Automatic control method for operating mechanism of molded case circuit breaker

Abstract

The invention discloses an automatic control method of a molded case circuit breaker operating mechanism, and particularly relates to the technical field of molded case circuit breakers. Electrical parameters are monitored in real time and transmitted to a control system, and comparative analysis is performed based on a preset threshold value and a fault model, so that faults are detected in time and a control instruction is generated, and the system can identify and sort multiple fault scenes, automatically execute switching-on or switching-off actions and ensure the reliability and stability of a power system; a judgment mechanism based on the circuit recovery condition is introduced, after the circuit is disconnected, whether recovery operation is carried out or not is reasonably judged according to the state of the circuit breaker and the recovery condition of the power system, the self-recovery capacity and the fault diagnosis capacity of the power system are effectively improved, the method can adapt to the influences of high loads, complex environments and power grid fluctuation, and the reliability of the power system is improved. And the reliability and the precision of an automatic control system are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir dam risk assessment, and specifically relates to an automatic control method for the operating mechanism of a molded case circuit breaker. Background Art

[0002] The automatic control of the operating mechanism of a molded case circuit breaker refers to the automatic operation of the operating mechanism of the molded case circuit breaker (such as functions like closing and opening the switch) through a control system. Specifically, it automatically monitors and controls the working state of the circuit breaker through devices such as sensors, actuators, and control circuits. In case of overload, short circuit, or other faults, the automatic control system can detect in real time and trigger the circuit breaker to act, quickly cutting off the circuit to ensure the safety of equipment and personnel. This system greatly improves the reliability and safety of power equipment, reduces the need for manual intervention, and improves the response speed.

[0003] The existing technologies have the following deficiencies: The existing automatic control methods for molded case circuit breakers have certain limitations, mainly reflected in aspects such as the accuracy of sensors, the response speed of the control system, and the robustness of algorithms. For example, in some high-load or complex environments, sensors may have data distortion or errors, resulting in the control system making incorrect judgments and triggering misoperations. In addition, the automatic control system has weak adaptability to power grid fluctuations, may fail to detect faults in a timely manner under extreme conditions or cause unnecessary tripping, affecting the normal operation of the power system. At the same time, the deficiencies of the existing system in terms of fault self-recovery ability and multi-fault diagnosis also limit its wide application in some key application scenarios. Summary of the Invention

[0004] The purpose of the present invention is to provide an automatic control method for the operating mechanism of a molded case circuit breaker to solve the deficiencies in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An automatic control method for the operating mechanism of a molded case circuit breaker, including: During the operation of the molded case circuit breaker, the electrical parameters in the circuit are monitored in real time, and the monitored electrical parameters are transmitted to the control system; The control system receives the electrical parameters from the circuit and conducts a comparative analysis based on preset electrical parameter thresholds and fault models to detect whether there is a fault situation; The control system judges the abnormal situation of the electrical parameters. If a fault is detected, a control instruction is generated, and the fault judgment process includes the identification and priority ranking of multiple fault scenarios; The control system automatically controls the operating mechanism of the molded case circuit breaker according to the result of the fault judgment to perform closing or opening actions; After the circuit is disconnected, the control system determines whether to resume the normal operation of the circuit based on the status of the circuit breaker and the recovery of the power system.

[0006] Preferably, the electrical parameters include current, voltage, temperature, frequency, and combinations thereof, and the sensor converts the analog signal into a digital signal through an analog-to-digital converter and transmits it to the control system for analysis.

[0007] Preferably, the control system judges the abnormal situation of the electrical parameters through a fuzzy logic algorithm, and the fuzzy rules include: if the current is high and the temperature is normal, then it is inferred that the temperature rises to high, and a circuit breaker opening operation needs to be performed.

[0008] Preferably, the fault judgment process is through the identification and priority ranking of multiple fault scenarios. The priority ranking is based on the severity and urgency of the faults, specifically including: the priority of short-circuit faults is higher than that of overload faults, and the priority of overvoltage faults is higher than that of undervoltage faults.

[0009] Preferably, the control system determines whether to resume the normal operation of the circuit based on the status of the circuit breaker and the recovery of the power system, including: analyzing the status monitoring data of the circuit breaker to generate a circuit breaker status abnormality score. The generation method is as follows: Collect monitoring data from the sensors of the circuit breaker and format it into a data set, where the data of each sample consists of n electrical parameter features: ; where each is a data point. Select the value of K, that is, find the k nearest neighbors of each data point. Calculate the distance between the data point to be detected and the non-data point to be detected through the Euclidean distance. The calculation formula is: ; where: is the feature vector of the data point , is the feature vector of the data point , m is the number of features, is the Euclidean distance between the data points ; For the data point of the circuit breaker status to be detected, calculate its Euclidean distance from all data points in the data set X, find the K nearest neighbors with the smallest distance, and obtain the circuit breaker status abnormality score by calculating the distances of the K neighbors and taking the average. The expression is: ; In the formula, ED is the circuit breaker status abnormality score.

[0010] Preferably, after analyzing the abnormal fluctuation of the electrical parameters, an electrical parameter abnormality score is generated. The generation method is as follows: Collect electrical parameter data and construct a time series data set, where each data point represents the value of an electrical parameter within a time period: ; where is the current data at each time point; Calculate the historical mean of the electrical parameters and the standard deviation , and the calculation expressions are: ; where is each electrical parameter value in the data set, m is the total number of data points, and the standard deviation represents the fluctuation range of the electrical parameter data. The calculation formula is: ; where the abnormal score Z-score of each data point is calculated, and the expression is: ; where is the abnormal score of the electrical parameter of the i-th data point, is the electrical parameter value at the i-th time point, and the overall abnormal score of the electrical parameter is obtained by summing and averaging the abnormal scores of the electrical parameters of all data points.

[0011] Preferably, convert the breaker status abnormal score and the electrical parameter abnormal score into a comprehensive feature vector, use the comprehensive feature vector as the input of the machine learning model, use the machine learning model to predict the circuit abnormal evaluation coefficient label for each group of comprehensive feature vectors as the prediction target, and use minimizing the sum of the prediction errors of all circuit abnormal evaluation coefficient labels as the training target to train the machine learning model until the sum of the prediction errors reaches convergence and then stop the model training. Determine the circuit abnormal evaluation coefficient according to the model output result, where the machine learning model is a polynomial regression model.

[0012] Preferably, compare the obtained circuit abnormal evaluation coefficient with a predetermined threshold to determine whether to restore the normal operation of the circuit. If the circuit abnormal evaluation coefficient is greater than or equal to the predetermined threshold, do not restore; if the circuit abnormal evaluation coefficient is less than the predetermined threshold, restore.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. By real-time monitoring of electrical parameters, combining advanced fault judgment algorithms and multiple fault scenario identifications, the present invention significantly improves the automatic control and fault response capabilities of the power system. This method can accurately judge abnormal conditions in the circuit and generate control instructions to automatically perform closing or opening operations, thereby improving the safety and stability of the system. Especially in high-load and complex environments, the control system can effectively avoid data distortion or misoperation and respond to power grid fluctuations in real time to ensure the normal operation of the power system.

[0014] 2. By comprehensively analyzing the abnormal score of the circuit breaker status and the abnormal score of electrical parameters, the present invention combines a machine learning model to evaluate circuit anomalies, and based on this, determines whether to perform a circuit restoration operation. Training using a polynomial regression model can not only accurately predict the circuit anomaly evaluation coefficient, but also automatically adjust the threshold to adapt to the working conditions of different power systems. This method greatly improves the fault self-restoration ability and multi-fault diagnosis level, enhances the adaptability and reliability of the power system in complex application scenarios, reduces manual intervention, and ensures the efficient and safe operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0016] Figure 1 It is a method mind map of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0018] For the embodiments, please refer to Figure 1 As shown, the automatic control method for the operating mechanism of the molded case circuit breaker in this embodiment includes; During the operation of the molded case circuit breaker, the electrical parameters in the circuit are monitored in real time, and the monitored electrical parameters are transmitted to the control system; The control system receives the electrical parameters from the circuit and conducts a comparative analysis based on the preset electrical parameter thresholds and fault models to detect whether there is a fault condition; The control system judges the abnormal conditions of the electrical parameters. If a fault is detected, a control instruction is generated. The fault judgment process includes the identification and priority ranking of multiple fault scenarios; The control system automatically controls the operating mechanism of the molded case circuit breaker according to the result of the fault judgment to perform closing or opening actions; When the circuit is disconnected, the control system judges whether to restore the normal operation of the circuit according to the status of the circuit breaker and the restoration situation of the power system.

[0019] The main objective of monitoring electrical parameters is to obtain key signals in the circuit to reflect the operating status of the electrical system in real time. Common electrical parameters during the operation of a molded case circuit breaker include: Current: Monitoring current changes can reflect the circuit load condition and whether abnormal situations such as overload or short circuit occur. Current monitoring is usually accomplished through a **Current Transformer (CT)**. The CT can convert high current into a proportional low current signal for easy measurement and monitoring.

[0020] Voltage: Voltage monitoring is used to determine whether there is overvoltage or undervoltage in the circuit. The voltage sensor converts the voltage signal of the circuit into a digital signal that can be processed by the control system. In the protection function of the circuit breaker, overvoltage and undervoltage are common fault triggering conditions.

[0021] Temperature: Monitoring the temperature of the circuit breaker and the circuit, especially in the case of heavy loads, temperature changes may indicate an overload risk for the equipment. Temperature sensors such as thermocouples or RTDs (Platinum Resistance Temperature Detectors) are used for real-time monitoring.

[0022] Frequency: Frequency changes may mean fluctuations in the grid frequency or power quality problems. Frequency monitoring can be achieved through a frequency sensor.

[0023] Other parameters: Include harmonics, power factor, phase angle, etc. These parameters help in comprehensively analyzing power quality, although they are not essential in some cases.

[0024] To obtain these electrical parameters in real time, a molded case circuit breaker is usually equipped with multiple sensors. Different types of sensors are installed at different positions to ensure accurate and comprehensive acquisition of circuit information: Current Transformer (CT): The CT is installed around the power cable or conductor, mainly used to monitor the current flowing through the circuit. When the current exceeds the set threshold, the CT can generate a corresponding signal to alert the control system of an abnormality.

[0025] Voltage sensor: The voltage sensor is installed at key positions such as the input end of the circuit and both ends of the circuit breaker to monitor voltage fluctuations in real time.

[0026] Temperature sensor: The temperature sensor is installed inside the molded case circuit breaker or on its key components to monitor temperature changes. High temperature may be caused by overload or other electrical faults, and the feedback from the temperature sensor can help determine whether the circuit needs to be disconnected.

[0027] Frequency sensor: The frequency sensor is mainly used to monitor the power supply frequency, which is particularly important in an unstable grid environment.

[0028] These sensors usually convert analog signals into digital signals through an analog-to-digital converter (ADC), and then transmit them to the control system for processing and analysis.

[0029] The electrical parameters collected by the sensors need to be transmitted to the control system for processing in real time. To ensure the accuracy and real-time nature of the data, the following technical means are usually adopted for transmission: Wired transmission: In some traditional power systems, electrical parameters are transmitted to the control system through wired communication (such as RS485, Modbus, etc. protocols). The transmission distance is relatively short and is usually applicable to local systems.

[0030] Wireless transmission: In some scenarios of remote monitoring and control, especially in power systems with wide distribution, wireless communication (such as Wi-Fi, Zigbee, LoRa, etc.) is used for data transmission. This method is suitable for wide coverage, especially in environments without fixed wiring conditions.

[0031] Industrial protocols: Commonly used protocols such as Modbus, CAN bus, Ethernet, etc. Through these protocols, reliable communication between the sensors and the control system is achieved, ensuring the real-time transmission and processing of data.

[0032] In the control system, a set of thresholds is preset for comparison and analysis with the electrical parameters monitored in real time. Each electrical parameter (such as current, voltage, temperature) has its own operating range, which is determined by the rated parameters of the equipment and the normal operating conditions. The control system compares the electrical parameters collected in real time with these preset thresholds: Current threshold: For example, the maximum value of the normal current is 1.2 times the rated current. When the current exceeds this threshold, it indicates the possibility of overload or short circuit, and the control system will consider it a fault signal.

[0033] Voltage threshold: For example, the voltage should fluctuate within ±10% of the rated voltage. If the voltage exceeds this range, it may indicate overvoltage or undervoltage faults in the power grid, and the system will mark it as abnormal.

[0034] Temperature threshold: For example, when the temperature exceeds the set safety upper limit (such as 80°C), it may indicate that the electrical equipment is overheating due to excessive load or poor heat dissipation, and the system should detect this abnormality.

[0035] In addition to simple threshold comparison, the control system will also use a fault model to further accurately determine whether the electrical parameters are in a fault state. The fault model is based on various fault scenarios that may occur during the operation of the power system, such as short circuit, overload, ground fault, etc., and determines the fault type through data analysis and pattern recognition. Specifically, it includes: Overload Fault Model: By comparing the real-time current data with the rated current of the device, if the current exceeds the set overload threshold and persists for a period of time, the overload protection logic is triggered and a fault is judged.

[0036] Short Circuit Fault Model: In current monitoring, if there is an instantaneous surge in current (e.g., more than 5 times the rated current) accompanied by a voltage drop, the control system may identify it as a short circuit fault. Short circuit faults often occur rapidly, and the system needs to respond quickly to cut off the power supply.

[0037] Under-Voltage and Over-Voltage Fault Models: By comparing the real-time voltage value with the set normal operating voltage range, if the voltage exceeds the normal range by a certain proportion and the duration exceeds the preset threshold, it is determined as a voltage anomaly fault. Under-voltage and over-voltage can damage electrical equipment and require timely response.

[0038] The control system compares the real-time electrical parameters with the parameters in the fault model. If it conforms to the characteristics of a certain fault model, it further confirms the fault type and prepares to execute the corresponding protection operation.

[0039] In a power system, the relationships between electrical parameters are usually not absolutely clear but rather have a certain degree of ambiguity. For example, the relationship between current and temperature may not always be linear. An increase in current may cause the temperature to rise, but not every increase in current will result in a significant temperature change. At this time, traditional digital logic may be difficult to accurately describe these fuzzy relationships, while **Fuzzy Logic** can handle such uncertainties through fuzzy rules and inference mechanisms, thus helping the control system make more reasonable judgments.

[0040] Fuzzy logic is a mathematical tool for dealing with uncertainty and ambiguity. Based on the "fuzzy set" theory, it can transform traditional precise mathematical models into models for handling unclear boundaries and uncertain information. In fuzzy logic, input data (such as current, voltage, temperature, etc.) are no longer precise numbers but are expressed in the form of fuzzy sets, and each input corresponds to a fuzzy value, which can more flexibly describe the relationships between parameters.

[0041] The relationship between current and temperature is usually not a simple linear one. An increase in current may cause the temperature to rise, but this depends on factors such as load, ambient temperature, and the thermal efficiency of the device. Therefore, a fuzzy logic system can build fuzzy rules based on these factors for reasoning and decision-making.

[0042] First, it is necessary to define fuzzy sets and linguistic variables. For example, current and temperature can be divided into several fuzzy categories: Current: Low, Normal, High Temperature: Low, Normal, High These categories are not precise numbers but are described through fuzzy sets. For example, "High" current does not mean the current exceeds a certain exact value but indicates that the current is within a certain range, close to the high value of the current.

[0043] Based on the empirical relationship between current and temperature, some fuzzy rules can be constructed to describe their relationship. For example: Rule 1: If the current is "Low", then the temperature is "Low".

[0044] Rule 2: If the current is "Normal", then the temperature is "Normal".

[0045] Rule 3: If the current is "High", then the temperature is "High".

[0046] Rule 4: If the current is "High" and the temperature is "Normal", then the temperature is "High".

[0047] Rule 5: If the current is "Low" and the temperature is "High", then it may be a device failure.

[0048] These rules describe the fuzzy relationship between current and temperature, helping the control system to make inferences during actual operation.

[0049] When the control system receives real-time data of current and temperature, it first converts these input data into fuzzy values. For example: If the current is 10A (low current), the current input can be classified into a certain range of the "Low" fuzzy set.

[0050] If the temperature is 70°C (normal temperature), the temperature input can be classified into a certain range of the "Normal" fuzzy set.

[0051] Next, the control system uses a fuzzy inference engine to make inferences on these fuzzy inputs based on the above rules. For example, if the current is "High" and the temperature is "Normal" currently, according to Rule 3 and Rule 4, the system will infer that the temperature may rise to "High", which requires attention.

[0052] The output obtained from fuzzy inference is a fuzzy value, while the system usually requires an exact output value to drive control instructions (such as the opening operation of a circuit breaker). To obtain specific control instructions, the fuzzy output value must be converted into a clear numerical value, and this process is called defuzzification.

[0053] Common defuzzification methods include the Centroid Method, which obtains an exact numerical value by calculating the centroid of the fuzzy output set. For example, if the fuzzy inference output is a fuzzy value between "high temperature" and "normal temperature", after defuzzification, a temperature value such as 85°C may be obtained.

[0054] Fuzzy logic can effectively handle the fuzzy relationships between input parameters. Especially when the relationship between current and temperature is not completely clear, fuzzy logic can make reasonable inferences based on rules rather than relying on an exact mathematical model. The fuzzy logic system can adjust the inference rules according to different operating conditions and equipment states, with strong flexibility. For example, in some special scenarios, the changes in current and temperature may not completely follow a linear pattern, and fuzzy logic can provide more accurate and adaptable decisions.

[0055] For example, in some high-load situations, an increase in current may cause the temperature to rise, but this is not always a direct linear relationship. In this case, fuzzy logic can help the control system determine whether the current is abnormal and, based on the fuzzy inputs of the current and temperature, infer whether there is a risk of overload or overheating. For example: Fuzzy input: The current is 18A (high current) and the temperature is 75°C (normal).

[0056] Fuzzy inference: Rules 4 and 3 are triggered, inferring that the temperature may rise further and protecting the circuit.

[0057] Output: The system generates a fuzzy output of "high temperature", obtains a temperature of 80°C after defuzzification, and decides whether to perform a protection operation (such as a circuit breaker operation).

[0058] Through such fuzzy inference, the control system can better identify electrical faults in the actual environment and ensure the safe operation of the equipment.

[0059] The control system first uses sensors to continuously monitor electrical parameters such as current, voltage, and temperature in the circuit and compares and analyzes them with preset thresholds and fault models. When the system detects that the electrical parameters exceed the safe range (such as overcurrent, overvoltage, overheating, etc.), the fault judgment algorithm (such as the fuzzy logic algorithm) will identify the specific fault type and generate control instructions based on the severity of the fault.

[0060] Types of fault judgment results: Overload fault: The current exceeds the set threshold but does not reach the short-circuit level, usually requiring a certain time delay.

[0061] Short-circuit fault: The current rises significantly within a very short time, which is a high-priority fault and requires immediate circuit interruption.

[0062] Overvoltage or undervoltage: The voltage exceeds the allowable range of the rated voltage, affecting the normal operation of the equipment.

[0063] Overhigh temperature: The temperature sensor monitors that the equipment temperature exceeds the safe range, which may cause equipment damage or fire.

[0064] According to the type of the fault, the system will judge whether to perform closing (restoring the circuit) or opening (cutting off the circuit) operations.

[0065] The control system generates corresponding operation instructions through comprehensive analysis of the fault. These instructions directly affect the operating mechanism of the molded case circuit breaker, instructing it to perform closing or opening actions.

[0066] Instruction type: Closing instruction: When the system fault is eliminated or the power grid returns to normal, a closing instruction is generated to restore the normal operation of the circuit.

[0067] Opening instruction: When the system detects a fault (such as overload, short circuit, overvoltage, etc.), an opening instruction is generated to immediately cut off the circuit, prevent the spread of the fault and protect electrical equipment.

[0068] The control system realizes the closing or opening of the circuit breaker through interface communication with the operating mechanism. The operating mechanism of the molded case circuit breaker usually consists of an electronic release device, an actuator (such as an electric actuator) and mechanical connection components, and the control system drives these components through electrical signals.

[0069] Electronic release device: It is mainly responsible for detecting abnormal signals such as current and voltage, and operating the circuit breaker according to the instructions of the control system. The electronic release device usually controls the opening and closing of the circuit breaker through relays or microprocessors.

[0070] Electric actuator: It is a key component for performing opening or closing operations. The control system realizes the actual action of the circuit breaker through the electric actuator. After receiving the control signal, the electric actuator will drive the mechanical structure of the circuit breaker to perform opening or closing.

[0071] When the control system generates a closing or opening instruction, the electric actuator performs specific operations through the driving mechanism.

[0072] Opening action: When detecting faults such as overload, short circuit, overvoltage, etc., the control system generates an opening instruction. After receiving this instruction, the electric actuator will immediately drive the breaker switch contacts to disconnect, cutting off the circuit to prevent the spread of the fault.

[0073] The opening action is usually fast. Especially in the case of a short circuit fault, the system will quickly perform the opening operation to prevent excessive current from damaging the equipment.

[0074] Closing operation: When the fault is eliminated or the power grid returns to normal, the control system generates a closing command, instructing the electric actuator to restore the closed state of the circuit breaker and reconnect the circuit.

[0075] The closing operation generally needs to ensure that the circuit state is normal. For example, when the current, voltage, and temperature are all within the safe range, the circuit can be safely closed.

[0076] In some cases, the system needs to consider delayed operation and retry mechanisms. For example: Overload protection delay: When the system detects an overload fault, it may not immediately trip but wait for a certain period (such as a few seconds) to determine whether the overload persists. If the overload persists, the system will trigger a trip; if the overload is an instantaneous fluctuation, the system may allow the circuit to resume.

[0077] Closing delay: If the system has tripped due to overvoltage or overload, the control system may delay for a certain time before closing, waiting for the electrical parameters to stabilize to ensure the safety of the circuit during closing.

[0078] Retry mechanism: During closing, if an instantaneous fault occurs, the system may automatically retry the closing operation until the electrical parameters stabilize or multiple attempts fail.

[0079] After performing the closing or tripping operation, the control system will continue to monitor the electrical parameters to ensure the normal state of the equipment. If the fault has been eliminated and the circuit returns to normal, the system will continuously track key parameters such as current and voltage to confirm the success of the closing operation. If the parameters are found to be abnormal again after closing, the control system may perform a tripping operation again.

[0080] At the same time, the system will also record the type of fault, the time of tripping and closing, and any abnormal situations during the execution process. These data can be used for subsequent analysis and maintenance.

[0081] In modern power systems, the control system not only performs closing and tripping operations locally but also supports monitoring and operation through a remote control platform. Through remote communication protocols (such as Modbus, IEC 61850, etc.), operators can view the status of the circuit breaker in real time, remotely execute closing or tripping commands, and perform fault diagnosis and system adjustment.

[0082] In the present invention, through precise fault judgment and analysis, the control system can automatically generate closing or tripping commands according to the changes in electrical parameters and implement specific operations through the electric actuator. The tripping operation is a rapid response to faults, while the closing operation ensures the normal operation of the circuit. This process not only improves the automation level of the system but also effectively protects the equipment, reduces human errors, and ensures the stability and safety of the power system.

[0083] After the circuit is disconnected, the control system determines whether to resume the normal operation of the circuit based on the status of the circuit breaker and the recovery of the power system, specifically including: In a power system, when a circuit breaker trips to cut off the circuit due to a fault (such as overload, short circuit, overvoltage, etc.), the task of the control system does not end. The system needs to continue monitoring the status of the circuit to ensure that the fault has been eliminated and the electrical parameters have returned to normal before performing the closing operation to resume the normal operation of the circuit. This process involves monitoring the status of the circuit breaker, real-time monitoring of electrical parameters, and fault recovery judgment.

[0084] Circuit breaker status monitoring: The control system will monitor the status of the circuit breaker in real time to determine whether the circuit has been cut off and the mechanical status of the circuit breaker. Modern circuit breakers usually have a status feedback function, including information such as switch status (open / closed), fault records, etc. When the circuit is disconnected due to a fault, the tripped status of the circuit breaker will be transmitted to the control system, and the system will confirm the "disconnected" status of the circuit breaker. At this time, the control system will start tracking the electrical parameters to determine whether the fault has been eliminated and whether the circuit can be restored. The circuit breaker usually records fault events and transmits the fault code or log to the control system for system analysis and fault diagnosis. The control system can use this information to determine the type of fault and whether further protection is required.

[0085] After analyzing the status monitoring data of the circuit breaker, a circuit breaker status anomaly score is generated. The generation method is as follows: Collect monitoring data from the sensors of the circuit breaker (such as current, voltage, temperature, etc.) and format it into a data set. Each data point will include different electrical parameters as features (such as current value, voltage value, temperature value, etc.), and a label (if it is supervised learning). If it is unsupervised learning, the label can be omitted.

[0086] Assume there is a data set, and the data of each sample consists of n electrical parameter features: ; where each is a data point, containing multiple features such as current, voltage, etc.

[0087] Select an appropriate value of K, that is, find the k nearest neighbors for each data point. The selection of K can be determined based on experience or through cross-validation. Generally speaking, a smaller value of K may be sensitive to noise, while a larger value of K may lead to an increase in computational complexity. Usually, the selection of K needs to be adjusted according to the data scale, features, and requirements of anomaly detection.

[0088] Find the most similar neighbors by calculating the distance between the data point to be detected and the non-data point to be detected through the Euclidean distance. The calculation formula is: ; where: is a data point 's eigenvector, is a data point 's eigenvector, m is the number of features, is a data point the Euclidean distance between.

[0089] For the circuit breaker status data point to be detected , calculate its Euclidean distance from all data points in the dataset X, find the K nearest neighbors with the smallest distance, and obtain the circuit breaker status anomaly score by calculating the average of the distances of the K neighbors. The expression is: ; where ED is the circuit breaker status anomaly score. When the circuit breaker status anomaly score is large, it means that this data point deviates far from the normal point, and the circuit breaker may have a fault.

[0090] Real-time monitoring of electrical parameters: After the circuit is disconnected, the control system will continue to monitor electrical parameters (such as current, voltage, temperature, etc.) to evaluate the recovery of the power system. The system obtains electrical parameters through sensors and compares them with reference values under normal operating conditions. The control system will monitor the magnitude and change of the current to determine whether the circuit has returned to the normal load state. For example, during the recovery period after a short-circuit fault, the current should stabilize near the rated value; if the current is still too high, it indicates that there may be an unresolved fault, and the control system should avoid closing the switch. Voltage fluctuations also need to be monitored in a timely manner. The control system will monitor whether the voltage has returned to the rated range. If the voltage is still abnormal (such as overvoltage or undervoltage), the closing operation will be postponed. In the case of equipment overheating, the information fed back by the temperature sensor will help the control system determine whether the equipment has returned to the safe operating range. The normal temperature recovery is one of the important bases for determining the closing of the switch.

[0091] After analyzing the abnormal fluctuation of electrical parameters, an electrical parameter anomaly score is generated. The generation method is: Collect electrical parameter data and construct a time series dataset, where each data point represents the electrical parameter value within a time period. The electrical parameter data at each time point can be current, voltage, temperature, etc. For example, the time series data of current is: ; where is the current data at each time point.

[0092] Calculate the historical mean and standard deviation , and the calculation expressions are: ; where is each electrical parameter value in the dataset, and m is the total number of data points. The standard deviation represents the fluctuation range of the electrical parameter data, and the calculation formula is: ; Among them, the anomaly score Z-score of each data point is calculated, and the expression is: ; Among them, is the electrical parameter anomaly score of the i-th data point, is the electrical parameter value (such as current, voltage) at the i-th time point. After summing and averaging the electrical parameter anomaly scores of all data points, the overall electrical parameter anomaly score is obtained.

[0093] Convert the breaker status anomaly score and the electrical parameter anomaly score into a comprehensive feature vector, use the comprehensive feature vector as the input of the machine learning model, and use the machine learning model to predict the circuit anomaly evaluation coefficient label for each set of comprehensive feature vectors as the prediction target. Minimize the sum of the prediction errors for all circuit anomaly evaluation coefficient labels as the training target, and train the machine learning model until the sum of the prediction errors converges and then stop the model training. Determine the circuit anomaly evaluation coefficient according to the model output result, where the machine learning model is a polynomial regression model.

[0094] Compare the obtained circuit anomaly evaluation coefficient with a predetermined threshold to determine whether to restore the normal operation of the circuit. If the circuit anomaly evaluation coefficient is greater than or equal to the predetermined threshold, do not restore; if the circuit anomaly evaluation coefficient is less than the predetermined threshold, restore.

[0095] The above formulas are all dimensionless and take their numerical calculations. The formula is obtained by collecting a large amount of data for software simulation to get a formula that is closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0096] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0097] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context. Those of ordinary skill in the art will realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this document can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0098] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application.

Claims

1. An automated control method for the operating mechanism of a molded case circuit breaker, characterized in that: Including: During the operation of the molded case circuit breaker, electrically parameters in the circuit are monitored in real time, and the monitored electrical parameters are transmitted to the control system; The control system receives the electrical parameters from the circuit and performs a comparative analysis based on a preset electrical parameter threshold and a fault model to detect whether there is a fault condition; The control system judges the abnormal conditions of the electrical parameters. If a fault is detected, a control instruction is generated. The fault judgment process includes the identification and prioritization of multiple fault scenarios; The control system automatically controls the operating mechanism of the molded case circuit breaker according to the result of the fault judgment to perform closing or tripping operations; After the circuit is disconnected, the control system judges whether to resume the normal operation of the circuit according to the state of the circuit breaker and the recovery situation of the power system.

2. The automatic control method of the operating mechanism of the plastic case circuit breaker according to claim 1, characterized in that: The electrical parameters include current, voltage, temperature, frequency and their combinations, and the sensor converts the analog signal into a digital signal through an analog-to-digital converter and transmits it to the control system for analysis.

3. The automatic control method for the operating mechanism of the molded case circuit breaker according to claim 2, wherein: The control system judges the abnormal conditions of the electrical parameters through a fuzzy logic algorithm, where the fuzzy rules include: if the current is high and the temperature is normal, then it is inferred that the temperature rises to high and a tripping operation needs to be performed.

4. The automated control method of the operating mechanism of the molded case circuit breaker according to claim 3, characterized in that: The fault judgment process is through the identification and prioritization of multiple fault scenarios. The prioritization is based on the severity and urgency of the faults, specifically including: the priority of the short-circuit fault is higher than that of the overload fault, and the priority of the overvoltage fault is higher than that of the undervoltage fault.

5. The automated control method for the operating mechanism of the molded case circuit breaker according to claim 1, characterized in that: The control system judges whether to resume the normal operation of the circuit according to the state of the circuit breaker and the recovery situation of the power system, including: generating an abnormal score of the circuit breaker state after analyzing the state monitoring data of the circuit breaker. The generation method is: Collect monitoring data from the sensors of the circuit breaker and format it into a data set, where the data of each sample consists of n electrical parameter features: ; where each is a data point. Select the value of K, that is, find the k nearest neighbors of each data point, and calculate the distance between the data point to be detected and the non-data point to be detected through the Euclidean distance. The calculation formula is: ; where: is the feature vector of the data point , is the feature vector of the data point , m is the number of features, is the Euclidean distance between the data points ; For the circuit breaker status data points to be detected , calculate the Euclidean distance between it and all data points in the dataset X, find the K nearest neighbors with the smallest distance, and obtain the circuit breaker status anomaly score by calculating the distances of the K neighbors and taking the average. The expression is as follows: ; where ED is the circuit breaker status anomaly score.

6. The automated control method for the operating mechanism of the plastic case circuit breaker according to claim 5, characterized in that: Generating an abnormal score of the electrical parameters after analyzing the abnormal fluctuation of the electrical parameters. The generation method is: Collect electrical parameter data and construct a time series data set, where each data point represents the value of an electrical parameter within a time period: ; among them, is the current data at each time point; Calculate the historical mean of electrical parameters and standard deviation , and the calculation expression is: ; where is each electrical parameter value in the dataset, m is the total number of data points, and the standard deviation represents the fluctuation range of the electrical parameter data. The calculation formula is: ; where the anomaly score Z-score of each data point is calculated, and the expression is: ; where is the electrical parameter anomaly score of the i-th data point, is the electrical parameter value at the i-th time point. After summing and averaging the electrical parameter anomaly scores of all data points, the overall electrical parameter anomaly score is obtained.

7. The automated control method for the operating mechanism of the molded case circuit breaker according to claim 6, characterized in that: Converting the abnormal score of the circuit breaker state and the abnormal score of the electrical parameters into a comprehensive feature vector, using the comprehensive feature vector as the input of a machine learning model. The machine learning model takes predicting the circuit abnormal evaluation coefficient label for each group of comprehensive feature vectors as the prediction target, and minimizing the sum of the prediction errors for all circuit abnormal evaluation coefficient labels as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence and then the model training is stopped. The circuit abnormal evaluation coefficient is determined according to the model output result. Among them, the machine learning model is a polynomial regression model.

8. The automatic control method for the operating mechanism of the plastic case circuit breaker according to claim 7, characterized in that: Comparing the obtained circuit abnormal evaluation coefficient with a predetermined threshold to judge whether to resume the normal operation of the circuit. If the circuit abnormal evaluation coefficient is greater than or equal to the predetermined threshold, no recovery is performed; if the circuit abnormal evaluation coefficient is less than the predetermined threshold, recovery is performed.

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