Method for the automatic control of the operating mechanism of a molded case circuit breaker

Abstract

This invention discloses an automated control method for the operating mechanism of a molded case circuit breaker (MCCB), specifically relating to the field of MCB technology. By monitoring electrical parameters in real time and transmitting them to the control system, and performing comparative analysis based on preset thresholds and fault models, the system can promptly detect faults and generate control commands. The system can identify and prioritize multiple fault scenarios, automatically executing closing or opening actions to ensure the reliability and stability of the power system. Furthermore, this invention introduces a judgment mechanism based on circuit recovery status. After the circuit is disconnected, the system rationally determines whether to perform a recovery operation based on the status of the circuit breaker and the recovery status of the power system, effectively improving the self-recovery capability and fault diagnosis capability of the power system. This method can adapt to the effects of high loads, complex environments, and power grid fluctuations, significantly improving the reliability and accuracy of the automated control system.

Description

Technical Field

[0001] This invention relates to the field of reservoir dam risk assessment technology, specifically to an automated control method for the operating mechanism of a molded case circuit breaker. Background Technology

[0002] The automated control of the operating mechanism of a molded case circuit breaker (MCCB) refers to the automated operation of the MCCB's operating mechanism (such as closing and opening functions) through a control system. Specifically, it uses sensors, actuators, and control circuits to automatically monitor and control the circuit breaker's operating status. In the event of overload, short circuit, or other faults, the automated control system can detect and trigger the circuit breaker in real time, quickly disconnecting the circuit and ensuring the safety of equipment and personnel. This system significantly improves the reliability and safety of power equipment, reduces the need for manual intervention, and increases response speed.

[0003] The existing technology has the following shortcomings:

[0004] Existing automated control methods for molded case circuit breakers (MCCBs) have certain limitations, primarily in the accuracy of sensors, the response speed of the control system, and the robustness of the algorithms. For example, under certain high-load or complex environments, sensors may exhibit data distortion or errors, leading to incorrect judgments and malfunctions by the control system. Furthermore, the automated control system has weak adaptability to power grid fluctuations, potentially failing to detect faults promptly under extreme conditions or causing unnecessary tripping, thus affecting the normal operation of the power system. Simultaneously, the shortcomings of existing systems in fault self-recovery capabilities and multi-fault diagnosis also limit their widespread application in some critical scenarios. Summary of the Invention

[0005] The purpose of this invention is to provide an automated control method for the operating mechanism of a molded case circuit breaker to address the shortcomings in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an automated control method for the operating mechanism of a molded case circuit breaker, comprising:

[0007] 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.

[0008] The control system receives electrical parameters from the circuit and performs comparative analysis based on preset electrical parameter thresholds and fault models to detect whether a fault exists.

[0009] The control system judges abnormal electrical parameters. If a fault is detected, a control command is generated. The fault judgment process includes the identification and priority ranking of multiple fault scenarios.

[0010] Based on the fault diagnosis results, the control system automatically controls the operating mechanism of the molded case circuit breaker to perform closing or opening actions.

[0011] When the circuit is disconnected, the control system determines whether to restore the circuit to normal operation based on the status of the circuit breaker and the recovery status of the power system.

[0012] 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.

[0013] Preferably, the control system uses a fuzzy logic algorithm to determine abnormal electrical parameters, wherein the fuzzy rules include: if the current is high and the temperature is normal, then it is inferred that the temperature has risen to high and a tripping operation needs to be performed.

[0014] Preferably, the fault judgment process involves the identification and priority ranking of multiple fault scenarios. The priority ranking is based on the severity and urgency of the fault, specifically including: short-circuit faults have a higher priority than overload faults, and overvoltage faults have a higher priority than undervoltage faults.

[0015] Preferably, the control system determines whether to restore normal circuit operation based on the circuit breaker's status and the power system's recovery status, including: analyzing the circuit breaker's status monitoring data to generate a circuit breaker status anomaly score, the generation method being:

[0016] Monitoring data is collected from the circuit breaker's sensors and formatted into a dataset, where each sample consists of n electrical parameter features: ; each of them For a given data point, we choose a value K, which means finding the k nearest neighbors of each data point. We then calculate the distance between the data point to be detected and the non-detected data points using Euclidean distance. The calculation formula is as follows: ;in: Data points eigenvectors, Data points The feature vector, where m is the number of features. Data points The Euclidean distance between them;

[0017] For the circuit breaker status data points to be tested Calculate the Euclidean distance between the circuit breaker and all data points in dataset X, find the K nearest neighbors with the smallest distance, and obtain the circuit breaker status anomaly score by calculating the average distance of the K nearest neighbors. The expression is as follows: In the formula, ED is the circuit breaker status anomaly score.

[0018] Preferably, after analyzing the abnormal fluctuations of electrical parameters, an electrical parameter anomaly score is generated. The generation method is as follows:

[0019] Collect electrical parameter data and construct a time series dataset, where each data point represents the electrical parameter value within a time period: ;in, These are current data at various points in time;

[0020] Calculate the historical average of electrical parameters and standard deviation The calculation expression is: ;in, This represents the value of each electrical parameter in the dataset, where 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: The outlier score Z-score for each data point is calculated using the following expression: ;in, It is the electrical parameter anomaly score of the i-th data point. The value of the electrical parameter at time point i is used to calculate the overall electrical parameter anomaly score by summing and averaging the electrical parameter anomaly scores of all data points.

[0021] Preferably, the circuit breaker status anomaly score and electrical parameter anomaly score are converted into a comprehensive feature vector. The comprehensive feature vector is used as the input of the machine learning model. The machine learning model uses the prediction of circuit anomaly evaluation coefficient labels for each set of comprehensive feature vectors as the prediction objective and minimizes the sum of prediction errors for all circuit anomaly evaluation coefficient labels as the training objective. The machine learning model is trained until the sum of prediction errors converges and the model training stops. The circuit anomaly evaluation coefficient is determined based on the model output. The machine learning model is a multinomial regression model.

[0022] Preferably, the obtained circuit anomaly evaluation coefficient is compared 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, no restoration is performed; if the circuit anomaly evaluation coefficient is less than the predetermined threshold, restoration is performed.

[0023] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0024] 1. This invention significantly improves the automation control and fault response capabilities of power systems by real-time monitoring of electrical parameters, combined with advanced fault diagnosis algorithms and multiple fault scenario identification. This method can accurately identify abnormal conditions in the circuit and generate control commands, automatically executing closing or opening operations, thereby improving system safety and stability. Especially under high load and complex environments, the control system can effectively avoid data distortion or misoperation, respond to grid fluctuations in real time, and ensure the normal operation of the power system.

[0025] 2. This invention comprehensively analyzes circuit breaker status anomaly scores and electrical parameter anomaly scores. It combines a machine learning model to assess circuit anomalies and determines whether circuit restoration operations should be performed. Using a multinomial regression model for training, it can not only accurately predict circuit anomaly assessment coefficients but also automatically adjust thresholds to adapt to different power system operating conditions. This method significantly improves fault self-recovery capabilities and multi-fault diagnosis levels, enhances the adaptability and reliability of power systems in complex application scenarios, reduces manual intervention, and ensures the efficient and safe operation of power systems. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0027] Figure 1 This is a mind map of the method of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] For examples, please refer to Figure 1 As shown, the automated control method for the operating mechanism of the molded case circuit breaker in this embodiment includes:

[0030] 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.

[0031] The control system receives electrical parameters from the circuit and performs comparative analysis based on preset electrical parameter thresholds and fault models to detect whether a fault exists.

[0032] The control system judges abnormal electrical parameters. If a fault is detected, a control command is generated. The fault judgment process includes the identification and priority ranking of multiple fault scenarios.

[0033] Based on the fault diagnosis results, the control system automatically controls the operating mechanism of the molded case circuit breaker to perform closing or opening actions.

[0034] When the circuit is disconnected, the control system determines whether to restore the circuit to normal operation based on the status of the circuit breaker and the recovery status of the power system.

[0035] The primary goal of monitoring electrical parameters is to acquire key signals in the circuit to reflect the real-time operating status of the electrical system. Common electrical parameters during the operation of molded case circuit breakers include:

[0036] Current: Monitoring current changes can reflect the circuit load and whether abnormal conditions such as overload or short circuit have occurred. Current monitoring is usually accomplished through a **current transformer (CT).** A CT can convert high current into a proportionally lower current signal, facilitating measurement and monitoring.

[0037] Voltage: Voltage monitoring is used to determine whether a circuit has overvoltage or undervoltage conditions. Voltage sensors convert the circuit's voltage signal into a digital signal that can be processed by the control system. In the protection function of circuit breakers, overvoltage and undervoltage are common fault triggering conditions.

[0038] Temperature: Monitoring the temperature in circuit breakers and circuits, especially under heavy loads, is crucial as temperature changes can indicate an overload risk. Temperature sensors such as thermocouples or RTDs (platinum resistance thermometers) are used for real-time monitoring.

[0039] Frequency: Frequency changes may indicate fluctuations in the power grid frequency or power quality problems. Frequency monitoring can be achieved through frequency sensors.

[0040] Other parameters, including harmonics, power factor, and phase angle, help in a comprehensive analysis of power quality, although they are not always necessary.

[0041] To acquire these electrical parameters in real time, molded case circuit breakers are typically equipped with multiple sensors. Different types of sensors are installed in different locations to ensure accurate and comprehensive acquisition of circuit information:

[0042] Current transformer (CT): CTs are installed around power cables or conductors, primarily to monitor the current flowing through a circuit. When the current exceeds a set threshold, the CT generates a corresponding signal to alert the control system to an abnormality.

[0043] Voltage sensor: Voltage sensors are installed at key locations such as the input terminal of the circuit and both ends of the circuit breaker to monitor voltage fluctuations in real time.

[0044] Temperature sensor: The temperature sensor is installed inside the molded case circuit breaker or on its critical components to monitor temperature changes. High temperatures may be caused by overload or other electrical faults, and the feedback from the temperature sensor helps determine whether the circuit needs to be disconnected.

[0045] Frequency sensor: Frequency sensors are mainly used to monitor power supply frequency, which is especially important in unstable power grid environments.

[0046] These sensors typically convert analog signals into digital signals using an analog-to-digital converter (ADC), which then transmits the signals to the control system for processing and analysis.

[0047] The electrical parameters collected by sensors need to be transmitted to the control system for processing in real time. To ensure data accuracy and real-time performance, the following techniques are typically used for transmission:

[0048] Wired transmission: In some traditional power systems, electrical parameters are transmitted to the control system via wired communication (such as RS485, Modbus, etc.). The transmission distance is relatively short and is usually suitable for local systems.

[0049] Wireless transmission: In some remote monitoring and control scenarios, especially in widely distributed power systems, wireless communication (such as Wi-Fi, Zigbee, LoRa, etc.) is used for data transmission. This method is suitable for large-area coverage, especially in environments without fixed wiring.

[0050] Industrial protocols: such as Modbus, CAN bus, Ethernet and other commonly used protocols, are used to achieve reliable communication between sensors and control systems, and to ensure real-time data transmission and processing.

[0051] In the control system, a set of preset thresholds is used for comparative analysis with real-time monitored electrical parameters. Each electrical parameter (such as current, voltage, and temperature) has its own operating range, which is determined by the equipment's rated parameters and normal operating conditions. The control system compares the real-time collected electrical parameters with these preset thresholds:

[0052] Current threshold: For example, the maximum normal current is 1.2 times the rated current. When the current exceeds this threshold, it indicates that there may be an overload or short circuit, and the control system will consider it a fault signal.

[0053] Voltage threshold: For example, the voltage should fluctuate within ±10% of the rated voltage. If the voltage exceeds this range, it may indicate an overvoltage or undervoltage fault in the power grid, and the system will mark it as abnormal.

[0054] Temperature threshold: For example, when the temperature exceeds the set safety 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 anomaly.

[0055] In addition to simple threshold comparisons, the control system utilizes fault models to further refine the determination of whether electrical parameters fall under a fault state. Fault models are based on various fault scenarios that may occur during power system operation, such as short circuits, overloads, and grounding faults, and determine the fault type through data analysis and pattern recognition. Specifically, this includes:

[0056] Overload fault model: By comparing real-time current data with the rated current of the equipment, if the current exceeds the set overload threshold and continues for a period of time, the overload protection logic is triggered and the fault is determined.

[0057] Short-circuit fault model: In current monitoring, if a sudden surge in current (e.g., exceeding five times the rated current) is accompanied by a voltage drop, the control system may identify it as a short-circuit fault. Short-circuit faults often occur rapidly, requiring the system to respond quickly to disconnect the power supply.

[0058] Undervoltage and overvoltage fault model: By comparing the real-time voltage value with the set normal operating voltage range, if the voltage exceeds the normal range by a certain percentage and the duration exceeds a preset threshold, it is determined to be a voltage abnormality fault. Undervoltage and overvoltage can damage electrical equipment and require timely response.

[0059] The control system compares the real-time electrical parameters with the parameters in the fault model. If the parameters match the characteristics of a certain fault model, the system further confirms the fault type and prepares to execute the corresponding protection operation.

[0060] In power systems, the relationships between electrical parameters are often not absolutely clear-cut, but rather involve a degree of fuzziness. For example, the relationship between current and temperature may not always be linear; an increase in current may lead to a rise in temperature, but not every increase in current results in a significant temperature change. In such cases, traditional digital logic may struggle to accurately describe these fuzzy relationships, while **fuzzy logic** can handle this uncertainty through fuzzy rules and reasoning mechanisms, thereby helping the control system make more rational judgments.

[0061] Fuzzy logic is a mathematical tool for handling uncertainty and fuzziness. Based on the theory of fuzzy sets, it can transform traditional precise mathematical models into models that handle 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. Each input corresponds to a fuzzy value, which allows for a more flexible description of the relationships between parameters.

[0062] The relationship between current and temperature is usually not a simple linear one. An increase in current may lead to an increase in temperature, but this depends on factors such as the load, ambient temperature, and the thermal efficiency of the equipment. Therefore, fuzzy logic systems can construct fuzzy rules based on these factors to perform reasoning and decision-making.

[0063] First, we need to define fuzzy sets and linguistic variables. For example, current and temperature can be divided into several fuzzy categories:

[0064] Current: Low current, Normal current, High current

[0065] Temperature: Low, Normal, High

[0066] These categories are not precise numbers, but are described using fuzzy sets. For example, "high" current does not mean that the current exceeds a certain exact value, but rather that the current is within a certain range, close to the high value of the current.

[0067] Based on the empirical relationship between current and temperature, some fuzzy rules can be constructed to describe their relationship. For example:

[0068] Rule 1: If the current is "low", then the temperature is "low".

[0069] Rule 2: If the current is "normal", then the temperature is "normal".

[0070] Rule 3: If the current is "high", then the temperature is "high".

[0071] Rule 4: If the current is "high" and the temperature is "normal", then the temperature is "high".

[0072] Rule 5: If the current is "low" and the temperature is "high", it may indicate a device malfunction.

[0073] These rules describe the fuzzy relationship between current and temperature, helping control systems to reason in practical operations.

[0074] When the control system receives real-time data on current and temperature, it first converts this input data into fuzzy values. For example:

[0075] If the current is 10A (low current), then the current input can be classified into a certain range of the "low" fuzzy set.

[0076] If the temperature is 70°C (normal temperature), then the temperature input can be classified into a certain range of the "normal" fuzzy set.

[0077] Next, the control system uses a fuzzy inference engine to reason about these fuzzy inputs based on the rules mentioned above. For example, if the current is "high" and the temperature is "normal", according to rules 3 and 4, the system will infer that the temperature may rise to "high", requiring alert.

[0078] The output obtained from fuzzy inference is a fuzzy value, while the system usually needs a precise output value to drive control commands (such as the opening operation of a circuit breaker). In order to obtain specific control commands, the fuzzy output value must be converted into a clear numerical value; this process is called defuzzification.

[0079] A commonly used defuzzification method is the centroid method, which obtains a precise value by calculating the centroid of the fuzzy output set. For example, if the fuzzy inference output is a vague value between "high temperature" and "normal temperature", after defuzzification, a temperature value such as 85°C may be obtained.

[0080] Fuzzy logic can effectively handle fuzzy relationships between input parameters, especially when the relationship between current and temperature is not entirely clear. Fuzzy logic can make reasonable inferences based on rules, rather than relying on precise mathematical models. Fuzzy logic systems can adjust inference rules according to different operating conditions and equipment states, exhibiting strong flexibility. For example, in certain special scenarios, changes in current and temperature may not perfectly follow linear laws; fuzzy logic can provide more accurate and adaptive decisions.

[0081] For example, under certain high-load conditions, an increase in current may lead to a rise in temperature, but this is not always a direct linear relationship. In such cases, fuzzy logic can help the control system determine whether the current is abnormal and, based on the fuzzy input of the current and temperature, infer whether there is a risk of overload or overheating. For example:

[0082] Fuzzy input: Current is 18A (high current), temperature is 75°C (normal).

[0083] Fuzzy reasoning: Rules 4 and 3 are triggered, inferring that the temperature may rise further and protecting the circuit.

[0084] Output: The system generates a fuzzy output of "high temperature", which is defuzzified to obtain a temperature of 80°C, and then determines whether to perform a protection operation (such as a circuit breaker tripping operation).

[0085] Through such fuzzy reasoning, the control system can better identify electrical faults in the real environment and ensure the safe operation of the equipment.

[0086] The control system first monitors electrical parameters such as current, voltage, and temperature in the circuit in real time using sensors, and compares and analyzes these parameters with preset thresholds and fault models. When the system detects that electrical parameters exceed safe limits (e.g., overcurrent, overvoltage, overheating), a fault diagnosis algorithm (such as a fuzzy logic algorithm) identifies the specific fault type and generates control commands based on the severity of the fault.

[0087] Types of fault diagnosis results:

[0088] Overload fault: The current exceeds the set threshold, but does not reach the short circuit level, and usually requires a certain time delay.

[0089] Short circuit fault: The current rises sharply in a very short time. It is a high-priority fault and the circuit needs to be cut off immediately.

[0090] Overvoltage or undervoltage: The voltage exceeds the allowable range of the rated voltage, affecting the normal operation of the equipment.

[0091] Overheating: If the temperature sensor detects that the device temperature exceeds the safe range, it may cause damage to the device or a fire.

[0092] Depending on the type of fault, the system will determine whether to perform a closing (restoring the circuit) or opening (disconnecting the circuit) operation.

[0093] The control system generates corresponding operating commands through comprehensive analysis of faults. These commands directly affect the operating mechanism of the molded case circuit breaker, instructing it to perform closing or opening actions.

[0094] Instruction type:

[0095] Closing command: When the system fault is cleared or the power grid is restored to normal, a closing command is generated to restore the normal operation of the circuit.

[0096] Tripping command: When the system detects a fault (such as overload, short circuit, overvoltage, etc.), it generates a tripping command to immediately disconnect the circuit, prevent the fault from spreading and protect electrical equipment.

[0097] The control system communicates with the operating mechanism to close or open the circuit breaker. The operating mechanism of a molded case circuit breaker typically consists of an electronic release device, an actuator (such as an electric actuator), and mechanical connection components. The control system drives these components through electrical signals.

[0098] Electronic release devices are primarily responsible for detecting abnormal signals such as current and voltage, and operating the circuit breaker according to instructions from the control system. Electronic release devices typically control the opening and closing of the circuit breaker using relays or microprocessors.

[0099] Electric actuator: This is a key component for performing opening or closing operations. The control system uses the electric actuator to realize the actual action of the circuit breaker. After receiving a control signal, the electric actuator drives the mechanical structure of the circuit breaker to perform opening or closing.

[0100] When the control system generates a closing or opening command, the electric actuator performs the specific operation through the drive mechanism.

[0101] Tripping action:

[0102] When an overload, short circuit, or overvoltage fault is detected, the control system generates a trip command. Upon receiving this command, the electric actuator immediately drives the circuit breaker switch contacts to open, cutting off the circuit and preventing the fault from spreading.

[0103] The tripping action is usually rapid, especially during short-circuit faults, when the system will quickly perform the tripping operation to prevent excessive current from damaging the equipment.

[0104] Closing operation:

[0105] When the fault is cleared or the power grid returns to normal, the control system generates a closing command, instructing the electric actuator to restore the circuit breaker to its closed state and reconnect the circuit.

[0106] The closing action generally requires ensuring that the circuit is in normal condition, such as when the current, voltage and temperature are within safe ranges, before the circuit can be closed safely.

[0107] In some cases, the system needs to consider delayed operations and retry mechanisms. For example:

[0108] Overload protection delay: When the system detects an overload fault, it may not immediately trip the circuit breaker, but instead waits for a certain period of time (such as a few seconds) to determine whether the overload is persistent. If the overload is persistent, the system will trigger the trip; if the overload is a momentary fluctuation, the system may allow the circuit to recover.

[0109] Closing delay: If the system has triggered the tripping due to overvoltage or overload, the control system may delay for a certain period of time before closing to wait for the electrical parameters to stabilize, so as to ensure the safety of the circuit when closing.

[0110] Retry mechanism: If a transient fault occurs during closing, the system may automatically retry the closing operation until the electrical parameters stabilize or multiple attempts fail.

[0111] After performing a closing or opening operation, the control system continues to monitor electrical parameters to ensure the equipment is in normal condition. If the fault has been cleared and the circuit has returned to normal, the system will continue to track key parameters such as current and voltage to confirm the success of the closing operation. If abnormal parameters are detected again after closing, the control system may perform an opening operation again.

[0112] The system also records the fault type, the time of opening and closing the circuit breaker, and any abnormal situations during the execution process. This data can be used for subsequent analysis and maintenance.

[0113] In modern power systems, control systems not only perform closing and opening operations locally, but also support monitoring and operation via remote control platforms. Through remote communication protocols (such as Modbus, IEC 61850, etc.), operators can view the status of circuit breakers in real time, remotely execute closing or opening commands, and perform fault diagnosis and system adjustment.

[0114] In this invention, through precise fault diagnosis and analysis, the control system can automatically generate closing or opening commands based on changes in electrical parameters, and implement the specific operations through electric actuators. The opening operation is a rapid response to the fault, while the closing operation ensures the circuit returns to normal operation. This process not only improves the system's automation level but also effectively protects equipment, reduces human error, and ensures the stability and safety of the power system.

[0115] When the circuit is disconnected, the control system determines whether to restore normal circuit operation based on the circuit breaker status and the power system recovery status. Specifically, this includes:

[0116] In power systems, the control system's task does not end after a circuit breaker trips and disconnects the circuit due to a fault (such as overload, short circuit, or overvoltage). The system needs to continue monitoring the circuit's status to ensure that the fault has been eliminated and electrical parameters have returned to normal before performing a closing operation to restore the circuit's normal operation. This process involves monitoring the circuit breaker's status, real-time monitoring of electrical parameters, and fault recovery assessment.

[0117] Circuit breaker status monitoring: The control system monitors the circuit breaker's status in real time, determining whether the circuit has been disconnected and the circuit breaker's mechanical condition. Modern circuit breakers typically have status feedback functions, including switch status (open / closed), fault records, and other information. When a circuit is disconnected due to a fault, the circuit breaker's tripping status is transmitted to the control system, which confirms the circuit breaker's "open" state. At this point, the control system begins tracking electrical parameters to determine whether the fault has been resolved and whether the circuit can be restored. The circuit breaker usually records fault events and transmits fault codes or logs to the control system for system analysis and fault diagnosis. The control system can use this information to determine the fault type and whether further protection is needed.

[0118] After analyzing the condition monitoring data of the circuit breaker, a circuit breaker condition anomaly score is generated. The generation method is as follows:

[0119] Monitoring data (such as current, voltage, and temperature) is collected from sensors on the circuit breaker and formatted into a dataset. Each data point will include different electrical parameters as features (e.g., current value, voltage value, temperature value, etc.) and a label (if it is supervised learning). If it is unsupervised learning, the label may be omitted.

[0120] Given a dataset where each sample consists of n electrical parameter features: ; each of them A data point contains multiple features, such as current and voltage.

[0121] Choosing an appropriate value for K involves finding the k nearest neighbors for each data point. The choice of K can be determined empirically or through cross-validation. Generally, a smaller K value may be more sensitive to noise, while a larger K value may increase computational complexity. Typically, the choice of K needs to be adjusted based on the data size, features, and anomaly detection requirements.

[0122] The most similar neighbors are found by calculating the distance between the data points to be detected and the non-data points to be detected using Euclidean distance. The calculation formula is as follows: ;in: Data points eigenvectors, Data points The feature vector, where m is the number of features. Data points The Euclidean distance between them.

[0123] For the circuit breaker status data points to be tested Calculate the Euclidean distance between the circuit breaker and all data points in dataset X, find the K nearest neighbors with the smallest distance, and obtain the circuit breaker status anomaly score by calculating the average distance of the K nearest neighbors. The expression is as follows: In the formula, ED is the circuit breaker status anomaly score. When the circuit breaker status anomaly score is large, it indicates that the data point deviates far from the normal point, and the circuit breaker may have a fault.

[0124] Real-time monitoring of electrical parameters: After a circuit is disconnected, the control system continues to monitor electrical parameters (such as current, voltage, and temperature) to assess the recovery status of the power system. The system acquires electrical parameters through sensors and compares them with reference values ​​under normal operating conditions. The control system monitors the magnitude and changes in current to determine whether the circuit has returned to normal load conditions. For example, during the recovery period after a short-circuit fault, the current should stabilize near its rated value; if the current remains too high, it indicates a possible unresolved fault, and the control system should avoid closing the circuit. Voltage fluctuations also need to be monitored promptly. The control system monitors whether the voltage has returned to the rated range. If the voltage remains abnormal (e.g., overvoltage or undervoltage), the closing operation will be postponed. In the event of equipment overheating, information from temperature sensors will help the control system determine whether the equipment has returned to a safe operating range. The return of temperature to normal is one of the important criteria for deciding whether to close the circuit.

[0125] After analyzing abnormal fluctuations in electrical parameters, an abnormality score for the electrical parameters is generated. The generation method is as follows:

[0126] Collect electrical parameter data and construct a time-series dataset, where each data point represents an electrical parameter value over a given time period. The electrical parameter data for each time point can be current, voltage, temperature, etc. For example, the time-series data for current would be: ;in, These are current data at various points in time.

[0127] Calculate the historical average of electrical parameters and standard deviation The calculation expression is: ;in, represents the value of each electrical parameter in the dataset, and m is the total number of data points. The standard deviation represents the range of fluctuation in the electrical parameter data, and is calculated using the following formula: The outlier score Z-score for each data point is calculated using the following expression: ;in, It is the electrical parameter anomaly score of the i-th data point. This represents the electrical parameter value (e.g., current, voltage) at the i-th time point. The overall electrical parameter anomaly score is obtained by summing and averaging the electrical parameter anomaly scores of all data points.

[0128] The circuit breaker status anomaly score and electrical parameter anomaly score are converted into a comprehensive feature vector. The comprehensive feature vector is used as the input of the machine learning model. The machine learning model uses the prediction of circuit anomaly evaluation coefficient labels for each set of comprehensive feature vectors as the prediction objective and minimizes the sum of prediction errors for all circuit anomaly evaluation coefficient labels as the training objective. The machine learning model is trained until the sum of prediction errors converges and the model training stops. The circuit anomaly evaluation coefficient is determined based on the model output. The machine learning model is a multinomial regression model.

[0129] The obtained circuit anomaly evaluation coefficient is compared 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, no restoration is performed; if the circuit anomaly evaluation coefficient is less than the predetermined threshold, restoration is performed.

[0130] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0131] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as 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, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. 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 via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0132] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Additionally, the character " / " in this document generally indicates an "or" relationship between the preceding and following related objects, but it may also indicate an "and / or" relationship; please refer to the context for specific understanding. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0133] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. An automated control method for the operating mechanism of a molded case circuit breaker, characterized in that: include: 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 electrical parameters from the circuit and performs comparative analysis based on preset electrical parameter thresholds and fault models to detect whether a fault exists. The control system judges abnormal electrical parameters. If a fault is detected, a control command is generated. The fault judgment process includes the identification and priority ranking of multiple fault scenarios. Based on the fault diagnosis results, the control system automatically controls the operating mechanism of the molded case circuit breaker to perform closing or opening actions. When the circuit is disconnected, the control system determines whether to restore normal circuit operation based on the circuit breaker status and the power system recovery status. Specifically, this includes: The control system determines whether to restore normal circuit operation based on the circuit breaker's status and the power system's recovery status. This includes: analyzing the circuit breaker's status monitoring data to generate a circuit breaker status anomaly score, the generation method being: Monitoring data is collected from the circuit breaker's sensors and formatted into a dataset where each sample consists of n electrical parameter features: ; each of them For a given data point, we choose a value K, which means finding the k nearest neighbors of each data point. We then calculate the distance between the data point to be detected and the non-detected data points using Euclidean distance. The calculation formula is as follows: ;in: Data points eigenvectors, Data points The feature vector, where m is the number of features. Data points The Euclidean distance between them; For the circuit breaker status data points to be tested Calculate the Euclidean distance between the circuit breaker and all data points in dataset X, find the K nearest neighbors with the smallest distance, and obtain the circuit breaker status anomaly score by calculating the average distance of the K nearest neighbors. The expression is as follows: In the formula, ED represents the circuit breaker status anomaly score. After analyzing abnormal fluctuations in electrical parameters, an abnormality score for the electrical parameters is generated. The generation method is as follows: Collect electrical parameter data and construct a time series dataset, where each data point represents the electrical parameter value within a time period: ;in, These are current data at various points in time; Calculate the historical average of electrical parameters and standard deviation The calculation expression is: ;in, This represents the value of each electrical parameter in the dataset, where 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: The outlier score Z-score for each data point is calculated using the following expression: ;in, It is the electrical parameter anomaly score of the i-th data point. The electrical parameter value at time point i is the sum of the electrical parameter anomaly scores of all data points, and the overall electrical parameter anomaly score is obtained by summing and averaging the scores of all data points. The circuit breaker status anomaly score and electrical parameter anomaly score are converted into a comprehensive feature vector. The comprehensive feature vector is used as the input of the machine learning model. The machine learning model uses the prediction of circuit anomaly evaluation coefficient labels for each set of comprehensive feature vectors as the prediction objective and minimizes the sum of prediction errors for all circuit anomaly evaluation coefficient labels as the training objective. The machine learning model is trained until the sum of prediction errors converges and the model training stops. The circuit anomaly evaluation coefficient is determined based on the model output. The machine learning model is a multinomial regression model.

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

3. The automated control method for the operating mechanism of a molded case circuit breaker according to claim 2, characterized in that: The control system uses a fuzzy logic algorithm to determine abnormal electrical parameters. The fuzzy rules include: if the current is high and the temperature is normal, then it is inferred that the temperature has risen to high and a tripping operation is required.

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

5. The automated control method for the operating mechanism of a molded case circuit breaker according to claim 1, characterized in that: The obtained circuit anomaly evaluation coefficient is compared 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, no restoration is performed; if the circuit anomaly evaluation coefficient is less than the predetermined threshold, restoration is performed.

Images