Intelligent power distribution comprehensive monitoring system integrating Internet of Things and cloud computing

Abstract

The invention discloses an intelligent power distribution integrated monitoring system fusing the Internet of Things and cloud computing, and relates to the technical field of power system monitoring, comprising an Internet of Things layer and a cloud computing layer. According to the invention, preliminary measurement data related to the operation state of the power distribution network is acquired through the Internet of Things terminal; performing time synchronization processing on sampling moments in all the preliminary measurement data, and if time synchronization cannot be completed at a sampling moment in a certain preliminary measurement data, not incorporating the preliminary measurement data corresponding to the sampling moment into the voting measurement data set; generating event window data from the data according to a sliding window; under the same synchronous timestamp, according to the voting measurement data sequence, uniquely associating and converging the voltage measurement quantity, the current measurement quantity and the switch state matched with the synchronous timestamp according to the measurement point identifier to generate a synchronous measurement data set; therefore, the consistency obstacle of misjudgment caused by voting for different event segments due to inconsistent time of different measurement points is relieved.

Description

Technical Field

[0001] This invention relates to the field of power system monitoring technology, and in particular to an intelligent power distribution integrated monitoring system that integrates the Internet of Things and cloud computing. Background Technology

[0002] Integrated power distribution monitoring refers to a set of technologies and services for unified and continuous monitoring and management of the entire operation process of a power distribution network. It collects, transmits, processes, and displays the operating data and status parameters of power distribution equipment and key nodes, thereby enabling functions such as perception of the operating status of the power distribution network, event identification, alarm prompts, fault location assistance, and handling records.

[0003] Currently, with the large-scale integration of distributed energy sources such as photovoltaics and energy storage into the distribution sides of industrial parks and residential areas, the power flow of the distribution network is gradually evolving from the traditional unidirectional power supply mode to a bidirectional power flow mode. When a fault occurs, the direction, amplitude, and duration of the fault current may change depending on the operating mode, the location of the distributed energy grid connection point, and the inverter's current limiting control. Traditional overcurrent protection based on single-point measurement, fixed setting, and direction discrimination is prone to the following problems: bidirectional power flow causes the overcurrent protection direction discrimination to fail, and non-faulty lines may be disconnected during a fault. Summary of the Invention

[0004] To address the technical problems existing in the background art, this invention proposes an intelligent power distribution integrated monitoring system that integrates the Internet of Things and cloud computing.

[0005] This invention proposes an intelligent power distribution integrated monitoring system that integrates the Internet of Things and cloud computing, comprising: Internet of Things (IoT) layer and cloud computing layer; The Internet of Things (IoT) layer includes: IoT data acquisition module: Uses IoT terminals to collect raw measurement data from measurement points in the power distribution network; Each measurement point in the power distribution network is assigned a unique measurement point identifier, which is used to uniquely identify the measurement point. Each measurement point in the distribution network is assigned a unique measurement point identifier. A globally unique 128-bit random string can be generated for each measurement point as the measurement point identifier using a universally unique identifier. Obtain the on / off status of the measurement point; The original measurement data of the measurement points, the measurement point identification, and the on / off status of the measurement points form the preliminary measurement data; Acquire preliminary measurement data from all measurement points to form a preliminary measurement data set; Transmit the preliminary measurement data set to the cloud computing layer; The cloud computing layer includes: Voting measurement data generation module: used to receive the preliminary measurement data set and generate the voting measurement data set based on the preliminary measurement data set; Event window data generation module: Generates an event window data set based on the voting measurement data set; Synchronous measurement data generation module: Generates a synchronous measurement data set based on the event window data set; obtains all synchronous measurement data sets to form a total synchronous measurement data set; Candidate event generation module: Generates candidate events based on the synchronous measurement data set and the event window data set, obtains all candidate events, and forms a candidate event set; Voting information generation module: Generates voting information for each candidate event in the candidate event set; obtains all voting information to form a voting set; Consistency determination module: Generates consistency determination results based on the voting set; Action instruction generation module: Generates an action instruction sequence based on the consistency judgment result. After executing the action instruction sequence, records the switch open / closed state as the disposal result.

[0006] Preferably, in the IoT acquisition module, the raw measurement data includes: Sampling time: The local timestamp at which the measurement point generates data; Voltage measurements: including the effective value or instantaneous sequence of phase voltage or line voltage; Current measurement quantities: including the effective value or instantaneous sequence of phase current; Frequency measurement: used to assist in event identification; IoT terminals are physical hardware installed at measurement points, such as smart sensors and fault indicators. Measurement points in a power distribution network refer to power distribution network nodes, such as switchgear, transformers, cable joints, and user meter boxes. The switch status includes the switch open / closed state associated with the measurement point and the moment when the switch open / closed position changes. The switch open / closed position change time refers to the timestamp corresponding to the moment when the switch state changes from one state to another. The switching states include the closed conducting state and the open isolation state.

[0007] Preferably, in the voting measurement data generation module, a voting measurement data set is generated based on the preliminary measurement data set, as follows: In the preliminary measurement data set, time synchronization processing is performed on the sampling time of all preliminary measurement data to generate a synchronization timestamp; The sampling time in the preliminary measurement data is replaced with a synchronization timestamp to form the voting measurement data; As an explanation, the preliminary measurement data includes the sampling time, while the voting measurement data includes the synchronization timestamp, hence the different names. The voting measurement data still includes voltage measurements, current measurements, frequency measurements, measurement point identifiers, and the on / off status of the measurement points; it simply replaces the sampling time in the preliminary measurement data with the synchronization timestamp. Acquire all voting measurement data to form a voting measurement data set; The time synchronization process uses NTP time synchronization technology to convert all sampling timestamps into synchronized timestamps under a unified time base. During time synchronization processing, if the sampling time in a certain preliminary measurement data cannot be synchronized, the preliminary measurement data corresponding to that sampling time will not be included in the voting measurement data set; The sampling time in a certain preliminary measurement data cannot be synchronized in time. This means that the deviation between the sampling time and the reference time in the NTP time synchronization technology exceeds the maximum allowable deviation. The maximum allowable deviation ranges from 1 to 10 ms. This section addresses the obstacle of time consistency in consensus algorithms: if the times at different measurement points are inconsistent, it will lead to misjudgments caused by voting on different event segments.

[0008] Preferably, in the event window data generation module, an event window data set is generated based on the voting measurement data set, as follows: For each measurement point in the voting measurement data set, the corresponding voting measurement data is identified. A sliding window is used to divide the data into multiple time windows according to a preset window length. The voting measurement data whose synchronization timestamps fall into the time windows are arranged in chronological order to generate event window data. For voting measurement data whose synchronization timestamps fall within the time window, arrange them in chronological order to generate event window data, as follows: For voting measurement data whose synchronization timestamps fall within the time window, the voting measurement data are arranged in chronological order according to the synchronization timestamps to form a voting measurement data sequence. Extract the voltage measurement sequence corresponding to the voltage measurement, the current measurement sequence corresponding to the current measurement, and the switch state sequence corresponding to the switch state from the voting measurement data sequence; Integrate voltage measurement sequences, current measurement sequences, and switch status sequences to form event window data; Retrieve the event window data corresponding to all time windows to form an event window data set; For illustration, the preset window length of the sliding window is a fixed duration in milliseconds. In one optional embodiment, the preset window length ranges from 10ms to 5000ms; further, the preset window length ranges from 50ms to 500ms. The sliding window has a fixed step size, which ranges from 5 to 100 ms; the fixed step size is greater than zero and less than the preset window length.

[0009] Preferably, in the synchronous measurement data generation module, a synchronous measurement data set is generated based on the event window data set; all synchronous measurement data sets are obtained to form a total synchronous measurement data set, as follows: Under the same synchronization timestamp, extract the voltage measurement, current measurement, and switch status that match the synchronization timestamp from the event window data set; Based on the voting measurement data sequence, the voltage measurement, current measurement, and switch status that match the synchronization timestamp are uniquely associated and aggregated according to the measurement point identifier to generate a synchronization measurement data set; The synchronous measurement data set includes synchronous timestamps, measurement point identifiers, voltage measurements, current measurements, and switch status; Obtain the set of synchronization measurement data corresponding to all synchronization timestamps to form the total set of synchronization measurement data; As an explanation, in the synchronous measurement data set, the synchronization timestamp is fixed and there is only one synchronization timestamp, while the measurement point identifier is the identifier of each measurement point in the voting measurement data sequence. That is to say, there are multiple measurement point identifiers in the synchronous measurement data set; and the voltage measurement, current measurement, and switch status are the voltage measurement, current measurement, and switch status corresponding to each measurement point identifier under the same synchronization timestamp.

[0010] Preferably, in the candidate event generation module, candidate events are generated based on the synchronous measurement data set and the event window data set, as follows: Based on the synchronized measurement data set and the event window data set, perform event detection and processing one by one according to the synchronized timestamp; When any synchronization timestamp meets the event triggering criterion, a candidate event is generated; Based on the synchronized measurement data set and the event window data set, event detection processing is performed one by one according to the synchronization timestamp. When any synchronization timestamp meets the event triggering criterion, a candidate event is generated, as follows: Event triggering criteria include any of the following: Switch change triggering criterion: In the synchronous measurement data set, for the switch state corresponding to the measurement point identifier, there exists at least one switch change time; the absolute value of the difference between the switch change time and the synchronous timestamp is less than or equal to the time tolerance value, which ranges from 1 to 10 ms. Current mutation triggering criterion: In the event window data set, identify the current measurement sequence in the event window data corresponding to the measurement point; obtain the maximum and minimum values ​​of the current measurement in each current measurement sequence; there exists at least one current measurement sequence in which the difference between the maximum and minimum values ​​of the current measurement is greater than or equal to the current increment threshold, and the current increment threshold ranges from 5 to 500A; Voltage mutation triggering criterion: In the event window data set, for the event window data corresponding to the measurement point identifier, the voltage measurement sequence is selected; the maximum and minimum voltage measurement values ​​in each voltage measurement sequence are obtained; there exists at least one voltage measurement sequence in which the difference between the maximum and minimum voltage measurement values ​​is greater than or equal to the voltage increment threshold, and the voltage increment threshold ranges from 5 to 2000V; For each candidate event that has been generated, construct the following in sequence: Use the synchronization timestamp that triggered the candidate event as the event time of the candidate event; Assign a unique event number to each candidate event; As an explanation, a globally unique 128-bit random string can be generated for each candidate event as an event number using a universally unique identifier; The synchronization measurement datasets corresponding to the synchronization timestamps that trigger candidate events are combined into an event-related measurement subset; Get multiple time windows corresponding to the synchronization timestamps that trigger candidate events, and use them as candidate time windows; from the candidate time windows, select the candidate time windows where the measurement point identifiers in the voting measurement data sequence are all located in the event-related measurement subset as candidate time windows; from the candidate time windows, select the time window with the smallest end time as the selected time window; and use the event window data of the selected time window as the event-related window subset. Event trigger types are generated based on a subset of event-related measurements and a subset of event-related windows. Event trigger types include operation trigger types and fault trigger types. Candidate events are formed based on event time, event number, subset of event-related measurements, subset of event-related windows, and event trigger type.

[0011] Preferably, the event trigger type is generated based on the event-related measurement subset and the event-related window subset, as follows: If any measurement point in the event-related measurement subset has a switch state change time corresponding to the switch state, and this switch state change time falls within the time window range of the selected time window, then the event trigger type is operation trigger type; otherwise, it is fault trigger type.

[0012] Preferably, in the voting information generation module, voting information is generated for each candidate event in the candidate event set, as follows: For each candidate event, extract all measurement point identifiers from the event-related measurement subset of the candidate event to form an event measurement point identifier set; Based on the event-related measurement subset and the event-related window subset, each measurement point identifier in the event measurement point identifier set is used as the voting generation object to generate corresponding voting sub-information; Voting sub-information includes the voting-related segment identifier and the voting value; The voting-related section identifier is generated in the following way: Obtain the topology mapping relationship of the power distribution network; The distribution network topology mapping relationship is a set of data used to characterize the correspondence between distribution network measurement points and distribution network sections; The distribution network topology mapping relationship includes: section identifier, measurement point identifier, and the correspondence between measurement point identifier and section identifier; among which, the section identifier is used to uniquely identify the power supply section formed by the separation of switching equipment; For any measurement point identifier in the event measurement point identifier set, based on the correspondence between measurement point identifiers and segment identifiers in the distribution network topology mapping relationship, the segment identifier corresponding to the measurement point identifier is obtained, and the segment identifier is used as the voting associated segment identifier; Voting values ​​are generated in the following way: If the event triggering type of the candidate event is fault triggering type, and the event triggering criterion of the candidate event is current change triggering criterion or voltage change triggering criterion, then the voting value is +1; If the event trigger type of the candidate event is an operation trigger type, then the voting value is -1; Otherwise, the vote count is 0; The voting information corresponding to the voting object is formed by using the event number, measurement point identifier, and voting sub-information.

[0013] Preferably, in the consistency determination module, a consistency determination result is generated based on the voting set, as follows: Within the voting set, statistical analysis is performed on the voting information for the same event number and the same voting-related segment identifier to obtain the number of votes with a value of +1 and the number of votes with a value of -1. Add the number of votes with a value of +1 to the number of votes with a value of -1, divide by 2 to get the total number of selected votes, and round up the total number of selected votes to get the required total number of votes. As an explanation, the number of votes with a value of +1 and the number of votes with a value of -1 are both positive integers. The total number of selected votes is rounded up to obtain the required total number of votes. This means that if the total number of selected votes is 0.5, 0.5 rounded up will result in 1, and if the total number of selected votes is 2.5, 2.5 rounded up will result in 3.

[0014] Generate a consistency determination result, which includes: event number, voting associated segment identifier, and commit status; The submission status includes submitted and unsubmitted; When the number of votes with a value of +1 is greater than or equal to the total number of votes required and the number of votes with a value of +1 is greater than the number of votes with a value of -1, a consistency determination result is submitted, and the submission status is "submitted". Otherwise, a consistency determination result is not submitted, and the submission status is "not submitted".

[0015] Preferably, in the action instruction generation module, an action instruction sequence is generated based on the consistency determination result, as follows: An action instruction sequence is generated based on the consistency determination result. The action instruction sequence includes the event number, the voting associated segment identifier, the action type, and the action sequence number. in: The action sequence number is an integer, and for each action instruction sequence generated, the action sequence number is incremented by 1 based on the previous action instruction sequence; When the commit status of the consistency determination result is uncommitted, the action type is locking; When the action type is locking, the switch open / closed state of the corresponding measurement point in the voting associated section identifier remains unchanged; When the commit status of the consistency determination result is "committed", the action type is "isolation". When the action type is isolation, the switch status of the corresponding measurement point in the voting associated segment identifier changes from the original closed conduction state to the open isolation state or from the original open isolation state to the closed conduction state.

[0016] The intelligent power distribution integrated monitoring system integrating the Internet of Things and cloud computing proposed in this invention has the following beneficial technical effects: This application collects preliminary measurement data related to the operating status of the power distribution network through an IoT terminal; performs time synchronization processing on the sampling times of all preliminary measurement data to generate synchronization timestamps; during time synchronization processing, if the sampling time of a certain preliminary measurement data cannot be synchronized, the preliminary measurement data corresponding to that sampling time is not included in the voting measurement data set; then, the data is processed into event window data using a sliding window; and under the same synchronization timestamp, voltage measurements, current measurements, and switch states matching the synchronization timestamp are extracted from the event window data set; based on the voting measurement data sequence, the voltage measurements, current measurements, and switch states matching the synchronization timestamp are uniquely associated and aggregated according to the measurement point identifier to generate a synchronized measurement data set; thereby alleviating the problem that if the times of different measurement points are inconsistent, voting will target different events. The system addresses the inconsistency issues caused by fragmented data and misjudgments. It then uses switch change triggering criteria, current surge triggering criteria, or voltage surge triggering criteria to generate candidate events. By leveraging the distribution network topology mapping, the votes from each measurement point are merged into the voting-related section identifier. Discrete vote values ​​are used to distinguish fault triggering types. Only when the number of votes with a value of +1 is greater than or equal to the total number of required votes and the number of votes with a value of +1 is greater than the number of votes with a value of -1, is a consistency judgment result submitted for isolation processing. Otherwise, the output action type is blocking, reducing the probability of cutting off non-faulty lines. This transforms the risk of erroneous cutting under bidirectional power flow conditions caused by traditional single-point measurement, fixed setting, and direction discrimination into cross-measurement point, cross-window, and section-based consistency judgment before isolation is executed. This alleviates the technical problem of overcurrent protection direction discrimination failure due to bidirectional power flow, which could lead to the cutting off of non-faulty lines during a fault. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a smart power distribution integrated monitoring system that integrates the Internet of Things and cloud computing according to the present invention. Detailed Implementation

[0018] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar symbols denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0019] like Figure 1 The intelligent power distribution integrated monitoring system shown includes: Internet of Things (IoT) layer and cloud computing layer; The Internet of Things (IoT) layer includes: IoT data acquisition module: Uses IoT terminals to collect raw measurement data from measurement points in the power distribution network; Each measurement point in the power distribution network is assigned a unique measurement point identifier, which is used to uniquely identify the measurement point. Each measurement point in the distribution network is assigned a unique measurement point identifier. A globally unique 128-bit random string can be generated for each measurement point as the measurement point identifier using a universally unique identifier. Obtain the on / off status of the measurement point; The original measurement data of the measurement points, the measurement point identification, and the on / off status of the measurement points form the preliminary measurement data; Acquire preliminary measurement data from all measurement points to form a preliminary measurement data set; Transmit the preliminary measurement data set to the cloud computing layer; In an optional embodiment, the raw measurement data in the IoT acquisition module includes: Sampling time: The local timestamp at which the measurement point generates data; Voltage measurements: including the effective value or instantaneous sequence of phase voltage or line voltage; Current measurement quantities: including the effective value or instantaneous sequence of phase current; Frequency measurement: used to assist in event identification; IoT terminals are physical hardware installed at measurement points, such as smart sensors and fault indicators. Measurement points in a power distribution network refer to power distribution network nodes, such as switchgear, transformers, cable joints, and user meter boxes. The switch status includes the switch open / closed state associated with the measurement point and the moment when the switch open / closed position changes. The switch open / closed position change time refers to the timestamp corresponding to the moment when the switch state changes from one state to another. The switch's open / closed states include a closed conducting state and an open isolation state; The cloud computing layer includes: Voting measurement data generation module: used to receive the preliminary measurement data set and generate the voting measurement data set based on the preliminary measurement data set; In an optional embodiment, the voting measurement data generation module generates a voting measurement data set based on the preliminary measurement data set, as follows: In the preliminary measurement data set, time synchronization processing is performed on the sampling time of all preliminary measurement data to generate a synchronization timestamp; The sampling time in the preliminary measurement data is replaced with a synchronization timestamp to form the voting measurement data; As an explanation, the preliminary measurement data includes the sampling time, while the voting measurement data includes the synchronization timestamp, hence the different names. The voting measurement data still includes voltage measurements, current measurements, frequency measurements, measurement point identifiers, and the on / off status of the measurement points; it simply replaces the sampling time in the preliminary measurement data with the synchronization timestamp. Acquire all voting measurement data to form a voting measurement data set; The time synchronization process uses NTP time synchronization technology to convert all sampling timestamps into synchronized timestamps under a unified time base. During time synchronization processing, if the sampling time in a certain preliminary measurement data cannot be synchronized, the preliminary measurement data corresponding to that sampling time will not be included in the voting measurement data set; The sampling time in a certain preliminary measurement data cannot be synchronized in time. This means that the deviation between the sampling time and the reference time in the NTP time synchronization technology exceeds the maximum allowable deviation. The maximum allowable deviation ranges from 1 to 10 ms. This section addresses the obstacle of time consistency in consensus algorithms: if the times at different measurement points are inconsistent, it will lead to misjudgments caused by voting on different event segments. Event window data generation module: Generates an event window data set based on the voting measurement data set; In an optional embodiment, the event window data generation module generates an event window data set based on the voting measurement data set, as follows: For each measurement point in the voting measurement data set, the corresponding voting measurement data is identified. A sliding window is used to divide the data into multiple time windows according to a preset window length. The voting measurement data whose synchronization timestamps fall into the time windows are arranged in chronological order to generate event window data. For voting measurement data whose synchronization timestamps fall within the time window, arrange them in chronological order to generate event window data, as follows: For voting measurement data whose synchronization timestamps fall within the time window, the voting measurement data are arranged in chronological order according to the synchronization timestamps to form a voting measurement data sequence. Extract the voltage measurement sequence corresponding to the voltage measurement, the current measurement sequence corresponding to the current measurement, and the switch state sequence corresponding to the switch state from the voting measurement data sequence; Integrate voltage measurement sequences, current measurement sequences, and switch status sequences to form event window data; Retrieve the event window data corresponding to all time windows to form an event window data set; For illustration, the preset window length of the sliding window is a fixed duration in milliseconds. In one optional embodiment, the preset window length ranges from 10ms to 5000ms; further, the preset window length ranges from 50ms to 500ms. The sliding window has a fixed step size, which ranges from 5 to 100 ms; the fixed step size is greater than zero and less than the preset window length. Synchronous measurement data generation module: Generates a synchronous measurement data set based on the event window data set; obtains all synchronous measurement data sets to form a total synchronous measurement data set; In an optional embodiment, the synchronous measurement data generation module generates a synchronous measurement data set based on the event window data set; and obtains all synchronous measurement data sets to form a total synchronous measurement data set, as follows: Under the same synchronization timestamp, extract the voltage measurement, current measurement, and switch status that match the synchronization timestamp from the event window data set; Based on the voting measurement data sequence, the voltage measurement, current measurement, and switch status that match the synchronization timestamp are uniquely associated and aggregated according to the measurement point identifier to generate a synchronization measurement data set; The synchronous measurement data set includes synchronous timestamps, measurement point identifiers, voltage measurements, current measurements, and switch status; Obtain the set of synchronization measurement data corresponding to all synchronization timestamps to form the total set of synchronization measurement data; As an explanation, in the synchronous measurement data set, the synchronization timestamp is fixed and there is only one synchronization timestamp, while the measurement point identifier is the identifier of each measurement point in the voting measurement data sequence. That is to say, there are multiple measurement point identifiers in the synchronous measurement data set; and the voltage measurement, current measurement, and switch status are the voltage measurement, current measurement, and switch status corresponding to each measurement point identifier under the same synchronization timestamp. Candidate event generation module: Generates candidate events based on the synchronous measurement data set and the event window data set, obtains all candidate events, and forms a candidate event set; In the candidate event generation module, candidate events are generated based on the synchronized measurement data set and the event window data set, as follows: Based on the synchronized measurement data set and the event window data set, perform event detection and processing one by one according to the synchronized timestamp; When any synchronization timestamp meets the event triggering criterion, a candidate event is generated; In an optional embodiment, event detection processing is performed sequentially according to the synchronization timestamp based on the synchronization measurement data set and the event window data set. When any synchronization timestamp meets the event triggering criterion, a candidate event is generated, as follows: Event triggering criteria include any of the following: Switch change triggering criterion: In the synchronous measurement data set, for the switch state corresponding to the measurement point identifier, there exists at least one switch change time; the absolute value of the difference between the switch change time and the synchronous timestamp is less than or equal to the time tolerance value, which ranges from 1 to 10 ms. Current surge triggering criterion: In the event window data set, for each measurement point, identify the current measurement sequence in the corresponding event window data; obtain the maximum and minimum values ​​of the current measurement in each current measurement sequence; there exists at least one current measurement sequence where the difference between the maximum and minimum values ​​of the current measurement is greater than or equal to a current increment threshold, the current increment threshold being in the range of 5-500A; in an optional embodiment, the current increment threshold is in the range of 10-200A. Voltage surge triggering criterion: In the event window data set, for the event window data corresponding to the measurement point identifier, the voltage measurement sequence is obtained; the maximum and minimum values ​​of the voltage measurement in each voltage measurement sequence are obtained; there exists at least one voltage measurement sequence in which the difference between the maximum and minimum values ​​of the voltage measurement is greater than or equal to the voltage increment threshold, the voltage increment threshold being in the range of 5-2000V; in an optional embodiment, the voltage increment threshold is in the range of 20-800V; For each candidate event that has been generated, construct the following in sequence: Use the synchronization timestamp that triggered the candidate event as the event time of the candidate event; Assign a unique event number to each candidate event; As an explanation, a globally unique 128-bit random string can be generated for each candidate event as an event number using a universally unique identifier; The synchronization measurement datasets corresponding to the synchronization timestamps that trigger candidate events are combined into an event-related measurement subset; Get multiple time windows corresponding to the synchronization timestamps that trigger candidate events, and use them as candidate time windows; from the candidate time windows, select the candidate time windows where the measurement point identifiers in the voting measurement data sequence are all located in the event-related measurement subset as candidate time windows; from the candidate time windows, select the time window with the smallest end time as the selected time window; and use the event window data of the selected time window as the event-related window subset. Event trigger types are generated based on a subset of event-related measurements and a subset of event-related windows. Event trigger types include operation trigger types and fault trigger types. In an optional embodiment, the event trigger type is generated based on a subset of event-related measurements and a subset of event-related windows, as follows: If any measurement point in the event-related measurement subset has a switch open / close position change time corresponding to its corresponding switch state, and this switch open / close position change time falls within the time window range of the selected time window, then the event trigger type is operation trigger type; otherwise, it is fault trigger type. Candidate events are formed based on event time, event number, subset of event-related measurements, subset of event-related windows, and event trigger type. Voting information generation module: Generates voting information for each candidate event in the candidate event set; obtains all voting information to form a voting set; In an optional embodiment, the voting information generation module generates voting information for each candidate event in the candidate event set, as follows: For each candidate event, extract all measurement point identifiers from the event-related measurement subset of the candidate event to form an event measurement point identifier set; Based on the event-related measurement subset and the event-related window subset, each measurement point identifier in the event measurement point identifier set is used as the voting generation object to generate corresponding voting sub-information; Voting sub-information includes the voting-related segment identifier and the voting value; The voting-related section identifier is generated in the following way: Obtain the topology mapping relationship of the power distribution network; The distribution network topology mapping relationship is a set of data used to characterize the correspondence between distribution network measurement points and distribution network sections; The distribution network topology mapping relationship includes: section identifier, measurement point identifier, and the correspondence between measurement point identifier and section identifier; among which, the section identifier is used to uniquely identify the power supply section formed by the separation of switching equipment; For any measurement point identifier in the event measurement point identifier set, based on the correspondence between measurement point identifiers and segment identifiers in the distribution network topology mapping relationship, the segment identifier corresponding to the measurement point identifier is obtained, and the segment identifier is used as the voting associated segment identifier; Voting values ​​are generated in the following way: If the event triggering type of the candidate event is fault triggering type, and the event triggering criterion of the candidate event is current change triggering criterion or voltage change triggering criterion, then the voting value is +1; If the event trigger type of the candidate event is an operation trigger type, then the voting value is -1; Otherwise, the vote count is 0; The voting information corresponding to the voting object is formed by using the event number, measurement point identifier, and voting sub-information; Consistency determination module: Generates consistency determination results based on the voting set; In an optional embodiment, the consistency determination module generates a consistency determination result based on the voting set, as follows: Within the voting set, statistical analysis is performed on the voting information for the same event number and the same voting-related segment identifier to obtain the number of votes with a value of +1 and the number of votes with a value of -1. Add the number of votes with a value of +1 to the number of votes with a value of -1, divide by 2 to get the total number of selected votes, and round up the total number of selected votes to get the required total number of votes. As an explanation, the number of votes with a value of +1 and the number of votes with a value of -1 are both positive integers. The total number of selected votes is rounded up to obtain the required total number of votes. This means that if the total number of selected votes is 0.5, 0.5 rounded up will result in 1, and if the total number of selected votes is 2.5, 2.5 rounded up will result in 3.

[0020] Generate a consistency determination result, which includes: event number, voting associated segment identifier, and commit status; The submission status includes submitted and unsubmitted; When the number of votes with a value of +1 is greater than or equal to the total number of votes required and the number of votes with a value of +1 is greater than the number of votes with a value of -1, a consistency determination result is submitted and the submission status is "submitted". Otherwise, a consistency determination result is not submitted and the submission status is "not submitted". Action instruction generation module: Generates an action instruction sequence based on the consistency judgment result. After executing the action instruction sequence, records the switch open / closed state as the disposal result.

[0021] In an optional embodiment, the action instruction generation module generates an action instruction sequence based on the consistency determination result, as follows: An action instruction sequence is generated based on the consistency determination result. The action instruction sequence includes the event number, the voting associated segment identifier, the action type, and the action sequence number. in: The action sequence number is an integer, and for each action instruction sequence generated, the action sequence number is incremented by 1 based on the previous action instruction sequence; When the commit status of the consistency determination result is uncommitted, the action type is locking; When the action type is locking, the switch open / closed state of the corresponding measurement point in the voting associated section identifier remains unchanged; When the commit status of the consistency determination result is "committed", the action type is "isolation". When the action type is isolation, the switch status of the corresponding measurement point in the voting associated segment identifier changes from the original closed conduction state to the open isolation state or from the original open isolation state to the closed conduction state.

[0022] This application collects preliminary measurement data related to the operating status of the power distribution network through an IoT terminal; performs time synchronization processing on the sampling times of all preliminary measurement data to generate synchronization timestamps; during time synchronization processing, if the sampling time of a certain preliminary measurement data cannot be synchronized, the preliminary measurement data corresponding to that sampling time is not included in the voting measurement data set; then, the data is processed into event window data using a sliding window; and under the same synchronization timestamp, voltage measurements, current measurements, and switch states matching the synchronization timestamp are extracted from the event window data set; based on the voting measurement data sequence, the voltage measurements, current measurements, and switch states matching the synchronization timestamp are uniquely associated and aggregated according to the measurement point identifier to generate a synchronized measurement data set; thereby alleviating the problem that if the times of different measurement points are inconsistent, voting will target different events. The system addresses the inconsistency issues caused by fragmented data and misjudgments. It then uses switch change triggering criteria, current surge triggering criteria, or voltage surge triggering criteria to generate candidate events. By leveraging the distribution network topology mapping, the votes from each measurement point are merged into the voting-related section identifier. Discrete vote values ​​are used to distinguish fault triggering types. Only when the number of votes with a value of +1 is greater than or equal to the total number of required votes and the number of votes with a value of +1 is greater than the number of votes with a value of -1, is a consistency judgment result submitted for isolation processing. Otherwise, the output action type is blocking, reducing the probability of cutting off non-faulty lines. This transforms the risk of erroneous cutting under bidirectional power flow conditions caused by traditional single-point measurement, fixed setting, and direction discrimination into cross-measurement point, cross-window, and section-based consistency judgment before isolation is executed. This alleviates the technical problem of overcurrent protection direction discrimination failure due to bidirectional power flow, which could lead to the cutting off of non-faulty lines during a fault.

[0023] To further explain, the root cause of traditional faulty disconnections lies in determining the direction based on single-point measurements, relying on the current direction / amplitude at a certain point to determine whether the fault is ahead or behind. In bidirectional power flow, the direction and amplitude of the fault current are affected by the output of distributed power sources, the location of the grid connection point, and the inverter's current limiting. The direction seen at a single point may be reversed, leading to the disconnection of a healthy line as a faulty line. However, the technical solution in this application changes the decision threshold for isolation to multi-point consistency within the same event and the same section. Therefore, even if bidirectional power flow confuses the direction, it is not easy to push non-faulty sections to the isolation stage. This is because disconnection only occurs when a majority of measurement points within the same section point to a "fault"; otherwise, it is not disconnected. A "fault," as described above, occurs when the number of votes with a value of +1 is greater than or equal to the total number of required votes and the number of votes with a value of +1 is greater than the number of votes with a value of -1. In this case, a consistency judgment result is submitted, and the submission status is "submitted." For clarification, "acquisition" in this application refers to obtaining the required content or data using existing technical means.

[0024] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0025] In the embodiments provided by this invention, it should be understood that the disclosed system or method can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for instance, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0026] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0027] Furthermore, the functional modules in the various embodiments of this invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in a combination of hardware and software functional modules.

[0028] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the basic characteristics of the present invention.

[0029] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart power distribution integrated monitoring system integrating the Internet of Things and cloud computing, characterized in that, include: Internet of Things (IoT) layer and cloud computing layer; The Internet of Things (IoT) layer includes: IoT data acquisition module: Uses IoT terminals to obtain preliminary measurement data from measurement points in the power distribution network, acquires preliminary measurement data from all measurement points to form a preliminary measurement data set, and transmits the preliminary measurement data set to the cloud computing layer; The cloud computing layer includes: Voting measurement data generation module: used to receive the preliminary measurement data set and generate the voting measurement data set based on the preliminary measurement data set; Event window data generation module: Generates an event window data set based on the voting measurement data set; Synchronous measurement data generation module: Generates a synchronous measurement data set based on the event window data set; obtains all synchronous measurement data sets to form a total synchronous measurement data set; Candidate event generation module: Generates candidate events based on the synchronous measurement data set and the event window data set, obtains all candidate events, and forms a candidate event set; Voting information generation module: Generates voting information for each candidate event in the candidate event set; obtains all voting information to form a voting set; Consistency determination module: Generates consistency determination results based on the voting set; Action instruction generation module: Generates an action instruction sequence based on the consistency judgment result. After executing the action instruction sequence, records the switch open / closed state as the disposal result.

2. The intelligent power distribution integrated monitoring system integrating the Internet of Things and cloud computing as described in claim 1, characterized in that, In the IoT data acquisition module, The system uses IoT terminals to collect raw measurement data from measurement points in the power distribution network; assigns a unique measurement point identifier to each measurement point in the power distribution network; and obtains the switch status of the measurement points. The original measurement data of the measurement points, the measurement point identification, and the on / off status of the measurement points form the preliminary measurement data; The raw measurement data includes: Sampling time: The local timestamp at which the measurement point generates data; Voltage measurements: including the effective value or instantaneous sequence of phase voltage or line voltage; Current measurement quantities: including the effective value or instantaneous sequence of phase current; Frequency measurement: used to assist in event identification; The switch status includes the switch open / closed state associated with the measurement point and the moment when the switch open / closed position changes. The switching states include the closed conducting state and the open isolation state.

3. The intelligent power distribution integrated monitoring system integrating the Internet of Things and cloud computing as described in claim 2, characterized in that, In the voting measurement data generation module, a voting measurement data set is generated based on the preliminary measurement data set, as follows: In the preliminary measurement data set, time synchronization processing is performed on the sampling time of all preliminary measurement data to generate a synchronization timestamp; The sampling time in the preliminary measurement data is replaced with a synchronization timestamp to form the voting measurement data; Acquire all voting measurement data to form a voting measurement data set; During time synchronization processing, if the sampling time in a certain preliminary measurement data cannot be synchronized, the preliminary measurement data corresponding to that sampling time will not be included in the voting measurement data set.

4. The intelligent power distribution integrated monitoring system integrating the Internet of Things and cloud computing as described in claim 3, characterized in that, In the event window data generation module, an event window data set is generated based on the voting measurement data set, as follows: For each measurement point in the voting measurement data set, the corresponding voting measurement data is identified. A sliding window is used to divide the data into multiple time windows according to a preset window length. The voting measurement data whose synchronization timestamps fall into the time windows are arranged in chronological order to generate event window data. For voting measurement data whose synchronization timestamps fall within the time window, arrange them in chronological order to generate event window data, as follows: For voting measurement data whose synchronization timestamps fall within the time window, the voting measurement data are arranged in chronological order according to the synchronization timestamps to form a voting measurement data sequence. Extract the voltage measurement sequence corresponding to the voltage measurement, the current measurement sequence corresponding to the current measurement, and the switch state sequence corresponding to the switch state from the voting measurement data sequence; Integrate voltage measurement sequences, current measurement sequences, and switch status sequences to form event window data; Retrieve the event window data corresponding to all time windows to form an event window data set.

5. The intelligent power distribution integrated monitoring system integrating the Internet of Things and cloud computing as described in claim 4, characterized in that, In the synchronous measurement data generation module, a synchronous measurement data set is generated based on the event window data set; all synchronous measurement data sets are then retrieved to form the overall synchronous measurement data set, as follows: Under the same synchronization timestamp, extract the voltage measurement, current measurement, and switch status that match the synchronization timestamp from the event window data set; Based on the voting measurement data sequence, the voltage measurement, current measurement, and switch status that match the synchronization timestamp are uniquely associated and aggregated according to the measurement point identifier to generate a synchronization measurement data set; The synchronous measurement data set includes synchronous timestamps, measurement point identifiers, voltage measurements, current measurements, and switch status; Obtain the set of synchronization measurement data corresponding to all synchronization timestamps to form the total set of synchronization measurement data.

6. The intelligent power distribution integrated monitoring system integrating the Internet of Things and cloud computing as described in claim 5, characterized in that, In the candidate event generation module, candidate events are generated based on the synchronized measurement data set and the event window data set, as follows: Based on the synchronized measurement data set and the event window data set, perform event detection and processing one by one according to the synchronized timestamp; When any synchronization timestamp meets the event triggering criterion, a candidate event is generated; Based on the synchronized measurement data set and the event window data set, event detection processing is performed one by one according to the synchronization timestamp. When any synchronization timestamp meets the event triggering criterion, a candidate event is generated, as follows: Event triggering criteria include any of the following: Switch change triggering criterion: In the synchronous measurement data set, for the switch state corresponding to the measurement point identifier, there exists at least one switch change time; the absolute value of the difference between the switch change time and the synchronous timestamp is less than or equal to the time tolerance value, which ranges from 1 to 10 ms. Current surge triggering criterion: In the event window data set, identify the current measurement sequence in the event window data corresponding to the measurement point; obtain the maximum and minimum values ​​of the current measurement in each current measurement sequence; There exists at least one current measurement sequence in which the difference between the maximum and minimum values ​​of the current measurement is greater than or equal to the current increment threshold, and the current increment threshold ranges from 5 to 500 A. Voltage surge triggering criterion: In the event window data set, identify the voltage measurement sequence in the event window data corresponding to the measurement point; obtain the maximum and minimum voltage measurement values ​​in each voltage measurement sequence; There exists at least one voltage measurement sequence in which the difference between the maximum and minimum voltage measurement values ​​is greater than or equal to a voltage increment threshold, the voltage increment threshold being in the range of 5-2000V; For each candidate event that has been generated, construct the following in sequence: Use the synchronization timestamp that triggered the candidate event as the event time of the candidate event; Assign a unique event number to each candidate event; The synchronization measurement datasets corresponding to the synchronization timestamps that trigger candidate events are combined into an event-related measurement subset; Obtain multiple time windows corresponding to the synchronization timestamps that triggered the candidate events, and use them as candidate time windows; From the candidate time windows, select the candidate time windows in which all the measurement point identifiers in the voting measurement data sequence are located in the event-related measurement subset as candidate time windows. From the candidate time windows, select the time window with the smallest end time as the selected time window. Use the event window data of the selected time window as the event-related window subset. Event trigger types are generated based on a subset of event-related measurements and a subset of event-related windows. Event trigger types include operation trigger types and fault trigger types. Candidate events are formed based on event time, event number, event-related measurement subset, event-related window subset, and event trigger type.

7. The intelligent power distribution integrated monitoring system integrating the Internet of Things and cloud computing as described in claim 6, characterized in that, The event trigger type is generated based on the event-related measurement subset and the event-related window subset, as follows: If any measurement point in the event-related measurement subset has a switch state change time corresponding to the switch state, and this switch state change time falls within the time window range of the selected time window, then the event trigger type is operation trigger type; otherwise, it is fault trigger type.

8. The intelligent power distribution integrated monitoring system integrating the Internet of Things and cloud computing as described in claim 6, characterized in that, In the voting information generation module, voting information is generated for each candidate event in the candidate event set, as follows: For each candidate event, extract all measurement point identifiers from the event-related measurement subset of the candidate event to form an event measurement point identifier set; Based on the event-related measurement subset and the event-related window subset, each measurement point identifier in the event measurement point identifier set is used as the voting generation object to generate corresponding voting sub-information; Voting sub-information includes the voting-related segment identifier and the voting value; The voting-related section identifier is generated in the following way: Obtain the topology mapping relationship of the power distribution network; The distribution network topology mapping relationship is a set of data used to characterize the correspondence between distribution network measurement points and distribution network sections; The distribution network topology mapping relationship includes: section identifier, measurement point identifier, and the correspondence between measurement point identifier and section identifier; among which, the section identifier is used to uniquely identify the power supply section formed by the separation of switching equipment; For any measurement point identifier in the event measurement point identifier set, based on the correspondence between measurement point identifiers and segment identifiers in the distribution network topology mapping relationship, the segment identifier corresponding to the measurement point identifier is obtained, and the segment identifier is used as the voting associated segment identifier; Voting values ​​are generated in the following way: If the event triggering type of the candidate event is fault triggering type, and the event triggering criterion of the candidate event is current change triggering criterion or voltage change triggering criterion, then the voting value is +1; If the event trigger type of the candidate event is an operation trigger type, then the voting value is -1; Otherwise, the vote count is 0; The voting information corresponding to the voting object is formed by using the event number, measurement point identifier, and voting sub-information.

9. The intelligent power distribution integrated monitoring system integrating the Internet of Things and cloud computing as described in claim 8, characterized in that, In the consistency determination module, a consistency determination result is generated based on the voting set, as follows: Within the voting set, statistical analysis is performed on the voting information for the same event number and the same voting-related segment identifier to obtain the number of votes with a value of +1 and the number of votes with a value of -1. Add the number of votes with a value of +1 to the number of votes with a value of -1, divide by 2 to get the total number of selected votes, and round up the total number of selected votes to get the required total number of votes. Generate a consistency determination result, which includes: event number, voting associated segment identifier, and commit status; The submission status includes submitted and unsubmitted; When the number of votes with a value of +1 is greater than or equal to the total number of votes required and the number of votes with a value of +1 is greater than the number of votes with a value of -1, a consistency determination result is submitted, and the submission status is "submitted". Otherwise, a consistency determination result is not submitted, and the submission status is "not submitted".

10. The intelligent power distribution integrated monitoring system integrating the Internet of Things and cloud computing as described in claim 9, characterized in that, In the action instruction generation module, an action instruction sequence is generated based on the consistency determination result, as follows: An action instruction sequence is generated based on the consistency determination result. The action instruction sequence includes the event number, the voting associated segment identifier, the action type, and the action sequence number. in: The action sequence number is an integer, and for each action instruction sequence generated, the action sequence number is incremented by 1 based on the previous action instruction sequence; When the commit status of the consistency determination result is uncommitted, the action type is locking; When the action type is locking, the switch open / closed state of the corresponding measurement point in the voting associated section identifier remains unchanged; When the commit status of the consistency determination result is "committed", the action type is "isolation". When the action type is isolation, the switch status of the corresponding measurement point in the voting associated segment identifier changes from the original closed conduction state to the open isolation state or from the original open isolation state to the closed conduction state.

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