Energy storage circuit system for medium voltage switchgear operating mechanism

Abstract

The application relates to the field of energy storage circuit monitoring, and specifically discloses an energy storage circuit system for a medium-voltage switch cabinet operating mechanism, which realizes soft starting and non-contact control of an energy storage motor by introducing a dynamic motor control circuit. A multi-stage stroke detection device replaces a mechanical limit switch, provides real-time and continuous position monitoring of the energy storage mechanism, and completely eliminates feedback lag and inaccuracy. A core energy storage state analysis unit can identify abnormal working conditions in the energy storage process in real time through intelligent acquisition, analysis and decision of signals. In addition, a power supply unit provides a stable operation basis, and a bidirectional communication interface ensures information interaction between the system and the external environment, thereby jointly constructing an intelligent energy storage ecological system that is closed-loop controllable, state feedback and fault prewarning, and comprehensively improving the response speed and operation stability of the medium-voltage switch cabinet operating mechanism.

Description

Technical Field

[0001] This application relates to the field of energy storage circuit monitoring, and more specifically, to an energy storage circuit system for medium-voltage switchgear operating mechanisms. Background Technology

[0002] Medium-voltage switchgear, as a critical control and protection device in power systems, directly impacts the reliability and safety of power grid operation through the performance of its operating mechanism. Among these mechanisms, the energy storage circuit is a core component enabling rapid and accurate closing actions. However, traditional energy storage circuit designs commonly suffer from issues such as low motor control precision, delayed energy storage status feedback, and insufficient overall system reliability. These shortcomings not only limit the response speed of circuit breaker closing actions but also pose a potential threat to the long-term operational stability of the equipment. Especially when facing complex operating conditions or sudden faults, traditional solutions often struggle to provide timely and effective response and protection.

[0003] Specifically, existing energy storage circuit systems typically employ relatively simple motor drive methods, such as start-stop control via traditional contactors or mechanical relays. This hard-switching mode is prone to arcing, shortening the lifespan of the motor and control components, and increasing maintenance costs. Furthermore, it lacks fine-grained adjustment capabilities for controlling the energy storage motor, making it difficult to achieve smooth soft starts and precise speed control. In terms of energy storage status monitoring, traditional solutions rely heavily on mechanical limit switches to determine whether the energy storage mechanism is in position. This discrete and discontinuous feedback method has limited accuracy and is susceptible to mechanical wear, leading to delayed and inaccurate feedback signals. In addition, existing systems generally lack the ability to intelligently identify and warn of abnormal operating conditions during energy storage. For example, when faults such as motor jamming or overload occur, they often only respond with simple overcurrent protection or delayed tripping, failing to perform fine-grained fault mode identification and proactive protection. This makes the system passive and vulnerable to potential risks, making it difficult to meet the stringent requirements of modern power systems for high reliability and intelligent operation.

[0004] Given the challenges faced by traditional energy storage loop systems, there is an urgent need for a solution to overcome core issues such as low motor control precision, delayed energy storage status feedback, and insufficient system reliability. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. According to this application, an energy storage circuit system for a medium-voltage switchgear operating mechanism includes:

[0006] Power supply unit, dynamic motor control circuit, multi-stage stroke detection device, energy storage status analysis unit and bidirectional communication interface;

[0007] Power supply unit, used to provide a stable power supply;

[0008] Dynamic motor control circuit, used for soft start and contactless control of energy storage motor;

[0009] A multi-stage travel detection device is used to monitor the travel of energy storage mechanisms in real time.

[0010] Energy storage status analysis unit, used for signal acquisition and intelligent analysis and decision-making;

[0011] A two-way communication interface is used to enable information exchange.

[0012] Compared with existing technologies, this application provides an energy storage loop system for medium-voltage switchgear operating mechanisms, aiming to solve core problems such as low motor control accuracy, lag in energy storage status feedback, and insufficient system reliability in traditional solutions. By introducing a dynamic motor control circuit, soft start and contactless control of the energy storage motor are achieved, effectively avoiding arc loss and shortened lifespan caused by traditional hard switching modes, and significantly improving the stability and accuracy of motor control. A multi-stage travel detection device replaces mechanical limit switches, providing real-time and continuous position monitoring of the energy storage mechanism, completely eliminating feedback lag and inaccuracy. The core energy storage status analysis unit, through intelligent signal acquisition, analysis, and decision-making, can identify abnormal operating conditions in the energy storage process in real time, thereby greatly improving the system's intelligent diagnosis and early warning capabilities and enhancing overall reliability. In addition, the power supply unit provides a stable operating foundation, and the bidirectional communication interface ensures information interaction between the system and the external environment, jointly constructing a closed-loop controllable, status-feedbackable, and fault-predictable intelligent energy storage ecosystem, comprehensively improving the response speed and operational stability of the medium-voltage switchgear operating mechanism. Attached Figure Description

[0013] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0014] Figure 1 This is a block diagram of an energy storage circuit system for a medium-voltage switchgear operating mechanism according to an embodiment of this application.

[0015] Figure 2 This is a block diagram of an energy storage state analysis unit in an energy storage loop system for a medium-voltage switchgear operating mechanism according to an embodiment of this application.

[0016] Figure 3 This is a schematic diagram of the data flow of the energy storage status analysis unit in an energy storage loop system for a medium-voltage switchgear operating mechanism according to an embodiment of this application.

[0017] Figure 4 This is a block diagram of a fault analysis subunit in an energy storage circuit system for a medium-voltage switchgear operating mechanism according to an embodiment of this application. Detailed Implementation

[0018] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0019] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0020] This application is proposed to address the core issues of low motor control accuracy, delayed energy storage status feedback, and insufficient system reliability in traditional solutions. Figure 1 This is a block diagram of an energy storage circuit system for a medium-voltage switchgear operating mechanism according to an embodiment of this application. Specifically, as shown... Figure 1 As shown, the energy storage circuit system 100 for the operating mechanism of a medium-voltage switchgear according to an embodiment of this application includes: a power supply unit 110, a dynamic motor control circuit 120, a multi-stage stroke detection device 130, an energy storage status analysis unit 140, and a bidirectional communication interface 150. The power supply unit 110 provides a stable power supply; the dynamic motor control circuit 120 performs soft-start and contactless control of the energy storage motor; the multi-stage stroke detection device 130 monitors the stroke of the energy storage mechanism in real time; the energy storage status analysis unit 140 acquires signals and performs intelligent analysis and decision-making; and the bidirectional communication interface 150 enables information exchange.

[0021] The Energy Storage Circuit System 100 for Medium-Voltage Switchgear Operating Mechanisms integrates multiple key units to jointly construct an intelligent, efficient, and reliable energy storage solution.

[0022] In detail, the power supply unit 110, providing a stable power supply, is the core energy supply module of the entire energy storage loop system. This unit integrates power management and regulation technology to ensure that all internal components receive a clean and constant operating voltage and current regardless of fluctuations in external grid conditions. To adapt to complex application environments, this unit has wide voltage input compatibility, flexibly adapting to AC or DC power supply modes, greatly enhancing the system's versatility and deployment flexibility. Through internal precision voltage regulation, filtering, and overcurrent / overvoltage protection circuits, the power supply unit 110 effectively suppresses transient interference and harmonics in the grid, outputting high-quality DC power, thus providing a solid guarantee for the stable operation of key components such as the dynamic motor control circuit, multi-stage travel detection device, and energy storage status analysis unit. The stable power supply it provides is crucial for solving the problems mentioned in the background technology, such as low motor control accuracy, delayed energy storage status feedback, and insufficient system reliability. Stable voltage input is a prerequisite for the dynamic motor control circuit to achieve precise soft start and contactless control, ensuring the smooth and efficient operation of the energy storage motor. At the same time, it ensures the accuracy and consistency of sensor data in the multi-stage travel detection device, avoids measurement errors or signal distortion caused by power fluctuations, and thus improves the real-time performance and accuracy of energy storage status feedback.

[0023] The dynamic motor control circuit 120, used for soft-start and contactless control of the energy storage motor, is the core module driving the energy storage motor. This circuit eliminates the drawbacks of traditional mechanical contactors, instead employing advanced power electronics technology. In one feasible technical solution, the dynamic motor control circuit includes a solid-state relay. Specifically, when the energy storage state analysis unit issues a start energy storage command, this circuit, through its internal solid-state relay, can smoothly and gradually increase the voltage applied to the energy storage motor. For example, it achieves soft start through precise pulse width modulation (PWM) signals, effectively avoiding the huge inrush current and mechanical stress generated at the moment of motor start-up in traditional hard-switching modes. This significantly extends the service life of the energy storage motor and related transmission components, and reduces noise and vibration during startup. Simultaneously, the contactless characteristic of the solid-state relay eliminates problems such as arcing, contact wear, and adhesion caused by frequent switching of traditional mechanical relays, greatly improving control reliability and response speed, and reducing maintenance requirements. This circuit can accurately respond to control signals from the energy storage state analysis unit, ensuring a smooth, efficient, and precise energy storage process, thereby directly solving the problems of low motor control accuracy and insufficient system reliability mentioned in the background technology.

[0024] The multi-stage stroke detection device 130 is used to monitor the stroke of the energy storage mechanism in real time, serving as a key component for accurately sensing the motion state of the energy storage mechanism. This device surpasses the limitations of traditional mechanical limit switches. Traditional limit switches can only provide discrete signals of being in or out of position and are susceptible to mechanical wear, leading to feedback lag and inaccuracy. The multi-stage stroke detection device 130, however, employs advanced non-contact sensor technology, such as high-sensitivity Hall sensors or high-precision encoders. These sensors can continuously measure the deformation of the energy storage spring or the displacement of the energy storage mechanism, thus providing continuous position feedback data. This means that the energy storage state analysis unit can acquire precise position information of the energy storage mechanism throughout the entire energy storage process, not just the final state. By providing this refined, multi-stage position data, the multi-stage stroke detection device 130 significantly improves the control accuracy and reliability of state judgment in the energy storage process. It provides a high-quality real-time data stream for the energy storage status analysis unit, enabling the unit to perform intelligent analysis based on precise location information, identify abnormal operating conditions in the energy storage process, and achieve more precise control of the energy storage motor. It solves the problems of lag and inaccuracy in energy storage status feedback mentioned in the background technology, and is an important foundation for realizing intelligent and highly reliable operation of the entire energy storage circuit.

[0025] The energy storage status analysis unit 140, used for signal acquisition, intelligent analysis, and decision-making, is the brain of the entire energy storage loop system. This unit receives real-time position data from multi-stage travel detection devices and key signals such as motor current feedback from the dynamic motor control circuit. Through built-in sub-units for baseline model loading, real-time data stream acquisition, expected current tolerance calculation, deviation fractional value stream calculation, and fault analysis, it performs in-depth analysis of real-time data during the energy storage process, intelligently identifying abnormal operating conditions such as motor jamming and overload. In actual operation, in a feasible technical solution, the energy storage status analysis unit receives the start energy storage command through a bidirectional communication interface. After confirming that the status is correct, the energy storage status analysis unit sends a control signal to the solid-state relay in the dynamic motor control circuit. This control signal is used to gradually increase the voltage applied to the energy storage motor to achieve a smooth soft start. Specifically, the control signal here is a PWM signal, ensuring precise control of motor speed and torque. Once the energy storage status analysis unit 140 detects an abnormality, it can automatically generate corresponding fault decisions and fault types, and can immediately trigger alarm information or automatically cut off the power supply when necessary to prevent equipment damage or accidents. This proactive fault warning and protection mechanism improves the operational reliability of the energy storage circuit and effectively solves the problem of the lack of intelligent diagnosis and early warning capabilities in traditional solutions in the background technology, ensuring the response speed and operational stability of the medium-voltage switchgear operating mechanism.

[0026] The bidirectional communication interface 150, used for information exchange, serves as a bridge for information interaction between the energy storage loop system and the external environment. This interface is responsible for data transmission and command reception between the energy storage loop and the upper-level monitoring system or other intelligent devices, thereby building an open and interconnected intelligent ecosystem. Specifically, this interface has high compatibility, supporting multiple industrial standard communication protocols, such as Modbus, CAN, or Ethernet. This multi-protocol support ensures seamless integration of the energy storage loop into existing power automation networks without complex adaptation work. Through this interface, the energy storage status analysis unit 140 can upload real-time energy storage status data, detailed fault alarm information, and accurate diagnostic results to the upper-level computer, thereby achieving remote visualization of energy storage status and fault prediction. This solves the problem of delayed energy storage status feedback in the prior art, enabling maintenance personnel to monitor equipment operation in real time. Simultaneously, the bidirectional communication interface 150 can also receive control commands from the upper-level computer, such as energy storage start commands, thereby achieving remote control and management, greatly improving the system's operability, maintainability, and remote intelligence level, and significantly enhancing the overall reliability of the system.

[0027] In other words, the entire energy storage circuit system 100 for the medium-voltage switchgear operating mechanism is provided with stable power by the power supply unit 110, the dynamic motor control circuit 120 realizes soft start and contactless precise control of the energy storage motor, and the multi-stage stroke detection device 130 provides real-time and continuous position feedback of the energy storage mechanism. This real-time data is collected by the energy storage status analysis unit 140, which acts as the intelligent core, performs in-depth analysis of the data, intelligently identifies abnormal operating conditions, and generates fault decisions and types. All key information is efficiently interacted with external systems through the bidirectional communication interface 150 to achieve remote monitoring and control.

[0028] It is understandable that medium-voltage switchgear, as a critical node device in the power network, directly determines the safe and stable operation of the power grid through the reliability of its operating mechanism. Among these mechanisms, the control scheme for the energy storage circuit is crucial for ensuring a rapid and accurate response. However, these devices are often deployed in outdoor prefabricated substations and other locations with variable environmental conditions, facing significant environmental stress challenges such as temperature and humidity. This places extremely high demands on the adaptability and intelligence of the energy storage circuit control scheme, making the development of a control scheme capable of adapting to dynamic environments a pressing technical challenge for the industry.

[0029] Existing energy storage loop control schemes, especially in fault protection logic, generally suffer from rigid design. Their overcurrent protection mechanisms rely on a fixed current threshold set at the factory based on standard room temperature. This static control logic leads to severe environmental mismatch problems when facing the dynamically changing physical world. For example, in low-temperature winter environments, the viscosity of the lubricating grease in the energy storage mechanism increases significantly, resulting in a surge in mechanical resistance. To overcome this resistance, the energy storage motor's operating current will normally and systematically exceed the room temperature value. In this case, the fixed overcurrent threshold will trigger an incorrect overcurrent fault alarm, causing the equipment to malfunction at low temperatures and generating false alarms. Conversely, in high-temperature summer environments, the viscosity of the lubricating oil decreases, and the normal operating current decreases. If slight mechanical jamming occurs, the abnormal current increment may still be below the fixed protection threshold, leading to missed alarms and missing the critical opportunity for early fault warning. The root of the problem is that the static control model cannot effectively cover all normal operating conditions and lacks a benchmark for judgment in different environments.

[0030] To address the false alarm and missed alarm issues caused by environmental mismatch in the aforementioned fixed threshold protection scheme, this invention proposes an innovative control scheme for an energy storage loop system. This control is implemented through an energy storage state analysis unit 140.

[0031] Specifically, the energy storage status analysis unit 140 is used for signal acquisition and intelligent analysis and decision-making. Figure 2 This is a block diagram of an energy storage state analysis unit in an energy storage loop system for a medium-voltage switchgear operating mechanism according to an embodiment of this application. Figure 3 This is a schematic diagram of the data flow in an energy storage state analysis unit within an energy storage loop system for a medium-voltage switchgear operating mechanism, according to an embodiment of this application. Figure 2 and Figure 3 As shown, the energy storage state analysis unit 140 includes: a baseline model loading subunit 141 for loading an active baseline model; a real-time data stream acquisition subunit 142 for acquiring a real-time data stream, which includes the current position and motor current; a desired current tolerance calculation subunit 143 for inputting the real-time data stream into the active baseline model to obtain a desired current sequence and a tolerance sequence; a deviation score calculation subunit 144 for calculating a deviation score based on the desired current sequence, the tolerance sequence, and the real-time data stream; and a fault analysis subunit 145 for performing anomaly accumulation judgment and fault mode recognition based on the deviation score to obtain fault decisions and fault types.

[0032] Specifically, the baseline model loading subunit 141 is used to load the active baseline model. It should be understood that, in order to achieve truly intelligent fault diagnosis, especially when facing complex environmental factors such as temperature changes, the control logic must first establish a normal behavior benchmark that can dynamically adapt to the current operating conditions. The fundamental reason why traditional fixed threshold protection schemes fail is that they define normal as a static and unchanging value, ignoring the objective facts that mechanical resistance, lubrication effects, etc., change with the environment in the physical world. Therefore, before making any anomaly judgments on real-time operating data, a dynamic, context-aware benchmark is needed. Therefore, designed to address this prerequisite, this application first selects and loads an active baseline model that best represents the normal operating characteristics under current conditions from a pre-built model library based on the current macroscopic working environment, providing an accurate and adaptive evaluation benchmark for subsequent intelligent analysis and decision-making.

[0033] In a feasible technical solution, the specific process of loading the baseline model into subunit 141 is as follows: First, the active baseline model needs to be defined and pre-built. The active baseline model is a set of mathematical models that can describe the functional relationship between the motor current and the travel position of the energy storage mechanism under different normal operating conditions. The construction of this model is completed before the equipment leaves the factory or during a specific maintenance phase. Specifically, technicians will place the medium-voltage switchgear in a controlled environmental laboratory at multiple typical ambient temperature points, such as -20 degrees Celsius, 0 degrees Celsius, 25 degrees Celsius, and 40 degrees Celsius. At each temperature point, the complete energy storage operation is repeatedly performed, and high-precision sensors are used to synchronously collect continuous position data of the energy storage mechanism and real-time current data of the energy storage motor, forming multiple datasets containing (temperature, position, current).

[0034] Based on these collected datasets, an independent baseline model can be constructed for each typical temperature point. In this approach, the model is built as a fine-grained lookup table. Specifically, the entire travel distance of the energy storage mechanism is discretized, for example, into hundreds of equally spaced location points. For each discrete location point, the statistical mean and standard deviation of the motor current are calculated during all normal energy storage operations at that temperature when the mechanism passes that point. The mean is defined as the expected current at that location, and three times the standard deviation is defined as the tolerance at that location. This results in a model library where each model is a large, location-indexed lookup table that accurately depicts the fingerprint curve of the expected current and tolerance of the energy storage motor as a function of travel location at a specific temperature.

[0035] The loading action of the baseline model loading subunit 141 is triggered during the power-on initialization of the energy storage loop system or at the start of each energy storage task. The core function of this subunit is to select and load the most suitable active baseline model from the model library based on one or more operating parameters that reflect the current physical state of the equipment. The method for obtaining the operating parameters and the model selection strategy can be flexibly designed, for example, but not limited to the following implementation methods:

[0036] Method 1: Based on the temperature of key components. This subunit acquires temperature values ​​from one or more temperature sensors. To improve accuracy, the temperature sensor is preferably directly installed on key components such as the housing of the operating mechanism, the outer casing of the energy storage motor, or the gearbox, to directly reflect the core temperature affecting mechanical resistance and lubrication. For example, if the system detects that the temperature of the mechanism housing is -15 degrees Celsius, then according to the preset temperature range division rules (e.g., below 0 degrees Celsius is the winter model, 0 to 30 degrees Celsius is the normal temperature model, and above 30 degrees Celsius is the summer model), the "winter model" is loaded.

[0037] Method 2: Adaptive selection based on initial operating data. In the initial stage of energy storage operation (e.g., the first 10% of the stroke), the system collects motor current and position data in real time. The loading subunit performs pattern matching or error comparison (e.g., calculating the root mean square error) between this initial data curve and the corresponding segments of various pre-stored baseline models in the model library. The baseline model that best matches the initial actual operating data and has the smallest error is selected as the active baseline model for this energy storage mission. This method requires no external sensors and can most accurately reflect the real-time state of the equipment.

[0038] Method 3: Multi-dimensional comprehensive judgment. This sub-unit can comprehensively consider multiple operating parameters, such as mechanism temperature, cabinet humidity, time interval since the last operation (to determine if lubricating oil has settled), and current power supply voltage, and select the most suitable baseline model through a preset decision tree or a simple weighted scoring model. Through any of the above methods, the loading sub-unit can ensure that the loaded active baseline model accurately reflects the normal behavior benchmark of the equipment under current operating conditions.

[0039] Next, the loading subunit reads the lookup table data file corresponding to the active baseline model from non-volatile memory such as flash memory and loads it completely into a designated area of ​​the dynamic random access memory (RAM) of the energy storage state analysis unit 140. For example, after loading is complete, an active baseline model representing winter operating conditions is successfully instantiated in memory. At this point, the baseline model loading subunit 141 outputs that an active baseline model matching the current environment is ready to change the system state.

[0040] Specifically, the real-time data stream acquisition subunit 142 is used to acquire real-time data streams, including the current position and motor current. Correspondingly, while a dynamic baseline model provides a theoretical benchmark for intelligent diagnostics, it cannot actively perceive the actual operating status of the equipment. For any meaningful comparison, analysis, or fault diagnosis, this theoretical model needs to be compared in real-time with the actual physical performance of the equipment during operation. Therefore, after loading a suitable active baseline model, the next crucial step is to accurately and continuously capture the key operating parameters of the energy storage mechanism at the moment of operation, continuously collecting core dynamic data during the energy storage process, providing the most direct and original on-site evidence for all subsequent intelligent analyses.

[0041] In a feasible technical solution, the specific process of the real-time data stream acquisition subunit 142 is as follows: The implementation process of the real-time data stream acquisition subunit 142 is a continuous high-frequency data acquisition cycle during energy storage operations. The real-time data stream is a sequence of data points arranged in chronological order, where each data point is an information tuple containing two dimensions: current position and motor current. The acquisition of this data stream depends on the close cooperation of other hardware units in the energy storage loop system.

[0042] Upon receiving the command to begin energy storage, the energy storage status analysis unit 140 immediately activates the real-time data stream acquisition subunit 142, which then begins operation at a preset high sampling frequency. This sampling frequency is crucial, ensuring the ability to capture rapid dynamic changes in current and location during energy storage. For example, it can be set to 100 times per second, with a sampling period of 10 milliseconds. Within each sampling period, the subunit performs a synchronous data acquisition operation once.

[0043] At a specific sampling moment, such as 500 milliseconds after energy storage begins, the real-time data stream acquisition subunit 142 simultaneously initiates data requests from two different sources. On one hand, it requests the current position of the energy storage mechanism from the multi-stage travel detection device 130. The high-precision encoder or Hall sensor in the multi-stage travel detection device 130 immediately returns a quantized position value, for example, a value indicating that 35.2% of the current travel has been completed. On the other hand, the subunit synchronously reads the instantaneous operating current of the energy storage motor from a high-precision current sensor integrated in the dynamic motor control circuit 120, such as a Hall effect current sensor or a sampling resistor with amplification circuitry. For example, the read current value is 7.8 amperes.

[0044] After acquiring these two synchronized raw data points, the real-time data stream acquisition subunit 142 combines them into a structured data point: (current position: 35.2, motor current: 7.8). This data point is then pushed into a first-in-first-out (FIFO) queue or circular buffer located in dynamic random access memory (RAM). This process is repeated every 10 milliseconds. As energy storage progresses, new data points continuously accumulate in the queue, forming a continuous, time-series-based real-time data stream. For example, at the next sampling time of 510 milliseconds, the acquired data point might be (current position: 36.1, motor current: 7.9), and it will be appended to the end of the queue. This real-time data stream completely records the actual trajectory of the motor current changing with the position of the energy storage mechanism during this energy storage operation.

[0045] Specifically, the expected current tolerance calculation subunit 143 is used to input the real-time data stream into the active baseline model to obtain the expected current sequence and tolerance sequence. It is understood that after the real-time data stream acquisition subunit 142 captures the actual trajectory of the energy storage mechanism's operation, a precise data sequence describing what it should be has been formed. However, for meaningful deviation analysis, a theoretical benchmark corresponding to each point and describing what it should be needs to be generated. This theoretical benchmark cannot be fixed but is dynamically generated based on the real-time changing travel position. Therefore, without using the active baseline model previously loaded by the baseline model loading subunit 141, the expected current value and reasonable fluctuation tolerance for each position point in the real-time data stream are calculated, thereby constructing a dynamic evaluation scale synchronized with the actual operating trajectory, providing a precise basis for subsequent deviation quantification.

[0046] In a feasible technical solution, the expected current tolerance calculation subunit 143 is used to: input each real-time position in the real-time data stream into the active baseline model to obtain the expected current sequence and the tolerance sequence.

[0047] In the above feasible technical solution, the specific process of the expected current tolerance calculation subunit 143 is as follows: The real-time data stream is a sequence of data points arranged in chronological order, where each data point contains two dimensions: current position and motor current. For example, a sequence segment might be: [(position: 35.2, current: 7.8), (position: 36.1, current: 7.9),...]. This subunit will process each data point in this data stream one by one.

[0048] The active baseline model is structured as either a lookup table or a function that, for any given position P, returns (expected current(P), tolerance(P)). When the expected current and tolerance calculation subunit 143 processes the first data point in the real-time data stream, such as (position: 35.2, current: 7.8), it extracts the position information, i.e., 35.2, and uses this position as an index to look up the active baseline model (lookup table) already loaded in memory. Since the real-time positions may be continuous and not necessarily correspond exactly to the discrete indices in the lookup table, the subunit finds the two index positions closest to 35.2 and performs linear interpolation calculations on the expected current and tolerance values ​​corresponding to these two positions to obtain the accurate expected current and tolerance at position 35.2. For example, if the expected current at position 35.0 is found to be 8.4 amps with a tolerance of 0.5 amps, and the expected current at position 36.0 is found to be 8.6 amps with a tolerance of 0.5 amps, then through interpolation, the expected current at position 35.2 can be calculated to be 8.44 amps with a tolerance of 0.5 amps. This expected current value represents the theoretical normal value of the motor current when the energy storage mechanism has reached 35.2% of its travel under the current operating conditions; while the tolerance represents the allowable normal fluctuation range around that point.

[0049] This sub-unit temporarily stores the calculation results (expected current: 8.44, tolerance: 0.5) and then continues processing the next data point in the real-time data stream. This query and interpolation calculation process continues as the real-time data stream is continuously input, throughout the entire energy storage process. As processing progresses, the expected current tolerance calculation sub-unit 143 generates two new sequences of the same length that are completely synchronized with the original real-time data stream. The first sequence is the expected current sequence, for example, [8.44, 8.53, ...]. The second sequence is the tolerance sequence, for example, [0.5, 0.5, ...]. These two sequences together constitute the dynamic definition of normal behavior.

[0050] Specifically, the deviation score calculation subunit 144 is used to calculate the deviation score stream based on the expected current sequence, tolerance sequence, and real-time data stream. It should be understood that after the expected current tolerance calculation subunit generates a dynamic evaluation scale synchronized with the actual operating trajectory, it simultaneously possesses both the data on what actually exists and the benchmark for what should exist. However, the original difference between these two, such as an absolute current difference, is not sufficient for direct fault diagnosis. A 0.5 ampere deviation generated at the beginning of the stroke may represent a completely different degree of anomaly than a 0.5 ampere deviation generated at the end of the stroke. Therefore, a standardized metric is needed to uniformly assess the severity of deviations, converting the original, physically-unit-based current deviation into a dimensionless, standardized deviation score, thereby providing a comparable and consistent input feature for subsequent time-series pattern analysis.

[0051] In a feasible technical solution, the specific process of the deviation score numerical stream calculation subunit 144 is as follows: The specific implementation process of the deviation score numerical stream calculation subunit 144 is a point-by-point mathematical operation. This subunit processes the three sequences iteratively. In each iteration, it synchronously extracts three data points located at the same time index position from the three sequences. Specifically, it extracts the motor current value from the real-time data stream, the expected current value from the expected current sequence, and the tolerance value from the tolerance sequence.

[0052] Subsequently, the subunit will strictly calculate these three values ​​according to the preset formula. In a feasible technical solution, the deviation fractional value flow calculation subunit 144 is used to: calculate the deviation fractional value flow based on the expected current sequence, tolerance sequence, and real-time data flow using the following formula, wherein the formula is: ;in, This is the motor current. For the desired current, For tolerance, This is the deviation fraction. The calculation process of this formula consists of two steps: First, calculate the absolute difference between the real-time motor current and the expected current at that position to obtain an initial deviation; then, divide this initial deviation by the tolerance value given by the model at that position. This division operation is the key to standardization, converting the absolute current deviation into a relative deviation in units of tolerance, i.e., the deviation fraction. Let's illustrate with a specific calculation example: For the first data point processed, the three synchronous data points received by the subunit are: the motor current in the real-time data stream is 7.8 amps, the expected current in the expected current sequence is 8.44 amps, and the tolerance in the tolerance sequence is 0.5 amps. The subunit substitutes these values ​​into the formula: Deviation fraction = |7.8 - 8.44| / 0.5 = |-0.64| / 0.5 = 1.28. This calculation result of 1.28 indicates that at the current point in time, the deviation of the actual current is 1.28 times the normal fluctuation range. This calculation process will be repeated continuously as the input data stream progresses. For each point in the sequence, a corresponding deviation score is generated. Finally, the output of the deviation score stream computation subunit 144 is a brand new time series of the same length as the input sequence, i.e., the deviation score stream. For example, a complete deviation score stream might be [1.28, 1.26, 1.35, ..., 4.5, 4.8, 5.1].

[0053] Specifically, the fault analysis subunit 145 is used to perform anomaly accumulation judgment and fault mode identification based on the deviation fractional value stream to obtain fault decisions and fault types. That is, after the deviation fractional value stream calculation subunit 144 successfully transforms the original physical deviation into a standardized, dimensionless deviation fractional value stream, a time series that can objectively reflect the degree of anomaly is ready. However, fault judgment is not based on instantaneous, isolated deviation values, but rather relies more on the dynamic patterns exhibited by these deviation values ​​over a period of time. A brief, accidental deviation spike may simply be harmless noise, while a persistent, even gradually increasing, deviation is highly likely to be a sign of a real fault. Therefore, a higher-level analysis unit is needed that can go beyond examining individual data points and instead start from the macroscopic patterns of the entire time series to perform intelligent anomaly accumulation judgment and fault root cause identification. To achieve this ultimate diagnostic goal, this application needs to perform anomaly accumulation judgment and fault mode identification on the deviation fractional value stream to deeply analyze the temporal characteristics of the deviation fractional value stream, thereby accurately identifying fault modes and ultimately generating clear and executable fault decisions.

[0054] In a feasible technical solution Figure 4 This is a block diagram of a fault analysis subunit in an energy storage circuit system for a medium-voltage switchgear operating mechanism according to an embodiment of this application. Figure 4As shown, the fault analysis subunit 145 includes: a deviation temporal pattern encoding secondary subunit 1451, used to input the deviation score stream into a sequence encoder based on an LSTM model to obtain a deviation temporal pattern feature encoding vector; a fault type generation secondary subunit 1452, used to input the deviation temporal pattern feature encoding vector into a classification head to obtain the fault type; and a fault decision secondary subunit 1453, used to generate the fault decision based on the fault type.

[0055] In the aforementioned feasible technical solution, the specific process of the fault analysis subunit 145 is as follows: The first step is executed by the deviation time-series pattern encoding secondary subunit 1451. The deviation score stream is, for example, a sequence containing hundreds of data points of the entire energy storage process: [1.28, 1.26, 1.35, ..., 4.5, 4.8, 5.1]. An LSTM-based sequence encoder is a special type of recurrent neural network. Its internal structure includes input gates, forget gates, and output gates. This gating mechanism enables it to effectively learn and remember long-term dependencies in time series, making it very suitable for analyzing data with clear time-series characteristics, such as energy storage processes. The specific architecture of this sequence encoder can be a two-layer stacked LSTM network, each layer having, for example, thirty-two hidden units. The encoder's role is not to classify each point in the sequence, but to compress the dynamic evolution information of the entire input sequence and encode it into a fixed-length high-dimensional vector, i.e., the deviation time-series pattern feature encoding vector. In specific implementation, the values ​​in the deviation score stream are fed into the first layer of the LSTM encoder one by one in chronological order. At each time step, the LSTM unit combines the current input deviation score with the hidden state and cell state passed from the previous time step, updates its own state through its internal gating logic, and outputs a new hidden state. This process is progressive, with information constantly flowing and being refined within the network. When the last data point of the sequence, such as 5.1, is processed, the hidden state vector output by the second-layer LSTM network at the last time step is used as the final representation of the entire sequence, i.e., the deviation temporal pattern feature encoding vector. This vector, for example, a 64-dimensional floating-point vector [-0.95, 0.82, -0.77, ...], no longer focuses on the specific value of a particular point, but rather contains deep temporal features such as the overall trend, volatility, and duration of the deviation sequence. It is worth mentioning that the weights and bias parameters of this LSTM encoder were obtained during the product development phase through supervised learning training on a large amount of pre-collected and labeled fault data.

[0056] The second step, generating a secondary subunit 1452 based on the fault type, continues execution. The classification head consists of one or more fully connected neural network layers. In a specific implementation, it can be a simple structure: a fully connected layer with an input dimension of 64 (matching the encoding vector dimension) and an output dimension of 4, such as a predefined four fault types: normal, slight stall, severe stall, and motor overload, followed by a Softmax activation function. When the 64-dimensional deviation temporal pattern feature encoding vector [-0.95, 0.82, -0.77, ...] is input into this classification head, it is first linearly transformed by the weight matrix of the fully connected layer and then biased to obtain a vector containing four raw scores. Subsequently, the Softmax function converts these four raw scores into a probability distribution. For example, the output might be [0.01, 0.04, 0.94, 0.01]. This probability vector means that the probability of the current energy storage process being judged as normal is 1%, the probability of slight jamming is 4%, the probability of severe jamming is 94%, and the probability of motor overload is 1%. The fault type generation sub-unit 1452 will select the category with the highest probability as the final diagnosis result. In this example, it will output severe jamming as the fault type of this energy storage process. The weights and bias parameters of this classification head are jointly trained end-to-end with the LSTM encoder. The training loss function (such as cross-entropy loss) will drive the entire model to learn how to accurately map the temporal feature vector to the corresponding fault label.

[0057] The third step is executed by the fault decision-making subunit 1453. Internally, this subunit is implemented as a rule-based decision logic module, functioning similarly to a lookup table or a conditional judgment structure. It maps abstract fault types to specific, executable operation instructions. This mapping relationship is pre-defined based on engineering experience, safety procedures, and equipment protection strategies.

[0058] For example, the internal logic of this subunit might be as follows: If the received fault type is normal, the fault decision is "operation successful, no alarm." If the received fault type is "minor stall," the fault decision is "send a general alarm message to the host computer, record the event log, and recommend preventative maintenance." If the received fault type is "severe stall," the fault decision is "immediately send an emergency stop command to the dynamic motor control circuit to cut off the motor power; simultaneously send the highest-level emergency alarm to the monitoring center through the bidirectional communication interface; lock the operating mechanism, and prohibit the next operation."

[0059] In this example, since the input fault type is severe jamming, the fault decision-making subunit 1453 will immediately generate and execute the corresponding emergency stop and alarm decisions. Specifically, this subunit will first immediately generate and broadcast a high-priority digital command, an emergency stop command, on the internal bus. This command is sent directly to the dynamic motor control circuit 120. Upon receiving this command, the dynamic motor control circuit 120 will instantly cut off the drive signal to the solid-state relay supplying the energy storage motor, thereby immediately stopping the energy supply to the motor and stopping it in the shortest possible time. This effectively avoids the possibility of coil overheating and burnout or irreversible mechanical damage to the transmission mechanism caused by continuous motor stalling. At the same time, the subunit will simultaneously encapsulate a structured emergency alarm data packet. This data packet is detailed, including not only a clear fault type identifier of severe jamming, but also a precise timestamp of the fault occurrence, the specific position reading of the energy storage mechanism when jammed, and a snapshot of the deviation score before the trigger judgment, among other key contextual information. The data packet is then passed to the bidirectional communication interface 150, which formats it according to a preset industrial communication protocol such as Modbus or CAN and immediately sends it to the upper-level monitoring system or remote operation and maintenance center. This ensures that operation and maintenance personnel can be informed of the occurrence of a serious fault and its detailed diagnostic information as soon as possible. In addition, this sub-unit will also set a serious fault lockout flag in the internal status register of the energy storage status analysis unit 140. Once this flag is set, the entire energy storage circuit will enter a safe lockout state, actively rejecting any subsequent energy storage commands until on-site maintenance personnel troubleshoot the fault and manually reset it. This constitutes a robust software-level safety protection, preventing misoperation under fault conditions.

[0060] In a preferred embodiment, the energy storage state analysis unit 140 includes: an active baseline model loading subunit 140-1, used to load an active baseline model, wherein the active baseline model is a probabilistic model based on Gaussian process regression; a data stream acquisition subunit 140-2, used to acquire a real-time data stream, the real-time data stream including the current position and motor current; a desired current tolerance generation subunit 140-3, used to input the real-time data stream into the active baseline model to obtain a desired current sequence and a tolerance sequence; a deviation calculation subunit 140-4, used to calculate a deviation score stream based on the desired current sequence, the tolerance sequence, and the real-time data stream; and a fault decision type analysis subunit 140-5, used to perform anomaly accumulation judgment and fault mode recognition based on the deviation score stream to obtain a fault decision and a fault type. Specifically, the implementation process of the data stream acquisition subunit 140-2 and the fault decision type analysis subunit 140-5 is the same as the implementation process of the real-time data stream acquisition subunit 142 and the fault analysis subunit 145 in the above optional embodiment, and therefore will not be specifically described.

[0061] Specifically, in traditional diagnostic schemes, the active baseline model stores the expected current and tolerance as independent variables in a lookup table. While this approach facilitates retrieval, its core flaw lies in treating the energy storage process as a series of isolated data points, thus completely losing the inherent continuity and correlation of the physical system. In a healthy mechanical-electrical system, the state variables (such as current) should change smoothly and continuously with position, and their uncertainties (i.e., tolerances) should be closely related to the system's dynamic characteristics (such as speed and load changes). Discretizing these continuous dynamic characteristics into a static lookup table leads to insufficient model accuracy, easily missing early minor anomalies in smooth curve regions due to excessively wide tolerances, or generating false alarms in regions of sharp curve changes due to excessively strict tolerances. Therefore, to overcome these limitations, this scheme adopts a probabilistic model based on Gaussian process regression as the active baseline model. Its fundamental purpose is to replace the simple, discretized lookup table model with a probabilistic model that more closely reflects physical reality and treats the entire current-position curve as a complete continuous function. In this way, the model can capture the inherent correlation between any two location points and output predictions in the form of a probability distribution. This makes the expected current and tolerance no longer two independent parameters, but rather intrinsic properties of the same prediction distribution. This non-parametric model can flexibly fit complex motor load curves and adaptively adjust its prediction confidence based on data density, thereby achieving a more accurate, robust, and intelligent fault diagnosis benchmark.

[0062] Specifically, the active baseline model loading subunit 140-1 is used to load the active baseline model, which is a probabilistic model based on Gaussian process regression. In detail, the first step is the definition of the Gaussian process model. This process establishes a fundamental mathematical assumption for the entire modeling process: the healthy current-location relationship is no longer considered a fixed curve, but rather defined as a set of functions that follow specific probabilistic laws and have infinite possibilities. Specifically, historical health data is first acquired, corresponding to the location... Health current value And learn the Gaussian process model. This Gaussian process is composed of the mean function. Sum of covariance functions The definition, its mathematical expression is: This formula declares an unknown function describing the variation of the health current with location. , representing the position The healthy current value itself follows a Gaussian process distribution. Here, the mean function This represents a priori guess about the current value when there is no observation data, and can be preset to a simple constant, such as 0 or a simple constant. Covariance function The kernel function, or kernel function, defines any two positions. and Current value on and The correlation between them. It elevates the analytical paradigm from simple numerical matching to probabilistic inference at the function level, laying the theoretical foundation for subsequent capture of curve continuity and adaptive adjustment tolerance. Next is the concretization of the covariance function (kernel function). This process requires giving the abstract concept of covariance in the previous step a clear and computable mathematical form. By introducing a specific covariance function, i.e., the kernel function, the model can quantify the correlation strength between any two travel location points. This scheme can use the squared exponential kernel function, also known as the radial basis function kernel, which has a clear physical meaning. Its formula is: This formula transforms the physical intuition that current values ​​are more correlated when locations are close into a precise mathematical rule, thereby enforcing the smoothness of the function curve in the model. The formula... and These are the current values ​​at two different locations. It is the signal variance, which is a hyperparameter learned from historical data. It is used to control the overall magnitude of the deviation of the expected current curve from its mean, corresponding to the overall drastic degree of current change in the energy storage process. For example, if it is 20.0, it means that the model expects the overall magnitude (standard deviation) of the healthy current curve from its mean to be about sqrt(20.0), which is about 4.5 amperes. The length scale is another key hyperparameter that describes the smoothness of the function's variation. For example, 5.0 indicates that the current has a strong local correlation at the travel location. The larger the value, the more the current value at one location point will affect a more distant location point; the smoother the function, the more stable and slowly changing the mechanical process. The smaller the value, the more drastic the function may change over a very small distance, corresponding to processes involving many rapid mechanical movements (such as gear meshing and over-dead points). These hyperparameters are automatically learned by applying optimization algorithms (such as maximizing the marginal likelihood function) to a large amount of historical health data, and are related to noise variance. Together through historical health data The optimal value is found by maximizing the marginal likelihood function.

[0063] Specifically, the expected current tolerance generation subunit 140-3 is used to input the real-time data stream into the active baseline model to obtain the expected current sequence and tolerance sequence. That is, it applies a pre-configured Gaussian process-based active baseline model to perform online prediction on the newly acquired data points and generate matching expected currents and dynamic tolerances. When real-time data, i.e., new location points, is received... At that time, the model will comprehensively utilize all historical health data and learned hyperparameters to calculate its corresponding predicted Gaussian distribution using the following formula: New expected current (predicted mean). The calculation formula is: New tolerance squared (prediction variance) The calculation formula is: In this way, it no longer outputs a fixed lookup table value, but a complete probability distribution. It is the most probable current value inferred based on all historical data and their correlations, i.e., the expected current. More importantly, It provides a dynamic, adaptive tolerance. In the formula, It is a current vector of historical health data, containing all the current measurements from the historical health data used for training. It involves substituting any two values ​​of the current vector from historical health data into the above... The covariance matrix calculated by the kernel function is used to represent the internal correlation between historical data. It is the identity matrix. It is the noise variance; yes and The correlation between these data points, such as the correlation vector composed of the reciprocals of the Euclidean distance, is a column vector representing the correlation between new real-time data and all historical health data. yes The current of various historical health data in the middle, It is a vector transpose operation; It is the prior variance of the new position itself, i.e. The magnitude of the prediction variance depends not only on the prior variance but also on corrections made up for by known information. If Very close to historical data, It will be very large, causing the subtracted portion to become larger. Smaller values ​​indicate higher certainty and tighter tolerance; conversely, if... Away from all historical data, The tolerance will increase, meaning the certainty is low and the tolerance will be widened. In this way, the model can tighten the tolerance in the smooth and predictable region of the energy storage curve to capture small anomalies, while loosening the tolerance in the region of sharp curve changes or sparse historical data to tolerate larger normal fluctuations.

[0064] In other words, after obtaining the real-time current and the expected current and dynamic tolerance predicted by the Gaussian process model, simply calculating the absolute difference between the real-time current and the expected current cannot effectively assess the degree of anomaly, because the amplitude of normal fluctuations varies at different operating stages. A fixed deviation threshold is difficult to adapt to the dynamic changes throughout the entire process, which may lead to missed detection of minor faults in the current stable region or a large number of false alarms in the current drastic change region. Therefore, it is necessary to standardize the original current deviation to generate a dimensionless index that can objectively measure the severity of the anomaly. This process, based on the expected current sequence, tolerance sequence, and real-time data stream generated in the previous steps, generates a deviation fractional stream through standardized absolute deviation calculation.

[0065] Specifically, the deviation calculation subunit 140-4 is used to calculate the deviation fractional stream based on the expected current sequence, tolerance sequence, and real-time data stream. It is based on the predicted mean output by the Gaussian process model. and the predicted standard deviation The standardized absolute deviation can be expressed by the formula Calculations show that This refers to the motor current in the real-time data stream. This formula converts an absolute deviation (in physical units, amperes) into a relative deviation (in units of the current location's prediction standard deviation, i.e., dynamic tolerance), or deviation fraction. This fraction represents how many times the observed current value deviates from the model's expected value predicted at the current location. A concrete calculation example illustrates this: at a certain moment, the motor current acquired by the real-time data stream... The current is 9.5 amperes, while the Gaussian process model predicts the expected current based on the current location. The value is 8.0 amperes, and the predicted standard deviation (i.e., dynamic tolerance) is also given. The current is 0.5 amperes. Substituting this into the formula, the deviation score is |9.5-8.0| / 0.5, which is 3.0. This result of 3.0 indicates that the currently observed current value deviates from the expected value predicted by the model by three times its own prediction standard deviation, which is a significant deviation signal. This calculation process is continuously performed for each point in the data stream, ultimately generating a new time series of the same length as the input sequence, namely the deviation score stream, which provides a standardized and information-rich input for subsequent fault mode identification.

[0066] The shift from deterministic models based on discrete point matching to probabilistic models based on function distributions leverages the inherent continuity and correlation of the physical system, as well as the uncertainty in the perception of the system state, to achieve adaptive tolerance and significantly reduce false alarms and false negatives. Specifically, in the smooth and predictable region of the energy storage curve, the model's prediction variance is significantly reduced. It will naturally shrink, tolerance This tightening makes the system more sensitive to minor early anomalies; while in regions of drastic curve changes or sparse historical data, the prediction variance increases, and the tolerance widens accordingly, effectively tolerating larger normal fluctuations. Furthermore, even if real-time data has not appeared precisely in historical data, the model can still provide reasonable expected current and prediction variance based on information from its neighbors. Moreover, the hyperparameters obtained during training can themselves serve as a quantitative description of the system's health status; for example, if an optimal length scale is found... The fact that the value decreases over time indicates that the smoothness of system operation is declining, which may be an early sign of mechanical wear and deterioration of lubrication, providing a new dimension for predictive maintenance.

[0067] In summary, the energy storage loop system 100 for medium-voltage switchgear operating mechanisms based on the embodiments of this application is explained, aiming to solve core problems in traditional solutions such as low motor control accuracy, lag in energy storage status feedback, and insufficient system reliability. By introducing a dynamic motor control circuit, soft start and contactless control of the energy storage motor are achieved, effectively avoiding arc loss and shortened lifespan caused by traditional hard switching modes, and significantly improving the stability and accuracy of motor control. A multi-stage travel detection device replaces the mechanical limit switch, providing real-time and continuous position monitoring of the energy storage mechanism, completely eliminating feedback lag and inaccuracy. The core energy storage status analysis unit, through intelligent acquisition, analysis, and decision-making of signals, can identify abnormal operating conditions in the energy storage process in real time, thereby greatly improving the system's intelligent diagnosis and early warning capabilities and enhancing overall reliability. In addition, the power supply unit provides a stable operating foundation, and the bidirectional communication interface ensures information interaction between the system and the external environment, jointly constructing a closed-loop controllable, status-feedbackable, and fault-predictable intelligent energy storage ecosystem, comprehensively improving the response speed and operational stability of the medium-voltage switchgear operating mechanism.

[0068] As described above, the energy storage loop system 100 for medium-voltage switchgear operating mechanisms according to embodiments of this application can be implemented in various wireless terminals, such as servers with energy storage loop algorithms for medium-voltage switchgear operating mechanisms. In one possible implementation, the energy storage loop system 100 for medium-voltage switchgear operating mechanisms according to embodiments of this application can be integrated into a wireless terminal as a software module and / or a hardware module. For example, the energy storage loop system 100 for medium-voltage switchgear operating mechanisms can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal; of course, the energy storage loop system 100 for medium-voltage switchgear operating mechanisms can also be one of many hardware modules of the wireless terminal.

[0069] Alternatively, in another example, the energy storage loop system 100 for the medium-voltage switchgear operating mechanism and the wireless terminal may also be separate devices, and the energy storage loop system 100 for the medium-voltage switchgear operating mechanism may be connected to the wireless terminal via wired and / or wireless networks, and transmit interactive information in accordance with an agreed data format.

[0070] Various implementations of this disclosure have been described above. The foregoing description is exemplary and not exhaustive. Furthermore, it is not limited to the disclosed implementations, and many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations.

Claims

1. A stored energy circuit system for a medium voltage switchgear operating mechanism, characterized by, include: Power supply unit, dynamic motor control circuit, multi-stage stroke detection device, energy storage status analysis unit and bidirectional communication interface; Power supply unit, used to provide a stable power supply; Dynamic motor control circuit, used for soft start and contactless control of energy storage motor; A multi-stage travel detection device is used to monitor the travel of energy storage mechanisms in real time. Energy storage status analysis unit, used for signal acquisition and intelligent analysis and decision-making; A two-way communication interface is used to enable information exchange; The energy storage state analysis unit includes: The baseline model loading sub-unit is used to load the active baseline model; A real-time data stream acquisition subunit is used to acquire real-time data streams, which include the current position and motor current. The desired current tolerance calculation subunit is used to input the real-time data stream into the active baseline model to obtain the desired current sequence and tolerance sequence. The deviation fractional numerical flow calculation subunit is used to calculate the deviation fractional numerical flow based on the desired current sequence, tolerance sequence, and real-time data stream; The fault analysis subunit is used to perform anomaly accumulation judgment and fault mode recognition based on the deviation score stream to obtain fault decision and fault type.

2. The energy storage circuit system for operating mechanism of medium voltage switchgear according to claim 1, characterized in that, The dynamic motor control circuit includes solid-state relays.

3. The energy storage circuit system for operating mechanism of medium voltage switchgear according to claim 2, characterized in that, The energy storage status analysis unit receives the start energy storage command through a two-way communication interface. After confirming that the status is correct, the energy storage status analysis unit sends a control signal to the solid-state relay in the dynamic motor control circuit. The control signal is used to gradually increase the voltage applied to the energy storage motor.

4. The energy storage circuit system for operating mechanism of medium voltage switchgear according to claim 3, characterized in that, The control signal is a PWM signal.

5. The energy storage circuit system for medium voltage switchgear operating mechanism according to claim 1, characterized in that, The desired current tolerance calculation subunit is used to input each real-time position in the real-time data stream into the active baseline model to obtain the desired current sequence and tolerance sequence.

6. The energy storage circuit system for medium voltage switchgear operating mechanism according to claim 1, characterized in that, The deviation score value flow calculation subunit is configured to calculate a deviation score value flow based on the expected current sequence, the tolerance sequence, and the real-time data flow according to the following formula: ; wherein, is a motor current, is an expected current, is a tolerance, is a deviation score value.

7. The energy storage circuit system for medium voltage switchgear operating mechanism according to claim 1, characterized in that, The fault analysis subunit includes: The deviation temporal pattern coding second-level sub-unit is used to input the deviation fractional numerical stream into the LSTM-based sequence encoder to obtain the deviation temporal pattern feature coding vector; A secondary sub-unit for generating fault types is used to input the deviation time-series pattern feature encoding vector into the classification head to obtain the fault type; The fault decision-making subunit is used to generate the fault decision based on the fault type.

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