Substation remote control automatic acceptance system and method based on cloud service

Abstract

The invention discloses a transformer substation remote control automatic acceptance system and method based on cloud service, and relates to the technical field of electrical automation. The system collects operation data of a transformer substation in real time through a sensor, transmits the data to a cloud platform, receives and preprocesses the data in real time, constructs a complete operation data set and stores the operation data set; the remote control optimization evaluation module carries out deep analysis on the operation data, calculates an environment influence factor Ei, carries out calculation based on the environment influence factor Ei to obtain a transformer substation predicted load P (t), calculates a distribution load L based on the transformer substation predicted load P (t), and obtains an optimization scheduling parameter Q through combination with calculation; and obtaining a load remote control scheduling error Wc, and comparing the error Wc with a preset error threshold value W for analysis. And when error abnormity is assessed for the first time, the system executes a secondary optimization algorithm, calculates a second optimization scheduling parameter Q2, and assesses an acceptance result after secondary optimization.

Description

Technical Field

[0001] The present invention relates to the field of electrical automation technology, and in particular to a substation remote control automatic acceptance system and method based on cloud services. Background Art

[0002] With the continuous increase in electricity demand and the development of smart grid technology, substations, as the core nodes of power transmission and distribution, are crucial to the safe and stable operation of the power system. Specifically, the acceptance and operation management of substations need to use advanced technical means to achieve real-time monitoring, data collection and intelligent scheduling to ensure the efficient operation and safety of substations.

[0003] The shortcomings of the current traditional substation management methods are mainly reflected in the incomplete collection of operating data, untimely data processing, and insufficient dispatch optimization. The existence of these problems will lead to abnormal conditions such as load imbalance and power quality degradation during the operation of the substation. For example, load imbalance may cause the substation equipment to be overloaded or idle, affecting the service life and safety of the equipment; the degradation of power quality will have a negative impact on the stability of power supply and the user's power experience. In addition, due to the lack of effective monitoring and dispatching means, it is difficult for the substation to respond and handle failures in a timely manner, which increases the difficulty of troubleshooting and repairing. In summary, these abnormal effects not only reduce the operating efficiency of the substation, but also pose a threat to the overall stability and safety of the power system. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a substation remote control automatic acceptance system and method based on cloud services, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: including a data acquisition module, a cloud processing module, a real-time monitoring and analysis module, a remote control scheduling and analysis module, and a remote control optimization and evaluation module;

[0006] The data acquisition module is used to collect the operation data of the substation in real time through sensors and monitoring equipment, and transmit the collected operation data to the cloud platform using the Internet of Things technology;

[0007] The cloud processing module receives the operation data in real time by building a cloud platform, and obtains the operation data set and stores the data after preprocessing the received operation data;

[0008] The real-time monitoring and analysis module uses a machine learning algorithm and loads the operating data set to calculate and obtain the environmental impact factor Ei, and then combines and calculates the substation predicted load P(t) based on the environmental impact factor Ei to identify the trend of electricity demand changes;

[0009] The remote control dispatch analysis module is used to calculate the distributed load L of the substation based on the obtained substation predicted load P(t), and then execute the intelligent remote control dispatch algorithm through the cloud platform to calculate the first optimized dispatch parameter Q to remotely optimize the substation load;

[0010] The remote control optimization and evaluation module performs comprehensive calculation to obtain the load remote control scheduling error Wc after optimizing the load remote control of the substation, and performs preliminary comparative evaluation and analysis of the remote control scheduling effect through the preset error threshold W and the load remote control scheduling error Wc. When the error is preliminarily evaluated to be abnormal, the secondary optimization algorithm formula is executed to calculate and obtain the second optimized scheduling parameter Q2 for load remote control scheduling, and the acceptance of the substation is evaluated and analyzed.

[0011] Preferably, the data acquisition module includes a sensor acquisition unit and a data transmission unit;

[0012] The sensor acquisition unit is used to convert the analog signal of the sensor group into a digital signal based on the sensor group installed around the transformer, and collect the operation data of the transformer in real time;

[0013] The operating data include ambient temperature Te, ambient humidity He, transformer internal oil temperature To, transformer current I, transformer voltage V, voltage fluctuation BD, frequency deviation PL, harmonic distortion Sz, load fluctuation frequency Fl and pressure fluctuation Pv;

[0014] The temperature sensor collects the ambient temperature Te;

[0015] The humidity sensor collects the ambient humidity He;

[0016] The oil temperature sensor collects the oil temperature To inside the transformer;

[0017] The current mutual inductance sensor collects the transformer current I;

[0018] The voltage mutual inductance sensor collects the transformer voltage V;

[0019] The power quality detector collects voltage fluctuation BD, frequency deviation PL and harmonic distortion Sz;

[0020] The load monitoring sensor collects the load fluctuation frequency Fl;

[0021] The pressure sensor collects pressure fluctuations Pv;

[0022] The data transmission unit is used to transmit the operating data collected by the sensor group to the cloud platform via wireless communication WIFi, and encrypt and authenticate the operating data based on the HTTP transport layer security protocol.

[0023] Preferably, the cloud processing module includes a data preprocessing unit and a data storage unit;

[0024] The data preprocessing unit is used to build a cloud platform and set up an MQTT server, receive and decrypt the transmitted operation data in real time, and then preprocess the decrypted operation data, the preprocessing includes load loss processing, power quality processing and comprehensive processing;

[0025] The load loss processing is performed by calculating the apparent power S=V*I according to the transformer current I and the transformer voltage V in the operation data, and performing a comprehensive calculation to obtain the active power P=S*cos(φ) according to the apparent power S combined with the power factor cos(φ) obtained from the power meter, and then calculating and analyzing the transformer input and output power to obtain the transformer load loss Lo=P input -P output , where P output Indicates output power, P input Indicates input power;

[0026] The power quality processing is performed by summarizing and calculating the voltage fluctuation BD, frequency deviation PL and harmonic distortion Sz in the operation data to obtain the power quality index Qf;

[0027] The power quality index Qf is calculated by the following algorithm formula:

[0028] Qf=w1*BD+w2*PL+w3*Sz;

[0029] Wherein, w1, w2 and w3 represent the preset weight values ​​of voltage fluctuation BD, frequency deviation PL and harmonic distortion Sz respectively; they are used to remotely adjust the influence of different indicators on the comprehensive power quality score, and the specific weight coefficient can be set according to the actual application scenario and power quality standard;

[0030] The comprehensive processing is used to remove noise and abnormal values ​​by combining the transformer load loss Lo and power quality index Qf obtained after the load loss processing and the power quality processing with the ambient temperature Te, ambient humidity He, transformer internal oil temperature To, load fluctuation frequency Fl and pressure fluctuation Pv in the operation data, and at the same time, after the data is verified and cleaned, the data is summarized to obtain the operation data set;

[0031] The data storage unit stores the operation data set obtained after preprocessing in time series by constructing a time series database.

[0032] Preferably, the real-time monitoring and analysis module includes an environment analysis unit and a load prediction unit;

[0033] The environmental analysis unit is used to perform correlation calculation to obtain the environmental impact factor Ei according to the ambient temperature Te, ambient humidity He and transformer internal oil temperature To in the operation data set;

[0034] The environmental impact factor Ei is calculated by the following algorithm formula:

[0035] Ei=α·Te+β·He+γ·To;

[0036] Wherein, α, β and γ represent the adjustment coefficients of ambient temperature Te, ambient humidity He and transformer internal oil temperature To respectively, and the specific parameters are set by the user.

[0037] Preferably, the load prediction unit extracts the stored operation data set and performs correlation calculation in combination with the obtained environmental impact factor Ei to obtain the substation predicted load P(t) and predict the future load;

[0038] The substation predicted load P(t) is calculated and obtained by the following algorithm formula;

[0039] P(t)=δ·Lo(t-1)+η·Qf(t)+θ·Fl(t)+κ·Pv(t)+ζ·Ei(t);

[0040] Wherein, Lo(t-1) represents the transformer load loss Lo at time t-1, Qf(t) represents the power quality index at time t, Fl(t) represents the load fluctuation frequency at time t, Pv(t) represents the pressure fluctuation at time t, Ei(t) represents the environmental impact factor at time t, δ represents the empirical coefficient of the transformer load loss Lo; η represents the empirical coefficient of the power quality index Qf(t); θ represents the empirical coefficient of the load fluctuation frequency Fl(t); κ represents the empirical coefficient of the pressure fluctuation Pv(t); ζ represents the empirical coefficient of the environmental impact factor Ei(t); and its specific values ​​are set by the user.

[0041] Preferably, the remote control scheduling analysis module includes a load balancing unit and an initial remote control scheduling unit;

[0042] The load balancing unit obtains the substation predicted load P(t) by performing correlation calculation in combination with the environmental impact factor Ei to obtain the substation distributed load L, and analyzes the distributed load of each substation;

[0043]

[0044] Where Li represents the distributed load of the ith substation, P total represents the total load of the substation, and N represents the number of substations.

[0045] Preferably, the initial remote control dispatching unit constructs an intelligent remote control dispatching algorithm, performs associated calculations based on the obtained distributed load L and the load fluctuation frequency Fl to obtain the first optimized dispatching parameter Q, minimizes the load difference and the load fluctuation frequency difference between the substations, optimizes the dispatching strategy, and then sends the obtained first optimized dispatching parameter Q to the substation through the LoRa communication technology to perform remote load distribution remote control adjustment;

[0046] The first optimization scheduling parameter Q is calculated and obtained by the following algorithm formula:

[0047]

[0048] Where Lj represents the distributed load of substation j, Fl represents the current load borne by substation j, i represents the load fluctuation frequency of substation i, Fl j represents the load fluctuation frequency of power station j, λ represents the adjustment coefficient, which is used to adjust the relative importance of load difference and load fluctuation frequency difference in optimal scheduling. If the adjustment coefficient λ is larger, the load fluctuation frequency difference has a greater impact on the optimal scheduling parameters. Qi represents the first optimal scheduling parameter Q of substation i.

[0049] Preferably, the remote control optimization evaluation module includes a remote control scheduling error analysis unit and a remote control scheduling optimization analysis unit;

[0050] The remote control scheduling error analysis unit includes a scheduling error calculation unit and a scheduling error evaluation unit;

[0051] After receiving the first optimized dispatching parameter Q, the dispatching error calculation unit remotely inputs the first optimized dispatching parameter Q into the dispatching system, and remotely dispatches the load of the substation. After the dispatching is completed, the substation load remote dispatching error Wc is comprehensively analyzed in combination with the operation data set of the substation, and the overall error of the load remote dispatching is analyzed;

[0052] The load remote control scheduling error Wc is calculated by the following algorithm formula:

[0053]

[0054] Where, Qf i represents the power quality index of substation i, Qf avg Represents the average power quality index, Pv i represents the pressure fluctuation of substation i, Pv avg represents the average pressure fluctuation, μ represents the adjustment coefficient of the power quality Qf error, and ν represents the adjustment coefficient of the pressure fluctuation Pv error;

[0055] The dispatch error evaluation unit presets an error threshold W through the safety and stability index of the substation load operation, and then makes a preliminary comparison and evaluation analysis of the effect of the dispatch strategy with the obtained load remote control dispatch error Wc;

[0056] The specific evaluation plan is as follows:

[0057] When the load remote control scheduling error Wc>error threshold W, it means that the current substation load remote control scheduling error is abnormal. At this time, the first remote control preset command is sent to the substation end, and the second scheduling optimization algorithm is executed to perform dynamic remote control adjustment;

[0058] When the load remote control scheduling error Wc ≤ error threshold W, it means that the current substation load remote control scheduling error is normal. At this time, a second remote control preset command is sent to the substation end to continue to execute the current scheduling optimization strategy and pass the acceptance.

[0059] Preferably, the remote control scheduling optimization analysis unit includes a secondary remote control scheduling unit and a remote control scheduling evaluation unit;

[0060] The secondary remote control dispatching unit is used to re-evaluate and identify the abnormality of the current substation load remote control dispatching error, and based on the optimization result of the current first optimized dispatching parameter Q, perform secondary calculation in combination with the environmental impact factor Ei to obtain the second optimized dispatching parameter Q2;

[0061] The second optimized scheduling parameter Q2 is calculated by the following algorithm formula:

[0062]

[0063] Where P(t) avg represents the average substation predicted load, Ei avg represents the average environmental impact factor;

[0064] The remote control dispatch evaluation unit is used to input the second optimized dispatch parameter Q2 into the dispatch system, remotely dispatch the load of the substation, and after the dispatch is completed, comprehensively analyze the secondary load remote control dispatch error Wc2 of the substation again, and perform a secondary comparative evaluation with the error threshold W, analyze the remote control adjustment effect after the secondary load is optimally dispatched, and execute an iterative strategy;

[0065] The specific evaluation plan is as follows:

[0066] When the secondary load remote control dispatch error Wc2>error threshold W, it means that the current substation secondary load remote control dispatch error is abnormal. At this time, a remote control preset command is sent to the protection and control device at the substation end to cut off the power transmission of the substation and mark it as unqualified for acceptance;

[0067] When the secondary load remote control scheduling error Wc2 ≤ error threshold W, it means that the secondary load remote control scheduling error of the current substation is normal, and it is marked as qualified for acceptance.

[0068] A cloud service-based remote control automatic acceptance method for a substation comprises the following steps:

[0069] S1. First, collect the substation operation data in real time through sensors and monitoring equipment, and use the Internet of Things technology to transmit the collected operation data to the cloud platform;

[0070] S2. Rebuild the cloud platform to receive the operation data in real time, perform data preprocessing on the received operation data, obtain the operation data set and store the data;

[0071] S3. Using big data analysis and machine learning algorithms, and loading the operating data set, calculate and obtain the environmental impact factor Ei, and combine and calculate the substation predicted load P(t) based on the environmental impact factor Ei to identify the trend of electricity demand changes;

[0072] S4, then calculate and obtain the distributed load L of the substation based on the obtained substation predicted load P(t), and then execute the intelligent remote control scheduling algorithm through the cloud platform to calculate and obtain the first optimized scheduling parameter Q to remotely optimize the substation load;

[0073] S5. Finally, after optimizing the load remote control of the substation, a comprehensive calculation is performed to obtain the load remote control scheduling error Wc, and a preliminary comparative evaluation and analysis of the remote control scheduling effect is performed through the preset error threshold W and the load remote control scheduling error Wc. When the error is initially evaluated to be abnormal, the secondary optimization algorithm formula is executed to calculate and obtain the second optimized scheduling parameter Q2 for load remote control scheduling, and the acceptance of the substation is evaluated and analyzed.

[0074] The present invention provides a substation remote control automatic acceptance system and method based on cloud services. It has the following beneficial effects:

[0075] (1) The system combines the data acquisition module and the cloud processing module, uses sensors and monitoring equipment to collect the substation operation data in real time, and uses the Internet of Things technology to transmit the data to the cloud platform. The data acquisition module includes a variety of sensors that can collect the substation operation data in real time. The data is transmitted to the cloud platform through wireless communication WIFI, and the HTTP transport layer security protocol is used for data encryption and authentication. The cloud platform performs preprocessing to obtain the operation data set and stores the data to ensure the security and reliability of data transmission. This real-time data collection and transmission method improves the efficiency of data processing and provides efficient and reliable data support for subsequent monitoring and analysis. Remote real-time monitoring and data analysis are achieved through the cloud platform. Acceptance personnel can perform real-time monitoring and acceptance operations at different locations, reducing the complexity of on-site operations.

[0076] (2) The system calculates the environmental impact factor Ei through the real-time monitoring analysis module and the remote control dispatch analysis module through the machine learning algorithm and the environmental analysis unit, and then extracts the stored operation data set through the load prediction unit, combines the environmental impact factor Ei, and uses the relevant formula to calculate the substation predicted load P(t) of the substation. The remote control dispatch analysis module then calculates the distribution load L based on the predicted load P(t), and then executes the intelligent remote control dispatch algorithm to calculate the first optimized dispatch parameter Q, minimize the load difference and load fluctuation frequency difference between substations, and thus optimize the dispatch strategy. Through these intelligent algorithms and optimized dispatch strategies, the system can dynamically remotely adjust the load distribution of the substation, improve the operating efficiency and stability of the substation, and at the same time introduce an intelligent algorithm to compare the remote control preset results of the centralized control system and the station-side monitoring system, realize automatic comparison, and improve acceptance efficiency and accuracy.

[0077] (3) The system calculates the dispatch error through the remote control dispatch error analysis unit and the remote control dispatch optimization analysis unit to obtain the load remote control dispatch error Wc, and then compares and evaluates the preset error threshold W through the safety and stability index of the substation load operation. When the load remote control dispatch error Wc exceeds the preset error threshold W, it indicates that the load remote control dispatch is abnormal. At this time, the system will execute the second optimization algorithm, calculate the second optimized dispatch parameter Q2, and perform further load remote control dispatch optimization. The secondary remote control dispatch unit then dispatches the load through the dispatch parameter Q2 adjusted by remote control, and re-evaluates the load remote control dispatch error Wc2. If the secondary error Wc2 is less than the error threshold W, the acceptance is qualified; otherwise, the acceptance is unqualified. Through multi-level error evaluation and optimization, the system can accurately identify and optimize the abnormal situation of load remote control dispatch, improve the accuracy and reliability of substation acceptance, and ensure the safe and stable operation of the substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1This is a schematic diagram of a process flow of a substation remote control automatic acceptance system based on cloud services of the present invention;

[0079] Figure 2 This is a schematic diagram of the steps of a remote control automatic acceptance method for a substation based on cloud services of the present invention. DETAILED DESCRIPTION

[0080] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0081] Example 1

[0082] See also Figure 1 The present invention provides a substation remote control automatic acceptance system based on cloud service. To achieve the above purpose, the present invention is implemented through the following technical scheme: including a data acquisition module, a cloud processing module, a real-time monitoring and analysis module, a remote control scheduling and analysis module, and a remote control optimization and evaluation module;

[0083] The data acquisition module is used to collect the substation operation data in real time through sensors and monitoring equipment, and transmit the collected operation data to the cloud platform using the Internet of Things technology;

[0084] The cloud processing module receives the operation data in real time by building a cloud platform, and after preprocessing the received operation data, obtains the operation data set and stores the data;

[0085] The real-time monitoring and analysis module uses machine learning algorithms and loads the operating data set to calculate and obtain the environmental impact factor Ei. It then combines and calculates the substation predicted load P(t) based on the environmental impact factor Ei to identify the changing trend of electricity demand.

[0086] The remote control dispatch analysis module is used to calculate the distributed load L of the substation based on the obtained substation predicted load P(t), and then execute the intelligent remote control dispatch algorithm through the cloud platform to calculate the first optimized dispatch parameter Q to remotely optimize the substation load;

[0087] After optimizing the remote control of the substation load, the remote control optimization evaluation module performs a comprehensive calculation to obtain the load remote control scheduling error Wc, and performs a preliminary comparative evaluation and analysis of the remote control scheduling effect through the preset error threshold W and the load remote control scheduling error Wc. When the error is initially evaluated to be abnormal, the secondary optimization algorithm formula is executed to calculate and obtain the second optimized scheduling parameter Q2 for load remote control scheduling, and the acceptance of the substation is evaluated and analyzed.

[0088] In this embodiment, the system uses sensors and monitoring equipment to collect the operation data of the substation in real time through the deployment of the data acquisition module, and combines the Internet of Things technology to seamlessly transmit the data to the cloud platform. This module realizes the efficient collection and safe transmission of the substation operation data, ensuring the timeliness and accuracy of the data. Through the cloud processing module, the system can receive and preprocess these data in real time, build a complete operation data set and store it. This processing process improves the efficiency and reliability of data processing and provides a solid data foundation for subsequent monitoring and analysis. The real-time monitoring and analysis module uses machine learning algorithms to conduct in-depth analysis of the operation data set, calculate the environmental impact factor Ei, and calculate the substation predicted load P(t) based on these factors. This intelligent analysis method can accurately identify the changing trend of electricity demand and provide more accurate load prediction. The remote control scheduling analysis module calculates the distribution load L based on the predicted load P(t), optimizes the load distribution through the intelligent remote control scheduling algorithm, and obtains the first optimized scheduling parameter Q. These intelligent prediction and scheduling strategies not only improve the efficiency of substation operation, but also effectively reduce the load differences between substations and improve the overall stability of the system. The remote control optimization evaluation module obtains the load remote control scheduling error Wc by comprehensively calculating the optimal scheduling of the substation load, and compares and analyzes it with the preset error threshold W. When the error is abnormal in the initial evaluation, the system will execute the secondary optimization algorithm to calculate the second optimization scheduling parameter Q2 for further optimization of the load remote control scheduling. This multi-level error evaluation and optimization mechanism significantly improves the accuracy of load remote control scheduling and the reliability of acceptance.

[0089] Example 2

[0090] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data acquisition module includes a sensor acquisition unit and a data transmission unit;

[0091] The sensor acquisition unit is used to convert the analog signal of the sensor group installed around the transformer into a digital signal, and collect the operation data of the transformer in real time;

[0092] The operating data include ambient temperature Te, ambient humidity He, transformer internal oil temperature To, transformer current I, transformer voltage V, voltage fluctuation BD, frequency deviation PL, harmonic distortion Sz, load fluctuation frequency Fl and pressure fluctuation Pv;

[0093] The temperature sensor collects the ambient temperature Te;

[0094] The humidity sensor collects the ambient humidity He;

[0095] The oil temperature sensor collects the oil temperature To inside the transformer;

[0096] The current mutual inductance sensor collects the transformer current I;

[0097] The voltage mutual inductance sensor collects the transformer voltage V;

[0098] The power quality detector collects voltage fluctuation BD, frequency deviation PL and harmonic distortion Sz;

[0099] The load monitoring sensor collects the load fluctuation frequency Fl;

[0100] The pressure sensor collects pressure fluctuations Pv;

[0101] The data transmission unit is used to transmit the operating data collected by the sensor group to the cloud platform through wireless communication WIFi, and encrypt and authenticate the operating data based on the HTTP transport layer security protocol.

[0102] In this embodiment, the data acquisition module of the system includes a sensor acquisition unit and a data transmission unit. The sensor acquisition unit converts analog signals into digital signals through a sensor group installed around the transformer, and collects the operation data of the transformer in real time. These data include ambient temperature Te, ambient humidity He, transformer internal oil temperature To, transformer current I, transformer voltage V, voltage fluctuation BD, frequency deviation PL, harmonic distortion Sz, load fluctuation frequency Fl and pressure fluctuation Pv. Various sensors are responsible for the collection of different parameters respectively, and the data is transmitted to the cloud platform through the data transmission unit using wireless communication WiFi, and the data is encrypted and authenticated based on the transport layer security protocol of HTTP. Through the data acquisition module, efficient, accurate collection and safe transmission of transformer operation data are achieved.

[0103] Example 3

[0104] This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: the cloud processing module includes a data pre-processing unit and a data storage unit;

[0105] The data preprocessing unit is used to build a cloud platform and set up an MQTT server to receive and decrypt the transmitted operating data in real time, and then preprocess the decrypted operating data. The preprocessing includes load loss processing, power quality processing and comprehensive processing;

[0106] The load loss processing is to calculate the apparent power S = V * I according to the transformer current I and transformer voltage V in the operating data, and to obtain the active power P = S * cos (φ) by combining the apparent power S with the power factor cos (φ) obtained from the power meter. Then, by analyzing the input and output power of the transformer, the transformer load loss Lo = P is calculated and analyzed. input -P output , where P output Indicates output power, P input Indicates input power;

[0107] Power quality processing obtains power quality index Qf by summarizing and calculating voltage fluctuation BD, frequency deviation PL and harmonic distortion Sz in the operation data;

[0108] The power quality index Qf is calculated by the following algorithm formula:

[0109] Qf=w1*BD+w2*PL+w3*Sz;

[0110] Wherein, w1, w2 and w3 represent the preset weight values ​​of voltage fluctuation BD, frequency deviation PL and harmonic distortion Sz respectively; they are used to remotely adjust the influence of different indicators on the comprehensive power quality score, and the specific weight coefficient can be set according to the actual application scenario and power quality standard;

[0111] The comprehensive processing is used to remove noise and abnormal values ​​by combining the transformer load loss Lo and power quality index Qf obtained after load loss processing and power quality processing with the ambient temperature Te, ambient humidity He, transformer internal oil temperature To, load fluctuation frequency Fl and pressure fluctuation Pv in the operating data. At the same time, after data verification and cleaning, data aggregation is performed to obtain the operating data set;

[0112] The data storage unit stores the running data set obtained after preprocessing in time series by constructing a time series database.

[0113] In this embodiment, the system receives and decrypts the transmitted operation data in real time by constructing a cloud platform and setting up an MQTT server through a data preprocessing unit. The decrypted data is subjected to load loss processing, power quality processing and comprehensive processing. The load loss processing calculates the apparent power S through the transformer current I and voltage V, and calculates the active power P in combination with the power factor cos(φ), and further analyzes the transformer input and output power to obtain the load loss Lo. The power quality processing calculates the power quality index Qf through voltage fluctuation BD, frequency deviation PL and harmonic distortion Sz, where w1, w2 and w3 are preset weight values. The comprehensive processing combines the above processing results with the ambient temperature Te, humidity He, transformer oil temperature To, load fluctuation frequency Fl and pressure fluctuation Pv, ​​performs noise removal and data cleaning, and finally summarizes them into an operation data set. The data storage unit stores the preprocessed data set in time series through a time series database. This module realizes the efficient preprocessing and storage of substation operation data, and improves the accuracy and reliability of data processing.

[0114] Example 4

[0115] This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the real-time monitoring and analysis module includes an ,environmental analysis unit and a load prediction unit;

[0116] The environmental analysis unit is used to perform correlation calculations to obtain the environmental impact factor Ei based on the ambient temperature Te, ambient humidity He and transformer internal oil temperature To in the operating data set;

[0117] The environmental impact factor Ei is calculated using the following algorithm formula:

[0118] Ei=α·Te+β·He+γ·To;

[0119] Wherein, α, β and γ represent the adjustment coefficients of ambient temperature Te, ambient humidity He and transformer internal oil temperature To respectively, and the specific parameters are set by the user.

[0120] The load prediction unit extracts the stored operation data set and combines it with the obtained environmental impact factor Ei to perform related calculations to obtain the substation predicted load P(t) and predict the future load;

[0121] The substation predicted load P(t) is calculated by the following algorithm formula;

[0122] P(t)=δ·Lo(t-1)+η·Qf(t)+θ·Fl(t)+κ·Pv(t)+ζ·Ei(t);

[0123] Wherein, Lo(t-1) represents the transformer load loss Lo at time t-1, Qf(t) represents the power quality index at time t, Fl(t) represents the load fluctuation frequency at time t, Pv(t) represents the pressure fluctuation at time t, Ei(t) represents the environmental impact factor at time t, δ represents the empirical coefficient of the transformer load loss Lo; η represents the empirical coefficient of the power quality index Qf(t); θ represents the empirical coefficient of the load fluctuation frequency Fl(t); κ represents the empirical coefficient of the pressure fluctuation Pv(t); ζ represents the empirical coefficient of the environmental impact factor Ei(t); and its specific values ​​are set by the user.

[0124] In this embodiment, the system calculates the environmental impact factor Ei according to the ambient temperature Te, ambient humidity He and the internal oil temperature To of the transformer through the environmental analysis unit. The load prediction unit extracts data from the stored operating data set in combination with the environmental impact factor Ei to calculate the predicted load P(t) of the substation. In the formula, the load loss Lo, the power quality index Qf, the load fluctuation frequency Fl and the pressure fluctuation Pv together with the environmental impact factor Ei affect the predicted load P(t). This module improves the accuracy and reliability of substation operation status monitoring through comprehensive analysis of environmental factors and load prediction. The system can more accurately predict future loads, provide accurate data support, and optimize scheduling strategies. These improvements make the operation of the substation more efficient and stable, help to respond to load changes in a timely manner, reduce potential risks, and improve the overall stability and reliability of the power system. The real-time monitoring and analysis module has achieved significant improvements in data accuracy, prediction capability and response speed, ensuring the safe and efficient operation of the substation.

[0125] Example 5

[0126] This embodiment is explained in Example 4. Please refer to Figure 1 ,Specifically: the remote control scheduling and analysis module includes a load balancing unit and an initial remote control scheduling unit;

[0127] The load balancing unit obtains the predicted load P(t) of the substation and performs correlation calculation in combination with the environmental impact factor Ei to obtain the distributed load L of the substation and analyze the distributed load of each substation;

[0128]

[0129] Where Li represents the distributed load of the ith substation, P total represents the total load of the substation, and N represents the number of substations.

[0130] The initial remote control dispatching unit constructs an intelligent remote control dispatching algorithm, performs correlation calculations based on the obtained distributed load L and the load fluctuation frequency Fl to obtain the first optimized dispatching parameter Q, minimizes the load difference and load fluctuation frequency difference between substations, optimizes the dispatching strategy, and then sends the obtained first optimized dispatching parameter Q to the substation through the LoRa communication technology to perform remote load distribution remote control adjustment;

[0131] The first optimization scheduling parameter Q is calculated by the following algorithm formula:

[0132]

[0133] Where Lj represents the distributed load of substation j, Fl represents the current load borne by substation j, i represents the load fluctuation frequency of substation i, Fl j represents the load fluctuation frequency of power station j, λ represents the adjustment coefficient, which is used to adjust the relative importance of load difference and load fluctuation frequency difference in optimal scheduling. If the adjustment coefficient λ is larger, the load fluctuation frequency difference has a greater impact on the optimal scheduling parameters. Qi represents the first optimal scheduling parameter Q of substation i.

[0134] In this embodiment, the system calculates the distribution load Li of the i-th substation by combining the predicted load P(t) of the substation and the environmental impact factor Ei through the load balancing unit. The initial remote control dispatching unit calculates the first optimized dispatching parameter Q according to the distribution load L and the load fluctuation frequency Fl through the intelligent remote control dispatching algorithm, minimizes the load difference and load fluctuation frequency difference between substations, and optimizes the dispatching strategy. This module realizes the intelligent distribution and balance of loads, and significantly reduces the load difference and fluctuation frequency difference between substations. Compared with traditional technical means, through the application of intelligent remote control dispatching algorithm, the system can more accurately distribute and optimize the load, and improve the efficiency and accuracy of the dispatching strategy. These improvements not only improve the operating efficiency of the substation, but also enhance the overall stability of the system, reduce equipment loss and operating costs. The remote control dispatching analysis module has achieved significant improvements in optimizing dispatching strategies and load balancing, ensuring the efficient and reliable operation of the power system.

[0135] Example 6

[0136] This embodiment is explained in Example 5. Please refer to Figure 1 ,Specifically: the remote control optimization evaluation module includes a remote control scheduling error ,analysis unit and a remote control scheduling optimization analysis unit;

[0137] The remote control dispatch error analysis unit includes a dispatch error calculation unit and a dispatch error evaluation unit;

[0138] After receiving the first optimized dispatching parameter Q, the dispatching error calculation unit remotely inputs the first optimized dispatching parameter Q into the dispatching system, and remotely dispatches the load of the substation. After the dispatching is completed, the substation load remote dispatching error Wc is comprehensively analyzed in combination with the operation data set of the substation, and the overall error of the load remote dispatching is analyzed;

[0139] The load remote control scheduling error Wc is calculated by the following algorithm formula:

[0140]

[0141] Where, Qf i represents the power quality index of substation i, Qf avg Represents the average power quality index, Pv i represents the pressure fluctuation of substation i, Pv avg represents the average pressure fluctuation, μ represents the adjustment coefficient of the power quality Qf error, and ν represents the adjustment coefficient of the pressure fluctuation Pv error;

[0142] The dispatch error evaluation unit presets the error threshold W through the safety and stability index of the substation load operation, and then makes a preliminary comparison and evaluation analysis of the effect of the dispatch strategy with the obtained load remote control dispatch error Wc;

[0143] The specific evaluation plan is as follows:

[0144] When the load remote control scheduling error Wc>error threshold W, it means that the current substation load remote control scheduling error is abnormal. At this time, the first remote control preset command is sent to the substation end, and the second scheduling optimization algorithm is executed to perform dynamic remote control adjustment;

[0145] When the load remote control scheduling error Wc ≤ error threshold W, it means that the current substation load remote control scheduling error is normal. At this time, a second remote control preset command is sent to the substation end to continue to execute the current scheduling optimization strategy and pass the acceptance.

[0146] In this embodiment, the remote control optimization evaluation module includes a remote control scheduling error analysis unit and a remote control scheduling optimization analysis unit. The remote control scheduling error analysis unit works in coordination with the scheduling error calculation unit and the scheduling error evaluation unit. After obtaining the first optimized scheduling parameter Q, the scheduling error calculation unit inputs Q into the scheduling system, schedules the substation load, and calculates the load remote control scheduling error Wc in combination with the substation operation data set. The scheduling error evaluation unit evaluates the effect of the scheduling strategy by comparing with the preset error threshold W. When the load remote control scheduling error Wc is greater than the error threshold W, the second scheduling optimization algorithm is executed for dynamic remote control adjustment; when the load remote control scheduling error Wc is less than or equal to the error threshold W, the current scheduling strategy continues to be executed, and the acceptance is qualified at this time. This module realizes the accurate evaluation and optimization of the remote control scheduling of substation loads, ensuring the effectiveness and safety of the scheduling strategy.

[0147] Example 7

[0148] This embodiment is explained in Example 6. Please refer to Figure 1 ,Specifically: the remote control scheduling optimization and analysis unit includes a secondary remote control scheduling unit and a remote control scheduling evaluation unit;

[0149] The secondary remote control dispatching unit is used to re-evaluate and identify the abnormality of the current substation load remote control dispatching error, and based on the optimization result of the current first optimized dispatching parameter Q, perform secondary calculation in combination with the environmental impact factor Ei to obtain the second optimized dispatching parameter Q2;

[0150] The second optimization scheduling parameter Q2 is calculated by the following algorithm formula:

[0151]

[0152] Where P(t) avg represents the average substation predicted load, Ei avg represents the average environmental impact factor;

[0153] The remote control dispatch evaluation unit is used to input the second optimized dispatch parameter Q2 into the dispatch system, and remotely dispatch the load of the substation. After the dispatch is completed, the remote control dispatch error Wc2 of the secondary load of the substation is comprehensively analyzed again, and a secondary comparative evaluation is performed with the error threshold W, the remote control adjustment effect after the secondary load is optimally dispatched is analyzed, and an iterative strategy is executed;

[0154] The specific evaluation plan is as follows:

[0155] When the secondary load remote control dispatch error Wc2>error threshold W, it means that the current substation secondary load remote control dispatch error is abnormal. At this time, a remote control preset command is sent to the protection and control device at the substation end to cut off the power transmission of the substation and mark it as unqualified for acceptance;

[0156] When the secondary load remote control scheduling error Wc2 ≤ error threshold W, it means that the secondary load remote control scheduling error of the current substation is normal, and it is marked as qualified for acceptance.

[0157] In this embodiment, the system calculates the second optimized dispatching parameter Q2 based on the first optimized dispatching parameter Q and the environmental impact factor Ei after the secondary remote control dispatching unit identifies the abnormal remote control dispatching error of the current substation load. The remote control dispatching evaluation unit inputs Q2 into the dispatching system, dispatches the substation load, and comprehensively analyzes the secondary load remote control dispatching error Wc2, compares it with the error threshold W, and evaluates the secondary optimization effect. If the secondary load remote control dispatching error Wc2 is greater than the error threshold W, it is marked as unqualified for acceptance; if the secondary load remote control dispatching error Wc2 is less than or equal to the error threshold W, it is marked as qualified for acceptance. This module further improves the accuracy and reliability of substation load remote control dispatching through secondary optimization dispatching and evaluation. Compared with traditional technical means, the system can perform secondary dynamic remote control adjustment on the basis of the initial optimization dispatching to ensure that the dispatching error is within an acceptable range, significantly improving the flexibility and accuracy of the dispatching strategy. Through a multi-level optimization and evaluation mechanism, the system can identify and correct the load remote control dispatching error more timely, ensure the efficient and stable operation of the substation, reduce equipment loss, and improve the overall operation reliability and safety of the power system. The remote control dispatch optimization and analysis unit has achieved significant improvements in load remote control dispatch optimization and evaluation, ensuring efficient and reliable operation of the power system.

[0158] Example 8

[0159] See also Figure 1 and Figure 2 , a substation remote control automatic acceptance method based on cloud service, comprising the following steps:

[0160] S1. First, an integrated sensor group is installed around the micro-hole processing of the engine turbine blade and on the pulse generator to collect material processing data and pulse data in real time;

[0161] S2. Upload the collected processing data and pulse data to the cloud platform for storage by wireless transmission, and pre-process the processing data and pulse data;

[0162] S3. Based on the pre-processed processing data and pulse data, an algorithm formula is constructed to calculate the cumulative total heat Rl and the temperature change rate △T, and the perforation temperature Ckwd is obtained by correlation calculation. Then, a safety threshold F1 is preset for comparison and evaluation to determine the triggering of the emergency stop strategy;

[0163] S4. Based on the pre-processed processing data and pulse data, the energy diffusion rate Ks and the heat affected zone area Ry are calculated and obtained, and combined with the piercing temperature Ckwd, the energy release control index Snkz is calculated and obtained;

[0164] S5. Finally, the preset pulse frequency threshold F2 is compared and evaluated with the obtained energy release control index Snkz, the pulse energy and frequency conditions are analyzed, and an energy release strategy is generated.

[0165] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A remote control automatic acceptance system for substations based on cloud services, Features: It includes data acquisition module, cloud processing module, real-time monitoring and analysis module, remote control scheduling and analysis module and remote control optimization and evaluation module; The data acquisition module is used to collect the operation data of the substation in real time through sensors and monitoring equipment, and transmit the collected operation data to the cloud platform using the Internet of Things technology; The cloud processing module receives the operation data in real time by building a cloud platform, and obtains the operation data set and stores the data after preprocessing the received operation data; The real-time monitoring and analysis module uses a machine learning algorithm and loads the operating data set to calculate and obtain the environmental impact factor Ei, and then combines and calculates the substation predicted load P(t) based on the environmental impact factor Ei to identify the trend of electricity demand changes; The remote control dispatch analysis module is used to calculate and obtain the distributed load L of the substation based on the obtained substation predicted load P(t), and then execute the intelligent remote control dispatch algorithm through the cloud platform to calculate and obtain the first optimized dispatch parameter Q to remotely optimize the substation load; The remote control optimization and evaluation module performs comprehensive calculation to obtain the load remote control scheduling error Wc after optimizing the load remote control of the substation, and performs preliminary comparative evaluation and analysis of the remote control scheduling effect through the preset error threshold W and the load remote control scheduling error Wc. When the error is preliminarily evaluated to be abnormal, the secondary optimization algorithm formula is executed to calculate and obtain the second optimized scheduling parameter Q2 for load remote control scheduling, and the acceptance of the substation is evaluated and analyzed.

2. According to the cloud service-based substation remote control automatic acceptance system according to claim 1, Features: The data acquisition module includes a sensor acquisition unit and a data transmission unit; The sensor acquisition unit is used to convert the analog signal of the sensor group into a digital signal based on the sensor group installed around the transformer, and collect the operation data of the transformer in real time; The operating data include ambient temperature Te, ambient humidity He, transformer internal oil temperature To, transformer current I, transformer voltage V, voltage fluctuation BD, frequency deviation PL, harmonic distortion Sz, load fluctuation frequency Fl and pressure fluctuation Pv; The data transmission unit is used to transmit the operating data collected by the sensor group to the cloud platform via wireless communication WIFi, and encrypt and authenticate the operating data based on the HTTP transport layer security protocol.

3. According to the cloud service-based substation remote control automatic acceptance system of claim 2, Features: The cloud processing module includes a data pre-processing unit and a data storage unit; The data preprocessing unit is used to build a cloud platform and set up an MQTT server, receive and decrypt the transmitted operation data in real time, and then preprocess the decrypted operation data, the preprocessing includes load loss processing, power quality processing and comprehensive processing; The load loss processing is performed by calculating the apparent power S=V*I according to the transformer current I and the transformer voltage V in the operation data, and performing a comprehensive calculation to obtain the active power P=S*cos(φ) according to the apparent power S combined with the power factor cos(φ) obtained from the power meter, and then calculating and analyzing the transformer input and output power to obtain the transformer load loss Lo=P input -P output , where P output Indicates output power, P input Indicates input power; The power quality processing is performed by summarizing and calculating the voltage fluctuation BD, frequency deviation PL and harmonic distortion Sz in the operation data to obtain the power quality index Qf; The power quality index Qf is calculated by the following algorithm formula: Qf=w1*BD+w2*PL+w3*Sz; Wherein, w1, w2 and w3 represent the preset weight values ​​of voltage fluctuation BD, frequency deviation PL and harmonic distortion Sz respectively; The comprehensive processing is used to remove noise and abnormal values ​​by combining the transformer load loss Lo and power quality index Qf obtained after the load loss processing and the power quality processing with the ambient temperature Te, ambient humidity He, transformer internal oil temperature To, load fluctuation frequency Fl and pressure fluctuation Pv in the operation data, and at the same time, after the data is verified and cleaned, the data is summarized to obtain the operation data set; The data storage unit stores the operation data set obtained after preprocessing in time series by constructing a time series database.

4. According to the cloud service-based substation remote control automatic acceptance system of claim 3, Features: The real-time monitoring and analysis module includes an environment analysis unit and a load prediction unit; The environmental analysis unit is used to perform correlation calculation to obtain the environmental impact factor Ei according to the ambient temperature Te, ambient humidity He and transformer internal oil temperature To in the operation data set; The environmental impact factor Ei is calculated by the following algorithm formula: Ei=α·Te+β·He+γ·To; Where α, β and γ represent the adjustment coefficients of ambient temperature Te, ambient humidity He and transformer internal oil temperature To respectively.

5. According to the cloud service-based substation remote control automatic acceptance system of claim 4, Features: The load prediction unit extracts the stored operation data set and performs correlation calculation in combination with the obtained environmental impact factor Ei to obtain the substation predicted load P(t); The substation predicted load P(t) is calculated and obtained by the following algorithm formula; P(t)=δ·Lo(t-1)+η·Qf(t)+θ·Fl(t)+κ·Pv(t)+ζ·Ei(t); Wherein, Lo(t-1) represents the transformer load loss Lo at time t-1, Qf(t) represents the power quality index at time t, Fl(t) represents the load fluctuation frequency at time t, Pv(t) represents the pressure fluctuation at time t, Ei(t) represents the environmental impact factor at time t, δ represents the empirical coefficient of the transformer load loss Lo; η represents the empirical coefficient of the power quality index Qf(t); θ represents the empirical coefficient of the load fluctuation frequency Fl(t); κ represents the empirical coefficient of the pressure fluctuation Pv(t); ζ represents the empirical coefficient of the environmental impact factor Ei(t).

6. A substation remote control automatic acceptance system based on cloud service according to claim 5, Features: The remote control scheduling analysis module includes a load balancing unit and an initial remote control scheduling unit; The load balancing unit obtains the substation predicted load P(t) by performing correlation calculation in combination with the environmental impact factor Ei to obtain the substation distributed load L, and analyzes the distributed load of each substation; Where Li represents the distributed load of the ith substation, P total represents the total load of the substation, and N represents the number of substations.

7. A substation remote control automatic acceptance system based on cloud service according to claim 6, Features: The initial remote control scheduling unit constructs an intelligent remote control scheduling algorithm, performs associated calculations based on the obtained distributed load L and the load fluctuation frequency Fl to obtain the first optimized scheduling parameter Q, and then sends the obtained first optimized scheduling parameter Q to the substation through the LoRa communication technology to perform remote load distribution remote control adjustment; The first optimization scheduling parameter Q is calculated and obtained by the following algorithm formula: Where Lj represents the distributed load of substation j, Fl i represents the load fluctuation frequency of substation i, Fl j represents the load fluctuation frequency of power station j, λ represents the adjustment coefficient, which is used to adjust the relative importance of load difference and load fluctuation frequency difference in optimal scheduling, and Qi represents the first optimal scheduling parameter Q of substation i.

8. A substation remote control automatic acceptance system based on cloud service according to claim 7, Features: The remote control optimization evaluation module includes a remote control scheduling error analysis unit and a remote control scheduling optimization analysis unit; The remote control scheduling error analysis unit includes a scheduling error calculation unit and a scheduling error evaluation unit; After receiving the first optimized dispatching parameter Q, the dispatching error calculation unit remotely inputs the first optimized dispatching parameter Q into the dispatching system, remotely dispatches the load of the substation, and after the dispatching is completed, combines the operation data set of the substation to comprehensively analyze the remote dispatching error Wc of the load of the substation; The load remote control scheduling error Wc is calculated by the following algorithm formula: Where, Qf i represents the power quality index of substation i, Qf avg Represents the average power quality index, Pv i represents the pressure fluctuation of substation i, Pv avg represents the average pressure fluctuation, μ represents the adjustment coefficient of the power quality Qf error, and ν represents the adjustment coefficient of the pressure fluctuation Pv error; The dispatch error evaluation unit presets an error threshold W through the safety and stability index of the substation load operation, and then makes a preliminary comparison and evaluation analysis of the effect of the dispatch strategy with the obtained load remote control dispatch error Wc; The specific evaluation plan is as follows: When the load remote control scheduling error Wc>error threshold W, it means that the current substation load remote control scheduling error is abnormal. At this time, the first remote control preset command is sent to the substation end, and the second scheduling optimization algorithm is executed to perform dynamic remote control adjustment; When the load remote control scheduling error Wc ≤ error threshold W, it means that the current substation load remote control scheduling error is normal. At this time, a second remote control preset command is sent to the substation end to continue to execute the current scheduling optimization strategy and pass the acceptance.

9. A substation remote control automatic acceptance system based on cloud service according to claim 8, Features: The remote control scheduling optimization analysis unit includes a secondary remote control scheduling unit and a remote control scheduling evaluation unit; The secondary remote control dispatching unit is used to re-evaluate and identify the abnormality of the current substation load remote control dispatching error, and based on the optimization result of the current first optimized dispatching parameter Q, perform secondary calculation in combination with the environmental impact factor Ei to obtain the second optimized dispatching parameter Q2; The second optimized scheduling parameter Q2 is calculated by the following algorithm formula: Where P(t) avg represents the average substation predicted load, Ei avg represents the average environmental impact factor; The remote control dispatch evaluation unit is used to input the second optimized dispatch parameter Q2 into the dispatch system, remotely dispatch the load of the substation, and after the dispatch is completed, comprehensively analyze the secondary load remote control dispatch error Wc2 of the substation again, and perform a secondary comparative evaluation with the error threshold W, analyze the remote control adjustment effect after the secondary load is optimally dispatched, and execute an iterative strategy; The specific evaluation plan is as follows: When the secondary load remote control dispatch error Wc2>error threshold W, it means that the current substation secondary load remote control dispatch error is abnormal. At this time, a remote control preset command is sent to the protection and control device at the substation end to cut off the power transmission of the substation and mark it as unqualified for acceptance; When the secondary load remote control scheduling error Wc2 ≤ error threshold W, it means that the secondary load remote control scheduling error of the current substation is normal, and it is marked as qualified for acceptance.

10. A cloud service-based substation remote control automatic acceptance method, applied to a cloud service-based substation remote control automatic acceptance system according to any one of claims 1 to 9, Features: The following steps are involved: S1. First, collect the substation operation data in real time through sensors and monitoring equipment, and use the Internet of Things technology to transmit the collected operation data to the cloud platform; S2. Rebuild the cloud platform to receive the operation data in real time, perform data preprocessing on the received operation data, obtain the operation data set and store the data; S3. Using big data analysis and machine learning algorithms, and loading the operating data set, calculate and obtain the environmental impact factor Ei, and combine and calculate the substation predicted load P(t) based on the environmental impact factor Ei to identify the trend of electricity demand changes; S4, then calculate and obtain the distributed load L of the substation based on the obtained substation predicted load P(t), and then execute the intelligent remote control scheduling algorithm through the cloud platform to calculate and obtain the first optimized scheduling parameter Q to remotely optimize the substation load; S5. Finally, after optimizing the load remote control of the substation, a comprehensive calculation is performed to obtain the load remote control scheduling error Wc, and a preliminary comparative evaluation and analysis of the remote control scheduling effect is performed through the preset error threshold W and the load remote control scheduling error Wc. When the error is initially evaluated to be abnormal, the secondary optimization algorithm formula is executed to calculate and obtain the second optimized scheduling parameter Q2 for load remote control scheduling, and the acceptance of the substation is evaluated and analyzed.

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