Processing system for electrical secondary control loop for high power scenarios

Abstract

The embodiment of the specification provides a processing system for an electrical secondary control loop in a high-power scene, and the processing system for the electrical secondary control loop in the high-power scene is modular collaborative processing system integrating real-time high-precision data acquisition, multi-level intelligent analysis and diagnosis, adaptive logic decision and digital twin simulation optimization, realizes closed-loop management and autonomous optimization from data to decision, significantly improves the overall reliability, action accuracy and response speed of the secondary control loop under high-power complex working conditions, and reduces the system debugging, operation and maintenance cost and potential risk through continuous verification and strategy optimization of the simulation module, and enhances the adaptability of the system to equipment state changes and operation mode conversion.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of electrical technology, and in particular to a processing system for electrical secondary control circuits in high-power scenarios. Background Technology

[0002] Secondary control loops in power systems monitor, control, and protect primary equipment. Especially in high-power and complex operating scenarios such as power transmission and distribution, traditional secondary control loops typically rely on fixed logic and discrete devices. The connections between functional links are loose, lacking overall coordination and adaptive capabilities. When faced with a wide variety of equipment, changing operating modes, and harsh electromagnetic environments, the system has limitations in reliability, response speed, and accurate fault identification. Furthermore, subsequent logic optimization relies on manual experience and offline testing, resulting in long iteration cycles and an inability to achieve continuous self-improvement during operation. This makes it difficult to meet the ever-increasing demands of modern power systems for safety, efficiency, and intelligent operation and maintenance.

[0003] Therefore, a better solution is urgently needed. Summary of the Invention

[0004] In view of this, embodiments of this specification provide a processing system for electrical secondary control circuits in high-power scenarios to address the technical deficiencies existing in the prior art.

[0005] According to a first aspect of the embodiments of this specification, a processing system for electrical secondary control circuits in high-power scenarios is provided, characterized in that the system includes a data acquisition and preprocessing module, an analysis and diagnosis module, a logic control and decision-making module, and a simulation optimization module;

[0006] The data acquisition and preprocessing module is configured to acquire raw signals from high-power electrical fields and process them to generate standard data frames; The analysis and diagnosis module is configured to receive standard data frames, extract features, perform multi-level analysis, and output diagnostic results. The logic control and decision-making module is configured to receive diagnostic results, generate and verify control strategies in conjunction with the current power grid operation mode, and output a sequence of control commands. The simulation optimization module is configured to build and maintain a digital twin model synchronized with the actual system, perform simulation verification based on standard data frames and control command sequences, and generate optimization schemes.

[0007] In one possible implementation, the data acquisition and preprocessing module is specifically configured as follows: The system acquires raw signals containing both analog and digital status signals through sensors. The analog signal is conditioned by amplification and filtering, and the conditioned analog signal is converted into a digital signal. Perform verification and standardization processing on digital signals and digital status signals to generate standard data frames; If a continuous abnormality is detected in the signal channel during signal conditioning, the redundant backup sensor data of the corresponding channel will be activated.

[0008] In one possible implementation, the data acquisition and preprocessing module performs real-time filtering of wideband electromagnetic interference and steep-rise surge voltages through a multi-stage active filter circuit during signal conditioning.

[0009] In one possible implementation, the analysis and diagnostic module is specifically configured as follows: Extract electrical characteristics and state logic sequences from standard data frames; The analysis and diagnostic module includes a rapid response layer, a deep analysis layer, and a collaborative diagnostic layer; The fast response layer performs a rapid initial judgment on the extracted electrical feature quantities and state logic sequences based on preset thresholds and rules; The deep analysis layer performs deep analysis based on a machine learning model that integrates electrical and non-electrical features; The collaborative diagnostic layer combines adjacent interval information to perform collaborative diagnosis. The analysis and diagnosis module outputs diagnostic results that include the equipment's health status, fault type, and confidence level.

[0010] In one possible implementation, the electrical features extracted in real time from the standard data frame include the fundamental RMS value of the current and voltage waveforms, harmonic content, phase angle, power, and fault transient features; the non-electrical features fused by the deep analysis layer include the cumulative operating time of the equipment and the ambient temperature.

[0011] In one possible implementation, the collaborative diagnostic layer is configured to perform joint reasoning by integrating information from the current interval, adjacent intervals, and station-level information when distinguishing fault types and locating fault points.

[0012] In one possible implementation, the logic control and decision-making module is specifically configured as having a built-in dynamically configurable control logic rule library. Based on the received diagnostic results and the current power grid operation mode, a control strategy is selected or generated from the control logic rule base. Before generating the control command sequence, an internal logic verification process is initiated, using Boolean algebra and timing logic to perform static and dynamic verification of the control strategy, and interlocking of hardware logic loops and software logic to prevent malfunctions; after the verification is passed, the control command sequence is generated and sent to the field actuators.

[0013] In one possible implementation, the control policies stored in the control logic rule base include policies for handling transient ground faults and automatically adapting to reclosing. After generating a sequence of control commands and sending them to the field actuators, the logic control and decision-making module continuously monitors the command execution feedback. If no correct feedback is received, the backup control channel is activated within a preset time.

[0014] In one possible implementation, the simulation optimization module is specifically configured as follows: A digital twin model is established based on the actual topology and parameters of a high-power electrical field. To synchronize the digital twin model with the state of the actual system; Using a digital twin model, offline simulation and online shadow mode simulation are performed based on standard data frames and control command sequences; Based on the differences between the simulation results and the actual records, an optimization scheme for the control logic rule base is generated.

[0015] In one possible implementation, online shadow mode simulation specifically involves feeding real-time data into a digital twin model without interfering with actual control, and simulating the execution of different backup control strategies in the current state to predict the outcome.

[0016] In one possible implementation, Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a processing system for an electrical secondary control circuit in a high-power scenario, provided by one embodiment of this specification. Detailed Implementation

[0018] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0019] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0020] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0021] This specification provides a processing system for electrical secondary control circuits in high-power scenarios, which will be described in detail in the following embodiments.

[0022] See Figure 1 , Figure 1 This document illustrates a system schematic diagram of a processing system for an electrical secondary control loop in a high-power scenario, according to an embodiment of this specification. Specifically, it includes a data acquisition and preprocessing module, an analysis and diagnosis module, a logic control and decision-making module, and a simulation optimization module. The data acquisition and preprocessing module is configured to acquire and process raw signals from the high-power electrical field to generate standard data frames. The analysis and diagnosis module is configured to receive the standard data frames, extract features, perform multi-level analysis, and output diagnostic results. The logic control and decision-making module is configured to receive the diagnostic results, generate and verify control strategies based on the current power grid operation mode, and output a control command sequence. The simulation optimization module is configured to establish and maintain a digital twin model synchronized with the actual system, perform simulation verification based on the standard data frames and control command sequences, and generate an optimization scheme.

[0023] The data acquisition and preprocessing module can refer to a functional unit deployed in an electrical control cabinet or local control unit, responsible for synchronously acquiring raw electrical quantities and status quantities from various primary equipment and secondary protection devices. The analysis and diagnosis module can refer to a software functional group running on a station control layer server or high-performance edge computing device; its core task is to perform deep analysis of the input data to identify system status and potential faults. The logic control and decision-making module can refer to a functional unit that makes final action decisions and drives actuators based on established protection and control logic and real-time diagnostic conclusions; it typically has high reliability and fast response capabilities. The simulation optimization module can refer to a software system running on an independent engineering workstation or cloud platform, used to build and run virtual models corresponding to the actual physical system for strategy verification and performance optimization. A standard data frame can refer to a uniformly formatted information packet containing payload data, timestamps, data source identifiers, and checksums. A diagnostic result can refer to a structured output that includes at least the status rating of the equipment or circuit, the identified anomaly or fault type, and its corresponding confidence level. A control command sequence can refer to a set of instructions arranged according to a specific timing and logical relationship, used to drive field devices such as circuit breakers, disconnect switches, or protection pressure plates. A digital twin model can refer to a computer model that accurately replicates the actual electrical primary wiring, secondary circuit topology, equipment parameters, and dynamic characteristics in virtual space.

[0024] The present invention will be further described below through a detailed embodiment: This processing system is applied to the high-power scenario monitoring and control of a critical outgoing circuit in a 110kV substation. Upon system startup, the data acquisition and preprocessing module begins operation. This module, connected to the outgoing current transformer, voltage transformer, and related intelligent terminals via cables, acquires analog signals of three-phase current and voltage in real time, as well as digital status signals such as circuit breaker auxiliary contact positions and protection device alarm signals. The acquired analog signals are amplified and preliminarily filtered by a signal conditioning circuit, and then converted into digital signals by a high-speed analog-to-digital converter. All digital signals and digital status signals are packaged, appended with precise timestamps and device identifiers, forming standard data frames, and sent to the analysis and diagnostic module via the substation's communication network.

[0025] The analysis and diagnosis module continuously receives standard data frames from the aforementioned data acquisition and preprocessing module. It first parses the instantaneous waveform data of current and voltage from the data frames and calculates their fundamental effective value, harmonic content, and phase relationship. Simultaneously, it performs logical analysis on the circuit breaker state change sequence. At a single operational instant, the module detects a sudden and sharp increase in the C-phase current, while the voltage drops. Its built-in fast response layer, based on a preset overcurrent threshold, immediately makes a preliminary determination of a "suspected short-circuit fault" and passes this preliminary conclusion, along with the extracted transient fault features, to the deep analysis layer. The deep analysis layer retrieves its pre-trained machine learning model, fusing the current electrical characteristics with historical equipment operating data and non-electrical characteristics such as ambient temperature. The model outputs a high-confidence "three-phase short circuit within the zone" diagnostic result. The collaborative diagnosis layer then integrates the voltage drop situation within the current bay and queries information from adjacent bays to confirm the absence of a through-fault current, thereby further confirming the location of the fault point. Finally, the module generates a diagnostic result containing "Fault type: Three-phase short circuit in the area, Confidence level: 99%, Recommended action: Instant trip", and sends it to the logic control and decision module.

[0026] Upon receiving the aforementioned diagnostic results, the logic control and decision-making module immediately initiates the decision-making process. The module, considering the current power grid as operating normally, retrieves a matching strategy from its dynamically configurable control logic rule base. For the case of a "three-phase short circuit within the zone" with extremely high confidence, the highest priority strategy in the rule base is selected: "Immediately issue a trip command." Before execution, the module's internal logic verification unit quickly reviews the logical conditions involved in the command and ensures through hardware loops that it will not malfunction due to a single software fault. After successful verification, the module generates a clear control command sequence for "trip outgoing line 101 circuit breaker," which is simultaneously sent to the substation's intelligent terminal and the circuit breaker itself via the optocoupler isolation output board and GOOSE message.

[0027] Meanwhile, the simulation optimization module runs continuously in the background. Based on the actual drawings and equipment parameters of the outgoing circuit, it has already constructed a corresponding digital twin model and receives standard data frames from the data acquisition and preprocessing module and copies of control commands from the logic control and decision-making module in real time. When the aforementioned fault occurs, the simulation optimization module synchronously injects the same fault conditions into the digital twin model and simulates the tripping command issued by the logic control module. By comparing the circuit breaker action time and fault clearing waveform in the virtual model with the actual system's recorded action events, the simulation module can assess whether the speed and selectivity of this action meet expectations. Over long-term operation, by analyzing numerous such cases, the simulation module may discover risks of misalignment in the current protection settings under certain edge operating modes, thereby generating optimized settings or modifying logic coordination relationships. After review by engineers, these optimized schemes can be used to update the rule base of the aforementioned logic control and decision-making module.

[0028] The beneficial effects of this embodiment are that by modularizing and forming a closed loop of four major functions—data acquisition, intelligent diagnosis, decision control, and simulation optimization—a secondary control system capable of autonomous perception, analysis, decision-making, and continuous self-optimization is constructed. This fundamentally changes the limitations of traditional discrete devices and fixed logic, significantly improving the overall response speed, action accuracy, and operational reliability of high-power complex electrical circuits in the face of instantaneous faults and abnormal operating conditions. At the same time, through the introduction of digital twin technology, the system is provided with powerful capabilities for offline verification and online self-evolution, reducing operation and maintenance costs and long-term operational risks.

[0029] In one possible implementation, the data acquisition and preprocessing module is specifically configured to: acquire raw signals containing analog signals and digital status signals through sensors; perform signal conditioning on the analog signals, including amplification and filtering, and convert the conditioned analog signals into digital signals; perform verification and standardization processing on the digital signals and digital status signals to generate standard data frames; and if a continuous abnormality is detected in the signal channel during the signal conditioning process, enable the redundant backup sensor data of the corresponding channel.

[0030] Analog signals can refer to continuously changing voltage or current signals, the amplitude of which is proportional to the electrical quantities (such as current and voltage) of the primary system. Digital status signals can refer to discrete, binary signals representing the switching position of equipment (such as on / off) or the activation / deactivation status of protection (such as activated / deactivated). Amplification refers to using electronic devices such as operational amplifiers to boost weak sensor signals to an amplitude range suitable for subsequent circuit processing. Filtering refers to using electronic filter circuits to remove unwanted frequency components from a signal, retaining the useful fundamental frequency or characteristic frequency band signal. Continuous signal channel abnormality refers to a fault state such as an open circuit, short circuit, signal exceeding limits, or prolonged lack of change in a sensor input circuit. Redundant backup sensor data refers to data collected by a second, independent sensor installed at the same measurement point to achieve high reliability.

[0031] In the embodiment where the aforementioned system is applied to a 110kV outgoing circuit, the specific implementation process of the data acquisition and preprocessing module is as follows: The module acquires analog current and voltage signals through precision sensors installed on the secondary sides of the current transformers and voltage transformers in phases A, B, and C. Simultaneously, it acquires the "closed / open" status signal of the circuit breaker through cable hard contacts. The analog current signal is typically a small signal in the milliampere range. It is first amplified by a high-input-impedance isolation operational amplifier to the volt level for subsequent processing. The amplified signal then enters an active filter circuit composed of multiple operational amplifiers for filtering. The "clean" analog signal after amplification and filtering is sent to a high-speed analog-to-digital converter with synchronous sampling, converting it into a 16-bit precision digital quantity. During this process, the module's channel self-test unit continuously monitors the signal quality of each analog channel, for example, checking whether the signal is continuously zero (possibly due to a broken wire) or exceeds a reasonable range (possibly due to transformer saturation or failure). If the module detects that the output of the B-phase current channel remains abnormally low for several power frequency cycles, the self-test unit determines that the channel has failed and immediately issues a control command to seamlessly switch the data source to a pre-configured redundant backup current transformer secondary side sensor installed in the same phase but with a different winding, thereby ensuring the continuity of the B-phase current data. All converted digital signals and the digital status signals acquired through optocoupler isolation are encapsulated together, and a unified synchronization time stamp and channel identifier are added to form a standard data frame output.

[0032] The beneficial effect of this embodiment is that by performing professional amplification, filtering and digitization processing on the original signal, and implementing a strict channel self-test and redundancy switching mechanism, it ensures that the data input to the subsequent intelligent analysis module has high precision, high anti-interference and high availability, providing a solid and reliable data foundation for the whole system to make correct decisions, and effectively avoiding the risk of system misjudgment or failure to operate due to a single sensor or channel failure.

[0033] In one possible implementation, the data acquisition and preprocessing module performs signal conditioning by using a multi-stage active filter circuit to filter wideband electromagnetic interference and steep-rise surge voltage in real time.

[0034] Among them, multi-stage active filter circuits can refer to electronic filter circuits composed of multiple operational amplifiers cascaded with resistor and capacitor networks, with each stage capable of suppressing interference in different frequency bands. Wideband electromagnetic interference can refer to various electromagnetic noises ranging from low frequency to high frequency (e.g., tens of kilohertz to hundreds of megahertz). Steeply rising edge surge voltage can refer to transient overvoltage pulses whose voltage amplitude rises sharply in an extremely short time (e.g., on the microsecond scale).

[0035] In the aforementioned data acquisition and preprocessing module's step of conditioning analog signals, the filtering function is implemented by a specially designed multi-stage active filter circuit. This circuit is not a simple single-stage filter, but rather consists of multiple filter units connected in series. The first stage is typically a low-pass filter with a high cutoff frequency, used to filter out high-frequency noise generated by the switching of power electronic equipment. The second stage may be designed as a notch filter for a specific frequency point to suppress known strong radiated interference. The third stage focuses on dealing with transient surges, and its circuit parameters are optimized to smoothly handle instantaneous pulses with extremely high voltage rise rates, preventing subsequent precision analog-to-digital converters from saturating or being damaged due to input overshoot. This multi-stage coordinated filtering design ensures that even sensors installed near high-power switchgear filled with strong electromagnetic interference such as circuit breaker opening and closing operations and frequency converter operation can maintain the purity and stability of the power frequency fundamental frequency component after being conditioned by this circuit, greatly improving the signal-to-noise ratio.

[0036] The beneficial effect of this embodiment is that the targeted design of multi-stage active filter circuits enables the system to have the excellent ability to work stably in harsh electromagnetic environments. It can effectively remove various wide-band interferences and extreme surges, ensuring the authenticity and accuracy of core electrical quantity measurement data, which is a prerequisite for achieving high-reliability protection and control.

[0037] In one possible implementation, the analysis and diagnosis module is specifically configured to: extract electrical characteristic quantities and state logic sequences from standard data frames; the analysis and diagnosis module includes a fast response layer, a deep analysis layer, and a collaborative diagnosis layer; the fast response layer performs a rapid preliminary judgment on the extracted electrical characteristic quantities and state logic sequences based on preset thresholds and rules; the deep analysis layer performs deep analysis based on a machine learning model that integrates electrical and non-electrical characteristic quantities; the collaborative diagnosis layer performs collaborative diagnosis by combining adjacent interval information; and the analysis and diagnosis module outputs diagnostic results including equipment health status, fault type, and confidence level.

[0038] Electrical characteristics can refer to quantifiable indicators calculated from current and voltage waveforms, such as RMS value, phase, harmonics, and power. State logic sequences refer to the chronological order of changes in the state quantities of various switches and protection devices. A fast response layer refers to a rapid judgment logic unit based on simple rules and thresholds, used for preliminary identification of obvious anomalies or faults. A deep analysis layer refers to a functional layer that uses complex algorithms or models to deeply mine and analyze multi-dimensional features. A machine learning model refers to an algorithmic model trained on historical data that can learn patterns from new data and make predictions or classifications. Non-electrical characteristics refer to features that are not directly related to electrical quantities but affect the system state, such as temperature, time, and equipment lifespan. A collaborative diagnostic layer refers to a functional layer that comprehensively utilizes information from multiple related devices or areas for joint analysis and reasoning. Equipment health status refers to the assessment level of the current operational integrity of the equipment. Confidence level refers to the probability estimate of the correctness or reliability of the diagnostic results.

[0039] In the aforementioned system embodiment, the internal operation of the analysis and diagnosis module is specifically described as follows: After receiving the standard data frame, the module first performs feature extraction. The electrical feature extraction unit calculates the real-time effective values, positive and negative sequence components, and harmonic amplitudes of the three-phase current and voltage, and performs transient analysis on the waveform to capture features such as the starting point of current mutations and the rising slope. The state logic sequence extraction unit analyzes the change events and timing of state quantities such as circuit breakers, disconnectors, and protective soft pressure plates. Subsequently, these features are sent in parallel to three levels. The fast response layer acts as a "sentinel," running a set of simple but extremely fast judgment logic, such as "any phase current is greater than 1.2 times the rated value for 10 milliseconds." If this condition is met, it is immediately marked as an "overcurrent alarm." This preliminary conclusion can be generated within milliseconds, providing focus and early warning for subsequent in-depth analysis. The deep analysis layer acts as an "expert," receiving alarms and all feature data from the fast response layer. This layer deploys a gradient boosting decision tree model trained on massive amounts of historical fault data. The model considers not only the current and voltage characteristics but also non-electrical characteristics such as the line's load curve over the past 24 hours and the current ambient temperature as input. For the aforementioned case of a sudden surge in C-phase current, the model comprehensively assesses whether it is a genuine short-circuit fault or a transient overcurrent caused by the starting of a large motor, and outputs a probabilistic deep diagnostic result. The collaborative diagnostic layer acts as a "commander." When the deep analysis layer is uncertain about the nature or location of the fault, this layer uses the station's communication network to obtain protection information from adjacent busbar sectionalizing switches, current directions from relevant feeders, etc., to perform regional collaborative reasoning, thereby making a more global and accurate final diagnosis, and attaching a comprehensive confidence level.

[0040] The beneficial effect of this embodiment is that by constructing a three-layer architecture of rapid response, in-depth analysis and collaborative diagnosis, the optimal balance between speed and accuracy in fault identification is achieved. This ensures the immediate perception of critical faults and overcomes the diagnostic challenges of complex working conditions and boundary faults by using intelligent algorithms and multi-source information fusion, thereby significantly improving the accuracy and robustness of fault identification.

[0041] In one possible implementation, the electrical features extracted in real time from the standard data frame include the fundamental RMS value of the current and voltage waveforms, harmonic content, phase angle, power, and fault transient features; the non-electrical features fused by the deep analysis layer include the cumulative operating time of the equipment and the ambient temperature.

[0042] The fundamental RMS value refers to the effective value of the power system's AC signal at power frequency. Harmonic content refers to the magnitude of each harmonic component in a periodic non-sinusoidal AC signal whose frequency is an integer multiple of the fundamental frequency. Phase angle refers to the angle corresponding to the time difference between two AC signals of the same frequency. Power refers to active power, reactive power, and apparent power. Fault transient characteristic quantities refer to the unique changes in electrical quantities that differ from steady-state characteristics at the moment a fault occurs or is cleared, such as traveling wave fronts and high-frequency components. Cumulative equipment operating time refers to the total energized operating time of the equipment since commissioning. Ambient temperature refers to the air temperature surrounding the equipment installation site.

[0043] In the specific implementation of feature extraction and deep analysis in the analysis and diagnosis module, the extraction of electrical features is comprehensive and multi-dimensional. The fundamental RMS value reflects the basic magnitude of electrical quantities; harmonic content reveals the power quality status of the power grid and the signs of certain specific faults; the phase angle between voltage and current is key to calculating the power factor and determining the power flow direction; active and reactive power directly reflect the load status of equipment. Fault transient features are particularly important, including the initial traveling wave front of current and voltage at the time of fault occurrence, energy distribution within a specific frequency band, etc., serving as fingerprint information to distinguish fault types. During model inference at the deep analysis layer, in addition to the rich electrical features mentioned above, the model also introduces important non-electrical features. Accumulated equipment operating time serves as an indirect indicator of equipment aging and insulation life, helping the model assess whether older equipment has a higher probability of failure under the same electrical stress. Ambient temperature directly affects the allowable current carrying capacity and heat dissipation conditions of the equipment. For example, in hot summers, the model may appropriately lower the overload alarm threshold or assign higher weight to temperature features when diagnosing overheating faults. This deep integration of electrical and non-electrical characteristics enables the analytical diagnostic model to possess a comprehensive judgment ability similar to that of an experienced senior engineer.

[0044] The beneficial effect of this embodiment is that by extracting and fusing multi-dimensional features covering steady state, transient state, power quality and the state of the equipment itself, a more comprehensive fault diagnosis model input that is closer to the real operating state of the physical world is constructed, which greatly enhances the intelligent diagnosis system's ability to identify complex and hidden faults and its predictive assessment ability of equipment health status.

[0045] In one possible implementation, the collaborative diagnostic layer is configured to perform joint reasoning by integrating information from the current interval, adjacent intervals, and station-level information when distinguishing fault types and locating fault points.

[0046] Specifically, "interval information" can refer to the measurement and protection information of the electrical circuit where the fault occurred or is suspected to have occurred. "Adjacent interval information" can refer to relevant information of other circuits on the same busbar or electrically closely connected to the faulty circuit. "Station-level information" can refer to public information within the entire substation or information that needs to be aggregated and processed by station control equipment. "Joint reasoning" can refer to the process of comprehensively utilizing information from multiple sources and of multiple types, and making a comprehensive judgment through logic, timing, or algorithms.

[0047] In the collaborative diagnostic layer operation of the aforementioned analysis and diagnostic module, its information integration and joint reasoning capabilities are specifically reflected in the handling of complex fault scenarios. For example, when the protection device of a 110kV outgoing line at this substation detects an increase in current and a decrease in voltage, but the voltage drop is not very severe, the fast response layer and the deep analysis layer may find it difficult to determine on their own whether this is a minor fault at the substation's outlet or a through-current caused by a busbar fault at the opposite substation. At this time, the collaborative diagnostic layer is activated. It first retrieves detailed information about this bay, including the impedance values ​​measured by the protection and the phase relationship between voltage and current. Next, it collects information from adjacent bays through the substation network: checking whether the protection of other outgoing lines on the same busbar has been activated and whether the current direction is pointing towards the busbar; and checking the current status of the bus tie switch. Furthermore, it may obtain protection action signals or fault recording data fragments of relevant lines at the opposite substation through wide area communication. By comparing the measured impedance of this station with the total impedance of the line and analyzing the relationship between the current direction and amplitude at multiple points, the collaborative diagnostic layer can perform regional fault location calculations and ultimately infer that the fault point is more likely to be located on the opposite side of the line, thereby avoiding unnecessary over-level maloperation of the station's protection or clearly distinguishing faults at this station from faults outside the area.

[0048] The beneficial effect of this embodiment is that by breaking through the information limitations of a single bay, it realizes information collaborative diagnosis across bays and even across substations, which significantly improves the accuracy of fault location and the selectivity of protection actions. It effectively solves the problems of false operation and failure to operate that traditional protection is prone to occur under the complex operation mode of the power grid, and enhances the safety and stability of the overall operation of the power grid.

[0049] In one possible implementation, the logic control and decision-making module is specifically configured as follows: it has a built-in dynamically configurable control logic rule base; based on the received diagnostic results and the current power grid operation mode, it selects or generates control strategies from the control logic rule base; before generating the control command sequence, it initiates an internal logic verification process, uses Boolean algebra and timing logic to perform static and dynamic verification of the control strategy, and prevents malfunctions through mutual interlocking of hardware logic loops and software logic; after the verification is passed, it generates the control command sequence and sends it to the field actuators.

[0050] Among these, the control logic rule base can refer to a knowledge base storing a series of "condition-action" rules. Dynamic configurability means that the content of the rule base can be modified, added to, or deleted during system operation through authorization, without requiring system downtime or program recompilation. Boolean algebra refers to a mathematical system based on logical variables (true / false, 1 / 0) and logical operations (AND, OR, NOT). Temporal logic refers to mathematical tools used to describe and verify logical states that change over time. Static verification refers to checking the completeness and consistency of logical conditions without considering time factors. Dynamic verification refers to checking the sequence, duration, and debouncing of actions in conjunction with timing. The interlocking of hardware logic loops and software logic refers to protective logic composed of independent hardware circuits that works in parallel with the software-implemented logic. When the hardware loop's condition is not met, it can physically block the output of the software logic.

[0051] In the aforementioned system embodiment, the specific decision-making and execution process of the logic control and decision-making module is as follows: The core of this module is a control logic rule base that can be edited and loaded online. Each rule in the rule base defines the sequence of actions to be executed under a specific combination of diagnostic results and specific power grid modes. When the module receives the result "three-phase short circuit in the area, confidence level 99%" from the diagnostic module, it first confirms that the current power grid is in normal dual-bus parallel operation mode. Subsequently, it matches the corresponding instantaneous tripping rule in the rule base. Before formally generating the "trip" command, the module's verification unit is activated. Static verification checks whether all the logical conditions involved in the command are met, such as: whether the line protection function is engaged, whether the circuit breaker is in the closed position, and whether there is no blocking signal. The "AND" operation result of these Boolean quantities must be "true". Dynamic verification checks the timing, such as confirming that the relevant start signal has lasted for a sufficient period of time before issuing the trip command. At the same time, an independent simple blocking loop composed of hardware relays or programmable logic devices is also running. This hardware loop monitors critical abnormal conditions. Once met, it directly and physically disconnects the trip output loop, preventing actual execution even if the software logic errs in issuing a trip command. Only when both the software logic verification and the hardware loop show no interlocking signals will the module ultimately generate a "trip 101 circuit breaker" command sequence with a unique operation ticket number, and drive the smart terminal through a reliable communication protocol and output board.

[0052] The beneficial effect of this embodiment is that by making the control strategy regular and dynamic, and introducing a strict hardware and software dual verification and interlocking mechanism, it not only ensures the flexibility and adaptability of the control action, but also builds a solid defense against malfunctions, achieving fast, accurate and absolutely reliable control output in complex and urgent fault handling processes.

[0053] In one possible implementation, the control strategies stored in the control logic rule base include strategies for handling transient ground faults and automatically adapting to reclosing; after the logic control and decision module generates a control command sequence and sends it to the field actuator, it continuously monitors the command execution feedback. If no correct feedback is received, the backup control channel is activated within a preset time.

[0054] Among them, transient grounding faults refer to grounding faults caused by lightning, bird damage, etc., in which the insulation can recover automatically after the arc extinguishes itself. Automatic adaptive reclosing refers to a strategy that automatically adjusts the reclosing delay time, number of reclosing operations (e.g., single-phase, triple-phase), and reclosing method (e.g., single-phase, three-phase) based on fault type, line parameters, historical success rate, and other information. Command execution feedback refers to the confirmation or status change signal returned by the field actuator (e.g., intelligent terminal, circuit breaker mechanism box) after receiving a control command. Preset time refers to a waiting time limit set based on the typical action time of the actuator. Backup control channel refers to a second set of control paths (e.g., second set of trip coils, backup fiber optic link) independent of the main control channel (e.g., first set of trip coils, main communication link).

[0055] In the application of the aforementioned logic control and decision-making module, its rule base includes intelligent handling strategies specifically for transient faults. For example, for a 10kV feeder equipped with automatic reclosing, when the diagnostic module determines it to be a "single-phase transient ground fault," the logic control module's matching strategy might be "first trip the faulty phase circuit breaker, and after a delay of 0.8 seconds, automatically issue a three-phase reclosing command." This delay and reclosing method can be dynamically adjusted based on line type, historical fault statistics, and other strategies, i.e., "automatic adaptation." Furthermore, the module's execution reliability is enhanced through feedback monitoring and channel redundancy. When the module issues a "trip" command, it does not passively wait but actively monitors the "trip position" signal returned from the intelligent terminal or circuit breaker mechanism. If no correct feedback signal is received within 150 milliseconds after the command is issued, the module determines that the main control channel may have failed. At this time, the module immediately and automatically activates the backup control channel, for example, through a second set of trip coils or a backup fiber optic link, to resend the same trip command, ensuring that critical operations can be executed.

[0056] The beneficial effects of this embodiment are that it improves power supply reliability through optimization strategies such as built-in intelligent reclosing; and greatly enhances the availability and robustness of the control system through command execution feedback monitoring and seamless switching of backup channels, ensuring the final execution of control commands under extreme conditions and avoiding system failure due to single point of failure in the execution process.

[0057] In one possible implementation, the simulation optimization module is specifically configured to: establish a digital twin model based on the actual topology and parameters of the high-power electrical field; synchronize the digital twin model with the state of the actual system; use the digital twin model to perform offline simulation and online shadow mode simulation based on standard data frames and control command sequences; and generate an optimization scheme for the control logic rule base based on the differences between the simulation results and the actual records.

[0058] In this context, "actual topology" refers to the physical connections between primary electrical equipment (such as transformers, busbars, lines, and circuit breakers). "Parameters" can refer to the electrical parameters (such as impedance, turns ratio, and time constant) and performance parameters (such as operating time and return coefficient) of the equipment. "State synchronization" refers to the process of ensuring that the equipment states (such as switch positions and power flow distribution) in the virtual model are consistent with their actual states in the physical world. "Offline simulation" refers to simulation calculations performed by manually setting faults and operating conditions during non-real-time system operation. "Online shadow mode simulation" refers to simulation deduction during real-time system operation, using real-time data to drive the virtual model for synchronization without affecting actual control.

[0059] In the aforementioned system embodiment, the simulation optimization module operates as follows: At the initial stage of the project, engineers utilize specialized modeling tools to construct a high-fidelity digital twin model corresponding to the 110kV outgoing circuit, based on the substation's design drawings and detailed parameters provided by the equipment manufacturer. The model includes not only primary equipment but also the logical model of secondary protection devices. During system operation, this module continuously acquires copies of standard data frames from the data acquisition and preprocessing module and control command sequences from the logic control and decision-making module by subscribing to the data bus. It then uses this data to drive the corresponding variables in the digital twin model, ensuring the virtual model's state remains synchronized with the actual system. Its simulation functions are divided into two categories: offline simulation, where engineers can arbitrarily set fault types, locations, and system operating modes in the model to pre-verify the correctness and coordination of the protection logic; and online "shadow mode" simulation, where, while the actual system handles real faults, the simulation module "reenacts" the entire process in the twin model using identical data and commands, without affecting actual control. By comparing the results of the "shadow simulation" with the fault waveforms and event sequence records of the actual system over a long period, the module can detect subtle mismatches. For example, if the actual tripping time is 5 milliseconds slower than the simulation expectation, it may indicate that the inherent delay of a certain protection element needs to be corrected. Based on these analyses, the module can automatically or assistedly generate optimization suggestions, such as "shortening the instantaneous overcurrent protection time setting by 5 milliseconds" or "modifying the activation conditions of the failure protection." Once confirmed, these solutions can be used to update the rule base of the aforementioned logic control and decision-making module.

[0060] The beneficial effect of this embodiment is that by constructing a dynamic digital twin that is highly consistent with the actual system and running online shadow simulation, a safe and efficient "testing ground" and "optimization engine" are provided for the secondary control loop. This enables continuous monitoring of system performance, early exposure of potential risks, and closed-loop optimization of control strategies, thus promoting the evolution of the system from "static configuration" to "dynamic self-optimization".

[0061] In one possible implementation, online shadow mode simulation specifically involves feeding real-time data into a digital twin model without interfering with actual control, and simulating the execution of different backup control strategies in the current state to predict the outcome.

[0062] In this context, "not interfering with actual control" means that all calculations and deductions in the shadow simulation are performed in an isolated virtual environment, and its output is used only for analysis and learning, without generating any real control commands to be sent to physical devices. Alternate control strategies refer to other control logic schemes that are not activated in the current system rule base but are considered as candidates or used for comparative analysis.

[0063] In the online shadow mode simulation of the aforementioned simulation optimization module, its "strategy pre-simulation" function is specifically demonstrated. For example, during an actual out-of-area fault crossing, the logic control and decision-making module correctly does not act according to the established strategy. Simultaneously, in shadow mode, the simulation optimization module, in addition to re-enacting this "no-action" scenario with actual data, can also perform virtual deduction: it hypothesizes, "What if a more aggressive blocking logic were used at this time?" Therefore, it loads a backup, more sensitive control strategy model into the twin model and re-performs the simulation calculation using the same set of real-time incoming fault data. The simulation results may show that using the backup strategy will lead to false activation of the line protection. This provides a valuable conclusion: under the current system configuration, the established conservative strategy is correct, while the backup strategy, although acting faster under certain specific internal faults, carries the risk of false activation in this out-of-area fault scenario. This "parallel world" style deduction is entirely completed in the background virtual environment, posing zero risk to the actual operating power system, but providing crucial data for maintenance personnel to evaluate the merits of strategies and for the system to accumulate adaptive knowledge of different strategies.

[0064] The beneficial effect of this embodiment is that the strategy pre-playing function in shadow simulation mode enables the system to explore and evaluate the possibilities of various control logics in a risk-free environment, accumulating decision-making knowledge to cope with unknown future operating scenarios, and significantly improving the system's forward-looking decision-making ability and adaptive evolution potential. Among these, It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0065] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0066] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A processing system for electrical secondary control circuits in high-power scenarios, characterized in that, The system includes a data acquisition and preprocessing module, an analysis and diagnosis module, a logic control and decision-making module, and a simulation optimization module; The data acquisition and preprocessing module is configured to acquire raw signals from the high-power electrical field and process them to generate standard data frames. The analysis and diagnosis module is configured to receive the standard data frame, extract features and perform multi-level analysis, and output diagnostic results. The logic control and decision-making module is configured to receive the diagnostic results, generate and verify control strategies in conjunction with the current power grid operation mode, and output a sequence of control commands. The simulation optimization module is configured to establish and maintain a digital twin model synchronized with the actual system, perform simulation verification based on the standard data frame and the control command sequence, and generate an optimization scheme.

2. The processing system according to claim 1, characterized in that, The data acquisition and preprocessing module is specifically configured as follows: The system acquires raw signals containing both analog and digital status signals through sensors. The analog signal is subjected to signal conditioning including amplification and filtering, and the conditioned analog signal is converted into a digital signal; The digital signal and the digital state signal are verified and standardized to generate a standard data frame. If a continuous abnormality is detected in the signal channel during signal conditioning, the redundant backup sensor data of the corresponding channel will be activated.

3. The processing system according to claim 2, characterized in that, The data acquisition and preprocessing module performs real-time filtering of wideband electromagnetic interference and steep rise-edge surge voltages through a multi-stage active filter circuit during signal conditioning.

4. The processing system according to claim 1, characterized in that, The analysis and diagnostic module is specifically configured as follows: Extract electrical characteristic quantities and state logic sequences from the standard data frames; The analysis and diagnosis module includes a fast response layer, a deep analysis layer, and a collaborative diagnosis layer; The fast response layer performs a rapid initial judgment on the extracted electrical feature quantities and the state logic sequence based on preset thresholds and rules; The deep analysis layer performs deep analysis based on a machine learning model that integrates the electrical and non-electrical features; The collaborative diagnostic layer combines adjacent interval information to perform collaborative diagnosis; The analysis and diagnosis module outputs diagnostic results that include the equipment's health status, fault type, and confidence level.

5. The processing system according to claim 4, characterized in that, The electrical features extracted in real time from the standard data frame include the fundamental RMS value of the current and voltage waveforms, harmonic content, phase angle, power, and fault transient features; the non-electrical features fused by the deep analysis layer include the cumulative operating time of the equipment and the ambient temperature.

6. The processing system according to claim 4, characterized in that, The collaborative diagnostic layer is configured to perform joint reasoning by integrating information from the current interval, adjacent intervals, and station-level information when distinguishing fault types and locating fault points.

7. The processing system according to claim 1, characterized in that, The logic control and decision-making module is specifically configured to have a built-in dynamically configurable control logic rule library. Based on the received diagnostic results and the current power grid operation mode, a control strategy is selected or generated from the control logic rule base. Before generating the control command sequence, an internal logic verification process is initiated. Boolean algebra and timing logic are used to perform static and dynamic verification of the control strategy, and the mutual interlocking of hardware logic loops and software logic is used to prevent malfunctions. After the verification is passed, the control command sequence is generated and sent to the field actuators.

8. The processing system according to claim 7, characterized in that, The control strategies stored in the control logic rule base include strategies for handling transient ground faults and automatically adapting to reclosing. After generating a control command sequence and sending it to the field actuator, the logic control and decision-making module continuously monitors the command execution feedback. If no correct feedback is received, the backup control channel is activated within a preset time.

9. The processing system according to claim 1, characterized in that, The simulation optimization module is specifically configured as follows: A digital twin model was established based on the actual topology and parameters of the high-power electrical site. Synchronize the digital twin model with the state of the actual system; Using the digital twin model, offline simulation and online shadow mode simulation are performed based on the standard data frame and the control command sequence; Based on the differences between the simulation results and the actual records, an optimization scheme for the control logic rule base is generated.

10. The processing system according to claim 9, characterized in that, The online shadow mode simulation specifically involves: without interfering with the actual control, real-time data is fed into the digital twin model, and different backup control strategies are simulated to predict the results under the current state.

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