Intelligent IoT devices for distribution networks with adaptive input and output

Abstract

The present invention relates to a distribution network intelligent object-end device with adaptive input and output, comprising a data classification module and a protection threshold selection module, a load prediction module, a load optimization module, a fault decision module, an equipment status module and a communication module connected to the data classification module, wherein the protection threshold selection module is connected to the load prediction module, the load optimization module is respectively connected to the fault decision module and the equipment status module, the fault decision module is connected to the equipment status module, and the communication module is respectively connected to the protection threshold selection module, the load prediction module, the load optimization module, the fault decision module, the equipment status module and a user side communication connection; wherein the data classification module is configured to collect electrical parameters and non-electrical parameters in the distribution network, and divide a normal operation database, a fault action information database, an equipment status information database and a communication database according to the collected electrical parameters and non-electrical parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network detection and maintenance, and more specifically to a distribution network intelligent physical terminal device with adaptive input and output. Background Art

[0002] The low-voltage distribution network is located at the end of the power grid. It is responsible for connecting the transmission network and directly supplying power to users. It is a key link in ensuring the quality of power supply. In recent years, with the improvement of the informationization and intelligence level of the power grid, the distribution network has gradually evolved from transmitting power and serving customers to the distribution Internet of Things stage of comprehensive perception, data fusion and intelligent application.

[0003] Intelligent terminals that implement monitoring and control in the power distribution Internet of Things are an important means of realizing the detection and maintenance of the distribution network. Most of the traditional distribution network intelligent terminals are designed separately to achieve a specific function, and the functions are preset in advance and relatively fixed, such as monitoring and collecting customer power consumption, monitoring the status of distribution switches, and monitoring the environmental status information of distribution facilities. However, the requirements for data collection and sharing of distribution Internet of Things terminals are increasing day by day. In addition to being business terminals, intelligent terminals need to be data terminals and communication gateways, so they need to have strong data processing capabilities and information interaction capabilities.

[0004] The current intelligent physical devices of the distribution network are limited by hardware devices and cannot perform edge computing. The distribution automation master station is required to collect data, process data, and control the equipment. The master station has a large workload and the equipment has low working efficiency. In addition, a single algorithm is generally used to make decisions on single variables such as faults, status, and load in the distribution network. It is difficult to deal with the various data collected, so that the decision is limited by the collected information and the reliability is low. The equipment cannot accurately make decisions and feedback on the distribution network status, fault information, and equipment status. Summary of the invention

[0005] In order to solve the problems in the above-mentioned prior art, the present invention provides a distribution network intelligent terminal device with adaptive input and output, which can directly process and analyze various types of data, reduce the workload of the PBX, improve work efficiency, and at the same time can quickly and accurately make decisions and provide feedback on the distribution network status, fault information and equipment status.

[0006] The distribution network intelligent object-end device with adaptive input and output provided by the present invention comprises a data classification module and a protection threshold selection module, a load prediction module, a load optimization module, a fault decision module, an equipment status module and a communication module connected to the data classification module, wherein the protection threshold selection module is connected to the load prediction module, the load optimization module is respectively connected to the fault decision module and the equipment status module, the fault decision module is connected to the equipment status module, and the communication module is respectively connected to the protection threshold selection module, the load prediction module, the load optimization module, the fault decision module, the equipment status module and the user side communication connection; wherein the data classification module is configured to: collect electrical parameters and non-electrical parameters in the distribution network, and divide the normal operation database, the fault action information library, the equipment status information library and the communication database according to the collected electrical parameters and non-electrical parameters.

[0007] Furthermore, the protection threshold selection module is configured to: obtain data in the normal operation database, and determine a current protection threshold of the distribution network loop according to the data in the normal operation database.

[0008] The protection threshold selection module has a decision tree algorithm, which is configured to: obtain the voltage and current during normal operation of the distribution network, input the voltage and current during normal operation of the distribution network into the decision tree algorithm, determine the circuit breaker model through the decision tree algorithm, and obtain the current protection threshold.

[0009] Furthermore, the load prediction module is configured to: obtain data from the normal operation database, predict load changes within a fixed prediction time after the current moment according to the data from the normal operation database, and optimize the action model of the circuit breaker according to the predicted load changes.

[0010] Furthermore, the load prediction module includes an action optimization module, which is configured to: obtain the current change of the distribution network loop according to the predicted load change, and if the current of the distribution network loop suddenly changes to a current less than 10mA, keep the circuit breaker disconnected within the time period when the current is less than 10mA.

[0011] Furthermore, the action optimization module is also configured to obtain a load power factor change according to the predicted load change, and if the load power factor changes to a value less than 0.3, keep the circuit breaker closed during a time period when the load power factor is less than 0.3.

[0012] Furthermore, the load optimization module is configured to communicate with the user side through the communication module, and perform load optimization according to the load disconnectability information of the user side during the time-of-use electricity price peak period at the location of the distribution network.

[0013] Furthermore, the load optimization module is configured to: inquire the user side through the communication module to obtain the peak period, valley period and flat period of the time-of-use electricity price at the location of the distribution network, and inquire whether the load connected to the user side is useful and the disconnectable time period of the load during the peak period during the time-of-use electricity price. If the load is useful at the current moment, the circuit breaker is kept closed; if the load is useless at the current moment, the circuit breaker is controlled to be disconnected, and the disconnectable time period of the load is recorded to control the circuit breaker to be disconnected in the same time period every subsequent day.

[0014] Furthermore, the fault decision module is configured to: obtain data in the fault action information library, make a fault decision according to the data in the fault action information library and the current protection threshold, and control the opening and closing of the circuit breaker.

[0015] Furthermore, the device status module is configured to: obtain electrical parameters and vibration information in the device status information library, determine whether the circuit breaker is disconnected successfully according to the electrical parameters in the device status information library, and determine whether the circuit breaker is abnormal or faulty according to the vibration information in the device status information library.

[0016] The present invention can make adaptive decisions and feedback on the distribution network status, fault information and circuit breaker status, and more efficiently complete the distribution network protection and equipment maintenance. Moreover, the operating current threshold can be adaptively optimized through the real-time data collected when the equipment is running. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural block diagram of a distribution network intelligent terminal device with adaptive input and output according to the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are given below in conjunction with the accompanying drawings and described in detail.

[0019] like Figure 1 As shown, the distribution network intelligent terminal device with adaptive input and output provided by the present invention includes a data classification module 1 and a protection threshold selection module 2, a load prediction module 3, a load optimization module 4, a fault decision module 5, a device status module 6 and a communication module 7 connected to the data classification module 1, and the protection threshold selection module 2 is connected to the load prediction module 3, the load optimization module 4 is connected to the fault decision module 5 and the device status module 6 respectively, the fault decision module 5 is connected to the device status module 6, and the communication module 7 is respectively connected to the protection threshold selection module 2, the load prediction module 3, the load optimization module 4, the fault decision module 5, the device status module 6 and the user side communication connection.

[0020] Among them, the data classification module 1 is set to: collect electrical parameters and non-electrical parameters in the distribution network through several different types of sensors, and divide the collected electrical parameters and non-electrical parameters into a normal operation database, a fault action information database, a device status information database and a communication database.

[0021] Electrical parameters include voltage, current, frequency, etc. Non-electrical parameters include communication information, vibration information, temperature, etc. Figure 1 A1, A2, A3, A4, and A5 listed in the table represent five of the above electrical parameters and non-electrical parameters. The normal operation database includes the electrical parameters of the distribution network during normal operation, the fault action information database includes the electrical parameters and non-electrical parameters when a fault occurs in the distribution network, the equipment status information database includes the electrical parameters of all circuit breakers during operation and the vibration information in the non-electrical parameters, and the communication database includes the above electrical parameters, non-electrical parameters, and information generated by other databases.

[0022] The protection threshold selection module 2 is configured to obtain data in the normal operation database, and determine the current protection threshold of the distribution network loop according to the data in the normal operation database.

[0023] Specifically, the protection threshold selection module 2 has an established decision tree algorithm, which obtains the voltage and current when the distribution network is in normal operation, and inputs the voltage and current when the distribution network is in normal operation into the established decision tree algorithm, and decides the circuit breaker model through the decision tree algorithm, thereby obtaining the current protection threshold. The decision tree algorithm is established according to the action value of the electrical appliance at different rated currents in the national standard. For example, the rated current specification sequence of the miniature circuit breaker is: 3A, 6A, 10A, 15A, 20A, 25A, 32A, 40A, 50A, 63A. If the current collected during the normal operation of the distribution network is 4A, and the value is obtained by comparing the decision tree within the range of 4A-6A, then the circuit breaker model with a rated current of 6A is selected, that is, the current protection threshold is 6A. In this way, the present invention can change the current protection threshold of the distribution network loop according to the real-time current, avoiding the problem that the action value cannot be reached or the circuit breaker disconnection time is long when a fault occurs when the load is small, and the present invention can be used in most distribution circuits that do not exceed the maximum protection value of the circuit breaker device, and has strong flexibility.

[0024] The load prediction module 3 is configured to obtain data from the normal operation database, predict the load change within a fixed prediction time (e.g., one week) after the current moment according to the data in the normal operation database, and optimize the action model of the circuit breaker according to the predicted load change. It should be noted that the load change predicted by this module is actually the change in current value, because after the loop is determined, the voltage remains unchanged, so the corresponding load change is the current change.

[0025] The present invention adopts the Prophet algorithm to predict the current change within a period of time after the current moment. The algorithm is an additive model for predicting time series, which is expressed as:

[0026] y(t)=g(t)+s(t)+h(t)+ε t

[0027] In the formula, y(t) represents the value of power W changing with time; g(t) is the trend term, which represents the non-periodic change of power W in the time series; s(t) is the periodic term, which represents the periodic change of power W in the time series; h(t) represents the holiday term, ε t represents the error term.

[0028] The Prophet algorithm decomposes the collected electrical parameters into time series, and then fits and accumulates the various decomposed data to finally obtain the predicted current value of the time series.

[0029] The load prediction module 3 includes an action optimization module, which is configured to obtain the current change of the distribution network loop according to the predicted current change. If the current of the distribution network loop suddenly changes to a current less than 10mA, the circuit breaker is kept disconnected during the time period when the current is less than 10mA, and the circuit breaker is kept closed during the remaining time periods to achieve load optimization.

[0030] Since the distribution network loop will be connected to a large-capacity inductive load, and this load will generate a large current when it starts, it may cause the circuit breaker to malfunction and disconnect the equipment. Therefore, the action optimization module is also set to: obtain the load power factor change according to the predicted load change. If the load power factor changes to a value less than 0.3, it indicates that a large-capacity inductive load is connected to the distribution network loop during this time period, and the circuit breaker is kept closed during the time period when the load power factor is less than 0.3. Among them, the load power factor is obtained based on the phase difference of the voltage and current waveforms of the load at the same time.

[0031] It should be noted that the data acquired by the load prediction module 3, such as load change, current size, load power factor, large-capacity inductive load, etc., are stored in the normal operation database. Through the load prediction module 3, the present invention can achieve power optimization to save power, and can avoid reclosing to reduce false operations.

[0032] The load optimization module 4 is configured to communicate with the user side through the communication module 7 and perform load optimization according to the load disconnectability information of the user side during the time-of-use electricity price peak period at the location of the distribution network.

[0033] Specifically, the load optimization module 4 inquires the user side through the communication module 7 to obtain the peak period, valley period and normal period of the time-of-use electricity price at the location of the distribution network, and inquires whether the load connected to the user side is useful and the time period during which the load can be disconnected during the peak period during the peak period of the time-of-use electricity price. If the load is useful at the current moment, the circuit breaker is kept closed; if the load is useless at the current moment, the circuit breaker is controlled to disconnect, and the time period during which the load can be disconnected is recorded to control the circuit breaker to disconnect in the same time period every day thereafter. During the valley period and the normal period, the circuit breaker is controlled in the manner of the aforementioned action optimization module. In this way, the load optimization module 4 can reduce electricity consumption during the peak period and save electric energy.

[0034] The fault decision module 5 is configured to obtain data in the fault action information library, make a fault decision based on the data in the fault action information library and the current protection threshold determined by the protection threshold selection module 2, and control the opening and closing of the circuit breaker.

[0035] Specifically, the fault decision module 5 obtains the current and voltage when the distribution network fails, and judges whether the load has a long-delay fault, a short-delay fault or an instantaneous fault according to the current and the current protection threshold when the distribution network fails, and uses the inverse time characteristic of the circuit breaker. If so, the load is correspondingly protected from overload, short-circuit or instantaneous action (i.e., the circuit breaker is controlled to be disconnected); judges whether the load has an overvoltage fault or an undervoltage fault according to the voltage when the distribution network fails and the voltage fluctuation of the distribution network shall not exceed 10% of the normal value. If so, the load is correspondingly protected from overvoltage or undervoltage (i.e., the circuit breaker is controlled to be disconnected). After determining the fault type, the fault decision module 5 communicates with the fault decision modules 5 in the lines at all levels in the distribution network through the communication module 7 to obtain the current and voltage when the distribution network at all levels fails and determine the line where the fault point is located (i.e., the protection line); at the same time, the breaking result of the circuit breaker in the equipment status module 6 is obtained (i.e., whether the circuit breaker of the line where the fault is located is successfully broken). If the breaking is successful, it indicates that the fault is eliminated, and a breaking success signal is sent to the upper level circuit breaker of the line where the fault is located to avoid the upper level circuit breaker from breaking; if the breaking is unsuccessful, it indicates that the fault is not eliminated, and a breaking failure signal is sent to the upper level circuit breaker of the line where the fault is located to enable the upper level circuit breaker to break. In this way, the present invention can collect fault information of the distribution network, and at the same time, it can realize the cooperation between the upper and lower levels to avoid malfunction.

[0036] The device status module 6 is configured to obtain electrical parameters and vibration information in the device status information library, determine whether the circuit breaker is disconnected successfully according to the electrical parameters in the device status information library, and determine whether the circuit breaker is abnormal or faulty according to the vibration information in the device status information library.

[0037] Specifically, when the current collected by the device status module 6 is zero, it indicates that the disconnection is successful. The device status module 6 obtains the vibration image of the circuit breaker, compares the obtained vibration image with the standard vibration image, and determines whether the circuit breaker is abnormal or faulty. For example, if it is determined based on the vibration image that the transmission guide rod of the circuit breaker is stuck, the circuit breaker is in an abnormal state, but it can still work normally, so the backup protection of the upper circuit breaker can reduce the coordination action time; if it is determined based on the vibration image that the circuit breaker has a core gap that is too large and refuses to move, then it is directly fed back to the upper circuit breaker to enable the upper circuit breaker to perform backup protection, and send a device fault signal to the user side through the communication module 7 for easy maintenance.

[0038] From the above description of each module, it can be seen that the data classification module 1 is the basis for establishing subsequent modules. It stores the data collected by different sensors according to different purposes: the electrical parameters of the distribution network during normal operation are stored in the normal operation database, the electrical parameters and non-electrical parameters during faults are stored in the fault action information library, and the electrical parameters and non-electrical parameters during the operation of all circuit breakers are stored in the equipment status information library. Since the communication information will always interact with each database, a separate communication module is established to store the electrical parameters, non-electrical parameters and information generated by other databases. The three modules of protection threshold selection module 2, load prediction module 3 and load optimization module 4 rely on the electrical parameters during normal operation of the distribution network to realize their respective functions. The fault decision module 5 runs in the fault action information library. When the distribution network fails, the circuit breaker is controlled according to the collected electrical parameters and non-electrical parameters. Its protection threshold is realized by the change of real-time load information to achieve input-output adaptation. The device status module 6 operates based on the device status information library, obtains the status of the circuit breaker according to the electrical parameters and non-electrical parameters when the circuit breaker is in operation, and judges it as normal, abnormal (can operate but some parts are damaged) or faulty, and feeds back the device status to the user through the communication module 7 for subsequent maintenance.

[0039] The present invention can make adaptive decisions and feedback on the distribution network status, fault information and circuit breaker status, and more efficiently complete the distribution network protection and equipment maintenance. Moreover, the operating current threshold can be adaptively optimized through the real-time data collected when the equipment is running.

[0040] The above is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. The above embodiment of the present invention can also be modified in various ways. That is, all simple, equivalent changes and modifications made according to the claims and the description of the present invention fall within the scope of protection of the claims of the present invention. The contents not described in detail in the present invention are all conventional technical contents.

Claims

1. A distribution network intelligent object terminal device with adaptive input and output, characterized in that: It includes a data classification module and a protection threshold selection module, a load prediction module, a load optimization module, a fault decision module, an equipment status module and a communication module connected to the data classification module, the protection threshold selection module is connected to the load prediction module, the load optimization module is respectively connected to the fault decision module and the equipment status module, the fault decision module is connected to the equipment status module, and the communication module is respectively connected to the protection threshold selection module, the load prediction module, the load optimization module, the fault decision module, the equipment status module and the user side communication connection; wherein the data classification module is configured to: collect electrical parameters and non-electrical parameters in the distribution network, and divide the normal operation database, the fault action information database, the equipment status information database and the communication database according to the collected electrical parameters and non-electrical parameters; The protection threshold selection module is configured to: obtain data in the normal operation database, and determine a current protection threshold of a distribution network loop according to the data in the normal operation database; The protection threshold selection module has a decision tree algorithm, which is configured to: obtain the voltage and current of the distribution network during normal operation, and input the voltage and current of the distribution network during normal operation into the decision tree algorithm, determine the circuit breaker model through the decision tree algorithm, and obtain the current protection threshold; The fault decision module is configured to: obtain data in the fault action information library, make a fault decision based on the data in the fault action information library and the current protection threshold, and control the breaking and closing of the circuit breaker; The fault decision module obtains the current and voltage when the distribution network fails, and judges whether the load has a long-delay fault, a short-delay fault or an instantaneous fault according to the current and the current protection threshold when the distribution network fails, and uses the inverse time characteristic of the circuit breaker. If so, the load is accordingly protected from overload, short circuit or instantaneous action; judges whether the load has an overvoltage fault or an undervoltage fault according to the voltage when the distribution network fails and the voltage fluctuation of the distribution network shall not exceed 10% of the normal value, and if so, the load is accordingly protected from overvoltage or undervoltage; After determining the fault type, the fault decision module communicates with the fault decision modules in the lines at all levels in the distribution network through the communication module to obtain the current and voltage at the distribution network at all levels when a fault occurs and determine the line where the fault point is located; at the same time, the breaking result of the circuit breaker in the equipment status module is obtained. If the breaking is successful, it indicates that the fault is eliminated, and a breaking success signal is sent to the upper-level circuit breaker of the line where the fault is located to avoid the upper-level circuit breaker from breaking; if the breaking is unsuccessful, it indicates that the fault is not eliminated, and a breaking failure signal is sent to the upper-level circuit breaker of the line where the fault is located to enable the upper-level circuit breaker to break.

2. The distribution network intelligent object terminal device with adaptive input and output according to claim 1, characterized in that: The load prediction module is configured to: obtain data in the normal operation database, predict load changes within a fixed prediction time after the current moment according to the data in the normal operation database, and optimize the action model of the circuit breaker according to the predicted load changes.

3. The distribution network intelligent object terminal device with adaptive input and output according to claim 2, characterized in that: The load prediction module includes an action optimization module, which is configured to obtain a current change in a distribution network loop according to a predicted load change, and if the current in the distribution network loop suddenly changes to a current less than 10mA, keep the circuit breaker disconnected within a time period when the current is less than 10mA.

4. The distribution network intelligent object terminal device with adaptive input and output according to claim 3, characterized in that: The action optimization module is also configured to obtain a load power factor change according to the predicted load change, and if the load power factor changes to a value less than 0.3, keep the circuit breaker closed during a time period when the load power factor is less than 0.

3.

5. The intelligent object-end device of the distribution network with adaptive input and output according to claim 1, characterized in that: The load optimization module is configured to: communicate with the user side through the communication module, and optimize the load according to the load disconnection information of the user side during the peak period of the time-of-use electricity price at the location of the distribution network; the load optimization module is configured to: inquire the user side through the communication module to obtain the peak period, valley period and flat period of the time-of-use electricity price at the location of the distribution network, and inquire whether the load connected to the user side is useful and the disconnectable time period of the load during the peak period during the peak period of the time-of-use electricity price. If the load is useful at the current moment, keep the circuit breaker closed; if the load is useless at the current moment, control the circuit breaker to disconnect, and record the disconnectable time period of the load to control the circuit breaker to disconnect in the same time period every subsequent day.

6. The intelligent object-end device of the distribution network with adaptive input and output according to claim 1, characterized in that: The device status module is configured to: obtain electrical parameters and vibration information in the device status information library, determine whether the circuit breaker is disconnected successfully according to the electrical parameters in the device status information library, and determine whether the circuit breaker is abnormal or faulty according to the vibration information in the device status information library.

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