Energy equipment servo control loop and safety state online diagnosis method and system thereof

Abstract

The invention discloses an energy equipment servo control loop and a safety state online diagnosis method and system thereof, and belongs to the field of energy equipment industrial control network safety. The method comprises the following steps of: hanging a gate on a steam turbine, establishing safe oil pressure of a hydraulic servo-motor, acquiring and storing LVDT voltage parameters of all regulating valves in a full-open state and a full-closed state when the DEH forced regulating valve opening degrees are respectively, and setting mechanical zero bias voltage at the same time; detecting an LVDT feedback voltage, a steam turbine comprehensive flow current and a servo card output voltage of a steam turbine servo control system in real time; the percentage value fed back by the LVDT opening degree, the corresponding reckoning opening degree of each valve under the comprehensive flow of the steam turbine and a servo card output instruction value are calculated; and judging whether the servo control loop is subjected to man-made forcing, network attack or valve jamming or not according to the calculated value. According to the invention, the jamming situation of the servo valve and the safety state of the servo control system can be diagnosed, and the intrinsic safety of energy equipment is realized.

Description

Technical Field

[0001] This invention relates to the field of cybersecurity technology for energy equipment industrial control systems, specifically to a servo control loop for energy equipment and an online diagnostic method for its safety status. Background Technology

[0002] With the promulgation and implementation of a series of laws and regulations, such as the Cybersecurity Law, the Data Security Law, the Personal Information Protection Law, and the Regulations on the Security Protection of Critical Information Infrastructure, cybersecurity has penetrated into all walks of life. Currently, cybersecurity protection for industrial control systems involves deploying firewalls, threat detection systems, and host security devices, and using whitelisting mechanisms and filtering information layer communication packets to strengthen the industrial control network. While this approach has some positive effects on cybersecurity, it cannot effectively prevent malicious manipulation of energy equipment by Trojan programs similar to the Stuxnet virus, which could send illegal operating commands from legitimate industrial control equipment, thereby disrupting processes and damaging energy equipment. Furthermore, it cannot prevent security incidents caused by the failure or loss of control of the energy equipment's own control loops.

[0003] The applicant's earlier Chinese invention patent, publication number CN118998431A, discloses an online diagnostic method for the solenoid valve circuit and its safety status in energy equipment. The method includes: a device connection step, where an online detection device is connected in parallel to the corresponding position in the solenoid valve circuit according to the solenoid valve's operating type; a calibration step, where the initial values ​​of the detection circuit in the online detection device are calibrated; a parameter configuration step, where the calibration values ​​of each detection channel and the state truth table of all solenoid valves in the energy equipment under various operating conditions are stored; an online diagnostic step, where the online detection device is used to detect the solenoid valve circuit and its safety status, and the detection results are combined with the state truth table to determine the safety status of the energy equipment; and a status early warning step, where real-time early warnings are provided for potential network attacks or illegal operations. This patent enables online detection of key control components of solenoid valves in energy equipment (steam turbines, gas turbines, wind turbines), which can prevent cybersecurity attacks targeting solenoid valves in energy equipment. However, the device cannot detect analog signals (i.e., servo valve control signals), cannot determine whether the servo valve signal conforms to the actual process instructions, and therefore cannot infer whether the process logic of the control DPU has been tampered with. Thus, it cannot detect cyberattacks targeting the servo card and its control DPU.

[0004] Nozzle-flap servo valves are key control components of steam turbines, primarily responsible for electro-hydraulic signal conversion, controlling steam valve opening, and consequently controlling the unit's speed and power generation load. Compared to proportional servo valves, nozzle-flap servo valves have higher dynamic response, faster control speed, higher cost, and lower resistance to contamination of the working medium. Therefore, during unit operation, servo valve jamming and sluggish operation frequently occur, affecting the safe operation of the entire unit. While there are many existing methods for servo valve testing, such as detecting oil pressure in the hydraulic actuator chamber, servo valve control oil flow, valve current, and valve voltage, current technologies for servo valve fault diagnosis in steam turbine control systems have many shortcomings. 1. For example, patent number CN106594000A, entitled "A Fault Diagnosis Method for an Electro-hydraulic Servo Valve," provides a method for collecting servo valve oil chamber pressure, valve inlet and outlet flow rates, shell temperature, and valve current, and then processing the data through a neural network model to obtain servo valve fault diagnosis results. This approach has the following disadvantages: ① It requires adding pressure, flow, temperature, and current sensors to the hydraulic actuator, increasing the system failure rate and equipment cost; ② The turbine control system contains numerous and dispersed devices, and the hydraulic actuators are installed in dispersed locations, often hundreds of meters or more away from the control system, making it impractical to add the aforementioned measuring points.

[0005] 2. For example, patent number CN113586173A, entitled "A Method for Judging the Jamming of a Steam Turbine Control Valve," provides a method to obtain the control valve command and valve position feedback information, or the servo voltage of the servo card, and determine whether the deviation between the control valve command and the valve position feedback information exceeds 3%, or whether the servo voltage exceeds ±1V, to determine whether the control valve is in a jammed state. This solution has the following drawbacks: ① The nozzle-baffle type servo card control principle is to calculate the deviation between the control valve command and the feedback, use a PID algorithm, output control current, and control the servo valve opening. This method only detects the servo card and control valve feedback information, and simply judges the relationship between the servo voltage and the feedback information, which does not conform to the control principle of the nozzle-baffle type servo valve and cannot achieve the goal of detecting the jamming of the steam turbine control valve. ② In order to ensure that the hydraulic system is in a safe closed state when the servo card fails, the servo valve will mechanically zero-bias. Therefore, during normal operation, the servo card must output a certain bias command to balance the mechanical zero bias.

[0006] 3. For example, patent number CN107939577B, entitled "An Online Fault Diagnosis Method for Proportional Servo Valve of a Hydropower Turbine Governor," provides a method for online fault diagnosis of the proportional servo valve of a hydropower turbine governor by using information from the servo valve control coil current, the proportional servo valve control input (PWM drive voltage), the proportional servo valve feedback, the main and auxiliary feedback, and the relay stroke feedback. This solution has the following drawbacks: ① Detecting the servo valve control coil current requires a sampling resistor in series, increasing the system failure rate and failing to meet the safety requirements of thermal power generation. ② The proportional servo valve control input is a PWM drive voltage converted to linear current, which does not meet the ±40mA control current requirement of the PID regulation output of the nozzle-baffle type servo valve. ③ It lacks the detection of the hydraulic actuator displacement feedback (LVDT) signal.

[0007] Therefore, there is an urgent need to provide an online diagnostic device for the servo control loop and its safety status of energy equipment, which can provide real-time online detection and diagnosis capabilities for the turbine servo system (servo valve and its control loop), as well as provide network security diagnostic capabilities for the servo card and its controller (DPU) of the turbine digital electro-hydraulic control system (DEH). Summary of the Invention

[0008] This invention aims to solve the aforementioned problems in the prior art by proposing an online diagnostic method and system for the servo control loop and its safety status of energy equipment. It diagnoses the jamming state of the servo valve by detecting the servo valve command voltage and LVDT feedback voltage; and diagnoses the safety status of the servo control system based on the unit's operating mechanism by detecting the unit's flow command. This can prevent security incidents such as cyberattacks and social engineering attacks targeting the energy equipment servo system, thus achieving inherent safety of the energy equipment.

[0009] To achieve the above-mentioned objectives, the technical solution of the present invention is as follows: A servo control loop for energy equipment and its online safety status diagnostic method include the following steps: The connection steps include: connecting the diagnostic device to the turbine servo control system in parallel and connecting it to the AO card and servo card in the servo control system; The setting steps include: engaging the turbine and establishing the hydraulic motor safety oil pressure, acquiring and storing the LVDT voltage parameters of all regulating valves in the DEH forced regulating valve opening states of fully open and fully closed, and setting the mechanical zero bias voltage; The detection steps include: real-time detection of the LVDT feedback voltage, turbine integrated flow current, and servo card output voltage of the turbine servo control system; The calculation steps include: calculating the percentage value of LVDT opening feedback based on LVDT feedback voltage; calculating the percentage value of turbine comprehensive flow based on turbine comprehensive flow current, and converting the estimated opening of each valve under turbine comprehensive flow by combining the turbine comprehensive flow-valve opening relationship table; and calculating the servo card output command value based on servo card output voltage. Diagnostic steps include: When the deviation between the actual valve opening obtained from the stored LVDT voltage parameters and the calculated opening is greater than 5%, it is judged to be either a forced operation or a network attack. When the calculated opening is less than 95% or greater than 5%, the PID is verified to be adjusting normally according to the setting and feedback based on the PID inverse algorithm. If not, the valve is judged to be stuck. When the percentage value of LVDT opening feedback is less than 100% and greater than 0%, and the servo card command voltage is greater than ±10V for more than the set time, it is judged that the valve is stuck.

[0010] Furthermore, the mechanical zero bias voltage is the servo card output voltage when the DEH forced adjustment gate opening is in a non-fully open or non-fully closed state.

[0011] Furthermore, the verification of whether the PID is adjusting normally according to the settings and feedback based on the PID inverse algorithm includes: The difference between the LVDT opening feedback and the calculated opening conversion is used as the input to the PID inverse algorithm. The output Out1 is obtained by combining the mechanical zero bias voltage with the PID inverse algorithm. Use the servo card command voltage as the servo card PID output Out2; Compare the deviations between Out1 and Out2. If the deviations are within the set threshold, it indicates that the PID adjustment is normal; otherwise, the PID adjustment is abnormal.

[0012] This invention also proposes an energy equipment servo control loop and its online safety status diagnostic system, including an MCU module and connected power supply module, storage module, servo command detection module, flow command detection module, LVDT detection module, and communication module; the flow command detection module is used to detect the unit flow output by the AO card in the turbine servo control system; the servo command detection module is used to detect the servo command voltage output by the servo card after PID calculation; the LVDT detection module is used to detect the LVDT feedback voltage; the storage module is used to store the correspondence between the flow curve and valve opening under various operating conditions of the turbine and the configuration information of the entire system; the power supply module is used for the internal power supply of the entire system; the MCU module is used for the implementation of the detection and diagnostic business of the entire system.

[0013] Furthermore, it also includes a communication module connected to the MCU module, which is used to communicate with other devices and collect the operating conditions of the energy equipment.

[0014] Furthermore, the MCU module includes a calculation submodule and a judgment submodule. The calculation submodule is used to calculate the percentage value of LVDT opening feedback based on LVDT feedback voltage; calculate the percentage value of turbine comprehensive flow based on turbine comprehensive flow current, and convert the estimated opening of each valve under turbine comprehensive flow by combining the turbine comprehensive flow-valve opening relationship table; and calculate the servo card output command value based on servo card output voltage. The judgment submodule is used when: If the deviation between the actual valve opening obtained from the stored LVDT voltage parameters and the calculated opening is greater than 5%, it is judged to be either a forced operation or a network attack. When the calculated opening is less than 95% or greater than 5%, the PID is verified to be adjusting normally according to the setting and feedback based on the PID inverse algorithm. If not, the valve is judged to be stuck. If the percentage value of the LVDT opening feedback is less than 100% and greater than 0%, and the servo card command voltage is greater than ±10V for more than the set time, it is judged that the valve is stuck.

[0015] Furthermore, the judgment submodule verifies whether the PID is adjusting normally according to the settings and feedback based on the PID inverse algorithm, including: The difference between the LVDT opening feedback and the calculated opening conversion is used as the input to the PID inverse algorithm. The output Out1 is obtained by combining the mechanical zero bias voltage with the PID inverse algorithm. Use the servo card command voltage as the servo card PID output Out2; Compare the deviations of Out1 and Out2. If the deviation is within the set threshold, it indicates that the PID adjustment is normal; otherwise, the PID adjustment is abnormal.

[0016] In summary, the present invention has the following advantages: 1. This invention can detect the status of servo cards, servo valves, and LVDTs in real time online, eliminating the need for maintenance personnel to go to the site to check the status of servo valves and their control circuits, reducing the risk of industrial equipment refusing to respond to control commands, and ensuring the safe operation of the equipment; 2. This invention can determine whether the servo control system is normal based on the relationship between the unit flow curve and valve opening and the equipment operation mechanism, and provide real-time early warning of network security attacks and malicious forced valve opening behavior. 3. This invention connects the probes to the object under test in parallel and uses a high-impedance acquisition circuit to acquire the relevant voltages of the LVDT and servo card, which reduces the risk of system failure caused by increasing the number of measurement points. Even if the detection device itself fails, it can be easily removed, reducing the impact on the safety of the unit.

[0017] 5. This invention fully utilizes the high precision and high reliability of the original system's servo card and LVDT. By detecting the command and feedback status of the two key electronic devices and comparing the deviation between the PID output command and the feedback, the faults of the servo card and servo valve can be determined without the need to install additional pressure, flow, temperature, current, and other sensors.

[0018] 6. This invention fully considers the relationship between the mechanical zero bias of the servo valve and the PID software algorithm of the servo card, and achieves a complete match with the zero bias compensation of the servo card PID algorithm, thereby minimizing detection errors. Attached Figure Description

[0019] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments, wherein: Figure 1 A schematic diagram of the existing steam turbine servo hydraulic control system architecture; Figure 2 This is a schematic diagram of the diagnostic system. Figure 3 The curve showing the relationship between the unit's steam flow rate and valve opening. Figure 4 This is the circuit diagram for the servo command detection module. Figure 5 This is the circuit diagram for the implementation of the flow command detection module; Figure 6 This is the circuit diagram for the LVDT detection module. Figure 7 This is the valve setting process in the present invention; Figure 8 This is the energy equipment safety status detection process in this invention. Detailed Implementation

[0020] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further clarifies the invention. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0021] Example 1: like Figure 1The diagram shows the architecture of a steam turbine hydraulic servo control system, which mainly consists of a hydraulic actuator, servo valves, a servo card, an LVDT (Low Voltage Controller), and cables. The servo card forms a closed-loop system through valve opening control commands issued by the servo control system's DPU (Digital Power Unit) and hydraulic actuator position feedback. A built-in PID control algorithm controls the servo valve opening, forming a stable control loop. The servo control system of the steam turbine generator set controls the opening of each valve. If the control system is subjected to a network attack, or if the control process parameters are maliciously tampered with or the valve opening is forcibly forced, the unit will be in a dangerous state.

[0022] Therefore, this embodiment proposes an online diagnostic method for the servo control loop of energy equipment and its safety status. The specific implementation steps are as follows: The connection steps include: connecting the diagnostic device to the turbine servo control system in parallel and connecting it to the AO card and servo card in the servo control system; For the tuning steps, please refer to... Figure 7 As shown, it includes: engaging the turbine and establishing the hydraulic motor safety oil pressure, acquiring and storing the LVDT voltage parameters of all regulating valves in the DEH forced regulating valve opening states of fully open and fully closed, and setting the mechanical zero bias voltage; The detection steps include: real-time detection of the LVDT feedback voltage, turbine reference current, and servo card output voltage of the turbine servo control system; The calculation steps include: calculating the percentage value of LVDT opening feedback based on LVDT feedback voltage; calculating the percentage value of turbine comprehensive flow based on turbine comprehensive flow current, and converting the estimated opening of each valve under turbine comprehensive flow by combining the turbine comprehensive flow-valve opening relationship table; and calculating the servo card output command value based on servo card output voltage. Diagnostic steps, refer to Figure 8 As shown, it includes: When the deviation between the actual valve opening obtained from the stored LVDT voltage parameters and the calculated opening is greater than 5%, it is judged to be either a forced operation or a network attack. When the calculated opening is less than 95% or greater than 5%, the PID is verified to be adjusting normally according to the setting and feedback based on the PID inverse algorithm. If not, the valve is judged to be stuck. When the percentage value of the LVDT opening feedback is less than 100% and greater than 0%, and the servo card command voltage is greater than ±10V for more than 5 seconds, it is judged that the valve is stuck.

[0023] like Figure 3The figure shows the relationship curve between the steam flow rate and valve opening of the unit. In the figure: HPG1 & 3 are the valve opening of high pressure regulating valve 1 and high pressure regulating valve 3 and the percentage of the turbine's total flow rate (reference); HPG2 is the valve opening of high pressure regulating valve 2 and the percentage of the turbine's total flow rate (reference); HPG4 is the valve opening of high pressure regulating valve 4 and the percentage of the turbine's total flow rate (reference).

[0024] This solution integrates a diagnostic device into the existing hydraulic servo control system of the steam turbine. By detecting the servo card command voltage and LVDT feedback voltage, it determines the jamming status of the servo valve. By detecting the unit flow command output by the AO card of the control system and based on the valve opening curve, it determines whether the servo command is valid and detects the safety status of the servo control system. This can prevent safety incidents such as network security attacks and malicious forced valve opening and closing, thus achieving inherent safety of energy equipment.

[0025] Application Scenario 1: Steam turbines are crucial energy equipment. Their system speed, power generation, and driving load are influenced by steam flow. The regulating valve controls the steam flow entering the cylinders to perform work, while the servo valve controls the opening of the regulating valve. Servo valves are precision devices, not only expensive but also requiring extremely high precision in controlling the temperature, pressure, and cleanliness of the working medium. In daily production, contamination of the working medium frequently causes valve jamming, leading to valve malfunction, leaving the valve fully open or fully closed, severely impacting unit load and potentially causing safety accidents. The digital electro-hydraulic controller (DEH) is the core control cabinet of the steam turbine, such as... Figure 1 As shown, it includes a controller (DPU), analog input / output (AI / AO) cards, digital input / output (AI / AO) cards, and servo cards (SVR).

[0026] All servo valves of the turbine unit are connected to the servo cards of the DEH via cables. By deploying diagnostic devices in the DEH cabinet, the safety status of all servo cards can be detected simultaneously, providing engineers with a reference for the lifespan of the servo valves so that servo valves in good condition can be replaced in advance during unit overhauls.

[0027] Application Scenario 2: The digital electro-hydraulic controller (DEH) is the core control equipment of a steam turbine, mainly composed of a DCS / PLC system, such as... Figure 1As shown, the DPU is the "brain" of the DEH (Dual Enclosure Unit), controlling the process logic during operation and connecting to I / O cards (DI / DO / AI / AO / SVR, etc.) via a fieldbus. In daily operation, the DPU receives control commands from the operator station and automatically operates according to its built-in process logic, controlling the turbine's field equipment, such as control valves, solenoid valves, and electrical switches. During a cyberattack, attackers can forge operator station control commands, causing DPU process malfunctions and equipment damage, or maliciously force the built-in process logic function blocks, rendering the I / O cards uncontrollable by the DPU's process logic and leading to adverse consequences. By deploying diagnostic devices, the normal operation of the control system can be verified based on the relationship between flow curves and valve openings, and the equipment's operating mechanism, thus realizing its function as a cybersecurity probe for critical infrastructure equipment.

[0028] Example 2 The diagnostic method in Example 1 is implemented using an energy equipment servo control loop and its online safety status diagnostic system, such as... Figure 2 As shown, the diagnostic system includes an MCU module and electrically connected to it a power supply module, a storage module, a servo command detection module, a flow command detection module, an LVDT detection module, and a communication module.

[0029] The system includes: a flow command detection module for detecting the unit flow rate output by the AO card in the turbine servo control system; a servo command detection module for detecting the servo command voltage output by the servo card after PID calculation; an LVDT detection module for detecting the LVDT feedback voltage (primary coil voltage and secondary coil voltage); a storage module for storing the correspondence between flow curves and valve openings under various operating conditions of the turbine, as well as the configuration information of the entire system; an MCU module for implementing the detection and diagnostic functions of the entire system; and a power supply module for internal power supply of the entire system.

[0030] Specifically, the MCU module includes a calculation submodule and a decision-making submodule, wherein: The calculation submodule is used to calculate the percentage value of LVDT opening feedback based on LVDT feedback voltage; calculate the percentage value of turbine comprehensive flow based on turbine comprehensive flow current, and convert the estimated opening of each valve under turbine comprehensive flow by combining the turbine comprehensive flow-valve opening relationship table; and calculate the servo card output command value based on servo card output voltage. The conditional submodule is used when: If the deviation between the actual valve opening obtained from the stored LVDT voltage parameters and the calculated opening is greater than 5%, it is judged to be either a forced operation or a network attack. When the calculated opening is less than 95% or greater than 5%, the PID is verified to be adjusting normally according to the setting and feedback based on the PID inverse algorithm. If not, the valve is judged to be stuck. If the percentage value of the LVDT opening feedback is less than 100% and greater than 0%, and the servo card command voltage is greater than ±10V for more than the set time, it is judged that the valve is stuck.

[0031] Specifically, the judgment submodule verifies whether the PID is adjusting normally according to the settings and feedback based on the PID inverse algorithm, including: The difference between the LVDT opening feedback and the calculated opening conversion is used as the input to the PID inverse algorithm. The output Out1 is obtained by combining the mechanical zero bias voltage with the PID inverse algorithm. Use the servo card command voltage as the servo card PID output Out2; Compare the deviations of Out1 and Out2. If the deviation is within the set threshold, it indicates that the PID adjustment is normal; otherwise, the PID adjustment is abnormal.

[0032] Example 3 This embodiment presents the circuit implementation structure of the servo command detection module in the above-mentioned diagnostic system.

[0033] The circuit implementation of the servo command detection module is as follows: Figure 4 As shown, this circuit is an industrial-grade differential signal high-precision amplification and conditioning circuit based on AD8221. It consists of five main parts: input impedance matching circuit, multi-stage filter circuit, amplifier circuit I, power supply decoupling circuit I, and output filter circuit I.

[0034] Specifically, in the input impedance matching circuit, the servo command voltage signal CRIN- is connected in series with R1 to the -IN pin (pin 1) of the AD8221, and CRIN+ is connected in series with R4 to the +IN pin (pin 4) of the AD8221. R2 is then connected in parallel at the input terminals of R1 and R4. This part achieves impedance symmetry for the differential input signal, overcurrent protection, and matching with the external signal source to prevent a decrease in the common-mode rejection ratio and reduce signal distortion.

[0035] In the multi-stage filter circuit, C4 is grounded by C2 and C8 respectively to filter out common-mode high-frequency noise at the input. The -IN / +IN differential parallel connection of C4 achieves differential-mode high-frequency filtering at the input, while suppressing high-frequency oscillation at the input of AD8221. Multi-stage filtering improves the stability of the input signal.

[0036] In amplifier circuit I, the RG pin (pin 2) and RG pin (pin 3) are shorted to use the default gain of 1 to ensure high-precision signal conditioning; REF (pin 6) is directly grounded (GND), and -VS (pin 5) is connected to -15V. The two share a decoupling filter network composed of C5 and C7 to ensure the potential stability of the output signal; this part relies on the internal precision symmetrical amplification architecture of AD8221 to achieve differential signal amplification with high common-mode rejection ratio, low offset voltage, and low noise, effectively suppressing common-mode interference in industrial environments.

[0037] In the power supply decoupling circuit I, the +VS (pin 8) to +15V and -VS (pin 5) to -15V use a decoupling filter network to achieve power supply stability at the power supply end.

[0038] In the output filter circuit I, the VOUT (pin 7) output is filtered out by an RC low-pass filter to remove residual high-frequency noise and then transmitted to the input terminal of the ADC side, thus realizing industrial-grade high-precision detection of servo command voltage.

[0039] Example 4 This embodiment presents the circuit implementation structure of the flow command detection module in the above diagnostic system.

[0040] The implementation circuit of the flow command detection module is as follows: Figure 5 As shown, this circuit is an industrial-grade differential signal high-precision current detection circuit based on AD8221. It consists of five main parts: sampling circuit, filtering circuit, amplifier circuit II, power supply decoupling circuit II, and output filtering circuit II.

[0041] Specifically, in the sampling circuit, the flow command current signal FlowAIIN- is directly connected to the -IN pin (pin 1) of AD8221, and FlowAIIN+ is directly connected to the +IN pin (pin 4) of AD8221. A high-precision, low-temperature coefficient sampling resistor Rs1 is connected in series at the differential input terminal to form a sampling loop. This part uses a low-resistance (49.4Ω) sampling resistor to minimize the impact on the constant current characteristics of the original circuit and reduce input signal distortion.

[0042] In the filter circuit, -IN / +IN differential parallel Cf1 and Rs1 form a low-pass filter network at the input end, which not only preserves the low-frequency characteristics of the flow command signal, but also accurately suppresses high-frequency differential noise; the two ends of Cf1 are grounded through C19 and C23 respectively to filter out common-mode high-frequency noise at the input end.

[0043] In amplifier circuit II, the RG pin (pin 2) and RG pin (pin 3) are shorted to use the default gain of 1 to ensure high-precision signal conditioning; REF (pin 6) is directly grounded (GND), and -VS (pin 5) is connected to -15V. The two share a filter network composed of C21 and C24 to ensure the potential stability of the output signal; this part relies on the internal precision symmetrical amplification architecture of AD8221 to achieve differential signal amplification with high common-mode rejection ratio, low offset voltage, and low noise, effectively suppressing common-mode interference in industrial environments.

[0044] In the power supply decoupling circuit II, +VS (pin 8) to +15V and -VS (pin 5) to -15V achieve symmetrical decoupling filtering at the power supply end, filtering out low-frequency ripple and high-frequency noise at the power supply end, thereby ensuring power supply stability.

[0045] In the output filter circuit II, the VOUT (pin 7) output is filtered out by the RC low-pass filter (R10 and C22) to remove residual high-frequency noise and is transmitted to the input terminal (AIN_2) on the ADC side, thus realizing the detection of the flow command current signal.

[0046] Example 5 This embodiment presents the circuit implementation structure of the LVDT detection module in the above diagnostic system.

[0047] The implementation circuit of the LVDT detection module is as follows: Figure 6 As shown, the core module of this circuit is the AD637 true RMS converter, which can calculate the true root mean square value of any complex waveform. Its circuit design is as follows: First, COMMON (pin 3) is the analog common ground of the AD637. Sharing this ground with OUTPUT OFFSET (pin 4) simplifies the circuit, while grounding OUTPUT OFFSET provides a default zero-point bias. The AD637's CS pin (pin 5) is connected to +15V through resistor R5, enabling it to operate in normal mode (disabling power-down mode). Resistor R5 is used to prevent accidental short circuits and transient current surges.

[0048] The power supply section uses filter networks composed of C9 / C10 and C11 / C13 for +VS (pin 13) and -VS (pin 12) respectively to filter out low-frequency ripple and suppress high-frequency noise at the power supply end, ensuring stable power supply to the AD637. The sampling signal is isolated from the DC component by the DC blocking resistor C15 before being transmitted to VIN (pin 15).

[0049] The core design of this detection circuit is the RMS conversion and second-order low-pass filtering section. A feedback-type second-order low-pass filter network is constructed using DEN INPUT (pin 6), Cav1, R6, R7, C12, and C14. This second-order low-pass filter network, through the internal buffer amplification of the AD637, achieves strict isolation between the input and output terminals, ensuring that the RMS operation and the input / output signals are not interfered with each other, and ensuring that the signal is not attenuated or distorted during internal processing. DEN INPUT is the divider input pin of the AD637. Connecting it to the connection node between Cav1 and RMS OUT (pin 11) enables "feedback adjustment of the RMS output," reducing the ripple caused by the small Cav1 and achieving a balance between high RMS conversion rate and low error rate.

[0050] Finally, the output signal after second-order filtering is input to the input terminal (AIN_1) of the ADC side, thus realizing the effective value detection of the LVDT signal.

Claims

1. A servo control loop for energy equipment and its online safety status diagnosis method, characterized in that, Includes the following steps: The connection steps include: connecting the diagnostic device to the turbine servo control system in parallel and connecting it to the AO card and servo card in the servo control system; The setting steps include: engaging the turbine and establishing the hydraulic motor safety oil pressure, acquiring and storing the LVDT feedback voltage parameters of all regulating valves in the DEH forced regulating valve opening states of fully open and fully closed, and setting the mechanical zero bias voltage; The detection steps include: real-time detection of the LVDT feedback voltage, turbine integrated flow current, and servo card output voltage of the turbine servo control system; The calculation steps include: calculating the percentage value of LVDT opening feedback based on LVDT feedback voltage; calculating the percentage value of turbine comprehensive flow based on turbine comprehensive flow current, and converting the estimated opening of each valve under turbine comprehensive flow by combining the turbine comprehensive flow-valve opening relationship table; and calculating the servo card output command value based on servo card output voltage. Diagnostic steps include: When the deviation between the actual valve opening obtained from the stored LVDT feedback voltage parameters and the above-calculated opening is greater than 5%, it is judged to be either a forced operation or a network attack. When the calculated opening is less than 95% or greater than 5%, the PID is verified to be adjusting normally according to the setting and feedback based on the PID inverse algorithm. If not, the valve is judged to be stuck. When the percentage value of LVDT opening feedback is less than 100% and greater than 0%, and the servo card command voltage is greater than ±10V for more than the set time, it is judged that the valve is stuck.

2. The servo control loop for energy equipment and its online safety status diagnosis method as described in claim 1, characterized in that, The mechanical zero bias voltage is the servo card output voltage when the DEH forced adjustment gate opening is in a non-fully open or non-fully closed state.

3. The energy equipment servo control loop and its online safety status diagnosis method as described in claim 1 or 2, characterized in that, The verification of whether the PID is adjusting normally according to the settings and feedback based on the PID inverse algorithm includes: The difference between the LVDT opening feedback and the calculated opening conversion is used as the input to the PID inverse algorithm. The output Out1 is obtained by combining the mechanical zero bias voltage with the PID inverse algorithm. Use the servo card command voltage as the servo card PID output Out2; Compare the deviations of Out1 and Out2. If the deviation is within the set threshold, it indicates that the PID adjustment is normal; otherwise, the PID adjustment is abnormal.

4. A servo control loop for energy equipment and its online safety status diagnostic system, characterized in that, The system includes an MCU module and connected power supply module, storage module, servo command detection module, flow command detection module, LVDT detection module, and communication module. The flow command detection module detects the unit flow rate output by the AO card in the turbine servo control system. The servo command detection module detects the servo command voltage output by the servo card after PID calculation. The LVDT detection module detects the LVDT feedback voltage. The storage module stores the correspondence between flow curves and valve openings under various turbine operating conditions, as well as the configuration information of the entire system. The power supply module provides internal power to the entire system. The MCU module enables the detection and diagnostic functions of the entire system.

5. The energy equipment servo control loop and its online safety status diagnostic system as described in claim 4, characterized in that, It also includes a communication module connected to the MCU module, which is used to communicate with other devices and collect the operating status of the energy equipment.

6. The energy equipment servo control loop and its online safety status diagnostic system as described in claim 4, characterized in that, The MCU module includes a calculation submodule and a judgment submodule. The calculation submodule is used to calculate the percentage value of LVDT opening feedback based on LVDT feedback voltage; calculate the percentage value of turbine comprehensive flow based on turbine comprehensive flow current, and convert the estimated opening of each valve under turbine comprehensive flow by combining the turbine comprehensive flow-valve opening relationship table; and calculate the servo card output command value based on servo card output voltage. The judgment submodule is used when: If the deviation between the actual valve opening obtained from the stored LVDT voltage parameters and the calculated opening is greater than 5%, it is judged to be either a forced operation or a network attack. When the calculated opening is less than 95% or greater than 5%, the PID is verified to be adjusting normally according to the setting and feedback based on the PID inverse algorithm. If not, the valve is judged to be stuck. If the percentage value of the LVDT opening feedback is less than 100% and greater than 0%, and the servo card command voltage is greater than ±10V for more than the set time, it is judged that the valve is stuck.

7. The energy equipment servo control loop and its online safety status diagnostic system as described in claim 6, characterized in that, The judgment submodule verifies whether the PID is adjusting normally according to the settings and feedback based on the PID inverse algorithm, including: The difference between the LVDT opening feedback and the calculated opening conversion is used as the input to the PID inverse algorithm. The output Out1 is obtained by combining the mechanical zero bias voltage with the PID inverse algorithm. Use the servo card command voltage as the servo card PID output Out2; Compare the deviations of Out1 and Out2. If the deviation is within the set threshold, it indicates that the PID adjustment is normal; otherwise, the PID adjustment is abnormal.

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