A substation disconnector position confirmation method and system

Abstract

The application discloses a kind of substation isolating switch position confirmation method and system, comprising have: through the real-time monitoring isolating switch position of deployment photoelectric, magnetic force and position sensor, and using redundancy design;The data of multiple sensors is fused and analyzed by artificial intelligence algorithm, and the state of isolating switch is judged by optimizing data;Environment monitoring system real-time monitoring substation environmental change, automatically adjust sensor sensitivity and working mode;Redundant sensor and communication network automatically switch to standby system when main sensor or network fails;Real-time position data of isolating switch is transmitted to dispatch center by remote monitoring system, alarm and generate fault information when abnormal;System has automatic maintenance and self-calibration function, periodically self-check or trigger self-calibration when abnormal;A kind of substation isolating switch position confirmation method and system of the application introduce intelligent sensor, redundancy design and artificial intelligence analysis technology, improve the accuracy and automation level of system.

Description

Technical Field

[0001] This invention belongs to the field of substation disconnector position confirmation, and specifically relates to a method and system for confirming the position of a substation disconnector. Background Technology

[0002] Substation disconnector switch position confirmation methods are used to ensure the correct and reliable status of disconnectors, preventing operational errors or equipment malfunctions. Disconnectors are typically equipped with mechanical indicators to display their current position; these indicators are usually located externally for operator observation. The actual position of the disconnector is monitored through electrical interlocking devices to ensure it does not conflict with other equipment during switching operations. Remote monitoring equipment or SCADA systems are used to monitor the disconnector's position in real time. This method utilizes sensors or position detection devices to transmit switch status information to the dispatch or monitoring center via network, allowing operators to understand the switch's open / closed status in real time. Operators can also manually or automatically check the switch via secondary circuits to verify if it is in the expected operating state. Operators can also conduct direct visual inspections on-site, confirming the switch position by observing the switch handle or indicator. Finally, using intelligent devices and sensors to confirm the disconnector's position in real time and transmit the information to the central monitoring system enhances the accuracy and reliability of the confirmation.

[0003] However, in existing substations, the status of disconnect switches is usually confirmed through mechanical indicators, electrical interlocking devices, and remote monitoring systems. Existing technologies have some shortcomings. Mechanical indicators are susceptible to environmental factors, leading to inaccurate position displays. If the electrical interlocking system malfunctions, position confirmation may fail. Remote monitoring systems are subject to communication delays and network interruptions. Existing sensor technologies may fail or lack accuracy in harsh environments. Manual confirmation methods rely on operator experience and are subject to the risk of human error. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for confirming the position of a substation disconnector. By introducing intelligent sensors, redundant design, and artificial intelligence analysis technology, the invention solves the problems of insufficient accuracy, reliability, and automation in existing technologies.

[0005] The technical solution adopted by this invention to solve its technical problem is:

[0006] A method for confirming the position of a substation disconnector includes the following steps:

[0007] The physical position of the disconnecting switch is monitored in real time by deploying photoelectric sensors, magnetic sensors, and position sensors, and is equipped with a redundant design;

[0008] Data collected from multiple sensors is fused, analyzed using artificial intelligence algorithms, and the status of the disconnecting switch is identified and determined. The sensor data is then optimized using the algorithm.

[0009] An environmental monitoring system is introduced to monitor environmental changes in the substation in real time and automatically adjust the sensitivity or operating mode of the sensors.

[0010] Redundant sensors and communication networks are used, and the system automatically switches to backup sensors or backup networks to confirm the location when the main sensor or network fails.

[0011] The real-time position data of the disconnecting switch is transmitted to the dispatch center through the remote monitoring system. The system provides real-time feedback on the switch position and immediately alarms and generates fault diagnosis information when any abnormality occurs.

[0012] The system employs automatic maintenance and self-calibration functions, automatically performing self-checks during periodic operation or when abnormalities occur.

[0013] As a preferred method, the physical position of the disconnecting switch is monitored in real time by deploying photoelectric sensors, magnetic sensors, and position sensors, and a redundant design is provided:

[0014] Sensor data acquisition and settings:

[0015] P 光 : Measurement values ​​from the photoelectric sensor;

[0016] P 磁 : Measurement values ​​from the magnetic sensor;

[0017] P 位 : Measurement values ​​from the position sensor;

[0018] The accuracy of each sensor is known and represented by weights. The weights of the sensors are set as follows:

[0019] w 光 Weights of photoelectric sensors

[0020] w 磁 Weight of the magnetic sensor

[0021] w 位 Weights of position sensors

[0022] The three weights are achieved as follows:

[0023] w 光 +w 磁 +w 位 =1

[0024] The combined position P after weighted fusion of sensors 综合 Calculated using the following formula:

[0025] P 综合 =w 光 ·P 光 +w 磁 ·P 磁 +w 位 ·P 位

[0026] Redundant design refers to deploying multiple redundant sensors in a system to improve its fault tolerance. This is illustrated by setting up two sensor systems, each with its own output:

[0027] Main system: P 主

[0028] Backup system: P 备

[0029] The calculation formula for redundancy design, by comparing the data of the two systems, sets a tolerance error range ∈, as follows:

[0030] |P 主 -P 备 |≤∈

[0031] If the output difference between the two systems exceeds the tolerance error ∈, an alarm will be triggered or a backup sensor will be activated.

[0032] Based on sensor weighted fusion and redundancy design, the system output P 最终 Calculated using the following formula:

[0033]

[0034] Among them, P 备光 P 备磁 P 备位 This indicates the output data of the backup sensor.

[0035] As a preferred approach, the data collected from multiple sensors is fused, analyzed using artificial intelligence algorithms, and the status of the disconnecting switch is identified and determined. The method for optimizing the sensor data through these algorithms is as follows:

[0036] The system deploys photoelectric sensors, magnetic sensors, and position sensors. The data collected by each sensor is represented as follows:

[0037] P 光 Data from photoelectric sensors;

[0038] P 磁 Data from the magnetic sensor;

[0039] P 位 Data from the position sensor;

[0040] Sensor data fusion is performed using a weighted average, with weights w. 光 w 磁 w 位 Based on the sensor's accuracy and reliability factors, the settings are as follows:

[0041] w 光 +w 磁 +w 位 =1

[0042] Fusion sensor data P 融合 The calculation is as follows:

[0043] P 融合 =w 光 ·P 光 +w 磁 ·P 磁 +w 位 ·P 位

[0044] Artificial intelligence algorithms are used to analyze and identify the state of the fused sensor data. The fused sensor data is set as input X, and the states include:

[0045] S 开 S 关 S 故障 ;

[0046] We will use support vector machines for state recognition. The model formula is as follows:

[0047] f(X) = w T X+b

[0048] Where w is the weight vector of the support vector machine, X is the input vector after sensor data fusion, and b is the bias term. The switch state is determined by calculating the decision function f(X) and its sign.

[0049] If f(X) > 0, then it is determined to be state S. 开 ;

[0050] If f(X) < 0, then it is determined to be state S. 关 ;

[0051] If f(X) exceeds the set threshold range, it indicates an abnormal or faulty state S. 故障 ;

[0052] To optimize sensor data and reduce errors, a feedback mechanism and optimization algorithm will be introduced, and P will be established. 优化 The Kalman filter update formula for the optimized sensor data is:

[0053]

[0054] in, For the optimized state estimate;

[0055] K k Kalman gain;

[0056] z k The sensor measurement value;

[0057] H k The observation matrix;

[0058] For prior estimation;

[0059] Based on sensor data fusion, AI algorithm analysis and optimization, the final disconnector state S is determined. final It is obtained through the following steps:

[0060] Sensor data fusion:

[0061] P 融合 =w 光 ·P 光 +w 磁 ·P 磁 +w 位 ·P 位

[0062] AI state recognition:

[0063] f(X) = w T X+b

[0064] The switch state S is determined based on the sign of f(X) or the threshold. final :

[0065] S final =S 开 if f(X)>0

[0066] S final =S 关 ff(X)<0

[0067] S final =S 故障 if |f(X)| exceeds the threshold

[0068] Data optimization:

[0069] P 优化 =Kalman(P 融合 )

[0070] Accurate state identification is obtained through optimized sensor data.

[0071] As a preferred option, an environmental monitoring system is introduced to monitor environmental changes in the substation in real time and automatically adjust the sensitivity or operating mode of the sensors.

[0072] The environmental monitoring system collects real-time environmental data on temperature, humidity, air pressure, and pollutant concentration at the substation. The environmental monitoring data is defined as follows:

[0073] E 温 E 湿 E 气压 E 污染物 ;

[0074] Sensor sensitivity S 传感器 The sensitivity is dynamically adjusted based on environmental data. The sensitivity adjustment is based on the impact of environmental changes on sensor performance. In high-temperature environments, the sensor will reduce sensitivity to avoid false alarms, while in low-temperature or high-humidity environments, the sensitivity will be increased.

[0075] The adjustment rules for sensor sensitivity are as follows:

[0076] S 传感器 (E 温 E 湿 E 气压 E 污染物 )

[0077] The formula for sensitivity adjustment is:

[0078] S 传感器 =f(E 温 E 湿 E 气压 E 污染物 )

[0079] Where f is the sensitivity adjustment function, which can be a weighted function, a linear function, or a nonlinear function. The following is a simplified model:

[0080] S 传感器 =S 基准 ·(1+α 温 ·E 温 +α 湿 ·E 湿 +α 气压 ·E 气压 +α 污染物 ·E 污染物 )

[0081] Among them, S 基准 This represents the initial sensitivity of the sensor;

[0082] α 温 α 湿 α 气压 α 污染物The weighting coefficients for the influence of environmental factors on sensor sensitivity;

[0083] In addition to sensitivity adjustment, the sensor's operating mode is dynamically adjusted according to environmental changes. The sensor's operating mode M is set. 传感器 For a function:;

[0084] M 传感器 =g(E 温 E 湿 E 气压 E 污染物 )

[0085] Where g is the working mode adjustment function, expressed as:

[0086]

[0087] The environmental monitoring system collects environmental data in real time and calculates the data using the aforementioned function. This dynamically adjusts the sensor sensitivity and operating mode. The integrated control system then adjusts the sensor behavior based on the adjusted sensitivity and operating mode, setting the substation temperature E... 温 When the value exceeds the set threshold, the system will take the following actions:

[0088] Reduce the sensor sensitivity to minimize the impact of temperature on the sensor:

[0089] S 传感器 =S 基准 ·(1-α 温 ·E 温 )

[0090] Adjust the sensor's operating mode to low-frequency sampling to reduce power consumption:

[0091] M 传感器 =Low frequency, high precision

[0092] Ultimately, the sensor output P 传感器 Adjusted by environmental changes, as expressed as:

[0093] P 传感器 (E 温 E 湿 E 气压 E 污染物 )=f(E 温 E 湿 E 气压 E 污染物 )·g(E 温 E 湿 E 气压 E 污染物 )

[0094] Where f and g are the adjustment functions for sensor sensitivity and operating mode, respectively, and the final output is the sensor's measured value.

[0095] As a preferred method, redundant sensors and communication networks are used, and the system automatically switches to backup sensors or backup networks for location confirmation when the main sensor or network fails.

[0096] Deploy multiple sensors, including a main sensor (S 主 ) and backup sensors (S 备 The sensor system works by acquiring sensor data to confirm location. When the main sensor fails, the system will automatically switch to the backup sensor.

[0097] set up:

[0098] S 主 (t) represents the measurement value of the main sensor at time t;

[0099] S 备 (t) represents the measured value of the backup sensor;

[0100] Fault determination function (S) 主 (t),S 备 (t) is used to identify and determine whether the main sensor has malfunctioned;

[0101] Define a fault determination function (S) 主 S 备 The system determines whether to switch based on whether the output of the main sensor is abnormal.

[0102]

[0103] If the fault determination function (S) 主 If ) = 1, it indicates that the main sensor has failed and the system switches to the backup sensor;

[0104] In addition to redundant sensors, a main communication network of N is established based on the redundancy of the communication network. 主 The backup communication network is N. 备 The system monitors the status of the main network in real time and switches to the backup network in case of failure.

[0105] Fault detection function Network fault determination function (N 主 Used to monitor the status of the main network:

[0106]

[0107] The logic of the automatic switching algorithm is as follows:

[0108] When the fault determination function (S) 主) = 1, and the network fault determination function (N) 主 When ) = 0, switch to the backup sensor S. 备 Confirm location;

[0109] If the network fault determination function (N) 主 If ) = 1, then switch to the backup network N. 备 And continue to acquire data from backup sensors;

[0110] To seamlessly switch to backup sensors or backup networks, the system's state transitions are described by the following formula:

[0111]

[0112] Location confirmation based on sensor data S 有效 The position (t) and the state of the communication network are dynamically updated. A position confirmation function, P(t), is established, and the position is calculated based on sensor data.

[0113] P(t)=f(S 有效 (t),N 有效 (t))

[0114] Where, N 有效 (t) represents the currently used communication network;

[0115] In summary, the calculation formula for redundant sensors and communication network systems is as follows:

[0116]

[0117] P(t)=f(S 有效 (t),N 有效 (t))

[0118] Where f is a function that calculates the position based on sensor data, dynamically switching to backup sensors and backup networks.

[0119] Preferably, the real-time position data of the disconnector is transmitted to the dispatch center through a remote monitoring system. The system provides real-time feedback on the switch position and immediately alarms in case of any abnormality. The method for generating fault diagnosis information is as follows:

[0120] The location data of the switch is transmitted to the dispatch center through a remote monitoring system. The switch position collected by the sensor is L(t), which includes the state of the switch, namely the position of the switch and the current operating mode of the switch.

[0121] L(t): Real-time position data of the disconnector switch, the switch state at time t;

[0122] If L(t) = 1, it means the switch is in the "on" state;

[0123] If L(t) = 0, it means that the switch is in the "off" state;

[0124] Location data is transmitted to the dispatch center via a remote monitoring system. The dispatch center receives and feeds back this data in real time. The feedback signal sent to the dispatch center is denoted as R(t), and is updated based on the real-time location data.

[0125] R(t)=L(t)

[0126] The dispatch center receives L(t) in real time and feeds it back to the system users;

[0127] In the application, the system monitors whether the status of the switch is normal. If the sensor data is abnormal or the remote transmission is interrupted, the system will immediately alarm. A threshold is set to identify and determine whether there is an abnormality.

[0128] Two main types of fault detection are established:

[0129] Sensor malfunction: The sensor failed to accurately acquire the switch position or returned a signal that did not match expectations;

[0130] Communication failure: The remote monitoring system experiences interruptions or delays in transmitting switch position data;

[0131] Define a fault determination function F(t) to determine whether a fault exists:

[0132]

[0133] Where F(t) = 1 indicates that a fault has been detected;

[0134] F(t) = 0 indicates that no fault was detected;

[0135] If the system detects a fault (F(t) = 1), it immediately generates fault diagnosis information and issues an alarm signal. Let the alarm signal be A(t), associated with the fault diagnosis information D(t). The alarm signal is defined as follows:

[0136]

[0137] Meanwhile, the fault diagnosis information D(t) is used to record and report the type of fault and the time of occurrence:

[0138]

[0139] If a fault occurs (A(t) = alarm), the system activates the emergency response mechanism, and the emergency response function is set as R. 应急 (t), which is defined as follows:

[0140]

[0141] As a preferred option, the system employs automatic maintenance and self-calibration functions. During periodic operation or when an anomaly occurs, the system automatically performs self-checks using the following method:

[0142] The system performs self-checks periodically, or automatically triggers a self-check process when an anomaly occurs. The self-check process includes the following steps:

[0143] Periodic self-check: The system performs a self-check operation periodically;

[0144] Anomaly detection: When the system detects an anomaly, it triggers a self-check;

[0145] Self-calibration: After self-test, if the system detects deviations or abnormalities, it will perform a self-calibration operation.

[0146] Periodic self-checks are a task that the system performs regularly to ensure that all parts of the system are operating normally. A period T is set. 自检 , represents the self-check time interval. Let the current time be t. The trigger condition for the self-check is expressed as:

[0147]

[0148] That is, the system every T 自检 A self-check is triggered by time;

[0149] Let E(t) be the anomaly detection function, defined as follows:

[0150]

[0151] When the system performs a self-test and detects an anomaly, it automatically performs a self-calibration. The self-calibration operation function is defined as self-calibration(t), which is initiated when an anomaly occurs. The trigger condition for self-calibration is expressed as follows:

[0152]

[0153] That is, when an anomaly is detected or a periodic self-test is performed, a self-calibration operation is triggered;

[0154] Based on the above, the system's self-test and self-calibration functions are performed as follows:

[0155] Periodic self-check trigger:

[0156]

[0157] Anomaly detection:

[0158]

[0159] Self-calibration triggered:

[0160]

[0161] If the periodic self-check trigger(t) = 1 or E(t) = 1, the system will perform a self-check to detect and repair problems. The specific content of the self-check includes:

[0162] Verify the sensor output;

[0163] Check the integrity of data transmission;

[0164] Check the control system for malfunctions;

[0165] If a problem is detected during the self-test, the system will automatically perform a self-calibration operation:

[0166]

[0167] The self-calibration process includes:

[0168] Adjust the sensor's calibration parameters;

[0169] Reset the system's thresholds or biases;

[0170] Repair or restart the malfunctioning system components.

[0171] A substation disconnector switch position confirmation system includes:

[0172] The sensor module is used to deploy photoelectric sensors, magnetic sensors, and position sensors to collect real-time physical position data of the disconnector switch;

[0173] The data fusion and intelligent analysis module is used to fuse and intelligently analyze the data collected by the sensors to identify and determine the switch position;

[0174] The environmental monitoring module is used to deploy temperature and humidity sensors and vibration sensors to monitor changes in the substation environment.

[0175] Redundant system modules are used to deploy redundant sensors and redundant communication networks to ensure that the backup system can take over in the event of a failure in the primary system.

[0176] The remote monitoring and feedback module is used to monitor the switch position in real time and provide fault alarms through the SCADA remote monitoring system.

[0177] The automatic maintenance and self-calibration module is used to periodically perform self-tests and self-calibrations on the system to ensure that all equipment is in optimal working condition.

[0178] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements a substation disconnector position confirmation method and system as described above.

[0179] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method and system for confirming the position of a substation disconnector.

[0180] The beneficial effects of this invention are:

[0181] Through redundant design (redundant sensors, communication networks, etc.), data loss or errors caused by the failure of a single sensor or communication link can be effectively avoided, ensuring that the system can still operate normally when the main sensor or main network malfunctions. By deploying multiple sensors and fusing data, the physical location of disconnecting switches can be monitored in real time and transmitted to the dispatch center via a remote monitoring system. By fusing data collected from multiple sensors and combining it with artificial intelligence algorithms for analysis and optimization, the status of disconnecting switches can be determined more accurately. The system's automatic maintenance and self-calibration functions ensure that the equipment can automatically perform self-checks and calibrations during regular operation or when anomalies occur, greatly reducing the need for manual inspections and improving operation and maintenance efficiency. Through redundant design and intelligent self-calibration mechanisms, the system can flexibly respond to future expansion and upgrade needs. The system can integrate data from different sensors and provide real-time diagnostic reports to help the dispatch center make more scientific decisions. This integrated data support enables operators to have a more comprehensive understanding of the substation's operating status and improves the accuracy of decision-making. Attached Figure Description

[0182] Figure 1 This is a schematic diagram of a substation disconnector position confirmation system according to the present invention. Detailed Implementation

[0183] The principles and features of the present invention are described below. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. The invention is described more specifically by way of example in the following paragraphs. The advantages and features of the invention will become clearer from the following description and claims.

[0184] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0185] Example

[0186] The technical solution adopted by this invention to solve its technical problem is:

[0187] A method for confirming the position of a disengaging switch in a substation includes the following steps:

[0188] The physical position of the disconnecting switch is monitored in real time by deploying photoelectric sensors, magnetic sensors, and position sensors, and is equipped with a redundant design;

[0189] Data collected from multiple sensors is fused, analyzed using artificial intelligence algorithms, and the status of the disconnecting switch is identified and determined. The sensor data is then optimized using the algorithm.

[0190] An environmental monitoring system is introduced to monitor environmental changes in the substation in real time and automatically adjust the sensitivity or operating mode of the sensors.

[0191] Redundant sensors and communication networks are used, and the system automatically switches to backup sensors or backup networks to confirm the location when the main sensor or network fails.

[0192] The real-time position data of the disconnecting switch is transmitted to the dispatch center through the remote monitoring system. The system provides real-time feedback on the switch position and immediately alarms and generates fault diagnosis information when any abnormality occurs.

[0193] The system employs automatic maintenance and self-calibration functions, automatically performing self-checks during periodic operation or when abnormalities occur.

[0194] Simultaneous monitoring by multiple sensors enables the acquisition of multi-dimensional data, effectively preventing system information loss or false alarms caused by the failure of a single sensor. The introduction of artificial intelligence algorithms enhances the system's intelligence level, automatically optimizing and adjusting sensor data to improve accuracy. Environmental monitoring and automatic adjustment ensure the stability of sensors under different climate or environmental conditions, preventing equipment failure or data errors caused by environmental factors. Redundancy increases system reliability; even if the main equipment fails, the system can continue to operate without affecting the normal monitoring and maintenance of the substation. Through a remote monitoring system, the dispatch center can monitor the status of substation equipment in real time, greatly improving management efficiency. Automatic maintenance and self-calibration reduce the need for human intervention, improve the long-term stability of the system, and reduce equipment failure rates.

[0195] The method involves real-time monitoring of the physical position of the disconnecting switch using photoelectric sensors, magnetic sensors, and position sensors, with redundant design features:

[0196] Sensor data acquisition and settings:

[0197] P光 : Measurement values ​​from the photoelectric sensor;

[0198] P 磁 : Measurement values ​​from the magnetic sensor;

[0199] P 位 : Measurement values ​​from the position sensor;

[0200] The accuracy of each sensor is known and is represented by a weight. The weight of each sensor is set as follows:

[0201] w 光 Weights of photoelectric sensors

[0202] w 磁 Weight of the magnetic sensor

[0203] w 位 Weights of position sensors

[0204] The three weights are achieved as follows:

[0205] w 光 +w 磁 +w 位 =1

[0206] The combined position P after weighted fusion of sensors 综合 Calculated using the following formula:

[0207] P 综合 =w 光 ·P 光 +w 磁 ·P 磁 +w 位 ·P 位

[0208] Redundant design refers to deploying multiple redundant sensors in a system to improve its fault tolerance. This is set up as two sensor systems, each with its own output:

[0209] Main system: P 主

[0210] Backup system: P 备

[0211] The calculation formula for redundancy design, by comparing the data of the two systems, sets a tolerance error range ∈, as follows:

[0212] |P 主 -P 备 |≤∈

[0213] If the output difference between the two systems exceeds the tolerance error ∈, an alarm will be triggered or a backup sensor will be activated.

[0214] Based on sensor weighted fusion and redundancy design, the system output P 最终 Calculated using the following formula:

[0215]

[0216] Among them, P 备光 P 备磁 P 备位 This indicates the output data of the backup sensor.

[0217] The introduction of redundancy design gives the system strong fault tolerance. Even if the main system fails, the backup system can seamlessly take over, ensuring the continuity and accuracy of position monitoring. By weighted fusion of data from multiple sensors, the system can fully utilize the advantages of each sensor, avoid data errors or instability from a single sensor, and improve overall position accuracy. The system can monitor the differences between sensors in real time and trigger alarms based on the tolerance error range, promptly identifying problems and avoiding potential risks during long-term operation. By setting sensor weights, the system can flexibly adjust the influence of each sensor according to its measurement accuracy and reliability, optimizing the accuracy of position calculation.

[0218] The method involves fusing data collected from multiple sensors, analyzing it using artificial intelligence algorithms, identifying and determining the status of the disconnector switch, and optimizing the sensor data through algorithms.

[0219] The system deploys photoelectric sensors, magnetic sensors, and position sensors. The data collected by each sensor is represented as follows:

[0220] P 光 Data from photoelectric sensors;

[0221] P 磁 Data from the magnetic sensor;

[0222] P 位 Data from the position sensor;

[0223] Sensor data fusion is performed using a weighted average, with weights w. 光 w 磁 w 位 Based on the sensor's accuracy and reliability factors, the settings are as follows:

[0224] w 光 +w 磁 +w 位 =1

[0225] Fusion sensor data P 融合 The calculation is as follows:

[0226] P 融合 =w光 ·P 光 +w 磁 ·P 磁 +w 位 ·P 位

[0227] Artificial intelligence algorithms are used to analyze and identify the state of the fused sensor data. The fused sensor data is set as input X, and the states include:

[0228] S 开 S 关 S 故障 ;

[0229] We will use support vector machines for state recognition. The model formula is as follows:

[0230] f(X) = w T X+b

[0231] Where w is the weight vector of the support vector machine, X is the input vector after sensor data fusion, and b is the bias term. The switch state is determined by calculating the decision function f(X) and its sign.

[0232] If f(X) > 0, then it is determined to be state S. 开 ;

[0233] If f(X) < 0, then it is determined to be state S. 关 ;

[0234] If f(X) exceeds the set threshold range, it indicates an abnormal or faulty state S. 故障 ;

[0235] To optimize sensor data and reduce errors, a feedback mechanism and optimization algorithm will be introduced, and P will be established. 优化 The Kalman filter update formula for the optimized sensor data is:

[0236]

[0237] in, For the optimized state estimate;

[0238] K k Kalman gain;

[0239] z k The sensor measurement value;

[0240] H k The observation matrix;

[0241] This is a priori estimation;

[0242] Based on sensor data fusion, AI algorithm analysis and optimization, the final disconnector state S is determined. final It is obtained through the following steps:

[0243] Sensor data fusion:

[0244] P 融合 =w 光 ·P 光 +w 磁 ·P 磁 +w 位 ·P 位

[0245] AI state recognition:

[0246] f(X) = w T X+b

[0247] The switch state S is determined based on the sign of f(X) or the threshold. final :

[0248] S final =S 开 if f(X)>0

[0249] S final =S 关 if f(X)<0

[0250] S final =S 故障 if |f(X)| exceeds the threshold

[0251] Data optimization:

[0252] P 优化 =Kalman(P 融合 )

[0253] Accurate state identification is obtained through optimized sensor data.

[0254] By weighted fusion of data from multiple sensors and analysis using AI algorithms (such as SVM), the accuracy of determining the status of disconnect switches can be improved. Kalman filters can effectively reduce noise in sensor data, and optimized data can reduce errors and improve system reliability. Using artificial intelligence algorithms for status recognition can identify potential faults or abnormal states in advance, helping the system to issue timely alarms or take measures to prevent equipment damage. Through sensor data fusion and AI algorithm analysis, even if the measurement data from a certain sensor is inaccurate, the system can still compensate through weighted averaging and optimization algorithms to maintain the stability of the overall system.

[0255] The method of introducing an environmental monitoring system to monitor environmental changes in the substation in real time and automatically adjust the sensitivity or operating mode of the sensors is as follows:

[0256] The environmental monitoring system collects real-time environmental data on temperature, humidity, air pressure, and pollutant concentration at the substation. The environmental monitoring data is defined as follows:

[0257] E 温 E 湿 E 气压 E 污染物 ;

[0258] Sensor sensitivity S 传感器 The sensitivity is dynamically adjusted based on environmental data. The sensitivity adjustment is based on the impact of environmental changes on sensor performance. In high-temperature environments, the sensor will reduce sensitivity to avoid false alarms, while in low-temperature or high-humidity environments, the sensitivity will be increased.

[0259] The adjustment rules for sensor sensitivity are as follows:

[0260] S 传感器 (E 温 E 湿 E 气压 E 污染物 )

[0261] The formula for sensitivity adjustment is:

[0262] S 传感器 =f(E 温 E 湿 E 气压 E 污染物 )

[0263] Where f is the sensitivity adjustment function, which can be a weighted function, a linear function, or a nonlinear function. The following is a simplified model:

[0264] S 传感器 =S 基准 ·(1+α 温 ·E 温 +α 湿 ·E 湿 +α 气压 ·E 气压 +α 污染物 ·E 污染物 )

[0265] Among them, S 基准 This represents the initial sensitivity of the sensor;

[0266] α 温 α 湿 α 气压 α 污染物 The weighting coefficients for the influence of environmental factors on sensor sensitivity;

[0267] In addition to sensitivity adjustment, the sensor's operating mode is dynamically adjusted according to environmental changes. The sensor's operating mode M is set. 传感器 For a function:;

[0268] M 传感器 =g(E 温 E 湿 E 气压 E 污染物 )

[0269] Where g is the working mode adjustment function, expressed as:

[0270]

[0271] The environmental monitoring system collects environmental data in real time and calculates the data using the aforementioned function. This dynamically adjusts the sensor sensitivity and operating mode. The integrated control system then adjusts the sensor behavior based on the adjusted sensitivity and operating mode, setting the substation temperature E... 温 When the value exceeds the set threshold, the system will take the following actions:

[0272] Reduce the sensor sensitivity to minimize the impact of temperature on the sensor:

[0273] S 传感器 =S 基准 ·(1-α 温 ·E 温 )

[0274] Adjust the sensor's operating mode to low-frequency sampling to reduce power consumption:

[0275] M 传感器 =Low frequency, high precision

[0276] Ultimately, the sensor output P 传感器 Adjusted by environmental changes, as expressed as:

[0277] P 传感器 (E 温 E 湿 E 气压 E 污染物 )=f(E 温 E 温 E 气压 E 污染物 )·g(E 温 E 湿 E 气压 E 污染物 )

[0278] Where f and g are the adjustment functions for sensor sensitivity and operating mode, respectively, and the final output is the sensor's measured value.

[0279] By collecting environmental data such as temperature, humidity, air pressure, and pollutant concentration in real time through an environmental monitoring system, the sensors can dynamically adjust their sensitivity and operating mode according to actual environmental changes, thereby improving the system's adaptability to complex environments. In extreme environments such as high temperatures, reducing sensor sensitivity can avoid false alarms or data anomalies caused by excessively high temperatures. In environments such as low temperatures or high humidity, increasing sensitivity helps ensure that the sensors capture and accurately identify environmental changes. When the temperature is too high, adjusting the sensor's operating mode to low-frequency sampling can effectively reduce sensor energy consumption, thereby extending the equipment's lifespan and reducing overall energy consumption. Through the combination of environmental monitoring and intelligent adjustment, this solution enhances the system's stability and reliability. The adjustment of sensor sensitivity and operating mode allows the system to maintain optimal operating conditions under different environmental conditions.

[0280] The method of using redundant sensors and communication networks to automatically switch to backup sensors or backup networks for location confirmation when the main sensor or network fails is as follows:

[0281] Deploy multiple sensors, including a main sensor (S 主 ) and backup sensors (S 备 The sensor system works by acquiring sensor data to confirm location. When the main sensor fails, the system will automatically switch to the backup sensor.

[0282] set up:

[0283] S 主 (t) represents the measurement value of the main sensor at time t;

[0284] S 备 (t) represents the measured value of the backup sensor;

[0285] Fault determination function (S) 主 (t), S 备 (t) is used to identify and determine whether the main sensor has malfunctioned;

[0286] Define a fault determination function (S) 主 S 备 The system determines whether to switch based on whether the output of the main sensor is abnormal.

[0287]

[0288] If the fault determination function (S) 主 If ) = 1, it indicates that the main sensor has failed and the system switches to the backup sensor;

[0289] In addition to redundant sensors, a main communication network of N is established based on the redundancy of the communication network. 主The backup communication network is N. 备 The system monitors the status of the main network in real time and switches to the backup network in case of failure.

[0290] Fault detection function Network fault determination function (N 主 Used to monitor the status of the main network:

[0291]

[0292] The logic of the automatic switching algorithm is as follows:

[0293] When the fault determination function (S) 主 ) = 1, and the network fault determination function (N) 主 When ) = 0, switch to the backup sensor S. 备 Confirm location;

[0294] If the network fault determination function (N) 主 If ) = 1, then switch to the backup network N. 备 And continue to acquire data from backup sensors;

[0295] To seamlessly switch to backup sensors or backup networks, the system's state transitions are described by the following formula:

[0296]

[0297] Location confirmation based on sensor data S 有效 The position (t) and the state of the communication network are dynamically updated. A position confirmation function, P(t), is established, and the position is calculated based on sensor data.

[0298] P(t)=f(S 有效 (t),N 有效 (t))

[0299] Where, N 有效 (t) represents the currently used communication network;

[0300] In summary, the calculation formula for redundant sensors and communication network systems is as follows:

[0301]

[0302] P(t)=f(S 有效 (t),N effective(t))

[0303] Where f is a function that calculates the position based on sensor data, dynamically switching to backup sensors and backup networks.

[0304] By setting up redundant sensors and redundant communication networks, the system can automatically switch to backup sensors or backup networks when the main sensor or main network fails, ensuring that location confirmation is not affected and avoiding system downtime. The system can monitor the status of sensors and communication networks in real time and automatically switch to backup resources, and the switching process does not have a significant impact on the operation of the system, ensuring smooth data flow and real-time performance. Through redundancy design, not only is the system's fault tolerance improved, but its stability in abnormal environments is also increased, making it suitable for scenarios with high accuracy requirements. The system reduces manual intervention and improves automation through automatic detection and switching, and optimizes the system's response speed and accuracy through intelligent algorithms.

[0305] The real-time position data of the disconnecting switch is transmitted to the dispatch center through a remote monitoring system. The system provides real-time feedback on the switch position and immediately alarms in case of any abnormality. The method for generating fault diagnosis information is as follows:

[0306] The location data of the switch is transmitted to the dispatch center through a remote monitoring system. The switch position collected by the sensor is L(t), which includes the state of the switch, namely the position of the switch and the current operating mode of the switch.

[0307] L(t): Real-time position data of the disconnector switch, the switch state at time t;

[0308] If L(t) = 1, it means the switch is in the "on" state;

[0309] If L(t) = 0, it means that the switch is in the "off" state;

[0310] Location data is transmitted to the dispatch center via a remote monitoring system. The dispatch center receives and feeds back this data in real time. The feedback signal sent to the dispatch center is denoted as R(t), and is updated based on the real-time location data.

[0311] R(t)=L(t)

[0312] The dispatch center receives L(t) in real time and feeds it back to the system users;

[0313] In the application, the system monitors whether the status of the switch is normal. If the sensor data is abnormal or the remote transmission is interrupted, the system will immediately alarm. A threshold is set to identify and determine whether there is an abnormality.

[0314] Two main types of fault detection are established:

[0315] Sensor malfunction: The sensor failed to accurately acquire the switch position or returned a signal that did not match expectations;

[0316] Communication failure: The remote monitoring system experiences interruptions or delays in transmitting switch position data;

[0317] Define a fault determination function F(t) to determine whether a fault exists:

[0318]

[0319] Where F(t) = 1 indicates that a fault has been detected;

[0320] F(t) = 0 indicates that no fault was detected;

[0321] If the system detects a fault (F(t) = 1), it immediately generates fault diagnosis information and issues an alarm signal. Let the alarm signal be A(t), associated with the fault diagnosis information D(t). The alarm signal is defined as follows:

[0322]

[0323] Meanwhile, the fault diagnosis information D(t) is used to record and report the type of fault and the time of occurrence:

[0324]

[0325] If a fault occurs (A(t) = alarm), the system activates the emergency response mechanism, and the emergency response function is set as R. 应急 (t), which is defined as follows:

[0326]

[0327] The system monitors the status of the disconnect switches in real time, enabling it to quickly detect and alarm when a fault occurs, ensuring timely response and minimizing the impact on the system. Through redundant design of sensors and communication, it can promptly detect faults and automatically trigger emergency response mechanisms, enhancing the system's reliability and fault tolerance. The system can automatically generate detailed fault diagnosis information when a fault occurs, accurately recording the fault type and occurrence time, providing support for subsequent fault investigation and repair. Upon the occurrence of a fault, the system can automatically activate the emergency response mechanism, quickly taking measures to prevent further impact on the system's stability or security.

[0328] The system employs automatic maintenance and self-calibration functions. During periodic operation or when an anomaly occurs, the system automatically performs self-checks using the following method:

[0329] The system performs self-checks periodically, or automatically triggers a self-check process when an anomaly occurs. The self-check process includes the following steps:

[0330] Periodic self-check: The system performs a self-check operation periodically;

[0331] Anomaly detection: When the system detects an anomaly, it triggers a self-check;

[0332] Self-calibration: After self-test, if the system detects deviations or abnormalities, it will perform a self-calibration operation.

[0333] Periodic self-checks are a task that the system performs regularly to ensure that all parts of the system are operating normally. A period T is set. 自检 , represents the self-check time interval. Let the current time be t. The trigger condition for the self-check is expressed as:

[0334]

[0335] That is, the system every T 自检 A self-check is triggered by time;

[0336] Let E(t) be the anomaly detection function, defined as follows:

[0337]

[0338] When the system performs a self-test and detects an anomaly, it automatically performs a self-calibration. The self-calibration operation function is defined as self-calibration(t), which is initiated when an anomaly occurs. The trigger condition for self-calibration is expressed as follows:

[0339]

[0340] That is, when an anomaly is detected or a periodic self-test is performed, a self-calibration operation is triggered;

[0341] Based on the above, the system's self-test and self-calibration functions are performed as follows:

[0342] Periodic self-check trigger:

[0343]

[0344] Anomaly detection:

[0345]

[0346] Self-calibration trigger:

[0347]

[0348] If the periodic self-check trigger(t) = 1 or E(t) = 1, the system will perform a self-check to detect and repair problems. The specific content of the self-check includes:

[0349] Verify the sensor output;

[0350] Check the integrity of data transmission;

[0351] Check the control system for malfunctions;

[0352] If a problem is detected during the self-test, the system will automatically perform a self-calibration operation:

[0353]

[0354] The self-calibration process includes:

[0355] Adjust the sensor's calibration parameters;

[0356] Reset the system's thresholds or biases;

[0357] Repair or restart the malfunctioning system components.

[0358] By automatically performing periodic self-checks and anomaly detection, the system can promptly identify and repair potential problems without manual intervention, greatly reducing human error and system downtime. Through automatic self-calibration, the system can immediately return to normal operation when anomalies occur, thereby improving system stability and reliability. By continuously performing self-checks and self-calibration, the system can always maintain optimal working condition, avoiding performance degradation due to prolonged operation. When anomalies occur, the system can quickly detect and self-repair, avoiding excessive system downtime and reducing the impact of failures on business operations.

[0359] A substation disconnector switch position confirmation system includes:

[0360] The sensor module is used to deploy photoelectric sensors, magnetic sensors, and position sensors to collect real-time physical position data of the disconnector switch;

[0361] The data fusion and intelligent analysis module is used to fuse and intelligently analyze the data collected by the sensors to identify and determine the switch position;

[0362] The environmental monitoring module is used to deploy temperature and humidity sensors and vibration sensors to monitor changes in the substation environment.

[0363] Redundant system modules are used to deploy redundant sensors and redundant communication networks to ensure that the backup system can take over in the event of a failure in the primary system.

[0364] The remote monitoring and feedback module is used to monitor the switch position in real time and provide fault alarms through the SCADA remote monitoring system.

[0365] The automatic maintenance and self-calibration module is used to periodically perform self-tests and self-calibrations on the system to ensure that all equipment is in optimal working condition.

[0366] The combination of multiple sensors provides redundant and diverse data sources, thereby improving system accuracy; intelligent analysis can more accurately determine the status of disconnecting switches, avoiding misidentification caused by the limitations of a single sensor; real-time monitoring of environmental changes ensures stable operation of sensors under various environmental conditions, avoiding the impact of environmental factors on sensor performance; redundancy design significantly improves system reliability and fault tolerance, allowing the backup system to quickly take over when the main system fails, reducing fault recovery time, avoiding system downtime due to equipment failure, and ensuring the safe and stable operation of the substation; remote monitoring modules greatly improve substation management efficiency, allowing operators to monitor the real-time status of the substation anytime, anywhere; automatic maintenance and self-calibration significantly improve system stability and reduce manual maintenance costs. During system operation, errors can be automatically identified and corrected, ensuring the long-term accuracy of sensors, equipment, and algorithms, and reducing performance degradation due to equipment aging or environmental changes.

[0367] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the substation disconnector position confirmation method and system as described above.

[0368] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the substation disconnector position confirmation method and system as described above.

[0369] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0370] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0371] The above embodiments of the present invention are not intended to limit the scope of protection of the present invention. The implementation of the present invention is not limited thereto. All other modifications, substitutions or alterations made to the above structure of the present invention based on the above content of the present invention, in accordance with ordinary technical knowledge and common practice in the field, without departing from the basic technical idea of ​​the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A method for confirming the position of a disengaging switch in a substation, characterized in that, Includes the following steps: The physical position of the disconnecting switch is monitored in real time by deploying photoelectric sensors, magnetic sensors, and position sensors, and is equipped with a redundant design; Data collected from multiple sensors is fused, analyzed using artificial intelligence algorithms, and the status of the disconnecting switch is identified and determined. The sensor data is then optimized using the algorithm. An environmental monitoring system is introduced to monitor environmental changes in the substation in real time and automatically adjust the sensitivity or operating mode of the sensors. Redundant sensors and communication networks are used, and the system automatically switches to backup sensors or backup networks to confirm the location when the main sensor or network fails. The real-time position data of the disconnecting switch is transmitted to the dispatch center through the remote monitoring system. The system provides real-time feedback on the switch position and immediately alarms and generates fault diagnosis information when any abnormality occurs. The system employs automatic maintenance and self-calibration functions, automatically performing self-checks during periodic operation or when abnormalities occur; The method involves real-time monitoring of the physical position of the disconnecting switch using photoelectric sensors, magnetic sensors, and position sensors, with redundant design features: Sensor data acquisition and settings: : The measured value of the photoelectric sensor; : Measurement values ​​from the magnetic sensor; : Measurement values ​​from the position sensor; The accuracy of each sensor is known and represented by weights. The weights of the sensors are set as follows: Weights of photoelectric sensors Weight of the magnetic sensor Weights of position sensors The three weights are achieved as follows: Integrated position after sensor weighted fusion Calculated using the following formula: Redundant design refers to deploying multiple redundant sensors in a system to improve its fault tolerance. This is illustrated by setting up two sensor systems, each with its own output: Main system: Backup system: The calculation formula for redundancy design sets a tolerance range by comparing data from two systems. ,for: If the output difference between the two systems exceeds the tolerance error If so, an alarm will be triggered or a backup sensor will be activated; Based on sensor weighted fusion and redundancy design, the system output Calculated using the following formula: in, , , This indicates the output data of the backup sensor.

2. The method for confirming the position of a substation disconnector according to claim 1, characterized in that, The method involves fusing data collected from multiple sensors, analyzing it using artificial intelligence algorithms, identifying and determining the status of the disconnector switch, and optimizing the sensor data through algorithms. The system deploys photoelectric sensors, magnetic sensors, and position sensors. The data collected by each sensor is represented as follows: Data from photoelectric sensors; Data from the magnetic sensor; Data from the position sensor; Sensor data fusion is performed using a weighted average method, with weights... , , Based on the sensor's accuracy and reliability factors, the settings are as follows: Fusion of sensor data The calculation is as follows: Artificial intelligence algorithms are used to analyze and identify the state of the fused sensor data, with the fused sensor data set as the input. The states include: 、 、 ； We will use support vector machines for state recognition. The model formula is as follows: in, For the weight vector of the support vector machine, The input vector is the result of sensor data fusion. As the bias term, the decision function is calculated. The switch state is determined by the sign of its value: like Then it is judged as a state. ; like Then it is judged as a state. ; like If the value exceeds the set threshold range, it indicates an abnormal or faulty state. ; To optimize sensor data and reduce errors, a feedback mechanism and optimization algorithm will be introduced, and a system will be established. The Kalman filter update formula for the optimized sensor data is: in, For the optimized state estimate; Kalman gain; The sensor measurement value; The observation matrix; This is a priori estimation; Based on sensor data fusion, AI algorithm analysis and optimization, the final disconnect switch status is determined. It is obtained through the following steps: Sensor data fusion: AI state recognition: according to The sign or threshold is used to determine the switch state. : Exceeding the threshold Data optimization: Accurate state identification is obtained through optimized sensor data.

3. The method for confirming the position of a substation disconnector according to claim 2, characterized in that, The method of introducing an environmental monitoring system to monitor environmental changes in the substation in real time and automatically adjust the sensitivity or operating mode of the sensors is as follows: The environmental monitoring system collects real-time environmental data on temperature, humidity, air pressure, and pollutant concentration at the substation. The environmental monitoring data is defined as follows: 、 、 、 ； Sensor sensitivity The sensitivity is dynamically adjusted based on environmental data. The sensitivity adjustment is based on the impact of environmental changes on sensor performance. In high-temperature environments, the sensor will reduce sensitivity to avoid false alarms, while in low-temperature or high-humidity environments, the sensitivity will be increased. The adjustment rules for sensor sensitivity are as follows: The formula for sensitivity adjustment is: in, The sensitivity adjustment function can be a weighted function, a linear function, or a nonlinear function. The following is a simplified model: in, This represents the initial sensitivity of the sensor; , , , The weighting coefficients for the influence of environmental factors on sensor sensitivity; In addition to sensitivity adjustment, the sensor's operating mode is dynamically adjusted according to environmental changes. The sensor's operating mode is then set. For a function:; in, The function for adjusting the working mode is expressed as: The environmental monitoring system collects environmental data in real time and calculates the data using the aforementioned functions. This dynamically adjusts the sensor sensitivity and operating mode. The integrated control system then adjusts the sensor behavior based on the adjusted sensitivity and operating mode to set the substation temperature... When the value exceeds the set threshold, the system will take the following actions: Reduce the sensor sensitivity to minimize the impact of temperature on the sensor: Adjust the sensor's operating mode to low-frequency sampling to reduce power consumption: Ultimately, the sensor's output Adjusted by environmental changes, as expressed as: in, and These are adjustment functions for sensor sensitivity and operating mode, respectively, and the final output is the sensor's measured value.

4. The method for confirming the position of a substation disconnector according to claim 3, characterized in that, The method of using redundant sensors and communication networks to automatically switch to backup sensors or backup networks for location confirmation when the main sensor or network fails is as follows: Deploy multiple sensors, including a main sensor. and backup sensors The sensor system works by acquiring sensor data to confirm the location. When the main sensor fails, the system will automatically switch to the backup sensor. set up: Indicates at time The measured value of the main sensor; This indicates the measured value from the backup sensor; Used to identify and determine whether the main sensor has malfunctioned; Define a fault determination function The system determines whether to switch based on whether the output of the main sensor is abnormal. like If the main sensor fails, the system switches to the backup sensor. In addition to redundant sensors, a main communication network is established based on the redundant communication network. The backup communication network is The system monitors the status of the main network in real time and switches to the backup network in case of failure. Fault detection function Used to monitor the status of the main network: The logic of the automatic switching algorithm is as follows: when ,and Switch to backup sensor when necessary. Confirm location; like Then switch to the backup network. And continue to acquire data from backup sensors; To seamlessly switch to backup sensors or backup networks, the system's state transitions are described by the following formula: Location confirmation based on sensor data The status of the communication network is dynamically updated, and the location confirmation function is set as follows: The location is calculated based on sensor data: in, The current communication network; In summary, the calculation formula for redundant sensors and communication network systems is as follows: in This is a function that calculates the location based on sensor data and dynamically switches to backup sensors and backup networks.

5. The method for confirming the position of a substation disconnector according to claim 4, characterized in that, The real-time position data of the disconnecting switch is transmitted to the dispatch center through a remote monitoring system. The system provides real-time feedback on the switch position and immediately alarms in case of any abnormality. The method for generating fault diagnosis information is as follows: The location data of the switches is transmitted to the dispatch center via a remote monitoring system, and the switch positions are collected by sensors. This data includes the state of the switch, including its position and current operating mode. Real-time position data of the disconnector switch, at time... The on / off state at any given time; like This indicates that the switch is in the "on" state; like This indicates that the switch is in the "off" state; Location data is transmitted to the dispatch center via a remote monitoring system. The dispatch center receives and sends back this data in real time. The feedback signal sent to the dispatch center is defined as follows: Updated based on real-time location data: The dispatch center receives in real time And feedback is sent to the system user; In the application, the system monitors whether the status of the switch is normal. If the sensor data is abnormal or the remote transmission is interrupted, the system will immediately alarm. A threshold is set to identify and determine whether there is an abnormality. Two main types of fault detection are established: Sensor malfunction: The sensor failed to accurately acquire the switch position or returned a signal that did not match expectations; Communication failure: The remote monitoring system experiences interruptions or delays in transmitting switch position data; Set fault determination function Determine if a fault exists: in, This indicates that a fault has been detected; This indicates that no fault was detected. If the system detects a fault If this occurs, fault diagnosis information will be generated immediately, and an alarm signal will be issued. The alarm signal will be set as follows: With fault diagnosis information Relatedly, the alarm signal is defined as: Meanwhile, fault diagnosis information Used to record and report the type of fault and when it occurred: If a fault occurs The system activates its emergency response mechanism and sets up an emergency response function. Its definition is as follows: 。 6. The method for confirming the position of a substation disconnector according to claim 5, characterized in that, The system employs automatic maintenance and self-calibration functions. During periodic operation or when an anomaly occurs, the system automatically performs self-checks using the following method: The system performs self-checks periodically, or automatically triggers a self-check process when an anomaly occurs. The self-check process includes the following steps: Periodic self-check: The system performs a self-check operation periodically; Anomaly detection: When the system detects an anomaly, it triggers a self-check; Self-calibration: After self-test, if the system detects deviations or abnormalities, it will perform a self-calibration operation. Periodic self-checks are a task that the system performs regularly to ensure that all parts of the system are operating normally. A period is set. , indicates the self-check time interval, with the current time set to . The self-check trigger condition is expressed as follows: That is, the system every A self-check is triggered by time; set up This is the anomaly detection function, defined as follows: When the system performs a self-test and detects an anomaly, the system automatically performs a self-calibration. The self-calibration operation function is set as follows: It is activated when an anomaly occurs. The trigger condition for self-calibration is expressed as follows: That is, when an anomaly is detected or a periodic self-test is performed, a self-calibration operation is triggered; Based on the above, the system's self-test and self-calibration functions are performed as follows: Periodic self-check trigger: Anomaly detection: Self-calibration triggered: like The system will perform a self-check to detect and repair problems. The specific content of the self-check includes: Verify the sensor output; Check the integrity of data transmission; Check the control system for malfunctions; If a problem is detected during the self-test, the system will automatically perform a self-calibration operation: The self-calibration process includes: Adjust the sensor's calibration parameters; Reset the system's thresholds or biases; Repair or restart the malfunctioning system components.

7. A substation disconnector position confirmation system, used to execute the method as described in claim 1, characterized in that, Including: The sensor module is used to deploy photoelectric sensors, magnetic sensors, and position sensors to collect real-time physical position data of the disconnector switch; The data fusion and intelligent analysis module is used to fuse and intelligently analyze the data collected by the sensors to identify and determine the switch position; The environmental monitoring module is used to deploy temperature and humidity sensors and vibration sensors to monitor changes in the substation environment. Redundant system modules are used to deploy redundant sensors and redundant communication networks to ensure that the backup system can take over in the event of a failure in the primary system. The remote monitoring and feedback module is used to monitor the switch position in real time and provide fault alarms through the SCADA remote monitoring system. The automatic maintenance and self-calibration module is used to periodically perform self-tests and self-calibrations on the system to ensure that all equipment is in optimal working condition.

8. An electronic device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a substation disconnector position confirmation method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a substation disconnector position confirmation method as described in any one of claims 1-6.

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