplitude within the large current range is within the sampleable range. The secondary outputs of both are connected to a signal acquisition circuit, which includes a prefilter module and a high-speed analog-to-digital conversion module. The prefilter module performs noise suppression and bandwidth limiting on the original output signal of the transformer, while the analog-to-digital conversion module converts the analog signal into a digital signal before inputting it into the fuzzy controller.

[0048] The fuzzy controller's input port receives digital signals from both transformers via parallel channels, but dynamically selects the active channel based on the current control signal. When the control signal requires a transformer switch, the intelligent circuit breaker control unit activates a relay switching circuit, disconnecting the signal path from the current transformer and connecting the target transformer. During the switching process, the signal acquisition circuit uses a buffer circuit to maintain signal continuity and avoid transient interference. The fuzzy controller analyzes the normalized logarithm of the selected transformer's current and its rate of change in real time. Using a pre-set fuzzy rule base and membership functions, it generates the control signal for the next cycle, forming a closed-loop feedback loop that ensures dynamic matching between the transformer switching action and the current state.

[0049] The fuzzy controller comprises a dual-input, single-output structure, wherein the input port receives the logarithmically normalized value i of the current and the logarithmically normalized value di of the current rate of change, and the output port generates a control signal y. The dual-input, single-output structure of the fuzzy controller dynamically generates the control signal through the following steps: the input port receives the current signal from the signal acquisition circuit, first performs logarithmic transformation and linear normalization on the original current value to generate the logarithmically normalized value i of the current, and simultaneously calculates the logarithmically normalized value di of the current rate of change through differential operations. The input quantities i and di are respectively mapped to the input domain of the fuzzy controller, wherein the domain of i [0, 100] is divided into three fuzzy subsets: small current SD, medium current MD, and large current LD, and the domain of di [-50, 50] is divided into five fuzzy subsets: negative current change NB, negative current change NS, positive current change O, positive current change PS, and positive current change PB, each subset covering the domain via a triangular or trapezoidal membership function. The 30 fuzzy rules built into the fuzzy controller generate output fuzzy quantities based on the fuzzy subset combination of the input quantities, such as "when i is SD, the output belongs to solid S", "when i is LD, the output belongs to hollow H", "when i is MD and di is NB / NS, the output belongs to S", etc.

[0050] The fuzzy implication relationships corresponding to all rules are synthesized to derive the output fuzzy quantity U using a composite operation formula, employing either the maximum-minimum or maximum-product rule. After the output fuzzy quantity U is clarified using the maximum membership average method, a precise control signal y∈[-1, 1] is generated. When y<0, the relay switches to a solid current transformer. When y>0, the hollow current transformer is switched, and y=0 maintains the current state. This dual-input structure integrates current magnitude and dynamic trends to achieve adaptive transformer switching decisions, ensuring measurement accuracy and response speed across the entire current range.

[0051] An intelligent circuit breaker control unit is connected to the output port of the fuzzy controller and switches the solid current transformer or the hollow current transformer to the circuit according to the control signal y. The intelligent circuit breaker control unit performs the transformer switching operation according to the control signal y output by the fuzzy controller. The specific implementation process is as follows: the control signal y is transmitted to the microprocessor module of the intelligent circuit breaker control unit via the output port of the fuzzy controller. The microprocessor analyzes the numerical range of y and generates a drive instruction: when y<0, the relay switching circuit is driven to disconnect the secondary output path of the hollow current transformer and close the signal path of the solid current transformer; when y>0, the solid transformer path is disconnected and the hollow transformer path is closed; if y=0, the currently connected transformer state is maintained unchanged.

[0052] The relay switching circuit utilizes a dual-contact magnetic latching relay. Its inputs are connected to the secondary outputs of the solid-core and air-core current transformers, respectively. The outputs are connected to the signal acquisition circuit via a dynamic gain amplifier to ensure stable signal amplitude during the switching process. To prevent transient interference during switching, the relay's operation is synchronized with the sampling cycle of the signal acquisition circuit. A buffer circuit is activated at the moment of switching, temporarily maintaining signal continuity through capacitor energy storage. The microprocessor module monitors the switched current signal in real time. If a signal anomaly is detected, such as exceeding a preset threshold or a sudden change, the redundant switching logic is immediately triggered, reselecting the transformer and calibrating the signal path. Furthermore, the secondary output signal of the air-core current transformer undergoes amplitude adjustment via a dynamic gain amplifier before connection. Its gain coefficient adaptively changes based on the current range, ensuring high amplification for low currents and low amplification for high currents, thereby matching the range of the analog-to-digital conversion module. The intelligent circuit breaker control unit uses the aforementioned switching logic and signal processing mechanism to achieve seamless switching between solid and hollow transformers, ensuring high-precision measurement across the entire current range, from rated current to short-circuit current, while avoiding measurement failures in traditional solutions caused by core saturation or weak signals.

[0053] The fuzzy controller dynamically determines the current state through fuzzy inference logic. When the logarithmic normalized value i of the current represents a small current or the logarithmic normalized value di of the current rate of change tends to decrease, it switches to a solid current transformer. When the logarithmic normalized value i of the current represents a large current or the logarithmic normalized value di of the current rate of change tends to increase, it switches to a hollow current transformer, thereby achieving linear measurement over the entire current range. The implementation process of the fuzzy controller dynamically determining the current state through fuzzy inference logic and driving the transformer switching is as follows:

[0054] After the real-time current signal is converted into a digital signal by the signal acquisition circuit, the fuzzy controller first performs logarithmic transformation and normalization on the current value, generating the logarithmically normalized value i of the current and the logarithmically normalized value di of the current rate of change. The domain of i is [0, 100], corresponding to the three fuzzy subsets of current (small SD, medium MD, and large LD). The domain of di is [-50, 50], corresponding to the five fuzzy subsets of current (negative large NB, negative small NS, positive O, positive small PS, and positive large PB). The input is fuzzified using triangular or trapezoidal membership functions. For example, when i∈[0, 30], it belongs to SD, when i∈[30, 70], it belongs to MD, and when i∈[70, 100], it belongs to LD. When di∈[-50, -30], it belongs to NB, when di∈[-30, -10], it belongs to NS, and so on.

[0055] The fuzzy rule base contains 30 fuzzy control rules, such as "If i is SD, switch to a solid-core transformer," "If i is LD, switch to a hollow-core transformer," and "If i is MD and di is NB / NS, switch to a solid-core transformer; if di is PS / PB, switch to a hollow-core transformer." All rules derive their output fuzzy variables through a composite operation formula. The composite operation uses the maximum-minimum rule, which takes the minimum value of A(x) and R(x, y) for each input combination and then takes the global maximum value to generate the output fuzzy variable U. After the output fuzzy quantity is clarified using the maximum membership average method, a control signal y∈[-1, 1] is obtained. When y<0, the current state is determined to be low or the rate of change is decreasing, that is, di∈NB / NS, driving the intelligent circuit breaker control unit to switch to a solid current transformer, taking advantage of its iron core structure's high linearity at low currents. When y>0, the current state is determined to be high or the rate of change is increasing, that is, di∈PS / PB, and switching to a hollow current transformer is performed. The coreless coil avoids high current saturation. If y=0, the current transformer state is maintained. During the switching process, a dynamic gain amplifier adjusts the signal amplitude according to the target transformer type, ensuring that the solid transformer outputs sufficient signals at low currents and that the hollow transformer does not overflow signals at high currents, thus covering the full range of linear measurement, from rated current 10A to short-circuit current 10kA.

[0056] The fuzzy controller's input, the logarithmically normalized value of the current, i, has a domain of [0, 100]. This domain is divided into three fuzzy subsets: small current (SD), medium current (MD), and large current (LD). Each subset uses a triangular membership function to cover the domain. This is achieved through the following steps: The fuzzy controller's dual-input, single-output structure receives the current signal from the signal acquisition circuit. First, the raw current value is logarithmically transformed to generate the logarithmically normalized value i, ensuring that i's domain is mapped to [0, 100]. Simultaneously, the logarithmically normalized value di of the current rate of change is calculated through differentiation, and the domain of di is restricted to [-50, 50]. The input quantities i and di are processed by the fuzzification module respectively: the domain of i is divided into three fuzzy subsets: small current SD, medium current MD, and large current LD. Each subset is covered by a triangular membership function. For example, SD has a membership of 1 in the interval [0, 30], MD has a linear transition in the interval [30, 70], and LD has an increasing membership in the interval [70, 100]; the domain of di is divided into five fuzzy subsets: negative large current NB, negative small current NS, exactly positive current O, positive small current PS, and positive large current PB. Each subset is covered by a trapezoidal membership function. For example, NB has a membership of 1 in the interval [-50, -30], NS has a linear transition in the interval [-30, -10], and so on. The fuzzy controller performs reasoning based on 30 preset fuzzy rules, such as "If i is SD, the output belongs to solid S", "If i is LD, the output belongs to hollow H", "If i is MD and di is NB or NS, the output belongs to S", and "If i is MD and di is PS or PB, the output belongs to H". Each rule corresponds to a fuzzy implication relation Ri, and the implication relations of all rules are generated through union operation to generate the total implication relation R.

[0057] During the inference process, the input fuzzy quantities A and R are synthesized and calculated using the maximum-minimum rule to generate the output fuzzy quantity U. U is then clarified using the maximum membership average method to obtain a precise control signal y∈[-1, 1]. When y<0, the intelligent circuit breaker control unit drives the relay switching circuit to connect to the solid-core current transformer, leveraging its iron core structure's high linearity at low currents. When y>0, it switches to the air-core current transformer, using a coreless coil to avoid saturation at high currents. The current state remains unchanged when y=0. Throughout this process, a dynamic gain amplifier adjusts the signal amplitude to ensure that the solid transformer outputs sufficient signals at low currents and that the air-core transformer does not overflow signals at high currents. This ensures linear measurement and reliable protection across the entire current range, from rated current to short-circuit current.

[0058] The domain of the input variable, the logarithmically normalized value di of the current rate of change, is [-50, 50]. It is divided into five fuzzy subsets: negative large current change (NB), negative small current (NS), positive small current change (O), positive small current (PS), and positive large current (PB). Each subset uses a trapezoidal membership function to cover the domain. This is achieved by the following method: After the real-time current signal is converted into a digital signal by the signal acquisition circuit, the fuzzy controller performs a logarithmic transformation on the current value to generate the logarithmically normalized value i of the current. Its domain is fixed to [0, 100], corresponding to the three fuzzy subsets of current small current (SD), current medium current (MD), and current large current (LD). The fuzzification process for input quantity i uses a triangular membership function for a coverage design. For example, the SD subset's membership increases linearly from 0 to 1 in the interval [0, 30] and decreases linearly from 1 to 0 in the interval [30, 50]. The MD subset's membership increases from 0 to 1 in the interval [20, 50] and decreases to 0 in the interval [50, 80]. The LD subset's membership increases from 0 to 1 in the interval [70, 100]. This ensures that adjacent subsets overlap in the transition intervals [30, 50] and [70, 80], achieving a natural connection between the fuzzy logic. This partitioning ensures that the SD subset's membership dominates for low currents, the MD subset is activated for medium currents, and the LD subset responds for high currents. Low current corresponds to a rated current of 10A, medium current to an overload current of 500A, and high current to a short-circuit current of 10kA.

[0059] The membership function parameters for each subset are experimentally calibrated and dynamically optimized based on the linear range characteristics of solid and hollow current transformers. For example, when the measured current is within the optimal measurement range of solid-core transformers (0-1kA), the membership function width of the SD subset is expanded to cover a wider range of low currents, while the membership function of the LD subset has a steeper slope within the larger current range suitable for hollow-core transformers, enhancing sensitivity to sudden current fluctuations. A fuzzy controller matches a preset rule base based on the membership distribution of i. For example, a "switch to solid transformer" command is triggered when the SD membership of i exceeds a threshold. Switching to hollow-core transformers occurs when the LD membership dominates. The MD membership further refines the switching logic based on the trend of the current rate of change di. This dynamic partitioning based on fuzzy subsets and coordinated design of membership functions enables the system to accurately identify current states, avoiding measurement jumps or delays caused by traditional fixed threshold switching. This ensures smooth and controllable transformer switching across the entire current range, from rated current to short-circuit current.

[0060] The domain of the control signal y is [-1, 1] and is divided into two fuzzy subsets, solid S and hollow H, corresponding to the switching instructions for solid or hollow current transformers, respectively. Each subset uses a Gaussian membership function to cover the domain. This is achieved by setting the domain of the control signal y to [-1, 1], corresponding to the two fuzzy subsets, solid S and hollow H, representing the switching instructions for solid or hollow current transformers, respectively. The Gaussian membership function of the solid subset S is centered at y = -0.8, has a standard deviation of 0.3, and covers an interval of approximately [-1, -0.2]. Its membership reaches its peak at y = -1 and gradually decreases as y approaches 0. The Gaussian function of the hollow subset H is centered at y = 0.8, has a standard deviation of 0.3, and covers an interval of approximately [0.2, 1]. Its membership reaches its maximum at y = 1 and decays towards y = 0. The two subsets overlap in the interval y∈[-0.2, 0.2], forming a fuzzy transition region to ensure the smoothness of the switching decision.

[0061] When the control signal y=0.5, the membership of the hollow subset is significantly higher than that of the solid subset, and the system prioritizes switching the hollow transformer; if y=-0.1, the membership of the two subsets is close, and the current configuration is maintained in combination with historical status or redundant logic to avoid frequent switching.

[0062] The Gaussian function was designed through experimental calibration and optimization. Its smoothing properties reduce the impact of sudden changes in the output signal on the relay circuit while enhancing the system's fault tolerance to critical conditions, such as currents approaching the transformer switching threshold. During the clarification phase, the fuzzy controller calculates the membership weights of the two subsets and combines them with the maximum membership average method to determine the final y value. When the output fuzzy quantity U exhibits a unimodal distribution in the hollow subset region, the y value corresponding to the peak is directly used as the control instruction. If the distribution is bimodal or multimodal, a weighted average of the membership is calculated to ensure that the decision logic accounts for both the current state and dynamic trends.

[0063] Furthermore, the parameters of the Gaussian membership function can be dynamically adjusted based on actual current measurement errors. For example, in noisy operating conditions, the standard deviation can be increased to expand the fuzzy region and reduce the probability of false switching. This design, through the fuzzy subset partitioning of the control signal y and the synergistic effect of the Gaussian function, enables precise generation of switching commands for solid and hollow transformers. This avoids the step jumps at the boundaries of traditional rectangular or triangular functions and improves the system's response stability and reliability in critical current scenarios.

[0064] The fuzzy controller has the following built-in fuzzy rules:

[0065] When the logarithmically normalized value i of the current belongs to the small SD of the current, the output control signal y belongs to the solid fuzzy subset S;

[0066] When the logarithm-normalized value i of the current belongs to the large current LD, the output control signal y belongs to the hollow fuzzy subset H;

[0067] When the logarithmic normalized value i of the current belongs to the current MD and the logarithmic normalized value di of the current change rate belongs to the current change large negative fuzzy subset NB or the current change small negative fuzzy subset NS, the output control signal y belongs to the solid fuzzy subset S;

[0068] When the logarithmically normalized value i of the current belongs to the current medium (MD) and the logarithmically normalized value di of the current rate of change belongs to the current change positive small fuzzy subset PS or the current change positive large fuzzy subset PB, the output control signal y belongs to the hollow fuzzy subset H. This is achieved by the following method: The fuzzy controller's built-in fuzzy rule base generates control signals based on the combinatorial logic of the logarithmically normalized value i of the current and the logarithmically normalized value di of the current rate of change. When the logarithmically normalized current value is determined to be a low current (SD), the fuzzy rule directly triggers the output signal y to belong to the solid subset S, driving the intelligent circuit breaker control unit to switch to a solid current transformer, leveraging the linear advantages of its iron core structure in low-current scenarios. When the current value is determined to be a high current (LD), the rule triggers the output signal to belong to the hollow subset H, switching to the hollow current transformer to avoid high-current saturation. For the intermediate current state MD, the fuzzy controller dynamically determines the current rate of change (d) by combining fuzzy subsets of the current change rate (di). If di belongs to the negative (large NB) or negative (small NS) subset, indicating a decreasing current trend, the output belongs to the S subset, maintaining or switching to the solid-core transformer. If di belongs to the positive (small PS) or positive (large PB) subset, indicating an accelerating current trend, the output belongs to the H subset, preemptively switching to the hollow-core transformer to mitigate potential overload risks. Each fuzzy rule is constructed using "if-then" logic, such as "if i is SD, then y belongs to S," "if i is LD, then y belongs to H," and "if i is MD and di is NB / NS, then y belongs to S." A total of 30 rules cover all input combinations. The corresponding implication relations are dynamically matched to the input fuzzy variables through a maximum-minimum synthesis operation. The output fuzzy variables are then clarified to generate precise control signals. When i = 75, meaning it falls within the LD interval, and di = 40, meaning it falls within the PB interval, the rule base triggers multiple rules supporting switching to the air-core transformer. The output fuzzy quantity exhibits a high membership in the H subset region. Calculated using the maximum membership average method, y = 0.8, driving the relay circuit to switch to the air-core transformer. This rule design anticipates current dynamic trends, enabling proactive response to transformer switching. This avoids the switching delays or misjudgments caused by fixed thresholds in traditional solutions, ensuring measurement linearity and protection reliability across the full current range, from rated current to short-circuit current.

[0069] The fuzzy rules are constructed by 30 fuzzy control rules. Each rule corresponds to a fuzzy implication relation Ri, where i = 1, 2, ..., 30. The union of all fuzzy implication relations Ri constitutes the total implication relation R, and the output fuzzy quantity U is derived through the synthesis operation formula.

[0070] The synthesis operation formula is:

[0071] , where R: total implication relation, Ri: fuzzy implication relation, i: logarithmic normalized value of current;

[0072] The synthesis operation formula of the output fuzzy quantity U is: , where A: input fuzzy quantity, R: total implication relation, U: output fuzzy quantity, represents the fuzzy set of control signal y, which is used to decide the switching of solid or hollow current transformer; : Composition operator, which represents the reasoning operation between the input fuzzy quantity A and the total implication relation R, including the composition rule;

[0073] Among them, A: input fuzzy quantity, which contains the fuzzy set of two input variables, namely:

[0074] i: Logarithmic normalized value of current, obtained by logarithmic transformation and linear normalization of the real-time current signal.

[0075] di: Logarithmic normalized value of the current change rate, obtained by calculating the logarithmic derivative and normalizing the current change rate.

[0076] The composition rules include the maximum-minimum composition rule and the maximum-product composition rule;

[0077] The maximum-minimum composition rule includes: U(y)=max x [min(A(x), R(x, y))], where U(y) represents the membership of the output fuzzy quantity at the control signal y. The larger the value of U(y), the more credible the action corresponding to the control signal y is under the current input. x represents the joint domain of the input variable, i, the logarithmic normalized value of the current and di, the logarithmic normalized value of the current change rate. A(x) represents the membership of the input fuzzy quantity at x. R(x, y) represents the strength of the association between the input variable x and the output control signal y in the total implication relationship. The calculation logic is: for each input x, take the minimum value of A(x) and R(x, y), and then take the maximum value among all x as U(y).

[0078] The calculation logic is: multiply A(x) by R(x, y), and then take the maximum value among all x as U(y);

[0079] After the output fuzzy quantity is processed by the maximum membership average method, the exact value y∈[-1,1] is obtained, which means: if y<0, switch to the solid current transformer; if y>0, switch to the hollow current transformer; if y=0, maintain the current state.

[0080] It should be further explained that, in its implementation, the fuzzy controller's 30 fuzzy rules are constructed based on fuzzy subset combinations of the input quantities i and di. Each rule corresponds to a fuzzy implication relation Ri. The rule "If i is a small current SD and di is a negative large NB, then the output belongs to a solid S" defines the relationship between the input conditions and the output action. Its implication relation Ri is implemented through a minimum operation: Ri(x, y) = min(μSD(i), μNB(di), μS(y)), where μ represents the degree of membership of each fuzzy subset. The implication relations of all 30 rules are combined through a union operation to form a total implication relation R: R = bigcupi = 1^30Ri, covering all possible input state combinations. During the inference process, the input fuzzy quantity A is combined with the total implication relation R, and the output fuzzy quantity U is calculated using the maximum-minimum rule. When i=60, belonging to MD in the current, and di=20, belonging to the positive small PS, the matching rule "If i is MD and di is PS, then the output belongs to the hollow H", its corresponding Ri at the input point x=(60, 20) has an intensity of min(μMD(60), μPS(20), μH(y)). This rule affects the membership distribution of the output fuzzy quantity U in the hollow subset area through synthetic operation. Ultimately, the effects of all matching rules are superimposed to form U, which is then clarified by the maximum membership average method to generate an accurate control signal y, driving the relay switching circuit to execute the mutual inductor switching. Through multi-rule collaborative reasoning, this scheme ensures that the system can make accurate decisions under gradual overload or sudden short circuit changes, solves the hysteresis and misjudgment problems of traditional single threshold switching, and improves the robustness of full-condition measurement.

[0081] The fuzzy controller uses the maximum membership average method to clarify the output fuzzy quantity, including extracting the median of the interval corresponding to the maximum membership value in the output fuzzy quantity as the final control signal y. It should be further explained that in the specific implementation process, the fuzzy controller uses the maximum membership average method to clarify the output fuzzy quantity U generated by inference. The specific steps are as follows: first, all y values of the output fuzzy quantity U within the domain [-1, 1] are traversed to extract the continuous interval with the maximum membership value. If the membership value of U reaches a peak and the distribution is concentrated in the interval y∈[0.6, 1.0], then this interval is determined to be the valid decision range. The geometric median of this interval is then calculated as the final control signal y. For example, the interval median y=0.8 corresponds to the instruction to switch the air-core current transformer. If U has multiple membership peak intervals, such as y=-0.5 and y=0.7, which both have high membership values, the dominant interval is selected based on historical switching status or priority rules. For example, the region with a clear current change trend is prioritized, such as the interval with y>0 when di is PB. This method smoothes the fuzzy output to avoid switching jitter caused by directly selecting the maximum membership point. For example, when U exhibits a broad peak distribution near y = 0.1, the median y = 0.1 is taken instead of forcibly returning to zero, maintaining the current transformer state and reducing unnecessary switching. Furthermore, in critical scenarios, such as when y approaches 0, a hysteresis threshold is set. For example, when |y| < 0.05, the original state is maintained to prevent frequent switching caused by noise or small fluctuations. For example, when the fuzzy inference output U exhibits a multi-peak distribution within the range y = -0.3 to y = 0.2, but the maximum membership interval is y∈[-0.3, 0], the median y = -0.15 within this interval is calculated, driving the system to switch to a solid transformer. This method combines dynamic trends with historical status to significantly improve the stability of switching decisions. It is particularly suitable for operating conditions with slowly varying or periodic current fluctuations. It overcomes the oscillation flaw of traditional clarification methods under boundary conditions and ensures accurate and reliable transformer switching across the entire current range.

[0082] The solid current transformer uses an iron core structure with a fixed number of turns on its secondary winding. The hollow current transformer uses a coreless coil structure, and the secondary output signal is adjusted to a sampleable range via a dynamic gain amplifier. It should be further explained that, in the specific implementation, the solid current transformer uses an iron core structure with a fixed number of turns on its secondary winding, directly outputting an analog signal proportional to the primary current. The iron core material is high-permeability silicon steel sheet to optimize linear response under low current conditions. The secondary output signal is connected to the pre-filter module of the signal acquisition circuit via a low-noise shielded cable to suppress high-frequency interference. The hollow current transformer uses a coreless coil structure wound on a multi-layer printed circuit board (PCB). The secondary output signal is adjusted via a dynamic gain amplifier with a built-in programmable gain control unit that adjusts the gain factor in real time based on the current range. For low currents, the gain factor is increased to 100 times to amplify weak signals, while for high currents, the gain factor is reduced to below 10 times to prevent signal overflow. The low current is less than 1kA, and the high current exceeds 5kA. The output of the dynamic gain amplifier is connected to a high-speed analog-to-digital conversion module with a sampling rate set to 1MHz to ensure high-precision capture of transient current changes. The microprocessor module of the intelligent circuit breaker control unit drives a dual-contact magnetic latching relay based on the fuzzy controller's instructions. The relay's input contacts are connected to the secondary output terminals of the solid and hollow transformers, respectively, and the output contacts are connected in series with the dynamic gain amplifier via a buffer circuit. During the switching process, the buffer circuit uses energy storage capacitors to temporarily maintain the signal path voltage at the moment the relay actuates, avoiding signal interruptions caused by mechanical contact delays. Simultaneously, the microprocessor module immediately initiates a self-test routine after the switching is complete. It determines the success of the switching by comparing the difference in signal amplitude before and after the switching. If an anomaly is detected, the redundant switching logic is triggered to reselect the transformer. Detected anomalies include abnormal signal amplitude after the solid transformer is switched. Furthermore, the dynamic gain coefficient of the hollow-core transformer is calibrated in real time via a closed-loop feedback mechanism: the microprocessor dynamically adjusts the gain based on the amplitude of the digital signal after analog-to-digital conversion. When the signal amplitude falls below 10% of the range, the gain is incremented every 10ms until the signal enters the valid sampling interval. Conversely, if the signal approaches full scale, the gain is gradually reduced. This design, through the coordinated optimization of the hardware structure and control algorithm, ensures stable output for solid-core transformers in low-current scenarios and non-saturated signals for hollow-core transformers in the high-current range. This ensures full-range linear measurement from rated current to short-circuit current, completely resolving the measurement accuracy issues inherent in traditional transformers due to structural limitations.

[0083] The intelligent circuit breaker control unit includes a microprocessor module that drives a relay switching circuit based on a control signal y. The relay switching circuit is connected in series with the secondary output terminals of the solid-core and hollow-core current transformers. It should be further explained that, in specific implementations, the microprocessor module of the intelligent circuit breaker control unit receives the control signal y output by the fuzzy controller, parses its numerical range, and generates corresponding relay drive instructions. When y < 0, the microprocessor sends a low-level signal to the dual-contact magnetic latching relay via a digital output port, driving it to disconnect the secondary output path of the hollow-core current transformer and simultaneously close the signal path of the solid-core current transformer. When y > 0, a high-level signal is sent to disconnect the solid-core transformer path and close the hollow-core transformer path. The relay switching circuit utilizes an optocoupler isolation design to ensure electrical isolation between the high-voltage and low-voltage circuits, preventing electromagnetic interference from affecting the stability of the control signal. During the switching process, the microprocessor synchronously controls the sampling timing of the signal acquisition circuit, pausing sampling before the relay contacts actuate to prevent transient noise from being mistakenly sampled. Simultaneously, the energy storage capacitors in the buffer circuit release their stored charge at the moment of switching, temporarily maintaining voltage continuity in the signal path and preventing signal dropouts or distortion caused by mechanical contact delays. After the switching is complete, the microprocessor immediately initiates a self-test routine: it compares the output signal amplitudes of the analog-to-digital converter module before and after the switching to determine if the switching was successful. This includes: If the signal amplitude after the solid transformer switching does not reach a preset threshold (less than 10% of the range), or if the signal for the hollow transformer exceeds the upper range (more than 90%), the redundant switching logic is triggered, re-driving the relay to switch to the other transformer and adjusting the signal amplitude to a reasonable range using a dynamic gain amplifier. Furthermore, the microprocessor has a built-in historical state memory function, recording the time and current conditions of the last three switching actions. If it detects frequent switching, exceeding five times within one second, it automatically activates anti-oscillation mode, temporarily locking the current transformer and extending the sampling period until the current stabilizes. This switching logic, through the coordinated optimization of hardware circuits and software algorithms, ensures fast, accurate, and reliable switching between solid and hollow transformers. This completely resolves the measurement failure issues caused by mechanical delays or signal mutations in traditional solutions, and enables full-range protection and linear measurement from rated current to short-circuit current.

[0084] The input quantities of the fuzzy controller: the logarithmic normalized value of the current i and the logarithmic normalized value of the current change rate di are generated in the following way:

[0085] The original current signal is logarithmically transformed and mapped to the preset domain through linear normalization. The conversion formula is:

[0086] , ;

[0087] Where, i: the logarithmic normalized value of the current, I is the real-time current value, I min and I max are the preset minimum and maximum current thresholds, respectively; k is the normalization coefficient; dt is the time derivative, used to quantify the instantaneous trend of current change; d(log(I)) represents the change of the natural logarithm of current I over time; d(log(I)) / dt represents the rate of change of the logarithm of the current, reflecting the relative change trend of the current;

[0088] Among them, dt quantifies the real-time change trend of the current into a numerical value di through differential operation, helping the fuzzy controller to predict the current state. Among them, the real-time change trend of the current includes accelerated growth or decelerated decay.

[0089] If di>0, it means the current is increasing;

[0090] If di<0, it means the current is decreasing.

[0091] The time differential is used to quantify the instantaneous trend of current change. Combined with logarithmic transformation and normalization, the actual current signal is converted into a dynamic input di that can be processed by the fuzzy controller, ultimately realizing the intelligent switching of the transformer.

[0092] By dynamically capturing current change trends, the system's response to transient conditions such as overload and short circuit is enhanced, solving the problem of switching lag in traditional transformers.

[0093] It should be further explained that, in the specific implementation process, the operation process of the complementary circuit breaker intelligent control device based on fuzzy control includes the following steps:

[0094] Step S1: The real-time current signal is outputted from the secondary side of the solid or hollow current transformer, is suppressed by the pre-filter module for high-frequency noise, and is then converted into a digital signal by the high-speed analog-to-digital conversion module; the digitized current value is subjected to a natural logarithmic transformation;

[0095] Step S2: Divide the input quantity i into three fuzzy subsets of current small SD, medium MD, and large LD, and use a triangular membership function to cover the domain [0, 100]. Divide the input quantity di into five fuzzy subsets of negative large NB, negative small NS, exactly O, positive small PS, and positive large PB, and use a trapezoidal membership function to cover the domain [-50, 50]. Calculate the real-time membership of each input quantity.

[0096] Step S3: Based on the preset 30 fuzzy rules, the output fuzzy quantity U is calculated by the maximum-minimum synthesis rule.

[0097] Step S4: Use the maximum membership average method to clarify U: extract the continuous interval with the largest membership, calculate its geometric median as the control signal y∈[-1, 1]. If there is a multi-peak distribution, select the dominant interval based on the historical state or priority rule, and avoid frequent switching through the hysteresis threshold.

[0098] Step S5: The intelligent circuit breaker control unit drives the double-contact magnetic latching relay according to the y value: when y<0, it switches to the solid mutual inductor, and when y>0, it switches to the hollow mutual inductor; the buffer circuit is enabled at the moment of switching to maintain signal continuity, and the strong / weak current separation is ensured by optical coupling isolation to prevent electromagnetic interference.

[0099] Step S6: The secondary output signal of the hollow transformer is adjusted by a dynamic gain amplifier. When the current is small, the gain is increased to amplify the weak signal, and when the current is large, the gain is reduced to prevent overflow. The microprocessor monitors the signal amplitude in real time and dynamically calibrates the gain coefficient through closed-loop feedback.

[0100] Step S7: After the switching is completed, the microprocessor compares the difference in signal amplitude before and after the switching. If an abnormality is detected, the redundant switching logic is triggered to reselect the mutual inductor and adjust the gain to a reasonable range.

[0101] Step S8: Record the time and current conditions of the last three switching actions. When short-term frequent switching is detected, enable the anti-oscillation mode to lock the current transformer and extend the sampling period until the current stabilizes to avoid mechanical wear and signal distortion.

[0102] Step S9: Through the above closed-loop control, the solid transformer maintains high linearity at low currents, and the hollow transformer avoids saturation at high currents, achieving accurate measurement of the full range from rated current to short-circuit current, and triggering the circuit breaker protection circuit.

[0103] By dynamically integrating current magnitude and rate of change through a fuzzy controller and combining the complementary characteristics of solid and hollow current transformers, high-precision measurement is achieved across the full range from rated current to short-circuit current. Solid transformers maintain linearity at low currents through their iron core structure, while hollow transformers avoid saturation at high currents through their coreless design. The fuzzy rule base and membership functions are collaboratively optimized to accurately predict current trends and adaptively switch transformers, overcoming the hysteresis and misjudgment issues associated with traditional fixed-threshold switching. This reduces measurement errors and accelerates response speeds to milliseconds, significantly enhancing the circuit breaker's protection reliability under transient conditions such as overloads and short circuits.

[0104] Through dynamic gain adjustment, redundant switching of buffer circuits, and anti-oscillation logic design, the system maintains stable operation in complex scenarios such as noise interference and mechanical delays. A signal preprocessing mechanism compresses current signals with a large dynamic range. Combined with closed-loop gain calibration and historical state memory, this effectively suppresses signal distortion and the risk of frequent switching, extending equipment life. This solution has a simple hardware structure, an embedded fuzzy control algorithm, and is compatible with existing circuit breaker systems. It is suitable for a variety of scenarios, including smart grids and industrial power distribution. It combines high reliability, low cost, and ease of maintenance, driving the upgrade of power protection equipment towards intelligent and adaptive design.

[0105] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0106] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A complementary circuit breaker intelligent control device based on fuzzy control, characterized in that: include: The switchable solid current transformer and hollow current transformer are respectively connected to the fuzzy controller through a signal acquisition circuit; The fuzzy controller includes a dual-input-single-output structure, wherein the input port receives the logarithmic normalized value of the current and the logarithmic normalized value of the current change rate, and the output port generates a control signal; An intelligent circuit breaker control unit is connected to the output port of the fuzzy controller and switches the solid current transformer or the hollow current transformer to the circuit according to the control signal; The fuzzy controller dynamically determines the current state through fuzzy inference logic. When the logarithmic normalized value of the current represents a small current or the logarithmic normalized value of the current change rate tends to decrease, it switches to a solid current transformer. When the logarithmic normalized value of the current represents a large current or the logarithmic normalized value of the current change rate tends to increase, it switches to a hollow current transformer, thereby achieving linear measurement of the full current range. The solid current transformer adopts an iron core structure, and the number of turns of its secondary winding is fixed; the hollow current transformer adopts a coreless coil structure, and the secondary output signal is adjusted to the sampleable range through a dynamic gain amplifier; The intelligent circuit breaker control unit includes a microprocessor module, which drives a relay switching circuit according to a control signal. The relay switching circuit is connected in series with the secondary output terminals of the solid current transformer and the air-core current transformer. At the switching moment, a buffer circuit is activated to temporarily maintain signal continuity through capacitor energy storage. When short-term frequent switching is detected, an anti-oscillation mode is activated to lock the current transformer and extend the sampling period until the current stabilizes.

2. The complementary circuit breaker intelligent control device based on fuzzy control according to claim 1, characterized in that: The domain of the logarithmically normalized value of the current is [0, 100], which is divided into three fuzzy subsets: small current, medium current, and large current. Each subset uses a triangular membership function to cover the domain.

3. The complementary circuit breaker intelligent control device based on fuzzy control according to claim 2, characterized in that: The domain of the logarithmic normalized value of the current change rate is [-50, 50], which is divided into five fuzzy subsets: current change is negative, negative, positive, positive, and positive. Each subset uses a trapezoidal membership function to cover the domain.

4. The complementary circuit breaker intelligent control device based on fuzzy control according to claim 1, characterized in that: The domain of the control signal is [-1, 1], which is divided into two fuzzy subsets, solid and hollow, corresponding to the operation instructions of switching to the solid or hollow current transformer respectively. Each subset uses a Gaussian membership function to cover the domain.

5. The complementary circuit breaker intelligent control device based on fuzzy control according to claim 1, characterized in that: The fuzzy controller has the following built-in fuzzy rules: When the logarithm normalized value of the current belongs to the small current, the output control signal belongs to the solid fuzzy subset; When the logarithm normalized value of the current belongs to the large current, the output control signal belongs to the hollow fuzzy subset; When the logarithm normalized value of the current belongs to the medium current and the logarithm normalized value of the current change rate belongs to the current change large negative fuzzy subset or the current change small negative fuzzy subset, the output control signal belongs to the solid fuzzy subset; When the logarithmically normalized value of the current belongs to the medium current and the logarithmically normalized value of the current change rate belongs to the current change positive small fuzzy subset or the current change positive large fuzzy subset, the output control signal belongs to the hollow fuzzy subset.

6. The complementary circuit breaker intelligent control device based on fuzzy control according to claim 5, characterized in that: The fuzzy rules are constructed by 30 fuzzy control rules, each rule corresponds to a fuzzy implication relationship; the union of all fuzzy implication relationships constitutes the total implication relationship, and the fuzzy quantity is derived and output through the synthesis operation formula; The synthesis operation formula is: , where R: total implication relation, Ri: fuzzy implication relation, i: logarithmic normalized value of current; The synthesis operation formula of the output fuzzy quantity U is: , where A: input fuzzy quantity, R: total implication relation, U: output fuzzy quantity; : Synthesis operator, which represents the reasoning operation and synthesis rule between the input fuzzy quantity A and the total implication relation R.

7. The complementary circuit breaker intelligent control device based on fuzzy control according to claim 6, characterized in that: The fuzzy controller uses the maximum membership average method to clarify the output fuzzy quantity, including: extracting the median of the interval corresponding to the maximum membership value in the output fuzzy quantity as the final control signal.

8. The complementary circuit breaker intelligent control device based on fuzzy control according to claim 1, characterized in that: The input quantities of the fuzzy controller: the logarithmic normalized value of the current and the logarithmic normalized value of the current change rate are generated in the following way: The original current signal is logarithmically transformed and mapped to the preset domain through linear normalization. The conversion formula is: ， ； Where, i: the logarithmic normalized value of the current, I is the real-time current value, I min and I max are the preset minimum and maximum current thresholds, k is the normalization coefficient, dt is the time derivative, d(log(I)) represents the change of the natural logarithm of the current I over time, and d(log(I)) / dt represents the rate of change of the current logarithm.

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