Fuzzy control-based complementary circuit breaker intelligent control device

Abstract

The invention discloses a complementary circuit breaker intelligent control device based on fuzzy control, which relates to the technical field of power electronics and comprises a solid current transformer and a hollow current transformer which can be switched and are respectively connected with a fuzzy controller through a signal acquisition circuit. According to the complementary circuit breaker intelligent control device based on fuzzy control, the magnitude and the change rate of the current are dynamically fused through the fuzzy controller, the complementary characteristics of the solid current transformer and the hollow current transformer are combined, and full-range high-precision measurement from rated current to short-circuit current is achieved. The linearity of the solid mutual inductor is kept through an iron core structure under small current, saturation of the hollow mutual inductor is avoided through a magnetic-core-free design under large current, a fuzzy rule base and a membership function are optimized in a collaborative mode, the current trend is accurately pre-judged, the mutual inductor is switched in a self-adaptive mode, the problems of hysteresis and misjudgment of traditional fixed threshold value switching are solved, the measurement error is reduced, and the detection accuracy is improved. The response speed is increased to millisecond level, and the protection reliability of the circuit breaker under the transient condition is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology, and more specifically to an intelligent control device for a complementary circuit breaker based on fuzzy control. Background Art

[0002] Intelligent circuit breakers play a crucial protection function in power systems. The core lies in accurately detecting the line current in real time to trigger corresponding protection actions. However, the current transformers used in existing technologies have significant limitations: Solid current transformers have good linearity in small current scenarios, but in large currents, the measurement becomes inaccurate due to core saturation; Hollow current transformers can adapt to wide-range measurements of large currents, but introduce large errors due to weak small current signals. A single transformer cannot meet the measurement requirements across the entire current range, resulting in potential protection failure of the circuit breaker due to measurement deviation under extreme conditions such as overload or short circuit, seriously threatening the safety and reliability of the power system. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the present invention provides an intelligent control device for a complementary circuit breaker based on fuzzy control, which solves the problem of dynamically judging the current state and realizing the intelligent switching control of two transformers, thereby ensuring high-precision measurement across the entire range from rated current to short-circuit current.

[0004] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent control device for a complementary circuit breaker based on fuzzy control, comprising: A switchable solid current transformer and a hollow current transformer, respectively connected to a fuzzy controller through signal acquisition circuits; The fuzzy controller includes a double-input single-output structure. The input ports receive the logarithmically normalized value i of the current and the logarithmically normalized value di of the current change rate, and the output port generates a control signal y; An intelligent circuit breaker control unit, connected to the output port of the fuzzy controller, switches the solid current transformer or the hollow current transformer to be connected to the circuit according to the control signal y; Among them, the fuzzy controller dynamically judges the current state through fuzzy inference logic. When the logarithmically normalized value i of the current represents a small current or the logarithmically normalized value di of the current change rate tends to decrease, it switches to the solid current transformer; when the logarithmically normalized value i of the current represents a large current or the logarithmically normalized value di of the current change rate tends to increase, it switches to the hollow current transformer, realizing linear measurement across the entire current range.

[0005] Preferably, for the input quantity of the fuzzy controller, that is, the domain of the logarithmically normalized value i of the current is [0, 100], which 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.

[0006] Preferably, the domain of the input quantity, i.e., the logarithmically normalized value di of the current change rate, is [-50, 50], which is divided into five fuzzy subsets: negative big NB, negative small NS, exactly O, positive small PS, and positive big PB of the current change. Each subset uses a trapezoidal membership function to cover the domain.

[0007] Preferably, the domain of the control signal y is [-1, 1], which is divided into two fuzzy subsets: solid S and hollow H, corresponding to the operation instructions for switching to solid or hollow current transformers respectively. Each subset uses a Gaussian membership function to cover the domain.

[0008] Preferably, the fuzzy controller incorporates the following fuzzy rules: When the logarithmically normalized value i of the current belongs to the small current SD, the output control signal y belongs to the solid fuzzy subset S; When the logarithmically normalized value i of the current belongs to the large current LD, the output control signal y belongs to the hollow fuzzy subset H; When the logarithmically normalized value i of the current belongs to the medium current MD and the logarithmically normalized value di of the current change rate belongs to the negative big fuzzy subset NB or the negative small fuzzy subset NS of the current change, the output control signal y belongs to the solid fuzzy subset S; When the logarithmically normalized value i of the current belongs to the medium current MD and the logarithmically normalized value di of the current change rate belongs to the positive small fuzzy subset PS or the positive big fuzzy subset PB of the current change, the output control signal y belongs to the hollow fuzzy subset H.

[0009] 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 through the composition operation formula; Among them, the composition operation formula is: , where R: total implication relation, Ri: fuzzy implication relation, i: logarithmically normalized value of the current; The composition operation formula for the output fuzzy quantity U is: , where A: input fuzzy quantity, R: total implication relation, U: output fuzzy quantity, representing the fuzzy set of the control signal y, used for decision-making on the switching of solid or hollow current transformers; : composition operator, representing the inference operation of the input fuzzy quantity A and the total implication relation R, including the composition rule; Among them, A: input fuzzy quantity, including the fuzzy sets of two input variables, namely: i: logarithmically normalized value of the current, obtained by logarithmic transformation and linear normalization of the real-time current signal.

[0010] $d_i$: The logarithm-normalized value of the current change rate, which is obtained by calculating the logarithmic derivative of the current change rate and normalizing it.

[0011] The composition rules include the max-min composition rule and the max-product composition rule; The max-min composition rule includes: $U(y)=\max$ x $[\min(A(x), R(x, y))]$, where, $U(y)$: represents the membership degree 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; $x$: the input variable, that is, the joint universe of discourse of the logarithm-normalized value $i$ of the current and the logarithm-normalized value $d_i$ of the current change rate; $A(x)$: the membership degree of the input fuzzy quantity at $x$, $R(x, y)$: the correlation strength 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 of $A(x)$ and $R(x, y)$, and then take the maximum value among all $x$ as $U(y)$.

[0012] The calculation logic is: multiply $A(x)$ and $R(x, y)$, and then take the maximum value among all $x$ as $U(y)$; After the output fuzzy quantity passes through the average value method of the maximum membership degree, the exact value $y\in[-1, 1]$ is obtained. Its meaning is: if $y\lt0$, then switch to the solid current transformer; if $y\gt0$, then switch to the hollow current transformer; if $y = 0$, then keep the current state.

[0013] Preferably, the fuzzy controller uses the average value method of the maximum membership degree to defuzzify the output fuzzy quantity, including: extracting the median value of the interval corresponding to the maximum membership degree in the output fuzzy quantity as the final control signal $y$.

[0014] Preferably, the solid current transformer adopts an iron core structure with a fixed number of turns in its secondary winding; the hollow current transformer adopts a non-magnetic core coil structure, and the secondary output signal is adjusted to the samplable range through a dynamic gain amplifier.

[0015] Preferably, the intelligent circuit breaker control unit includes a microprocessor module, which drives the relay switching circuit according to the 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.

[0016] Preferably, the input quantities of the fuzzy controller: the logarithm-normalized value $i$ of the current and the logarithm-normalized value $d_i$ of the current change rate are generated in the following way: Perform a logarithmic transformation on the original current signal and map it to a preset universe of discourse through linear normalization. Its conversion formula is: , ; where, i: logarithmically normalized value of current, I is the real-time current value, I min and I max are respectively the preset minimum and maximum current thresholds, k is the normalization coefficient, dt: differential of time, 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 change rate of the logarithm of current, reflecting the relative change trend of current; where, dt quantifies the real-time change trend of current into a numerical value di through differential operation, helping the fuzzy controller to predict the current state. The real-time change trend of current includes accelerating growth or decelerating decay.

[0017] If di>0, it means the current is increasing; If di<0, it means the current is decreasing.

[0018] The differential of time is used to quantify the instantaneous trend of current change. Combining 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 current transformer.

[0019] By dynamically capturing the current change trend, the response ability of the system to transient conditions such as overload and short circuit is enhanced, and the problem of lag in switching of traditional current transformers is solved.

[0020] The present invention provides an intelligent control device for a complementary circuit breaker based on fuzzy control. It has the following beneficial effects: (1). The intelligent control device for the complementary circuit breaker based on fuzzy control dynamically fuses the current magnitude and change rate through a fuzzy controller, combines the complementary characteristics of solid and hollow current transformers, and realizes high-precision measurement in the full range from rated current to short-circuit current. The solid current transformer maintains linearity using the iron core structure at small currents, and the hollow current transformer avoids saturation through a non-magnetic core design at large currents. The fuzzy rule base and membership function are optimized synergistically to accurately predict the current trend and adaptively switch the current transformer, overcoming the hysteresis and misjudgment problems of traditional fixed-threshold switching, reducing the measurement error, and improving the response speed to the millisecond level, significantly enhancing the protection reliability of the circuit breaker under transient conditions such as overload and short circuit.

[0021] (2) The intelligent control device for complementary circuit breakers based on fuzzy control can maintain stable operation in complex scenarios such as noise interference and mechanical delay 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, and combined with the closed-loop gain calibration and historical state memory functions, effectively suppresses the risk of signal distortion and frequent switching, and extends the device life. The hardware structure of this solution is simple, the fuzzy control algorithm can be embedded, compatible with existing circuit breaker systems, applicable to multiple scenarios such as smart grids and industrial power distribution, taking into account high reliability, low cost, and easy maintenance, and promoting the upgrade of power protection equipment to intelligence and adaptability. Description of the Drawings

[0022] Figure 1 It is a schematic framework diagram of the whole invention; Figure 2 It is a schematic flow diagram of the invention. Detailed Embodiments

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0024] Please refer to Figure 1 and Figure 2 The present invention provides a technical solution: an intelligent control device for complementary circuit breakers based on fuzzy control, including: A switchable solid current transformer and a hollow current transformer are respectively connected to a fuzzy controller through a signal acquisition circuit; the implementation of the switchable solid current transformer and the hollow current transformer is realized through the collaborative work of the signal acquisition circuit and the fuzzy controller. The solid current transformer adopts an iron core structure, with a fixed number of turns in its secondary winding, and directly outputs an analog signal proportional to the primary current, which is suitable for small current scenarios; the hollow current transformer adopts a non-magnetic core coil structure, and the signal output from the secondary side is adjusted by a dynamic gain amplifier to ensure that the signal amplitude is within the samplable range within a large current range. The secondary output terminals of both are respectively connected to the signal acquisition circuit, which includes a pre-filtering module and a high-speed analog-to-digital conversion module. Among them, the pre-filtering module suppresses noise and limits the bandwidth of the original signal output by the current transformer, and the analog-to-digital conversion module converts the analog signal into a digital signal and then inputs it into the fuzzy controller.

[0025] The input ports of the fuzzy controller receive the digital signals of two current transformers through parallel channels, but only dynamically select the effective channel according to the current control signal: when the control signal requires switching the current transformer, the intelligent circuit breaker control unit drives the relay switching circuit to disconnect the signal path of the current transformer and connect the target current transformer. The signal acquisition circuit maintains signal continuity through the buffer circuit during the switching process to avoid transient interference. The fuzzy controller analyzes the logarithmically normalized value and the change rate of the current of the selected current transformer in real time, and generates the control signal for the next cycle through the preset fuzzy rule base and membership function, forming a closed-loop feedback to ensure the dynamic matching of the current transformer switching action and the current state.

[0026] The fuzzy controller includes a double-input single-output structure. The input ports receive the logarithmically normalized value i of the current and the logarithmically normalized value di of the current change rate, and the output port generates the control signal y. The double-input single-output structure of the fuzzy controller realizes the dynamic generation of the control signal through the following steps: The input ports receive the current signals from the signal acquisition circuit. First, the original current value is logarithmically transformed and linearly normalized to generate the logarithmically normalized value i of the current. At the same time, the logarithmically normalized value di of the current change rate is calculated through differential operation. The input quantities i and di are respectively mapped to the input universe of discourse of the fuzzy controller. Among them, the universe of discourse [0, 100] of i is divided into three fuzzy subsets: small current SD, medium current MD, and large current LD, and the universe of discourse [-50, 50] of di is divided into five fuzzy subsets: large negative current change NB, small negative current change NS, exactly zero O, small positive current change PS, and large positive current change PB. Each subset covers the universe of discourse through triangular or trapezoidal membership functions. The 30 fuzzy rules built in the fuzzy controller generate the output fuzzy quantity based on the combination of the fuzzy subsets of the input quantities. For example, "when i is SD, the output membership is solid S", "when i is LD, the output membership is hollow H", "when i is MD and di is NB / NS, the output membership is S", etc.

[0027] The fuzzy implication relationships corresponding to all the rules are used to deduce the output fuzzy quantity U through the composition operation formula. The composition operation adopts the maximum-minimum or maximum-product rule. After the output fuzzy quantity U is defuzzified by the average value method of the maximum membership degree, the precise control signal y ∈ [-1, 1] is generated. When y < 0, the relay switching circuit is driven to connect the solid current transformer, when y > 0, the hollow current transformer is connected, and when y = 0, the current state is maintained. This double-input structure realizes the adaptive decision-making of the current transformer switching by integrating the current magnitude and the dynamic change trend, ensuring the measurement accuracy and response speed within the full current range.

[0028] The 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 access 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 through the output port of the fuzzy controller. The microprocessor analyzes the numerical range of y and generates a drive instruction: when y < 0, the drive relay switches the circuit 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 state of the currently connected transformer remains unchanged.

[0029] The relay switching circuit uses a double-contact magnetic latching relay. Its input terminals are respectively connected to the secondary output terminals of the solid and hollow current transformers, and the output terminal is connected to the signal acquisition circuit through a dynamic gain amplifier to ensure the stability of the signal amplitude during the switching process. To prevent switching transient interference, the relay action is synchronized with the sampling period of the signal acquisition circuit, and a buffer circuit is enabled at the moment of switching to temporarily maintain signal continuity through capacitor energy storage. The microprocessor module monitors the switched current signal in real time. If an abnormal signal is detected, such as exceeding the preset threshold or mutation, the redundant switching logic is immediately triggered to re-select the transformer and calibrate the signal path. In addition, the secondary output signal of the hollow current transformer is amplitude-adjusted by a dynamic gain amplifier before being connected, and its gain coefficient changes adaptively according to the current range, ensuring a high magnification factor for small currents and a low magnification factor for large currents, so as to match the range of the analog-to-digital conversion module. Through the above switching logic and signal processing mechanism, the intelligent circuit breaker control unit realizes the seamless switching between the solid and hollow transformers, ensures high-precision measurement in the full range from the rated current to the short-circuit current, and at the same time avoids the measurement failure problem caused by core saturation or weak signal in the traditional scheme.

[0030] Among them, the fuzzy controller dynamically judges the current state through fuzzy inference logic. When the logarithmically normalized value i of the current represents a small current or the logarithmically normalized value di of the current change rate tends to decrease, it switches to the solid current transformer. When the logarithmically normalized value i of the current represents a large current or the logarithmically normalized value di of the current change rate tends to increase, it switches to the hollow current transformer to achieve linear measurement in the full current range. The implementation process of the fuzzy controller dynamically judging the current state and driving the transformer switching through fuzzy inference logic is as follows: 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 processing on the current value to generate the logarithmically normalized value i of the current and the logarithmically normalized value di of the current change rate. Among them, the domain of i is [0, 100], corresponding to three fuzzy subsets of small SD, medium MD, and large LD of the current, and the domain of di is [-50, 50], corresponding to five fuzzy subsets of negative large NB, negative small NS, exactly O, positive small PS, and positive large PB of the change. The input quantity is fuzzified through 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.

[0031] The fuzzy rule base contains 30 fuzzy control rules, such as "switch to a solid-core current transformer if i is SD", "switch to an air-core current transformer if i is LD", "switch to a solid core if i is MD and di is NB / NS, and switch to an air core if di is PS / PB", etc. All rules derive and output fuzzy quantities through the synthesis operation formula. The synthesis operation adopts the maximum-minimum rule, that is, take the minimum of A(x) and R(x, y) for each input combination, and then take the global maximum to generate the output fuzzy quantity U. After the output fuzzy quantity is defuzzified by the average value method of the maximum membership degree, the control signal y ∈ [-1, 1] is obtained. When y < 0, it is determined that the current state is small current or the change rate tends to decrease, that is, di ∈ NB / NS, and the intelligent circuit breaker control unit is driven to switch to a solid-core current transformer, using its high linearity at small currents; when y > 0, it is determined that the current is large or the change rate tends to increase, that is, di ∈ PS / PB, then switch to an air-core current transformer to avoid the problem of large current saturation through a non-magnetic core coil; if y = 0, maintain the current current transformer state. During the switching process, the dynamic gain amplifier adjusts the signal amplitude according to the target current transformer type to ensure that the solid-core current transformer outputs sufficient signals at small currents and the air-core current transformer does not overflow signals at large currents, so as to cover the full-range linear measurement from the rated current of 10 A to the short-circuit current of 10 kA.

[0032] The input variables of the fuzzy controller, that is, the domain of the logarithmically normalized value i of the current is [0, 100], which 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. It is achieved through the following steps: The double-input single-output structure of the fuzzy controller receives the current signal from the signal acquisition circuit. First, a logarithmic transformation is performed on the original current value to generate the logarithmically normalized value i of the current, ensuring that the domain of i is mapped to [0, 100]. At the same time, the logarithmically normalized value di of the current change rate is calculated through differential operation, and the domain of di is restricted to [-50, 50]. The input variables 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, the membership degree of SD is 1 in the interval [0, 30], MD linearly transitions in the interval [30, 70], and the membership degree of LD increases in the interval [70, 100]. The domain of di is divided into five fuzzy subsets: large negative change in current NB, small negative change in current NS, exactly zero O, small positive change in current PS, and large positive change in current PB. Each subset is covered by a trapezoidal membership function. For example, the membership degree of NB is 1 in the interval [-50, -30], NS linearly transitions in the interval [-30, -10], and so on. The fuzzy controller performs inference based on 30 preset fuzzy rules, such as "if i is SD, then the output membership is solid S", "if i is LD, then the output membership is hollow H", "if i is MD and di is NB or NS, then the output membership is S", "if i is MD and di is PS or PB, then the output membership is H". Each rule corresponds to a fuzzy implication relation Ri, and the implication relations of all rules are generated into the total implication relation R through union operation.

[0033] During the inference process, the input fuzzy quantity A is combined with R using the max-min rule to generate the output fuzzy quantity U, and then U is defuzzified by the average value method of the maximum membership degree to obtain the precise control signal y ∈ [-1, 1]. When y < 0, the intelligent circuit breaker control unit drives the relay to switch the circuit to connect the solid current transformer, taking advantage of its high linearity in the small current scenario due to its iron core structure; when y > 0, it switches to the hollow current transformer to avoid the large current saturation problem through the non-magnetic core coil; when y = 0, the current state is maintained. The entire process adjusts the signal amplitude through a dynamic gain amplifier to ensure that the solid transformer outputs sufficient signals in small currents and the hollow transformer does not overflow signals in large currents, thereby achieving full-range linear measurement and reliable protection from the rated current to the short-circuit current.

[0034] The domain of the input quantity, i.e., the logarithmically normalized value di of the current change rate, is [-50, 50], which is divided into five fuzzy subsets: negative big NB, negative small NS, exactly O, positive small PS, and positive big PB for the current change. Each subset uses a trapezoidal membership function to cover the domain. This is achieved as follows: 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, whose domain is fixed at [0, 100], corresponding to three fuzzy subsets: small current SD, medium current MD, and large current LD. The fuzzyfication process of the input quantity i is designed by using a triangular membership function for coverage. For example, the membership degree of the SD subset rises linearly from 0 to 1 in the interval [0, 30] and drops linearly from 1 to 0 in the interval [30, 50]; the membership degree of the MD subset rises from 0 to 1 in the interval [20, 50] and drops to 0 in the interval [50, 80]; the membership degree of the LD subset rises from 0 to 1 in the interval [70, 100], ensuring that there is an overlap in the transition intervals: [30, 50] and [70, 80] for adjacent subsets to achieve the natural connection of fuzzy logic. This division method makes the membership degree of the SD subset dominant for small currents, the MD subset activated for medium currents, and the LD subset responsive for large currents. Among them, small current corresponds to the rated current of 10 A, medium current corresponds to the overload current of 500 A, and large current corresponds to the short-circuit current of 10 kA.

[0035] The membership function parameters of each subset are calibrated through experiments and dynamically optimized in combination with the linear interval characteristics of solid and hollow current transformers. For example, when the measured current is within the best measurement range (0 - 1 kA) of the solid current transformer, the width of the membership function of the SD subset is extended to cover a wider small current interval, while the slope of the membership function of the LD subset increases within the large current range applicable to the hollow current transformer to enhance the sensitivity to sudden current changes. The fuzzy controller matches the preset rule base according to the membership degree distribution of i. For example, when the SD membership degree of i exceeds the threshold, the "switch to solid current transformer" instruction is triggered, and when the LD membership degree dominates, it switches to the hollow current transformer. The MD membership degree further refines the switching logic in combination with the trend of the current change rate di. Through this collaborative design of dynamic division based on fuzzy subsets and membership functions, the system can accurately identify the current state, avoid measurement jumps or delays caused by traditional fixed-threshold switching, and ensure smooth switching of the current transformer and controllable linearity within the full range from the rated current to the short-circuit current.

[0036] The universe of discourse of the control signal y is [-1, 1], which is divided into two fuzzy subsets: solid S and hollow H, corresponding to the operation instructions for switching to solid or hollow current transformers respectively. Each subset uses a Gaussian membership function to cover the universe of discourse. This is achieved as follows: The universe of discourse of the control signal y is set to [-1, 1], corresponding to the two fuzzy subsets of solid S and hollow H, representing the operation instructions for switching to solid or hollow current transformers respectively. The center of the Gaussian membership function of the solid subset S is located at y = -0.8, with a standard deviation of 0.3, covering an interval of approximately [-1, -0.2]. Its membership degree reaches a peak at y = -1 and gradually decreases as y approaches 0; the center of the Gaussian function of the hollow subset H is located at y = 0.8, with a standard deviation of 0.3, covering an interval of approximately [0.2, 1]. The membership degree is 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.

[0037] When the control signal y = 0.5, the membership degree of the hollow subset is significantly higher than that of the solid subset, and the system preferentially executes the operation of switching to the hollow current transformer; if y = -0.1, the membership degrees of the two subsets are close, and the current configuration is maintained by combining historical states or redundant logic to avoid frequent switching.

[0038] The design of the Gaussian function is optimized through experimental calibration. Its smooth characteristics can reduce the impact of sudden changes in the output signal on the relay circuit, and at the same time enhance the fault tolerance of the system in critical states, where critical states include currents close to the switching threshold of the current transformer. In the defuzzification stage, the fuzzy controller determines the final y value by calculating the membership degree weights of the two subsets and combining the average value method of the maximum membership degree; when the output fuzzy quantity U shows a single-peak distribution in the hollow subset region, the y value corresponding to the peak is directly taken as the control instruction; if the distribution is bimodal or multi-modal, the weighted average of the membership degrees is calculated to ensure that the decision-making logic takes into account both the current state and the dynamic trend.

[0039] In addition, the parameters of the Gaussian membership function can be dynamically adjusted according to the actual current measurement error. For example, in working conditions with high noise, the standard deviation is increased to expand the fuzzy region and reduce the probability of mis-switching. Through the above design, the fuzzy subset division of the control signal y and the Gaussian function work together to accurately generate the switching instructions for solid and hollow current transformers, avoiding the step jumps at the boundaries of traditional rectangular or triangular functions and improving the response stability and reliability of the system in critical current scenarios.

[0040] The fuzzy controller incorporates the following fuzzy rules: When the logarithmically normalized value i of the current belongs to the small current SD, the output control signal y belongs to the solid fuzzy subset S; When the logarithmically normalized value i of the current belongs to the large current LD, the output control signal y belongs to the hollow fuzzy subset H; When the logarithmically normalized value \(i\) of the current belongs to MD in the current and the logarithmically normalized value \(di\) of the current change rate belongs to the negative large fuzzy subset NB or the negative small fuzzy subset NS of the current change, the output control signal \(y\) belongs to the solid fuzzy subset S; When the logarithmically normalized value \(i\) of the current belongs to MD in the current and the logarithmically normalized value \(di\) of the current change rate belongs to the positive small fuzzy subset PS or the positive large fuzzy subset PB of the current change, the output control signal \(y\) belongs to the hollow fuzzy subset H. This is achieved as follows: The fuzzy rule base built into the fuzzy controller generates a control signal based on the combined logic of the logarithmically normalized value \(i\) of the current and the logarithmically normalized value \(di\) of the current change rate. When the current value is determined to be a small current SD after logarithmic normalization, 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 the solid current transformer and utilizing its linear advantage in the small current scenario; when the current value is determined to be a large current LD, the rule triggers the output to belong to the hollow subset H and switches to the hollow current transformer to avoid the large current saturation problem. For the intermediate current state MD, the fuzzy controller makes a dynamic decision in combination with the fuzzy subset of the current change rate \(di\): if \(di\) belongs to the negative large NB or negative small NS subset, indicating that the current trend tends to decrease, then the output belongs to the S subset, maintaining or switching to the solid current transformer; if \(di\) belongs to the positive small PS or positive large PB subset, indicating that the current trend is accelerating upwards, then the output belongs to the H subset and switches to the hollow current transformer in advance to cope with the potential overload risk. Each fuzzy rule is constructed through "if-then" logic, such as "if \(i\) is SD, then \(y\) belongs to S", "if \(i\) is LD, then \(y\) belongs to H", "if \(i\) is MD and \(di\) is NB / NS, then \(y\) belongs to S", etc. A total of 30 rules cover all input combinations. The implication relationships corresponding to the rules are dynamically matched with the input fuzzy quantities through the max-min composition operation, and the output fuzzy quantity is defuzzified to generate an accurate control signal. When \(i = 75\), that is, belonging to the LD interval, and \(di = 40\), that is, belonging to the PB interval, the rule base triggers multiple rules supporting the switching to the hollow current transformer, and the output fuzzy quantity shows a high membership degree in the H subset region. After calculation by the average value of the maximum membership degree method, \(y = 0.8\), driving the relay circuit to switch to the hollow current transformer. This rule design anticipates the current dynamic trend, realizes the early response of the current transformer switching, avoids the switching delay or misjudgment caused by the fixed threshold in the traditional scheme, and ensures the measurement linearity and protection reliability in the full range from the rated current to the short-circuit current.

[0041] The fuzzy rules are constructed by 30 fuzzy control rules, and each rule corresponds to a fuzzy implication relationship \(R_i\), where \(i = 1, 2, \ldots, 30\); the union of all fuzzy implication relationships \(R_i\) constitutes the total implication relationship \(R\), and the output fuzzy quantity \(U\) is derived through the composition operation formula; Among them, the composition operation formula is: , where R represents the total implication relationship, Ri represents the fuzzy implication relationship, and i represents the logarithmically normalized value of the current; The composite operation formula for the output fuzzy quantity U is: , where A represents the input fuzzy quantity, R represents the total implication relationship, U represents the output fuzzy quantity, which is the fuzzy set representing the control signal y and is used to decide the switching of the solid or hollow current transformer; : The composite operator represents the inference operation between the input fuzzy quantity A and the total implication relationship R and includes the composite rule; Among them, A represents the input fuzzy quantity, which includes the fuzzy sets of two input variables, namely: i represents the logarithmically normalized value of the current, which is obtained by logarithmic transformation and linear normalization of the real-time current signal.

[0042] di represents the logarithmically normalized value of the current change rate, which is obtained by logarithmic derivative calculation and normalization of the current change rate.

[0043] The composite rules include the max-min composite rule and the max-product composite rule; The max-min composite rule includes: U(y)=max x [min(A(x), R(x, y))], where U(y) represents the membership degree 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 under the current input; x represents the input variable, that is, the joint universe of discourse of the logarithmically normalized value i of the current and the logarithmically normalized value di of the current change rate; A(x) represents the membership degree of the input fuzzy quantity at x, and R(x, y) represents the correlation strength 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).

[0044] The calculation logic is: multiply A(x) by R(x, y), and then take the maximum value among all x as U(y); After the output fuzzy quantity is processed by the average method of the maximum membership degree, an accurate value y ∈ [-1, 1] is obtained, and its meaning is: if y < 0, then switch to the solid current transformer; if y > 0 →, then switch to the hollow current transformer; if y = 0 →, then keep the current state.

[0045] It should be further noted that in the specific implementation process, the 30 fuzzy rules of the fuzzy controller are constructed based on the fuzzy subset combinations of the input variables i and di, and each rule corresponds to a fuzzy implication relation Ri. The rule "if i is small current SD and di is large negative NB, then the output membership is solid S" defines the association between the input conditions and the output actions, and its implication relation Ri is realized through the minimum operation, that is, Ri(x, y) = min(μSD(i), μNB(di), μS(y)), where μ represents the membership degrees of each fuzzy subset. The implication relations of all 30 rules are combined into the total implication relation R through the union operation, that is, R = ⋃i = 1^30 Ri, covering all possible combinations of input states. In the reasoning process, the input fuzzy quantity A is composed with the total implication relation R, and the maximum-minimum rule is used to calculate the output fuzzy quantity U. When i = 60, belonging to MD in the membership current, and di = 20, belonging to positive small PS, the matching rule "if i is MD and di is PS, then the output membership is hollow H" is matched, and the strength of its corresponding Ri at the input point x = (60, 20) is min(μMD(60), μPS(20), μH(y)). This rule affects the membership degree distribution of the output fuzzy quantity U in the hollow subset region through the composition operation. Finally, the effects of all matching rules are superimposed to form U, and then it is defuzzified by the average value method of the maximum membership degree to generate an accurate control signal y to drive the relay switching circuit to perform the mutual inductor switching. This scheme ensures accurate decision-making of the system under both overload gradual change or short-circuit mutation through multi-rule collaborative reasoning, solves the hysteresis and misjudgment problems of traditional single-threshold switching, and improves the robustness of full-condition measurement.

[0046] The fuzzy controller defuzzifies the output fuzzy quantity using the average value method of the maximum membership degree, including: extracting the median value of the interval corresponding to the maximum membership degree in the output fuzzy quantity as the final control signal y. It should be further noted that in the specific implementation process, when the fuzzy controller defuzzifies the output fuzzy quantity U generated by inference, the average value method of the maximum membership degree is adopted. The specific steps are as follows: First, traverse all y values of the output fuzzy quantity U within the universe of discourse [-1, 1], and extract the continuous interval with the maximum membership degree. If the membership degree of U reaches the peak and is concentrated within the interval y ∈ [0.6, 1.0], then this interval is determined as the effective decision-making range; subsequently, calculate the geometric median value of this interval as the final control signal y. For example, the median value of the interval y = 0.8 corresponds to the instruction to switch the hollow current transformer. If there are multiple intervals with peak membership degrees in U, such as y = -0.5 and y = 0.7 being both high membership degrees at the same time, then select the dominant interval according to the historical switching state or priority rules. For example, give priority to responding to the area where the current change trend is obvious. When di is PB, select the interval where y > 0. This method smooths the fuzzy output quantity, avoiding the switching jitter caused by directly taking the maximum membership degree point. For example, when U shows a wide peak distribution near y = 0.1, take the median value y = 0.1 instead of forcing it to zero, and maintain the current state of the transformer to reduce unnecessary switching actions. In addition, in critical scenarios, such as when y is close to 0, by setting a hysteresis threshold, such as keeping the original state when |y| < 0.05, to prevent frequent switching caused by noise or small fluctuations. For example, when the fuzzy inference output U shows a multi-peak distribution within the range from y = -0.3 to y = 0.2, but the interval with the maximum membership degree is y ∈ [-0.3, 0], then calculate the median value of this interval y = -0.15 to drive the system to switch to the solid transformer. This method combines the dynamic trend and the historical state, significantly improving the stability of the switching decision, especially suitable for working conditions with slow current change or periodic fluctuations, overcoming the oscillation defect of traditional defuzzification methods under boundary conditions, and ensuring the accuracy and reliability of the transformer switching within the full current range.

[0047] The solid current transformer adopts a core structure with a fixed number of turns in its secondary winding; the hollow current transformer adopts a non-magnetic core coil structure, and the secondary output signal is adjusted to the measurable range through a dynamic gain amplifier. It should be further noted that in the specific implementation process, the solid current transformer adopts a core structure with a fixed number of turns in its secondary winding, directly outputs an analog signal proportional to the primary current, and the core material is selected as high-permeability silicon steel sheets to optimize the linear response under small currents. The secondary output signal is connected to the pre-filter module of the signal acquisition circuit through a low-noise shielded cable to suppress high-frequency interference; the hollow current transformer adopts a non-magnetic core coil structure and is wound by multiple layers of printed circuit boards (PCBs). The secondary output signal is adjusted by a dynamic gain amplifier, which is built with a programmable gain control unit to adjust the gain coefficient in real time according to the current range. That is, when the current is small, the gain coefficient is increased to 100 times to amplify weak signals, and when the current is large, the gain coefficient is reduced to less than 10 times to prevent signal overflow. Among them, the small current is less than 1 kA, and the large current exceeds 5 kA. The output end of the dynamic gain amplifier is connected to a high-speed analog-to-digital conversion module, and its sampling rate is set to 1 MHz to ensure high-precision capture of transient current changes. The microprocessor module of the intelligent circuit breaker control unit drives a double-contact magnetic latching relay according to the instructions of the fuzzy controller. The input contacts of the relay are respectively connected to the secondary output ends of the solid and hollow transformers, and the output contacts are connected in series with the dynamic gain amplifier through a buffer circuit. During the switching process, the buffer circuit uses an energy storage capacitor to temporarily maintain the signal path voltage at the moment of relay operation, avoiding signal interruption caused by mechanical contact delay; at the same time, the microprocessor module immediately starts a self-check program after the switching is completed, and judges whether the switching is successful by comparing the signal amplitude differences before and after the switching. If an abnormality is detected, the redundant switching logic is triggered to re-select the transformer. Among them, the detected abnormality includes the abnormal signal amplitude after the solid transformer is switched. In addition, the dynamic gain coefficient of the hollow transformer is calibrated in real time through a closed-loop feedback mechanism: the microprocessor dynamically adjusts the gain according to the amplitude of the digital signal after analog-to-digital conversion. When the signal amplitude is lower than 10% of the full scale, the gain is increased every 10 ms until the signal enters the effective sampling range. Conversely, if the signal is close to the full scale, the gain is gradually reduced. The above design optimizes the cooperation of the hardware structure and the control algorithm to ensure that the solid transformer outputs stably in small current scenarios and the hollow transformer does not saturate in large current ranges, so as to cover the full-range linear measurement from the rated current to the short-circuit current, and completely solve the problem of insufficient measurement accuracy caused by the structural limitations of traditional transformers.

[0048] 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. It should be further explained that in the specific implementation process, after the microprocessor module of the intelligent circuit breaker control unit receives the control signal y output by the fuzzy controller, it parses its numerical range and generates a corresponding relay drive instruction. When y<0, the microprocessor sends a low-level signal to the double-contact magnetic latching relay through the digital output port, driving it to disconnect the secondary output path of the hollow current transformer and close the signal path of the solid current transformer; when y>0, a high-level signal is sent to disconnect the solid transformer path and close the hollow transformer path. The relay switching circuit adopts an optocoupler isolation design to ensure the electrical isolation of the strong and weak current parts and prevent 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, suspends sampling before the relay contact is actuated, and avoids the transient noise of the switching from being mistakenly collected; at the same time, the energy storage capacitor in the buffer circuit releases the stored charge at the moment of switching, temporarily maintains the voltage continuity of the signal path, and prevents signal drop or distortion caused by mechanical contact delay. After the switching is completed, the microprocessor immediately starts the self-test program: by comparing the difference in the output signal amplitude of the analog-to-digital conversion module before and after the switching, it is determined whether the switching is successful, including: if the signal amplitude of the solid transformer does not reach the preset threshold after switching, that is, less than 10% of the range, or the hollow transformer signal exceeds the upper limit of the range, that is, more than 90%, then the redundant switching logic is triggered, the relay is re-driven to switch to another transformer, and the signal amplitude is adjusted to a reasonable range through the dynamic gain amplifier. In addition, the microprocessor has a built-in historical state memory function to record the time and current conditions of the last three switching actions. When frequent switching of more than 5 times within 1 second is detected in a short period of time, the anti-oscillation mode is automatically enabled, the current transformer is temporarily locked and the sampling period is extended until the current state stabilizes. The above switching logic ensures that the switching action of solid and hollow transformers is fast, accurate and reliable through the coordinated optimization of hardware circuits and software algorithms, completely solving the measurement failure problem caused by mechanical delays or signal mutations in traditional solutions, and realizing full-range protection and linear measurement from rated current to short-circuit current.

[0049] 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 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 maxThey are respectively the preset minimum and maximum current thresholds, k is the normalization coefficient, dt is the differential of time, which is used to quantify the instantaneous trend of current change, d(log(I)) represents the change of the natural logarithm of current I over time, and d(log(I)) / dt represents the change rate of the logarithm of current, reflecting the relative change trend of current; Among them, through differential operation, dt quantifies the real-time change trend of current into a numerical value di, which helps the fuzzy controller to predict the current state. The real-time change trend of current includes accelerating growth or decelerating decay.

[0050] If di>0, it means the current is increasing; If di<0, it means the current is decreasing.

[0051] The differential of time is used to quantify the instantaneous trend of current change. Combining logarithmic transformation and normalization, the actual current signal is converted into a dynamic input di that can be processed by the fuzzy controller, and finally the intelligent switching of the current transformer is realized.

[0052] By dynamically capturing the current change trend, the response ability of the system to transient conditions such as overload and short circuit is enhanced, and the problem of lag in switching of traditional current transformers is solved.

[0053] It should be further noted 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: Step S1: The real-time current signal is output from the secondary side of the solid or hollow current transformer. After suppressing high-frequency noise by the pre-filtering module, it is converted into a digital signal by the high-speed analog-to-digital conversion module; the natural logarithm transformation is performed on the digitized current value; Step S2: The input quantity i is divided into three fuzzy subsets: small current SD, medium MD, and large LD, and the triangular membership function is used to cover the domain [0, 100]; the input quantity di is divided into five fuzzy subsets: negative large NB, negative small NS, exactly O, positive small PS, and positive large PB, and the trapezoidal membership function is used to cover the domain [-50, 50], and the real-time membership degrees of each input quantity are calculated; Step S3: Based on the preset 30 fuzzy rules, the output fuzzy quantity U is calculated through the maximum-minimum composition rule.

[0054] Step S4: The maximum membership degree average method is used to defuzzify U: extract the continuous interval with the largest membership degree, calculate its geometric median as the control signal y∈[-1, 1]. If there is a multi-peak distribution, the dominant interval is selected in combination with the historical state or priority rules, and frequent switching is avoided through the hysteresis threshold.

[0055] 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; at the moment of switching, a buffer circuit is enabled to maintain signal continuity, and optocoupler isolation is used to ensure the separation of strong / weak electricity and prevent electromagnetic interference.

[0056] Step S6: The signal output from the secondary side of the hollow mutual inductor is adjusted by a dynamic gain amplifier. The gain is increased when the current is small to amplify weak signals, and the gain is decreased when the current is large to prevent overflow; the microprocessor monitors the signal amplitude in real - time and dynamically calibrates the gain coefficient through closed - loop feedback.

[0057] Step S7: After the switching is completed, the microprocessor compares the signal amplitude differences before and after switching. If an abnormality is detected, it triggers the redundant switching logic to re - select the mutual inductor and adjusts the gain to a reasonable range.

[0058] Step S8: Record the time and current conditions of the last three switching operations. When short - time frequent switching is detected, the anti - oscillation mode is enabled to lock the current mutual inductor, and the sampling period is extended until the current is stable to avoid mechanical wear and signal distortion.

[0059] Step S9: Through the above - mentioned closed - loop control, the solid mutual inductor maintains high linearity at small currents, and the hollow mutual inductor avoids saturation at large currents, realizing accurate measurement in the full range from rated current to short - circuit current, and triggering the breaker action protection circuit.

[0060] By dynamically fusing the current magnitude and change rate through a fuzzy controller, and combining the complementary characteristics of solid and hollow current transformers, accurate measurement in the full range from rated current to short - circuit current is achieved. The solid mutual inductor maintains linearity using the iron - core structure at small currents, and the hollow mutual inductor avoids saturation through a non - magnetic - core design at large currents. The fuzzy rule base and membership function cooperate to optimize, accurately predict the current trend and adaptively switch the mutual inductor, overcoming the hysteresis and misjudgment problems of traditional fixed - threshold switching, reducing the measurement error, and improving the response speed to the millisecond level, significantly enhancing the protection reliability of the circuit breaker under transient conditions such as overload and short - circuit.

[0061] Through dynamic gain adjustment, redundant switching of the buffer circuit, and anti - oscillation logic design, the system still operates stably under complex scenarios such as noise interference and mechanical delay. The signal pre - processing mechanism compresses large - dynamic - range current signals, combined with closed - loop gain calibration and historical state memory functions, effectively suppressing the risk of signal distortion and frequent switching, and extending the equipment life. The hardware structure of this solution is simple, the fuzzy control algorithm can be embedded, compatible with existing circuit breaker systems, suitable for multiple scenarios such as smart grids and industrial power distribution, taking into account high reliability, low cost, and easy maintenance, and promoting the upgrade of power protection equipment to be intelligent and adaptive.

[0062] It should be noted that in this text, relational terms such as first and second are only used 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 "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0063] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control device for a complementary circuit breaker based on fuzzy control, characterized in that, Including: A switchable solid current transformer and a hollow current transformer, which are respectively connected to a fuzzy controller through a signal acquisition circuit; The fuzzy controller includes a double-input single-output structure, where the input ports receive the logarithmically normalized value of the current and the logarithmically normalized value of the current change rate, and the output port generates a control signal; An intelligent circuit breaker control unit, connected to the output port of the fuzzy controller, switches the solid current transformer or the hollow current transformer to be connected to the circuit according to the control signal; Among them, the fuzzy controller dynamically judges the current state through fuzzy inference logic. When the logarithmically normalized value of the current represents a small current or the logarithmically normalized value of the current change rate tends to decrease, it switches to the solid current transformer. When the logarithmically normalized value of the current represents a large current or the logarithmically normalized value of the current change rate tends to increase, it switches to the hollow current transformer to achieve linear measurement in the full current range.

2. The intelligent control device for complementary circuit breakers based on fuzzy control according to claim 1, characterized in that: The universe of discourse 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 universe of discourse.

3. The intelligent control device for complementary circuit breakers based on fuzzy control according to claim 2, wherein: The universe of discourse of the logarithmically normalized value of the current change rate is [-50, 50], which is divided into five fuzzy subsets: large negative current change, small negative current change, exactly zero current change, small positive current change, and large positive current change. Each subset uses a trapezoidal membership function to cover the universe of discourse.

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

5. The intelligent control device for complementary circuit breakers based on fuzzy control according to claim 1, wherein: The fuzzy controller internally has the following fuzzy rules: When the logarithmically normalized value of the current belongs to the small current, the output control signal belongs to the solid fuzzy subset; When the logarithmically normalized value of the current belongs to the large current, the output control signal belongs to the hollow 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 large negative current change fuzzy subset or the small negative current change 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 small positive current change fuzzy subset or the large positive current change fuzzy subset, the output control signal belongs to the hollow fuzzy subset.

6. The intelligent control device for complementary circuit breakers based on fuzzy control according to claim 5, characterized in that: The fuzzy rules are constructed by 30 fuzzy control rules, and each rule corresponds to a fuzzy implication relationship; the union of all fuzzy implication relationships constitutes the total implication relationship, and the output fuzzy quantity is deduced through the synthesis operation formula; Among them, the synthesis operation formula is: , where R: total implication relationship, Ri: fuzzy implication relationship, i: logarithmically normalized value of current; The synthesis operation formula for the output fuzzy quantity U is as follows: , where A: input fuzzy quantity, R: total implication relation, U: output fuzzy quantity; : synthesis operator, representing the inference operation and synthesis rule of the input fuzzy quantity A and the total implication relation R.

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

8. The intelligent control device for complementary circuit breakers based on fuzzy control according to claim 1, characterized in that: 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 non-magnetic core coil structure, and the secondary output signal is adjusted to the samplable range through a dynamic gain amplifier.

9. The intelligent control device for complementary circuit breakers based on fuzzy control according to claim 8, characterized in that: The intelligent circuit breaker control unit includes a microprocessor module, which drives a relay to switch the circuit according to the control signal, and the relay switching circuit is connected in series with the secondary output terminals of the solid current transformer and the hollow current transformer.

10. The intelligent control device for complementary circuit breakers based on fuzzy control according to claim 1, characterized in that: The input variables of the fuzzy controller: the logarithmically normalized value of the current and the logarithmically normalized value of the current change rate are generated in the following manner: Perform a logarithmic transformation on the original current signal and map it to a preset universe of discourse through linear normalization. The conversion formula is: ， ； where, i: logarithmically normalized value of 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: differential of time, d(log(I)): represents the change of the natural logarithm of current I with time, and d(log(I)) / dt represents the change rate of the logarithm of current.

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