Method and system for confirming position of isolating switch of transformer substation

Abstract

The invention discloses a substation disconnecting switch position confirmation method and system, and the method comprises the steps: monitoring the position of a disconnecting switch in real time through arranging photoelectric, magnetic and position sensors, and employing a redundancy design; performing fusion analysis on the data of the plurality of sensors through an artificial intelligence algorithm, optimizing the data and judging the state of the disconnecting switch; the environment monitoring system monitors the environment change of the transformer substation in real time and automatically adjusts the sensitivity and the working mode of the sensor; the redundant sensor and the communication network are automatically switched to the standby system when the main sensor or the network fails; real-time position data of the disconnecting switch is transmitted to a dispatching center through a remote monitoring system, and an alarm is given and fault information is generated when abnormity occurs; the system has automatic maintenance and self-calibration functions, and self-calibration is triggered during regular self-inspection or abnormity; according to the substation disconnecting switch position confirmation method and system, intelligent sensors, redundancy design and artificial intelligence analysis technologies are introduced, and the accuracy and automation level of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the field of transformer substation disconnector position confirmation, and in particular relates to a transformer substation disconnector position confirmation method and system. Background Art

[0002] The method for confirming the position of the substation disconnector is used to ensure that the status of the disconnector is correct and reliable, and to prevent operational errors or equipment failures. The disconnector is usually equipped with a mechanical indicator to display the current position of the switch. The indicator is usually set on the outside of the switch for the operator to observe; the actual position of the disconnector is monitored by an electrical interlocking device to ensure that it does not conflict with other equipment during switching operations; the position of the disconnector is monitored in real time using remote monitoring equipment or SCADA. This method uses sensors or position detection equipment to transmit switch status information to the dispatching or monitoring center through the network, so that the operator can understand the open and closed status of the switch in real time; the operator can manually or automatically check the switch through the secondary circuit to verify whether the disconnector is in the expected operating state; the operator conducts a direct visual inspection on site and confirms the switch position by observing the switch handle or indicator; the position of the disconnector is confirmed in real time using smart devices and sensors, and the information is transmitted to the central monitoring system to enhance the accuracy and reliability of the confirmation.

[0003] However, in existing substations, the status of disconnectors is usually confirmed through mechanical indicators, electrical interlocking devices and remote monitoring systems. The existing technology has some shortcomings. Mechanical indicators are easily affected by environmental factors, resulting in inaccurate position display. If the electrical interlocking system fails, the position confirmation may fail. The remote monitoring system is subject to the risk of communication delays and network interruptions. Existing sensor technology may fail or lack accuracy in harsh environments. Manual confirmation methods rely on the operator's experience and there is a risk of human error. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method and system for confirming the position of a substation disconnector, which solves the problems of insufficient accuracy, reliability and automation level in the prior art by introducing intelligent sensors, redundant design and artificial intelligence analysis technology.

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

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

[0007] Photoelectric sensors, magnetic sensors, and position sensors are deployed to monitor the physical position of the disconnector in real time, and a redundant design is provided;

[0008] The data collected by multiple sensors are integrated and analyzed using artificial intelligence algorithms to identify and judge the status of the disconnector, and the sensor data is optimized through algorithms;

[0009] Introducing an environmental monitoring system to monitor substation environmental changes in real time and automatically adjust sensor sensitivity or operating mode;

[0010] Use redundant sensors and communication networks to automatically switch to backup sensors or networks for location confirmation when the primary sensor or network fails.

[0011] The real-time position data of the disconnector is transmitted to the dispatch center via the remote monitoring system. The system provides real-time feedback on the switch position and immediately issues an alarm when any abnormality occurs, generating fault diagnosis information.

[0012] The system adopts automatic maintenance and self-calibration functions. During regular operation or when an abnormality occurs, the system automatically performs self-inspection.

[0013] As a preferred method, a photoelectric sensor, a magnetic sensor, and a position sensor are deployed to monitor the physical position of the disconnector in real time, and a redundant design is provided as follows:

[0014] Sensor data collection, settings:

[0015] P 光 : The measured value of the photoelectric sensor;

[0016] P 磁 : The measurement value of the magnetic sensor;

[0017] P 位 : The measured value of the position sensor;

[0018] The accuracy of each sensor is known and is represented by a weight. The weight of the sensor is set as:

[0019] w 光 : Weight of the photoelectric sensor

[0020] w 磁 : Weight of the magnetic sensor

[0021] w 位 : Weight of the position sensor

[0022] The three weights are:

[0023] w 光 +w 磁 +w 位 =1

[0024] The comprehensive position P after sensor weighted fusion 综合 Calculated by the following formula:

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

[0026] Redundant design means deploying multiple redundant sensors in the system to improve the system's fault tolerance. Two sensor systems are set up, and the output of each system is:

[0027] Main system: P 主

[0028] Backup system: P 备

[0029] The calculation formula of redundant design sets a tolerance error range ∈ by comparing the data of the two systems:

[0030] |P 主 -P 备 |≤∈

[0031] If the output difference between the two systems exceeds the tolerance error∈, an alarm is issued or the backup sensor is activated;

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

[0033]

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

[0035] As a preferred method, the data collected by multiple sensors are integrated, analyzed using an artificial intelligence algorithm, the state of the disconnector is identified and determined, and the sensor data is optimized by the algorithm as follows:

[0036] Photoelectric sensors, magnetic sensors, and position sensors are deployed. The collected data of each sensor is expressed as:

[0037] P 光 : Photoelectric sensor data;

[0038] P 磁 : Magnetic sensor data;

[0039] P 位 : data from the position sensor;

[0040] The fusion of sensor data is performed by weighted averaging, with weight w 光 , w 磁 , w 位 According to the accuracy and reliability factors of the sensor, it is set as:

[0041] w 光 +w 磁 +w 位 =1

[0042] The fused sensor data P 融合 Calculated as:

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

[0044] Through artificial intelligence algorithms, the fused sensor data is analyzed and the status is identified. The sensor fusion data is set as input X. The status includes:

[0045] S 开 、S 关 、S 故障 ;

[0046] The support vector machine is used for state recognition, and the model formula is:

[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) based on the sign of its value:

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

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

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

[0052] We will optimize sensor data and reduce errors, introduce feedback mechanisms and optimization algorithms, and establish P 优化 For the optimized sensor data, the update formula of Kalman filter is:

[0053]

[0054] in, is the optimized state estimate;

[0055] K k is the Kalman gain;

[0056] z k is the sensor measurement value;

[0057] H k is the observation matrix;

[0058] is a priori estimate;

[0059] Based on sensor data fusion, AI algorithm analysis and optimization, the final isolation switch status S final 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 status recognition:

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

[0064] Determine the switch state S based on the sign or threshold of f(X) 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 recognition is achieved through optimized sensor data.

[0071] As a preferred method, an environmental monitoring system is introduced to monitor the environmental changes of the substation in real time and automatically adjust the sensitivity or working mode of the sensor as follows:

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

[0073] E 温 、E 湿 、E 气压 、E 污染物 ;

[0074] Sensor sensitivity S 传感器 Dynamically adjust according to environmental data. Set up sensitivity adjustment based on the impact of environmental changes on sensor performance. If the sensor is in a high temperature environment, the sensitivity will be reduced to avoid false alarms. In a low temperature or high humidity environment, the sensitivity will be increased.

[0075] The adjustment rules for setting the 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 uses 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 is the initial sensitivity of the sensor;

[0082] α 温 , α 湿 , α 气压 , α 污染物is the weight coefficient of the impact of environmental factors on sensor sensitivity;

[0083] In addition to adjusting the sensitivity, the working mode of the sensor is dynamically adjusted according to environmental changes. 传感器 For a function:

[0084] M 传感器 =g(E 温 ,E 湿 ,E 气压 ,E 污染物 )

[0085] Among them, g is the working mode adjustment function, which is expressed as:

[0086]

[0087] The environmental data collected by the environmental monitoring system in real time is calculated through the above functions, and the sensitivity and working mode of the sensor are dynamically adjusted. The integrated control system adjusts the behavior of the sensor according to the adjusted sensitivity and working mode, and establishes the substation temperature E 温 When the value exceeds the set threshold, the system takes the following actions:

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

[0089] S 传感器 =S 基准 ·(1-α 温 ·E 温 )

[0090] Adjust the sensor working mode to low-frequency sampling to reduce energy consumption:

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

[0092] Finally, the output of the sensor P 传感器 is regulated by environmental changes, 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] Among them, f and g are the adjustment functions of sensor sensitivity and working mode, respectively, and the final output is the sensor's measured value.

[0095] Preferably, redundant sensors and communication networks are used to automatically switch to backup sensors or networks for position confirmation when a primary sensor or network fails. The method is as follows:

[0096] Deploy multiple sensors, including the main sensor (S 主 ) and backup sensor (S 备 ), the work of the sensor system is to confirm the position by obtaining sensor data. 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 measurement value of the backup sensor;

[0100] Fault judgment function (S 主 (t),S 备 (t)) is used to identify whether the main sensor is faulty;

[0101] Define a fault judgment function Fault judgment function (S 主 , S 备 ), determine whether to switch based on whether the output of the main sensor is abnormal:

[0102]

[0103] If the fault judgment function (S 主 )=1, it means that the main sensor fails and the system switches to the backup sensor;

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

[0105] Fault detection function Network fault judgment function (N 主 ) is 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 judgment function (S 主)=1, and the network fault judgment function (N 主 )=0, switch to the backup sensor S 备 Perform location confirmation;

[0109] If the network fault judgment function (N 主 )=1, then switch to the backup network N 备 , and continue to obtain data from the backup sensor;

[0110] In order to seamlessly switch to the backup sensor or backup network, the state transition of the system is described by the following formula:

[0111]

[0112] Position confirmation based on sensor data S 有效 (t) and the status of the communication network are dynamically updated, and the position confirmation function is set as P(t). The position is calculated based on the sensor data:

[0113] P(t)=f(S 有效 (t),N 有效 (t))

[0114] Among them, N 有效 (t) is the communication network currently in use;

[0115] In summary, the calculation formula of the redundant sensor and communication network system is:

[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 the sensor data and dynamically switches to the backup sensor and backup network.

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

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

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

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

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

[0124] The location data is transmitted to the dispatch center through the remote monitoring system. The dispatch center receives and feeds back the data in real time. The feedback signal to the dispatch center is set as R(t) and is updated according to 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 switch status is normal. If the sensor data is abnormal or the remote transmission is interrupted, the system will immediately alarm and determine whether there is an abnormality by setting a threshold;

[0128] Two main fault types are detected:

[0129] Sensor failure: The sensor fails to accurately obtain the switch position or the signal returned does not match the expected signal;

[0130] Communication failure: interruption or delay in the transmission of switch position data by the remote monitoring system;

[0131] Set the fault judgment function F(t) to determine whether there is a fault:

[0132]

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

[0134] F(t) = 0 means no fault is detected;

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

[0136]

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

[0138]

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

[0140]

[0141] As a preference, the system adopts automatic maintenance and self-calibration functions. During regular operation or when an abnormality occurs, the system automatically performs self-checking in the following ways:

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

[0143] Periodic self-test: The system performs self-test operations regularly;

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

[0145] Self-calibration: After self-test, if the system finds deviation or abnormality, it will perform self-calibration operation;

[0146] Periodic self-check is 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-test time interval. Set the current time as t, and the trigger condition of the self-test is expressed as:

[0147]

[0148] That is, the system is 自检 Time triggers a self-test;

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

[0150]

[0151] When the system performs self-test and detects an anomaly, it automatically performs self-calibration. The self-calibration operation function is set as selfcalibration(t), which is started when an anomaly occurs. The trigger condition of self-calibration is expressed as:

[0152]

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

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

[0155] Periodic self-test trigger:

[0156]

[0157] Anomaly Detection:

[0158]

[0159] Self-calibration trigger:

[0160]

[0161] If the periodic self-test trigger (t) = 1 or E(t) = 1, the system will perform a self-test to detect and repair the problem. The specific contents of the self-test include:

[0162] Verify the output of the sensor;

[0163] Check the integrity of data transmission;

[0164] Check whether the control system has any faults;

[0165] If any problems are found during the self-test, the system will automatically perform self-calibration:

[0166]

[0167] The self-calibration process includes:

[0168] Adjust the sensor's calibration parameters;

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

[0170] Repair or restart failed system components.

[0171] A substation disconnector position confirmation system, comprising:

[0172] Sensor module, used to deploy photoelectric sensors, magnetic sensors, and position sensors to collect physical position data of the disconnector in real time;

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

[0174] Environmental monitoring module, used to deploy temperature, humidity, and vibration sensors to monitor substation environmental changes;

[0175] Redundant system modules, used to deploy redundant sensors and redundant communication networks to ensure that the backup system can take over when the main system fails;

[0176] Remote monitoring and feedback module, used for real-time monitoring of switch position and fault alarm through SCADA remote monitoring system;

[0177] The automatic maintenance and self-calibration module is used to regularly perform self-inspection and self-calibration on the system to ensure that all devices are 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 runnable on the processor. When the processor executes the program, a method and system for confirming the position of a substation disconnector as described above is implemented.

[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 the present invention are:

[0181] Redundant design (redundant sensors, communication networks, etc.) effectively prevents data loss or errors caused by failure of a single sensor or communication link, ensuring that the system continues to operate normally even when problems arise with the primary sensor or network. By deploying multiple sensors and fusing data, the physical location of the disconnector can be monitored in real time and transmitted to the dispatch center via a remote monitoring system. By fusing data collected by multiple sensors and combining it with artificial intelligence algorithms for analysis and optimization, the status of the disconnector can be more accurately determined. The system's automatic maintenance and self-calibration functions ensure that the equipment automatically performs self-inspection and calibration during regular operation or when an anomaly occurs, significantly reducing the need for manual inspection 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 informed decisions. This integrated data support gives operators a more comprehensive understanding of the substation's operating status, improving the accuracy of their decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0182] Figure 1 The figure is a flow chart of a substation disconnector position confirmation system according to the present invention. DETAILED DESCRIPTION

[0183] The principles and features of the present invention are described below. The examples provided are intended to illustrate the present invention only and are not intended to limit the scope of the present invention. The following paragraphs describe the present invention in more detail by way of example. The advantages and features of the present invention will become more apparent 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 those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present 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 the present invention to solve its technical problem is:

[0187] A method for confirming the position of a transformer substation disconnector comprises the following steps:

[0188] Photoelectric sensors, magnetic sensors, and position sensors are deployed to monitor the physical position of the disconnector in real time, and a redundant design is provided;

[0189] The data collected by multiple sensors are integrated and analyzed using artificial intelligence algorithms to identify and judge the status of the disconnector, and the sensor data is optimized through algorithms;

[0190] Introducing an environmental monitoring system to monitor substation environmental changes in real time and automatically adjust sensor sensitivity or operating mode;

[0191] Use redundant sensors and communication networks to automatically switch to backup sensors or networks for location confirmation when the primary sensor or network fails.

[0192] The real-time position data of the disconnector is transmitted to the dispatch center via the remote monitoring system. The system provides real-time feedback on the switch position and immediately issues an alarm when any abnormality occurs, generating fault diagnosis information.

[0193] The system adopts automatic maintenance and self-calibration functions. During regular operation or when an abnormality occurs, the system automatically performs self-inspection.

[0194] Simultaneous monitoring by multiple sensors can obtain multi-dimensional data, effectively avoiding system information loss or false alarms caused by the failure of a single sensor; the introduction of artificial intelligence algorithms can improve the intelligence level of the system, automatically optimize and adjust sensor data, and improve data accuracy; environmental monitoring and automatic adjustment can ensure the stability of sensors under different climatic or environmental conditions, and prevent equipment failures or data errors caused by environmental factors; redundant systems increase system reliability, and even if the main equipment fails, the system can continue to operate without affecting the normal monitoring and maintenance of the substation; through the remote monitoring system, the dispatching center can grasp 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 the failure rate of equipment.

[0195] The method of deploying photoelectric sensors, magnetic sensors, and position sensors to monitor the physical position of the disconnector in real time and equipping it with redundant design is as follows:

[0196] Sensor data collection, settings:

[0197] P光 : The measured value of the photoelectric sensor;

[0198] P 磁 : The measurement value of the magnetic sensor;

[0199] P 位 : The measured value of the position sensor;

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

[0201] w 光 : Weight of the photoelectric sensor

[0202] w 磁 : Weight of the magnetic sensor

[0203] w 位 : Weight of the position sensor

[0204] The three weights are:

[0205] w 光 +w 磁 +w 位 =1

[0206] The comprehensive position P after sensor weighted fusion 综合 Calculated by the following formula:

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

[0208] Redundant design means deploying multiple redundant sensors in the system to improve the system's fault tolerance. Two sensor systems are set up, and the output of each system is:

[0209] Main system: P 主

[0210] Backup system: P 备

[0211] The calculation formula of redundant design sets a tolerance error range ∈ by comparing the data of the two systems:

[0212] |P 主 -P 备 |≤∈

[0213] If the output difference between the two systems exceeds the tolerance error∈, an alarm is issued or the backup sensor is activated;

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

[0215]

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

[0217] The introduction of redundant design gives the system a strong fault-tolerant capability. Even if the main system fails, the backup system can take over seamlessly, ensuring the continuity and accuracy of position monitoring. By weightedly fusing data from multiple sensors, the advantages of each sensor can be fully utilized, avoiding data errors or instability of a single sensor, and improving the overall position accuracy. The system can monitor the differences between sensors in real time and trigger alarms within the tolerance error range to detect problems in a timely manner and avoid potential risks in long-term operation. By setting sensor weights, the system can flexibly adjust the influence of each sensor according to the measurement accuracy and reliability of each sensor, and optimize the accuracy of position calculation.

[0218] The method of fusing the data collected by multiple sensors, analyzing it using artificial intelligence algorithms, identifying and judging the status of the disconnector, and optimizing the sensor data through algorithms is as follows:

[0219] Photoelectric sensors, magnetic sensors, and position sensors are deployed. The collected data of each sensor is expressed as:

[0220] P 光 : Photoelectric sensor data;

[0221] P 磁 : Magnetic sensor data;

[0222] P 位 : data from the position sensor;

[0223] The fusion of sensor data is performed by weighted averaging, with weight w 光 , w 磁 , w 位 According to the accuracy and reliability factors of the sensor, it is set as:

[0224] w 光 +w 磁 +w 位 =1

[0225] The fused sensor data P 融合 Calculated as:

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

[0227] Through artificial intelligence algorithms, the fused sensor data is analyzed and the status is identified. The sensor fusion data is set as input X. The status includes:

[0228] S 开 、S 关 、S 故障 ;

[0229] The support vector machine is used for state recognition, and the model formula is:

[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) based on the sign of its value:

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

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

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

[0235] We will optimize sensor data and reduce errors, introduce feedback mechanisms and optimization algorithms, and establish P 优化 For the optimized sensor data, the update formula of Kalman filter is:

[0236]

[0237] in, is the optimized state estimate;

[0238] K k is the Kalman gain;

[0239] z k is the sensor measurement value;

[0240] H k is the observation matrix;

[0241] is a priori estimate;

[0242] Based on sensor data fusion, AI algorithm analysis and optimization, the final isolation switch status S final 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 status recognition:

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

[0247] Determine the switch state S based on the sign or threshold of f(X) 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 recognition is achieved 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 the disconnector can be improved. Kalman filters can effectively reduce noise in sensor data, and the optimized data can reduce errors and improve system reliability. Using artificial intelligence algorithms for state recognition can identify potential faults or abnormal conditions 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 of 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 the environmental changes of the substation in real time and automatically adjust the sensitivity or working mode of the sensor is as follows:

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

[0257] E 温 、E 湿 、E 气压 、E 污染物 ;

[0258] Sensor sensitivity S 传感器 Dynamically adjust according to environmental data. Set up sensitivity adjustment based on the impact of environmental changes on sensor performance. If the sensor is in a high temperature environment, the sensitivity will be reduced to avoid false alarms. In a low temperature or high humidity environment, the sensitivity will be increased.

[0259] The adjustment rules for setting the 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 uses 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 is the initial sensitivity of the sensor;

[0266] α 温 , α 湿 , α 气压 , α 污染物 is the weight coefficient of the impact of environmental factors on sensor sensitivity;

[0267] In addition to adjusting the sensitivity, the working mode of the sensor is dynamically adjusted according to environmental changes. 传感器 For a function:

[0268] M 传感器 =g(E 温 ,E 湿 ,E 气压 ,E 污染物 )

[0269] Among them, g is the working mode adjustment function, which is expressed as:

[0270]

[0271] The environmental data collected by the environmental monitoring system in real time is calculated through the above functions, and the sensitivity and working mode of the sensor are dynamically adjusted. The integrated control system adjusts the behavior of the sensor according to the adjusted sensitivity and working mode, and establishes the substation temperature E 温 When the value exceeds the set threshold, the system takes the following actions:

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

[0273] S 传感器 =S 基准 ·(1-α 温 ·E 温 )

[0274] Adjust the sensor working mode to low-frequency sampling to reduce energy consumption:

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

[0276] Finally, the output of the sensor P 传感器 is regulated by environmental changes, 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] Among them, f and g are the adjustment functions of sensor sensitivity and working 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 the environmental monitoring system, sensors can dynamically adjust their sensitivity and operating mode based on 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 excessive temperature. In environments such as low temperatures or high humidity, increasing sensitivity helps ensure that sensors capture environmental changes and accurately identify them. When the temperature is too high, adjusting the sensor's operating mode to low-frequency sampling can effectively reduce the sensor's energy consumption, thereby extending the device's service life and reducing overall energy consumption. By combining environmental monitoring with intelligent regulation, this solution enhances the system's stability and reliability. Adjusting sensor sensitivity and operating mode enables the system to maintain optimal operating conditions under different environmental conditions.

[0280] Using redundant sensors and communication networks, when the primary sensor or network fails, the method of automatically switching to the backup sensor or backup network for position confirmation is as follows:

[0281] Deploy multiple sensors, including the main sensor (S 主 ) and backup sensor (S 备 ), the work of the sensor system is to confirm the position by obtaining sensor data. 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 measurement value of the backup sensor;

[0285] Fault determination letter (S 主 (t), S 备 (t)) is used to identify whether the main sensor is faulty;

[0286] Define a fault judgment function fault judgment function (S 主 , S 备 ), determine whether to switch based on whether the output of the main sensor is abnormal:

[0287]

[0288] If the fault judgment function (S 主 )=1, it means that the main sensor fails and the system switches to the backup sensor;

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

[0290] Fault detection function Network fault judgment function (N 主 ) is 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 judgment function (S 主 )=1, and the network fault judgment function (N 主 )=0, switch to the backup sensor S 备 Perform location confirmation;

[0294] If the network fault judgment function (N 主 )=1, then switch to the backup network N 备 , and continue to obtain data from the backup sensor;

[0295] In order to seamlessly switch to the backup sensor or backup network, the state transition of the system is described by the following formula:

[0296]

[0297] Position confirmation based on sensor data S 有效 (t) and the status of the communication network are dynamically updated, and the position confirmation function is set as P(t). The position is calculated based on the sensor data:

[0298] P(t)=f(S 有效 (t),N 有效 (t))

[0299] Among them, N 有效 (t) is the communication network currently in use;

[0300] In summary, the calculation formula of the redundant sensor and communication network system is:

[0301]

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

[0303] Where f is a function that calculates the position based on the sensor data and dynamically switches to the backup sensor and backup network.

[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 position 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 has no significant impact on the operation of the system, ensuring data smoothness and real-time performance; the redundant design not only improves the system's fault tolerance, but also increases the system's stability in abnormal environments, making it suitable for scenarios with high accuracy requirements; the system reduces manual intervention and improves the degree of 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 disconnector is transmitted to the dispatch center via the remote monitoring system. The system provides real-time feedback on the switch position and immediately issues an alarm if any abnormality occurs. The method for generating fault diagnosis information is as follows:

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

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

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

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

[0310] The location data is transmitted to the dispatch center through the remote monitoring system. The dispatch center receives and feeds back the data in real time. The feedback signal to the dispatch center is set as R(t) and is updated according to 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 switch status is normal. If the sensor data is abnormal or the remote transmission is interrupted, the system will immediately alarm and determine whether there is an abnormality by setting a threshold;

[0314] Two main fault types are detected:

[0315] Sensor failure: The sensor fails to accurately obtain the switch position or the signal returned does not match the expected signal;

[0316] Communication failure: interruption or delay in the transmission of switch position data by the remote monitoring system;

[0317] Set the fault judgment function F(t) to determine whether there is a fault:

[0318]

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

[0320] F(t) = 0 means no fault is detected;

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

[0322]

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

[0324]

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

[0326]

[0327] The system monitors the status of the disconnector in real time and can quickly detect and alarm when a fault occurs, ensuring timely response to the fault and reducing the impact on the system; through the redundant design of sensors and communications, it can detect faults in a timely manner and automatically trigger the emergency response mechanism, enhancing the reliability and fault tolerance of the system; the system can automatically generate detailed fault diagnosis information when a fault occurs, accurately record the fault type and occurrence time, and provide support for subsequent troubleshooting and repair; when a fault occurs, the system can automatically activate the emergency response mechanism and take quick measures to avoid further impact on the stability or safety of the system.

[0328] The system uses automatic maintenance and self-calibration functions. During regular operation or when an abnormality occurs, the system automatically performs self-checking in the following ways:

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

[0330] Periodic self-test: The system performs self-test operations regularly;

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

[0332] Self-calibration: After self-test, if the system finds deviation or abnormality, it will perform self-calibration operation;

[0333] Periodic self-check is 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-test time interval. Set the current time as t, and the trigger condition of the self-test is expressed as:

[0334]

[0335] That is, the system is 自检 Time triggers a self-test;

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

[0337]

[0338] When the system performs self-test and detects an anomaly, it automatically performs self-calibration. The self-calibration operation function is set as selfcalibration(t), which is started when an anomaly occurs. The trigger condition of self-calibration is expressed as:

[0339]

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

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

[0342] Periodic self-test trigger:

[0343]

[0344] Anomaly Detection:

[0345]

[0346] Self-calibration trigger:

[0347]

[0348] If the periodic self-test trigger (t) = 1 or E(t) = 1, the system will perform a self-test to detect and repair the problem. The specific contents of the self-test include:

[0349] Verify the output of the sensor;

[0350] Check the integrity of data transmission;

[0351] Check whether the control system has any faults;

[0352] If any problems are found during the self-test, the system will automatically perform self-calibration:

[0353]

[0354] The self-calibration process includes:

[0355] Adjust the sensor's calibration parameters;

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

[0357] Repair or restart failed system components.

[0358] By automatically performing periodic self-checks and anomaly detection, the system can promptly identify and fix potential problems without manual intervention, significantly reducing human error and system downtime. Automatic self-calibration allows the system to immediately return to normal when an anomaly occurs, thereby improving system stability and reliability. Continuous self-checks and self-calibration ensure the system remains in optimal working condition, avoiding performance degradation caused by prolonged operation. When an anomaly occurs, the system can quickly detect and self-repair, avoiding extended system downtime and minimizing the impact of the failure on the business.

[0359] A substation disconnector position confirmation system, comprising:

[0360] Sensor module, used to deploy photoelectric sensors, magnetic sensors, and position sensors to collect physical position data of the disconnector in real time;

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

[0362] Environmental monitoring module, used to deploy temperature, humidity, and vibration sensors to monitor substation environmental changes;

[0363] Redundant system modules, used to deploy redundant sensors and redundant communication networks to ensure that the backup system can take over when the main system fails;

[0364] Remote monitoring and feedback module, used for real-time monitoring of switch position and fault alarm through SCADA remote monitoring system;

[0365] The automatic maintenance and self-calibration module is used to regularly perform self-inspection and self-calibration on the system to ensure that all devices are in optimal working condition.

[0366] The combination of multiple sensors can provide redundant and diverse data sources, thereby improving the accuracy of the system; intelligent analysis can more accurately determine the status of the disconnector and avoid misidentification due to the limitations of a single sensor; through real-time monitoring of environmental changes, it can ensure that the sensor can operate stably under various environmental conditions and avoid the impact of environmental factors on sensor performance; the redundant design significantly improves the reliability and fault tolerance of the system. When the main system fails, the backup system can quickly take over, reducing the fault recovery time, avoiding system downtime due to equipment failure, and ensuring the safety and stability of substation operation; the remote monitoring module can greatly improve the management efficiency of the substation, and operators can grasp the real-time status of the substation anytime and anywhere; automatic maintenance and self-calibration can significantly improve the stability of the system and reduce manual maintenance costs. During system operation, it can automatically identify and correct errors to ensure the long-term accuracy of sensors, equipment and algorithms, and reduce 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 computer program, a method and system for confirming the position of a substation disconnector as described above is implemented.

[0368] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method and system for confirming the position of a substation disconnector as described above are implemented.

[0369] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database 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), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0370] Those skilled in the art will clearly understand that for the sake of convenience and brevity in description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by 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, and the implementation methods of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in this field, without departing from the above basic technical ideas of the present invention, should fall within the scope of protection of the present invention.

Claims

1. A method for confirming the position of a substation disconnector, characterized in that: The following steps are involved: Photoelectric sensors, magnetic sensors, and position sensors are deployed to monitor the physical position of the disconnector in real time, and a redundant design is provided; The data collected by multiple sensors are integrated and analyzed using artificial intelligence algorithms to identify and judge the status of the disconnector, and the sensor data is optimized through algorithms; Introducing an environmental monitoring system to monitor substation environmental changes in real time and automatically adjust sensor sensitivity or operating mode; Use redundant sensors and communication networks to automatically switch to backup sensors or networks for location confirmation when the primary sensor or network fails. The real-time position data of the disconnector is transmitted to the dispatch center via the remote monitoring system. The system provides real-time feedback on the switch position and immediately issues an alarm when any abnormality occurs, generating fault diagnosis information. The system adopts automatic maintenance and self-calibration functions. During regular operation or when an abnormality occurs, the system automatically performs self-inspection.

2. A method for confirming the position of a substation disconnector according to claim 1, characterized in that: The method of deploying photoelectric sensors, magnetic sensors, and position sensors to monitor the physical position of the disconnector in real time and equipping it with redundant design is as follows: Sensor data collection, settings: P 光 : The measured value of the photoelectric sensor; P 磁 : The measurement value of the magnetic sensor; P 位 : The measured value of the position sensor; The accuracy of each sensor is known and is represented by a weight. The weight of the sensor is set as: w 光 : Weight of the photoelectric sensor w 磁 : Weight of the magnetic sensor w 位 : Weight of the position sensor The three weights are: w 光 +w 磁 +w bit = 1 The comprehensive position P after sensor weighted fusion 综合 Calculated by the following formula: P 综合 =w 光 ·P 光 +w 磁 ·P 磁 +w 位 ·P 位 Redundant design means deploying multiple redundant sensors in the system to improve the system's fault tolerance. Two sensor systems are set up, and the output of each system is: Main system: P 主 Backup system: P 备 The calculation formula of redundant design sets a tolerance error range ∈ by comparing the data of the two systems: |P 主 -P 备 |≤∈ If the output difference between the two systems exceeds the tolerance error∈, an alarm is issued or the backup sensor is activated; Based on sensor weighted fusion and redundant design, the system output P 最终 Calculated by the following formula: Among them, P 备光 , P 备磁 , P 备位 Indicates the output data of the backup sensor.

3. A method for confirming the position of a substation disconnector according to claim 2, characterized in that: The method of fusing the data collected by multiple sensors, analyzing it using artificial intelligence algorithms, identifying and judging the status of the disconnector, and optimizing the sensor data through algorithms is as follows: Photoelectric sensors, magnetic sensors, and position sensors are deployed. The collected data of each sensor is expressed as: P 光 : Photoelectric sensor data; P 磁 : Magnetic sensor data; P 位 : data from the position sensor; The fusion of sensor data is performed by weighted averaging, with weight w 光 , w 磁 , w 位 According to the accuracy and reliability factors of the sensor, it is set as: In 光 +in 磁 +in 位 =1 The fused sensor data P 融合 Calculated as: P 融合 =w 光 ·P 光 +w 磁 ·P 磁 +w 位 ·P 位 Through artificial intelligence algorithms, the fused sensor data is analyzed and the status is identified. The sensor fusion data is set as input X. The status includes: S 开 、S 关 、S 故障 ; The support vector machine is used for state recognition, and the model formula is: f(X)=w T X+b 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) based on the sign of its value: If f(X)>0, then it is judged to be state S 开 ; If f(X) < 0, then it is judged to be state S 关 ; If f(X) exceeds the set threshold range, it is an abnormal or fault state S 故障 ; We will optimize sensor data and reduce errors, introduce feedback mechanisms and optimization algorithms, and establish P 优化 For the optimized sensor data, the update formula of Kalman filter is: in, is the optimized state estimate; K k is the Kalman gain; z k is the sensor measurement value; H k is the observation matrix; is a priori estimate; Based on sensor data fusion, AI algorithm analysis and optimization, the final isolation switch status S final Obtained through the following steps: Sensor Data Fusion: P 融合 =w 光 ·P 光 +w 磁 ·P 磁 +w 位 ·P 位 AI status recognition: f(X)=w T X+b Determine the switch state S based on the sign or threshold of f(X) final : S final =S 开 if f(X)>0 S final =S 关 if f(X)<0 S final =S 故障 if|f(X)|exceeds the threshold Data optimization: P 优化 =Kalman(P 融合 ) Accurate state recognition is achieved through optimized sensor data.

4. A method for confirming the position of a substation disconnector according to claim 3, characterized in that: The method of introducing an environmental monitoring system to monitor the environmental changes of the substation in real time and automatically adjust the sensitivity or working mode of the sensor is as follows: The environmental monitoring system collects real-time environmental data on substation temperature, humidity, air pressure, and pollutant concentration. The environmental monitoring data is set as follows: E 温 、E 湿 、E 气压 、E 污染 object; Sensor sensitivity S 传感器 Dynamically adjust based on environmental data. Sensitivity adjustment is based on the impact of environmental changes on sensor performance. If the sensor is in a high temperature environment, the sensitivity will be reduced to avoid false alarms. In a low temperature or high humidity environment, the sensitivity will be increased. The adjustment rules for setting the sensor sensitivity are as follows: S 传感器 (AND 温 ,AND 湿 ,AND 气压 ,AND 污染物 ) The formula for sensitivity adjustment is: S 传感器 =f(E 温 ,AND 湿 ,AND 气压 ,AND 污染物 ) Where f is the sensitivity adjustment function, which uses a weighted function, a linear function, or a nonlinear function. The following is a simplified model: S 传感器 =S 基 (1+a 温 ·E 温 +a 湿 ·E 湿 +a 气压 ·E 气压 +a 污染物 ·E 污染物 ) Among them, S 基准 is the initial sensitivity of the sensor; α 温 , α 湿 , α 气压 , α 污染物 is the weight coefficient of the impact of environmental factors on sensor sensitivity; In addition to adjusting the sensitivity, the working mode of the sensor is dynamically adjusted according to environmental changes. 传感器 For a function: M 传感器 =g(E 温 ,E 湿 ,E 气压 ,E 污染物 ) Among them, g is the working mode adjustment function, which is expressed as: The environmental data collected by the environmental monitoring system in real time is calculated through the above functions, and the sensitivity and working mode of the sensor are dynamically adjusted. The integrated control system adjusts the behavior of the sensor according to the adjusted sensitivity and working mode, and establishes the substation temperature E 温 When the value exceeds the set threshold, the system takes the following actions: Reduce the sensitivity of the sensor to reduce the impact of temperature on the sensor: S 传感器 =S 基准 ·(1-a 温 ·E 温 ) Adjust the sensor working mode to low-frequency sampling to reduce energy consumption: M 传感器 =Low frequency, high precision Finally, the output of the sensor P 传感器 is regulated by environmental changes, expressed as: P 传感器 (AND 温 ,AND 湿 ,AND 气压 ,AND 污然物 )=f(E 温 ,AND 湿 ,AND 气压 ,AND 污染物 )·g(E 温 ,AND 湿 ,AND 气压 ,AND 污染物 ) Among them, f and g are the adjustment functions of sensor sensitivity and working mode, respectively, and the final output is the sensor's measured value.

5. A method for confirming the position of a substation disconnector according to claim 4, characterized in that: Using redundant sensors and communication networks, when the primary sensor or network fails, the method of automatically switching to the backup sensor or backup network for position confirmation is as follows: Deploy multiple sensors, including the main sensor (S 主 ) and backup sensor (S 备 ), the work of the sensor system is to confirm the position by obtaining sensor data. When the main sensor fails, the system will automatically switch to the backup sensor; set up: S 主 (t) represents the measurement value of the main sensor at time t; S 备 (t) represents the measurement value of the backup sensor; Fault judgment function (S 主 (t), S 备 (t)) is used to identify whether the main sensor is faulty; Define a fault judgment function Fault judgment function (S 主 , S 备 ), determine whether to switch based on whether the output of the main sensor is abnormal: If the fault judgment function (S 主 )=1, it means that the main sensor fails and the system switches to the backup sensor; In addition to redundant sensors, a redundant communication network is established, with the main communication network being N 主 , the backup communication network is N 备 ,The system monitors the status of the primary network in real time and switches to the backup network in case of failure; Fault detection function Network fault judgment function (N 主 ) is used to monitor the status of the main network: The logic of the automatic switching algorithm is as follows: When the fault judgment function (S 主 )=1, and the network fault judgment function (N 主 )=0, switch to the backup sensor S 备 Conduct location confirmation; If the network fault judgment function (N 主 )=1, then switch to the backup network N 备 , and continue to obtain data from the backup sensor; In order to seamlessly switch to the backup sensor or backup network, the state transition of the system is described by the following formula: Position confirmation based on sensor data S 有效 (t) and the status of the communication network are dynamically updated, and the position confirmation function is set as P(t). The position is calculated based on the sensor data: P(t)=f(S 有效 (t),N 有效 (t)) Among them, N 有效 (t) is the communication network currently in use; In summary, the calculation formula of the redundant sensor and communication network system is: P(t)=f(S 有效 (t),N 有效 (t)) Where f is a function that calculates the position based on sensor data and dynamically switches to backup sensors and backup networks.

6. A method for confirming the position of a substation disconnector according to claim 5, characterized in that: The real-time position data of the disconnector is transmitted to the dispatch center via the remote monitoring system. The system provides real-time feedback on the switch position and immediately issues an alarm if any abnormality occurs. The method for generating fault diagnosis information is as follows: The position data of the switch is transmitted to the dispatch center through the remote monitoring system. The switch position collected by the sensor is L(t). This data includes the state of the switch, which is the position of the switch and the current operating mode of the switch; L(t): real-time position data of the disconnector, the switch status at time t; If L(t) = 1, it means the switch is in the "on" state; If L(t) = 0, it means the switch is in the "off" state; The location data is transmitted to the dispatch center through the remote monitoring system. The dispatch center receives and feeds back the data in real time. The feedback signal to the dispatch center is set as R(t) and is updated according to the real-time location data: R(t)=L(t) The dispatch center receives L(t) in real time and feeds it back to the system users; In the application, the system monitors whether the switch status is normal. If the sensor data is abnormal or the remote transmission is interrupted, the system will immediately alarm and determine whether there is an abnormality by setting a threshold; Two main fault types are detected: Sensor failure: The sensor fails to accurately obtain the switch position or the signal returned does not match the expected signal; Communication failure: interruption or delay in the transmission of switch position data by the remote monitoring system; Set the fault judgment function F(t) to determine whether there is a fault: Where F(t) = 1 indicates that a fault is detected; F(t) = 0 means no fault is detected; If the system detects a fault (F(t) = 1), it immediately generates fault diagnosis information and issues an alarm signal. The alarm signal is set as A(t) and is associated with the fault diagnosis information D(t). The alarm signal is defined as: At the same time, the fault diagnosis information D(t) is used to record and feedback the type and occurrence time of the fault: If a fault occurs (A(t) = alarm), the system starts the emergency processing mechanism and sets the emergency response function as R 应急 (t), which is defined as follows:

7. A method for confirming the position of a substation disconnector according to claim 6, characterized in that: The system uses automatic maintenance and self-calibration functions. During regular operation or when an abnormality occurs, the system automatically performs self-checking in the following ways: The system performs self-tests periodically or automatically triggers a self-test when an anomaly occurs. The self-test process includes the following steps: Periodic self-test: The system performs self-test operations regularly; Anomaly detection: When the system detects an abnormal situation, it triggers a self-check; Self-calibration: After self-test, if the system finds deviation or abnormality, it will perform self-calibration operation; Periodic self-check is 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-test time interval. Set the current time as t, and the trigger condition of the self-test is expressed as: That is, the system is 自检 Time triggers a self-test; Let E(t) be the anomaly detection function, which is defined as follows: When the system performs self-test and detects an anomaly, it automatically performs self-calibration. The self-calibration operation function is set as selfcalibration(t), which is started when an anomaly occurs. The trigger condition of self-calibration is expressed as: That is, when an abnormality or periodic self-test is detected, a self-calibration operation is triggered; Based on the above, the system's self-test and self-calibration functions operate as follows: Periodic self-test trigger: Anomaly Detection: Self-calibration trigger: If the periodic self-test trigger (t) = 1 or E(t) = 1, the system will perform a self-test to detect and repair the problem. The specific contents of the self-test include: Verify the output of the sensor; Check the integrity of data transmission; Check whether the control system has any faults; If any problems are found during the self-test, the system will automatically perform self-calibration: The self-calibration process includes: Adjust the sensor's calibration parameters; Resetting the system's thresholds or biases; Repair or restart failed system components.

8. A substation disconnector position confirmation system, characterized in that: Includes: Sensor module, used to deploy photoelectric sensors, magnetic sensors, and position sensors to collect physical position data of the disconnector in real time; Data fusion and intelligent analysis module, used to fuse and intelligently analyze the data collected by the sensors to identify and determine the switch position; Environmental monitoring module, used to deploy temperature, humidity, and vibration sensors to monitor substation environmental changes; Redundant system modules, used to deploy redundant sensors and redundant communication networks to ensure that the backup system can take over when the main system fails; Remote monitoring and feedback module, used for real-time monitoring of switch position and fault alarm through SCADA remote monitoring system; The automatic maintenance and self-calibration module is used to regularly perform self-inspection and self-calibration on the system to ensure that all devices are in optimal working condition.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, a method for confirming the position of a substation disconnector according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a method for confirming the position of a substation disconnector as described in any one of claims 1 to 7 is implemented.

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