An economic operation control method for distribution transformer based on intelligent fusion terminal

Abstract

The application discloses an economic operation control and switching strategy of distribution transformer based on intelligent fusion terminal, and mainly comprises the following steps: 1) under the construction system of distribution Internet of Things "cloud management edge terminal", a cloud edge collaborative framework is designed; 2) a cloud edge collaborative mechanism based on MQTT protocol is established, and the MQTT protocol is adopted to complete the sending and receiving operation of cloud edge data; 3) a cloud edge collaborative control model supported by the cloud edge collaborative mechanism is constructed, and the model comprises index calculation, prediction, real-time research and judgment, control strategy and switching control module; and 4) the cloud edge collaborative control model of distribution transformer is utilized to select a suitable switching strategy of switch to switch the switch of distribution transformer. The method has good universality and applicability, and the switching scheme of the switch can ensure the economic operation of the distribution transformer under the condition of meeting the stable operation of the distribution transformer.

Description

Technical Field

[0001] This invention relates to the field of smart distribution networks, specifically a method for economical operation control of distribution transformers based on a smart fusion terminal. Background Technology

[0002] As a core component of the power distribution network, distribution transformers inevitably generate active and reactive power losses during power transmission. According to relevant data, transformer power losses account for approximately 50% of the total line losses in the entire power system.

[0003] The conventional economical operation method for distribution transformers mainly involves collecting operating data to calculate the load rate and determining whether the transformer is under light or heavy load conditions based on the calculation results, thus selecting the appropriate combination of operating modes on-site. However, frequent switching and adjusting the operating modes of distribution transformers not only affects the stable operation of the distribution network but also shortens the service life of the transformers. With the widespread application of intelligent integrated terminals in distribution networks, the amount of online monitoring data for distribution transformers is increasing. If real-time analysis of this monitoring data can be performed, and reasonable switching measures can be taken for distribution transformers based on the results, it will be significant for achieving economical operation of distribution transformers and reducing losses.

[0004] The intelligent converged terminal is a new generation of distribution IoT terminal. As the most powerful brain of a distribution substation, it meets the needs of marketing and distribution operations, integrating the collection of distribution and consumption information to achieve unified data collection for both marketing and distribution businesses. Designed with a "software-defined terminal" concept, it uses the "State Grid Chip" series chips and the proprietary "Hub OS" operating system, supported by edge computing capabilities. Through an APP application, it supports the integrated collection of marketing and distribution data and flexible business configuration. Equipped with edge device management components, it supports integrated deployment with IoT management platforms, enabling remote terminal management and business deployment.

[0005] With the development of IoT technology, combining IoT technology with the operation control of distribution transformers can reduce unnecessary switching and lower transformer losses. Distribution transformer substations, with the substation intelligent fusion terminal as their core, deploy various low-voltage sensing devices on the power supply side, low-voltage line side (including branches), and user side. This enables the collection and monitoring of electrical data, power data, and environmental data such as temperature and humidity from distribution transformers, low-voltage branches, and low-voltage users. It can also connect to external industrial cameras for remote video viewing. The substation intelligent fusion terminal analyzes, calculates, and organizes the substation data as needed, then transmits it via fiber optic or wireless private / public networks to the distribution automation master station, IoT management platform, and electricity information collection system master station. Summary of the Invention

[0006] The purpose of this invention is to adjust the operating status of distribution transformers based on intelligent fusion terminals, reduce power losses during the operation of distribution transformers, and achieve economical operation.

[0007] The technical solution adopted to achieve the purpose of this invention is as follows: an economical operation control method for distribution transformers based on intelligent fusion terminals, mainly including the following steps:

[0008] 1) Under the construction system of "cloud-pipe-edge-device" in the distribution Internet of Things, a cloud-edge collaboration framework is designed. This mechanism includes two main parts: cloud and edge. The cloud-edge collaboration mechanism supports the deployment of cloud and edge. It can realize multi-dimensional monitoring and economic operation switching control of distribution transformers through intelligent fusion terminals. Data acquisition, feature extraction and index calculation functions are deployed on the edge, while real-time analysis and prediction models are deployed in the cloud center. This forms an operation strategy and internal logic of data collaboration, business collaboration and computing resource collaboration. The cloud-edge collaboration mechanism can improve data processing capabilities, so that the operation status assessment and prediction of distribution transformers can meet real-time response, thereby realizing precise operation and maintenance management of distribution transformers.

[0009] 2) Establish a cloud-edge collaboration mechanism based on the MQTT protocol, and use the MQTT protocol to complete the sending and receiving of cloud-edge data.

[0010] Furthermore, regarding cloud-edge interaction based on the MQTT protocol, the list of pre-configured topics on the cloud platform is shown in the table below:

[0011] Table 1. List of pre-configured topics on the cloud platform

[0012] Transmitted content Data Topic Upload custom data $oc / devices / {device_id} / sys / messages / up Send custom format data $oc / devices / {device_id} / sys / messages / down Terminal reports attribute data $oc / devices / {device_id} / sys / properties / report Downlink response $oc / devices / {device_id} / sys / shadow / get / response / request_id={request_id} Cloud command delivery $oc / devices / {device_id} / sys / commands / request_id={request_id}

[0013] 3) Construct a cloud-edge collaborative control model. The cloud-edge collaborative control model for distribution transformers uses the cloud-edge collaborative mechanism in 2) to upload and distribute information and collaborate on data.

[0014] 3.1) Furthermore, the cloud-edge collaborative control model is deployed on both the cloud and edge devices with the support of the cloud-edge collaborative mechanism, completing the uploading and downloading of information and data collaboration, and includes the following modules:

[0015] 3.1.1) Index Calculation Module: The index calculation module is located at the edge. It receives data collected from the distribution transformer, calculates the load rate, cumulative switching time, etc. of the distribution transformer, and calculates the economic load coefficient and optimal operating range of the distribution transformer and transmits the results to the cloud.

[0016] 3.1.2) Prediction Module: To determine the optimal timing for adjusting transformer operating modes and reduce the frequency of adjustments, short-term load forecasting is introduced into the transformer operation analysis. The cloud-based prediction module receives distribution transformer operating data from the database and performs multi-step predictions, laying the groundwork for the control strategy module's adjustment strategies. Through short-term load forecasting, and based on the predicted load curve and the basic theory of economic transformer operation, it determines the appropriate action to take on the distribution transformer switches, ultimately achieving optimized distribution transformer operating combinations. The predicted data provides strong data support for the control strategy module's adjustment strategies, avoiding the problem of excessive switching operations.

[0017] 3.1.3) Real-time analysis module: By receiving the edge calculation results and cloud prediction results, the real-time analysis module determines whether the distribution transformer is outside the economic operating range. If it meets the requirements, it enters the control strategy module.

[0018] 3.1.4) Control Strategy Module: The control module analyzes the situation in real time based on the operating data calculated at the edge and the prediction results of the cloud prediction module, selects an appropriate control strategy, and sends the switching scheme to the edge distribution transformer switching control module.

[0019] 3.1.5) Switching control module: Controls the distribution transformer switch according to the control strategy of the control strategy module.

[0020] 4) The economical operation control and switching procedures for distribution transformer switches are as follows:

[0021] 4.1) Receive indicator data from the indicator calculation module , , , , , , , Specifically, this includes: apparent power. Overall power loss Critical load power of distribution transformer Comprehensive power loss during separate operation Load rate Load distribution coefficient of distribution transformer A Load distribution coefficient of distribution transformer B Superimposed load power .

[0022] Apparent power The calculation formula is as follows:

[0023]

[0024] Overall power loss The calculation formula is as follows:

[0025]

[0026]

[0027]

[0028] It is the total power loss under no-load conditions. This is the total power loss under rated load. It is the load factor.

[0029] Critical load power The calculation formula is as follows:

[0030]

[0031] The combined power loss of the two distribution transformers operating separately The calculation formula is as follows:

[0032]

[0033] In the formula: This is the sum of the load power of the two transformers. ;

[0034] , These are the load powers of transformers A and B, respectively, in kVA;

[0035] , These are the load distribution coefficients for transformers A and B, respectively.

[0036] This refers to the no-load comprehensive power loss of distribution transformer A. This refers to the comprehensive power loss of distribution transformer A under rated load. This refers to the no-load comprehensive power loss of distribution transformer B. This is the comprehensive power loss of the rated load of distribution transformer B.

[0037] The code for uploading metric data is shown below:

[0038] {

[0039] "services": [

[0040] {

[0041] "service_id":"service_id",

[0042] "properties":{

[0043] "apparent power": apparent power,

[0044] "Comprehensive power loss": Comprehensive power loss,

[0045] "critical load power": critical load power,

[0046] "Separate the operating comprehensive power loss": Separate the operating comprehensive power loss,

[0047] "load_rate": load_rate,

[0048] "event_time": "event_time"

[0049] }

[0050] } ]

[0052] }

[0053] 4.2) In step 3.1.2), the prediction module uses the LSTM-SVM model (using the publicly available patented data-driven trend prediction method for the operating status of distribution transformers, publication number: CN113393102B) to predict the distribution transformer index data for the next t time period and obtain the results.

[0054] 4.3) Adaptively adjust the locking and switching time based on the prediction results. The steps are as follows;

[0055] 4.3.1) The intelligent fusion terminal uploads indicator data to the prediction module;

[0056] In module 4.3.2), the LSTM-SVM model performs multi-step predictions on data such as load factor (i.e., the first step). At that moment, );

[0057] 4.3.3) Determine the opening / closing status of the bus tie;

[0058] 4.3.4) After judgment Does the load exceed the critical load power at any given moment?

[0059] 4.3.5) Confirm the interlocking time .

[0060] 4.4) The real-time analysis module determines whether the system is in an uneconomical operating range and whether it is within the switching / locking time. outside;

[0061] 4.5) Select the operating scheme for the designed distribution transformer The steps are as follows:

[0062] 4.5.1) Determine the rated capacity of the two distribution transformers. , Size details;

[0063] 4.5.2) Determine the superimposed load power of the distribution transformer Critical load power of distribution transformers A and B Size details;

[0064] 4.5.3) Select the operating scheme for the distribution transformer.

[0065] 4.6) Determine the duration of overload exceeding limits and Size or light load over-limit duration and For both large and small cases, calculate the number of times the limit is exceeded based on the duration of the exceeded limit. Determine the relationship between the number of times the limit is exceeded and the value of Y.

[0066] 4.7) Start checking synchronization (default duration) Within a certain time period (seconds), the power supplies on both sides were detected to meet the extreme values ​​of voltage amplitude difference (0-20V), phase angle difference (0-5 degrees), and frequency difference (0.1-0.2 Hz), which met the switching requirements.

[0067] 4.8) Select the design switch control scheme The process is as follows:

[0068] 4.8.1) Determine whether the rated capacities of the two distribution transformers are equal;

[0069] 4.8.2) Determine the superimposed load power of the distribution transformer With critical load power The size of the transformer determines its operating status.

[0070] 4.8.3) Selecting a switch control scheme .

[0071] 4.9) The control scheme is sent to the switching control module to switch the high and low voltage switches and the bus tie switch.

[0072] In the context of the power Internet of Things, this invention proposes an economical operation control and switching strategy for distribution transformers based on intelligent fusion terminals. This method has good versatility and applicability; the switching scheme can minimize the number of switching operations while ensuring stable operation of the distribution transformer, thus guaranteeing its economical operation. Attached Figure Description

[0073] Figure 1 A cloud-edge collaborative architecture for economic operation control and switching strategies of distribution transformers based on intelligent fusion terminals;

[0074] Figure 2 Overall flowchart;

[0075] Figure 3 Select a flowchart for the solution;

[0076] Figure 4 A flowchart for adjusting the switching and locking time of the prediction module;

[0077] Figure 5 This is a topology diagram of two distribution transformers in the embodiment;

[0078] Figure 6 A diagram showing the calculated load rate of the distribution transformer;

[0079] Figure 7 This is a flowchart of the switching strategy for distribution transformers. Detailed Implementation

[0080] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0081] Example

[0082] This example uses 24-hour operating data from a distribution transformer area of ​​a power grid on a certain day in August 2022. The example is as follows:

[0083] 1) Under the construction system of "cloud-pipe-edge-device" in the distribution Internet of Things, a cloud-edge collaboration framework is designed. This mechanism includes two main parts: cloud and edge. The cloud-edge collaboration mechanism supports the deployment of cloud and edge. It can realize multi-dimensional monitoring and economic operation switching control of distribution transformers through intelligent fusion terminals. Data acquisition, feature extraction and index calculation functions are deployed on the edge, while real-time analysis and prediction models are deployed in the cloud center. This forms an operation strategy and internal logic of data collaboration, business collaboration and computing resource collaboration. The cloud-edge collaboration mechanism can improve data processing capabilities, so that the operation status assessment and prediction of distribution transformers can meet real-time response, thereby realizing precise operation and maintenance management of distribution transformers.

[0084] 2) Establish a cloud-edge collaboration mechanism based on the MQTT protocol, and use the MQTT protocol to complete the sending and receiving of cloud-edge data.

[0085] Furthermore, regarding cloud-edge interaction based on the MQTT protocol, the list of pre-configured topics on the cloud platform is shown in the table below:

[0086] Table 1. List of pre-configured topics on the cloud platform

[0087] Transmitted content Data Topic Upload custom data $oc / devices / {device_id} / sys / messages / up Send custom format data $oc / devices / {device_id} / sys / messages / down Terminal reports attribute data $oc / devices / {device_id} / sys / properties / report Downlink response $oc / devices / {device_id} / sys / shadow / get / response / request_id={request_id} Cloud command delivery $oc / devices / {device_id} / sys / commands / request_id={request_id}

[0088] 3) Construct a cloud-edge collaborative control model. The cloud-edge collaborative control model for distribution transformers uses the cloud-edge collaborative mechanism in 2) to upload and distribute information and collaborate on data.

[0089] 3.1) Furthermore, the cloud-edge collaborative control model is deployed on both the cloud and edge devices with the support of the cloud-edge collaborative mechanism, completing the uploading and downloading of information and data collaboration, and includes the following modules:

[0090] 3.1.1) Index Calculation Module: The index calculation module is located at the edge. It receives data collected from the distribution transformer, calculates the load rate, cumulative switching time, etc. of the distribution transformer, and calculates the economic load coefficient and optimal operating range of the distribution transformer and transmits the results to the cloud.

[0091] 3.1.2) Prediction Module: To determine the optimal timing for adjusting transformer operating modes and reduce the frequency of adjustments, short-term load forecasting is introduced into the transformer operation analysis. The cloud-based prediction module receives distribution transformer operating data from the database and performs multi-step predictions, laying the groundwork for the control strategy module's adjustment strategies. Through short-term load forecasting, and based on the predicted load curve and the basic theory of economic transformer operation, it determines the appropriate action to take on the distribution transformer switches, ultimately achieving optimized distribution transformer operating combinations. The predicted data provides strong data support for the control strategy module's adjustment strategies, avoiding the problem of excessive switching operations.

[0092] 3.1.3) Real-time analysis module: By receiving the edge calculation results and cloud prediction results, the real-time analysis module determines whether the distribution transformer is outside the economic operating range. If it meets the requirements, it enters the control strategy module.

[0093] 3.1.4) Control Strategy Module: The control module analyzes the situation in real time based on the operating data calculated at the edge and the prediction results of the cloud prediction module, selects an appropriate control strategy, and sends the switching scheme to the edge distribution transformer switching control module.

[0094] 3.1.5) Switching control module: Controls the distribution transformer switch according to the control strategy of the control strategy module.

[0095] 4) The economical operation control and switching procedures for distribution transformer switches are as follows:

[0096] 4.1) Receive indicator data from the indicator calculation module , , , , , , , Specifically, this includes: apparent power. =178.66KVA, total power loss =2463.83KW, critical load power of distribution transformer =176.40KW, total power loss during split operation =2393.83KW, load rate =56.7%, load distribution factor of distribution transformer A =0.66, load distribution coefficient of distribution transformer B =0.34, superimposed load power =178.66KVA.

[0097] Apparent power The calculation formula is as follows:

[0098]

[0099] Overall power loss The calculation formula is as follows:

[0100]

[0101]

[0102]

[0103] It is the total power loss under no-load conditions. This is the total power loss under rated load. It is the load factor.

[0104] Critical load power The calculation formula is as follows:

[0105]

[0106] The combined power loss of the two distribution transformers operating separately The calculation formula is as follows:

[0107]

[0108] In the formula: This is the sum of the load power of the two transformers. ;

[0109] , These are the load powers of transformers A and B, respectively, in kVA;

[0110] , These are the load distribution coefficients for transformers A and B, respectively.

[0111] This refers to the no-load comprehensive power loss of distribution transformer A. This refers to the comprehensive power loss of distribution transformer A under rated load. This refers to the no-load comprehensive power loss of distribution transformer B. This is the comprehensive power loss of the rated load of distribution transformer B.

[0112] The code for uploading metric data is shown below:

[0113] {

[0114] "services": [

[0115] {

[0116] "service_id":"5fed8387aaafca02dba*****",

[0117] "properties":{

[0118] "apparent power": 209.56KVA

[0119] "Comprehensive power loss":2463.83KW,

[0120] "critical load power":176.40KW,

[0121] "Separate the operating comprehensive power loss":2393.83KW,

[0122] "load_rate": 56.7%,

[0123] "event_time": "20220821T121212Z"

[0124] }

[0125] } ]

[0127] }

[0128] 4.2) In step 3.1.2), the prediction module uses the LSTM-SVM model (using the publicly available patented data-driven trend prediction method for the operating status of distribution transformers, publication number: CN113393102B) to predict the distribution transformer index data for the next t=4 time period and obtain the results.

[0129] 4.3) Adaptively adjust the locking and switching time based on the prediction results. =9h, the steps are as follows;

[0130] 4.3.1) The intelligent fusion terminal uploads indicator data to the prediction module;

[0131] In module 4.3.2), the LSTM-SVM model performs multi-step predictions on data such as load factor (i.e., the first step). At that moment, );

[0132] 4.3.3) Determine if the bus tie opening / closing status is closed;

[0133] 4.3.4) After judgment =There are 4 times when the load exceeds the critical load power;

[0134] 4.3.5) Confirm the interlocking time =9h.

[0135] 4.4) The real-time analysis module determines whether the system is in an uneconomical operating range or during the switching / locking period. outside;

[0136] 4.5) Select the operating scheme for the designed distribution transformer The steps are as follows:

[0137] 4.5.1) Determine the rated capacity of the two distribution transformers. , Size cases are =315KVA> =160KVA;

[0138] 4.5.2) Determine the magnitude of the superimposed load power of the distribution transformer compared to the critical load power of distribution transformers A and B. =178.66KVA > Critical load power of distribution transformer A =176.40KW;

[0139] 4.5.3) Selecting the operating scheme for distribution transformers Distribution transformers A and B operate separately.

[0140] 4.6) Determine the duration of overload exceeding limits and The size case is the duration of overload exceeding the limit > Calculate the number of times the limit is exceeded using the duration of the exceeded limit. =3, check the number of times the limit is exceeded. =2 is the case where the size is > .

[0141] 4.7) Start checking synchronization (default duration) Within a certain time period (seconds), the power supplies on both sides were detected to meet the extreme values ​​of voltage amplitude difference (0-20V), phase angle difference (0-5 degrees), and frequency difference (0.1-0.2 Hz), which met the switching requirements.

[0142] 4.8) Select the design switch control scheme The process is as follows:

[0143] 4.8.1) Determine the rated capacity of the two distribution transformers. =315KVA> =160KVA;

[0144] 4.8.2) Determine the superimposed load power of the distribution transformer With critical load power The size is the superimposed load power =178.66KVA > Critical Load Power =176.40KW, the operation status of the distribution transformer is determined to be: distribution transformer A operates independently → distribution transformers A and B operate separately;

[0145] 4.8.3) Selecting a switch control scheme 1) Close the high-voltage switch of transformer B, 2) Close the low-voltage switch of transformer B, 3) Disconnect the bus tie switch.

[0146] 4.9) The control scheme is sent to the switching control module to switch the high and low voltage switches and the bus tie switch.

[0147] In the context of the power Internet of Things, this invention proposes an economical operation control and switching strategy for distribution transformers based on intelligent fusion terminals. This method has good versatility and applicability; the switching scheme can minimize the number of switching operations while ensuring stable operation of the distribution transformer, thus guaranteeing its economical operation.

Claims

1. A method for economical operation control of distribution transformers based on intelligent fusion terminals, characterized in that, The main steps include: Step 1) Under the construction system of "cloud-pipe-edge-device" of distribution Internet of Things, design a cloud-edge collaboration framework and establish a cloud-edge collaboration mechanism based on the MQTT protocol. This mechanism includes two main parts: cloud and edge. The cloud-edge collaboration mechanism supports the deployment of cloud and edge. Multi-dimensional monitoring and economic operation of distribution transformers can be realized through intelligent fusion terminals. Step 1.1) Cloud-edge interaction based on the MQTT protocol mainly includes the following: Step 1.1.1) Call the interface to report the terminal's custom data to the platform. The platform forwards the message reported by the terminal to the application server or other cloud services for storage and processing. Step 1.1.2) When the terminal cannot issue commands in the format defined in the product model, data in a custom format can be sent to the terminal using the interface; Step 1.1.3) The terminal reports the attribute data to the platform according to the format defined in the product model; Step 1.1.4) The terminal obtains the stored data from the platform. Users modify device attributes through the application server or IoT console. When the terminal goes online, it subscribes to the topic and can obtain the data stored on the platform to synchronize the terminal attribute values, thereby completing the modification of the terminal attribute values. Step 1.1.5) The platform sends terminal control commands to the terminal. After the platform sends the commands, the terminal needs to return the execution results of the commands to the platform in a timely manner. If the terminal does not respond, the platform will consider the command execution to have timed out. Step 2) Construct a cloud-edge collaborative control model supported by the cloud-edge collaboration mechanism, including modules for indicator calculation, prediction, real-time analysis, control strategy, and switching control. Step 2.1) Index Calculation Module: The index calculation module is located at the edge. It receives the data collected by the distribution transformer, calculates the load rate and cumulative switching time of the distribution transformer, and calculates the economic load factor and optimal operating range of the distribution transformer and transmits the results to the cloud. Step 2.2) Prediction Module: In order to determine the optimal timing for adjusting the transformer operation mode and reduce the number of adjustments, short-term load forecasting is introduced into the transformer operation analysis. After receiving the distribution transformer operation data sent by the database, the cloud-based prediction module performs multi-step predictions on the data, laying the groundwork for the control strategy module to adjust its strategy. Through short-term load forecasting, and based on the predicted load curve and the basic theory of economic operation of transformers, it determines what action to take for the distribution transformer switch, ultimately achieving the optimization of the distribution transformer operation combination. The prediction data provides strong data support for the control strategy module to adjust its strategy, which can avoid the problem of excessive switching frequency. Step 2.3) Real-time analysis module: By receiving the edge calculation results and cloud prediction results, the real-time analysis module determines whether the distribution transformer is outside the economic operating range. If it meets the requirements, it enters the control strategy module. Step 2.4) Control Strategy Module: The control module analyzes the situation in real time based on the operating data calculated at the edge and the prediction results of the cloud prediction module, selects an appropriate control strategy, and sends the switching scheme to the edge distribution transformer switching control module. Step 2.5) Switching control module: Control the distribution transformer switch according to the control strategy of the control strategy module; Step 3) Collect data using intelligent fusion terminals to generate indicator data. , , , , , The calculations are uploaded to the cloud based on the formula, and the results are as follows: Apparent power : Overall power loss : It is the total power loss under no-load conditions, P KZ This is the total power loss under rated load. It is the load factor; Critical load power : The combined power loss of the two distribution transformers operating separately : It is the no-load comprehensive power loss of distribution transformer A, P KZA This refers to the comprehensive power loss of distribution transformer A under rated load. It is the no-load comprehensive power loss of distribution transformer B, P KZB This refers to the comprehensive power loss of the rated load of distribution transformer B. Step 4) The prediction module uses the LSTM-SVM model to predict the distribution transformer index data for the next t time periods; Step 4.1) The cloud-based real-time analysis and control strategy module selects a suitable operating scheme for the distribution transformer. The steps are as follows: Step 4.1.1) Determine the rated capacity of the two distribution transformers. , Size details; Step 4.1.2) If they are equal, determine whether the load power of a single distribution transformer is greater than the critical load power of distribution transformer A. If not, choose Option 1, where transformer A operates independently, and the cumulative time reaches [a certain threshold]. Option 2 is selected, with transformer B operating independently; otherwise, Option 3 is selected, with transformers A and B operating separately. When the combined load power of the two transformers is lower than the critical load power of one transformer, and the duration reaches [a certain value], [the following conditions apply]. Adjusted to Option 1; Step 4.1.3) If the rated capacity of the distribution transformer Determine whether the superimposed load power exceeds the critical load power of distribution transformer A. If the load does not exceed the limit, select option four: distribution transformer A operates independently. If the load exceeds the limit, determine the critical load power with distribution transformer B. The size, if less than the critical load power of distribution transformer B Option 5 is selected: Distribution transformer B operates independently; if the load exceeds the critical load capacity of distribution transformer B... Option 6 is selected, with distribution transformers A and B operating separately; Step 4.1.4) If the rated capacity of the distribution transformer Determine whether the superimposed load power exceeds the critical load power of distribution transformer B. If the load does not exceed the limit, select option seven, and distribution transformer B will operate independently; if the load exceeds the limit, determine whether it exceeds the critical load power of distribution transformer A. If the load is less than the critical load power of distribution transformer A, select option eight, where distribution transformer A operates independently; if the load is greater than the critical load power of distribution transformer A, select option eight, where distribution transformer A operates independently. Option nine is selected, with distribution transformers A and B operating separately; Step 4.2) Adaptively adjust the locking and switching time based on the prediction results. The steps are as follows: Step 4.2.1) The intelligent fusion terminal uploads the indicator data to the prediction module; In step 4.2.2), the LSTM-SVM model in the module performs multi-step prediction of the load factor data, i.e., at time 1, 2, x, ..., t, where t≤4; Step 4.2.3) Determine if the bus tie switch is locked. If so, proceed with the determination. At any given moment, is there a load exceeding the critical load power, and the interlocking time is adjusted accordingly? ; If not, maintain the initial locking time. constant; Step 4.3) Determine if it is during the switching and locking time. In addition, if the overload over-limit duration is outside the switching and locking time, determine whether it is within the specified time. Or whether the duration of light load exceeding the limit is Calculate the number of times the limit is exceeded using the duration of the exceeded limit. Determine if the number of times exceeds the limit is exceeded. Start the synchronous inspection, duration is Within seconds, if the power supplies on both sides are found to meet the extreme values ​​of voltage amplitude difference (0-20V), phase angle difference (0-5 degrees), and frequency difference (0.1-0.2 Hz), a suitable switching control scheme is selected. Step 5) According to the switch control scheme, the switching control module switches the high and low voltage switches and the bus tie switch.

Images