IoT-based low-voltage distribution network loop closing monitoring system
By using an IoT-based low-voltage distribution network loop-closing monitoring system, the loop-closing operation can be monitored and controlled in real time, solving the problem of electrical parameter mismatch in the low-voltage distribution network loop-closing operation. This enables efficient and reliable loop-closing operation and fault prediction, improving power grid operation efficiency and user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUANGZHOU POWER SUPPLY BUREAU GUANGDONG POWER GRID CO LTD
- Filing Date
- 2024-11-22
- Publication Date
- 2026-07-07
AI Technical Summary
In low-voltage distribution networks, complex transformer topology and chaotic phase sequence during loop closing operations can lead to inrush currents that affect the stable operation of upstream power sources and even cause relay protection devices to trip, thus impacting grid safety.
The low-voltage distribution network closed-loop monitoring system based on IoT includes a sensing layer module, a communication network layer module, and an application layer module. It utilizes sensors, processors, edge computing units, wireless communication networks, servers, and client modules to monitor and control the closed-loop operation in real time, and performs data processing and fault prediction through edge computing.
It enables rapid response to electrical parameters such as current, voltage, and phase, reduces the amount of data transmitted over the network, improves system reliability and security, ensures the accuracy and reliability of loop closing operations, reduces network load and maintenance costs, and improves power grid operating efficiency and user electricity experience.
Smart Images
Figure CN119602469B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power distribution network monitoring technology, specifically an IoT-based low-voltage power distribution network loop monitoring system. Background Technology
[0002] People's quality of life is closely related to a safe and stable power supply. Currently, urban low-voltage distribution networks are characterized by high load density, concentrated electricity consumption, and high requirements for power supply reliability. To further reduce users' perception of power outages, low-voltage loop-connection technology has become an important research topic. With the rapid development of Internet of Things (IoT) technology, intelligent monitoring and control of low-voltage distribution networks has become an important component of power system modernization.
[0003] However, low-voltage distribution networks suffer from complex transformer topologies and chaotic outgoing phase sequences, making it difficult for operators to determine in advance whether low-voltage loop closure meets the requirements. Furthermore, the moment of loop closure may generate a large inrush current, affecting the stable operation of the upstream power source, or a large circulating current may be generated after the loop stabilizes. This could cause overload of electrical equipment or lines, or even tripping of relay protection devices, thus seriously impacting the safe operation of the entire power grid. Summary of the Invention
[0004] The purpose of this invention is to provide an IoT-based low-voltage distribution network loop monitoring system to solve the problems mentioned above.
[0005] The technical solution adopted in this invention is as follows: a low-voltage distribution network closed-loop monitoring system based on IoT, characterized in that: the system includes: a sensing layer module, a communication network layer module, and an application layer module;
[0006] The perception layer module is internally equipped with a sensor module and a processor module;
[0007] The communication network layer module is internally equipped with a wireless communication network and a server.
[0008] The application layer module internally includes a client module and a data analysis module.
[0009] The sensor module is responsible for collecting current, voltage, and phase electrical parameter data, and an edge computing unit is set inside the sensor module;
[0010] The processor module processes and analyzes the data collected by the sensor and controls the loop closing operation;
[0011] Communication module: transmits the data processed by the processor to the network layer;
[0012] The wireless communication network connects the perception layer module and the application layer module, and is responsible for data transmission;
[0013] The server receives data from the perception layer module, stores, processes, and analyzes it, and transmits the results to the application layer module.
[0014] The client module receives data transmitted from the server and displays it to the user, allowing the user to perform loop merging operations;
[0015] The data analysis module analyzes the data transmitted from the server and provides a visual representation.
[0016] In a preferred embodiment, the sensor module uses a Hall sensor, which is installed at the output terminal of the distribution transformer to collect current data in real time.
[0017] The sensor module uses a voltage transformer, which is installed at the output end of the distribution transformer to collect voltage data in real time.
[0018] The sensor module uses a digital phase measuring instrument, which is installed at the output end of the distribution transformer to collect phase data of voltage or current in real time.
[0019] The sensor module is used as a sensor for the 0.4kV low-voltage distribution network through a real-time acquisition unit, including a high-precision current transformer, a voltage transformer, and a phase sensor, to acquire electrical parameters of the low-voltage outgoing lines of the two distribution areas in real time, including voltage difference and phase angle difference.
[0020] In a preferred embodiment, the processor module uses an STM32 microcontroller to handle data processing, control logic, and communication functions; it filters, amplifies, and converts current, voltage, and phase data to remove noise and improve data accuracy and reliability; the processor module determines whether the loop closing conditions are met based on the loop closing model and preset conditions, including a voltage difference of less than 5% and a phase angle difference of less than 2°, and controls the loop closing operation, including closing and opening.
[0021] The processor module transmits the processed data to the server via the 5G module and receives control commands from the server.
[0022] In a preferred embodiment, the communication module transmits the data processed by the processor module to the server and receives control commands from the server.
[0023] In a preferred embodiment, the wireless communication network uses a mobile operator's 5G network to connect the sensing layer module and the server to achieve wireless data transmission.
[0024] The server is equipped with a loop closure determination logic. Based on real-time collected data such as differential pressure, phase angle difference, loop closure inrush current, steady-state current, and load rate difference between the two transformers, it automatically calculates whether the loop closure conditions are met. When the loop closure conditions are not met, the client module can issue an early warning message to remind the operators to pay attention to safety.
[0025] In a preferred embodiment, the sensor interface of the edge computing unit is responsible for connecting to current, voltage, and phase sensors and receiving data collected by the sensors;
[0026] The edge computing unit uses an analog-to-digital converter to convert the analog signals collected by the sensors into digital signals for subsequent processing.
[0027] The edge computing unit filters digital signals through filters to remove noise and improve data accuracy.
[0028] In a preferred embodiment, the edge computing unit performs an anomaly detection module using a current overload detection algorithm, specifically including:
[0029] S1: Data Acquisition: The current sensor collects current data and transmits it to the data acquisition module;
[0030] S2: Analog-to-digital conversion: The data acquisition module converts analog current signals into digital signals;
[0031] S3: Filtering: The data processing module filters the digital current signal to remove noise;
[0032] S4: RMS value calculation: The data processing module calculates the RMS value of the current, using the same formula as described above;
[0033] S5: Current Change Rate Calculation: The data processing module calculates the rate of change of the effective current value between two adjacent sampling points, using the following formula:
[0034] dI_eff=(I_eff(t+Δt)-I_eff(t)) / Δt;
[0035] Where dI_eff is the rate of change of current, in amperes per second (A / s), I_eff(t+Δt) is the effective value of current at time t+Δt, I_eff(t) is the effective value of current at time t, and Δt is the sampling time interval, in seconds (s).
[0036] S6: Threshold judgment: The anomaly detection module compares the calculated current change rate with the preset current change rate threshold.
[0037] S7: Warning: If the rate of change of current exceeds the threshold, the warning module issues a red warning and transmits the warning information to the server and client through the communication module.
[0038] In a preferred embodiment, the edge computing unit uses the ARIMA model in time series analysis to predict loop-closed faults in the IoT low-voltage distribution network. The calculation process includes:
[0039] S1: Data Collection: Collect historical fault data of IoT low-voltage distribution network loop closure;
[0040] S2: Data preprocessing: Stabilize and remove outliers from the loop-closed data of the low-voltage distribution network;
[0041] The first-order difference formula for stabilizing and removing outliers from low-voltage distribution network loop data is as follows:
[0042] X′ t =X t -X t-1 ;where X t The value of the time series at time t, i.e., the observation value at the current time point of the loop closure data; X t-1 The value of the looped data time series at time t-1, i.e., the observation value at the previous looped data time point;
[0043] The L-lag operator, when applied to time series data with loops, L k X t =X t-k That is, L raised to the power of k acts on X t The value at time tk;
[0044] d: The order of differencing, indicating the number of times the time series of the loop data is differencing is performed to achieve stationarity.
[0045] S3: Model Identification: The parameters (p, d, q) of the ARIMA model are determined using ACF and PACF plots. ACF measures the correlation between the looped data time series and its lags. PACF measures the partial correlation between the time series and its lags, removing the influence of intermediate lags.
[0046] The formula for identifying the autocorrelation function (ACF) is:
[0047] ρ k It is the autocorrelation coefficient of lag k; γ k γ is the autocovariance of lag k; γ0 is the variance of the sequence.
[0048] The formula for identifying the partial autocorrelation function (PACF) is as follows:
[0049] in:
[0050] φk is the partial autocorrelation coefficient of lag k;
[0051] φkj is the partial autocorrelation coefficient of lag j;
[0052] S4: Model estimation: Estimate the model parameters using the maximum likelihood estimation method;
[0053] The formula for finding the maximum value of the following log-likelihood function using the ARIMA model is as follows:
[0054] Where θ is the vector of model parameters; X is the observed data; T is the number of observations; σ² is the variance of the error term; and εt is the residual at time t.
[0055] S5: Model Validation: Use residual analysis to test the effectiveness of the model;
[0056] S6: Prediction: Using models to predict future failures;
[0057] The ARIMA model is represented as ARIMA(p,d,q), where:
[0058] p is the order of the autoregressive term;
[0059] d is the degree of the difference;
[0060] q is the order of the moving average term;
[0061] The calculation formulas for the ARIMA model include:
[0062] The formula for calculating the autoregressive (AR) component is as follows: Where Yt is the observed value at time t; c is a constant term; Φi is the autoregressive coefficient; and t is the white noise error term.
[0063] The formula for calculating the difference part is:
[0064] in It is a difference operator;
[0065] B is the lag operator; BYt = Yt-1;
[0066] d is the degree of the difference;
[0067] The formula for calculating the moving average (MA) component is as follows:
[0068] Where μ is the mean of the time series;
[0069] Θi is the moving average coefficient;
[0070] t and ti are error terms.
[0071] In a preferred embodiment, the client module displays the data collected by the sensing layer module in the form of charts and curves, and allows users to perform loop-closing operations according to system prompts, including closing and opening the circuit breaker; at the same time, the client module also has an early warning function, and issues an early warning message when it detects that the loop-closing conditions are not met, reminding users to pay attention to safety.
[0072] In a preferred embodiment, the data analysis module, in conjunction with the edge computing unit, uses the ARIMA model in time series analysis to perform trend analysis on current, voltage, and phase data, predicting potential future problems, including loop-closing faults in the 0.4kV low-voltage distribution network. The data analysis module displays the data analysis results in the form of charts and curves, making it easy for users to intuitively understand the operating status of the 0.4kV low-voltage distribution network.
[0073] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:
[0074] 1. In this invention, the edge computing unit can process data in real time at the source of data generation, reducing the bandwidth and latency required for data transmission to the cloud or server, thereby achieving rapid response to electrical parameters such as current, voltage, and phase. Since the edge computing unit can perform preliminary data processing locally, only necessary data or analysis results need to be uploaded to the server, which greatly reduces the amount of data transmitted over the network and lowers the network load. Even if the network connection is unstable or interrupted, the system can still maintain basic monitoring and control functions locally by processing data on the edge computing unit, improving system reliability. The edge computing unit can process sensitive data locally, reducing the risk of data transmission over the network and improving data security.
[0075] 2. In this invention, the edge computing unit runs current change rate detection algorithms and ARIMA models for time series analysis. These algorithms enable local fault prediction and anomaly detection, allowing the system to make smarter decisions faster. Custom data processing and analysis algorithms provide greater flexibility. Cost-effectiveness: By processing data at the edge, reliance on a central server is reduced, thereby lowering data storage and computation costs. The edge computing unit can receive control commands from the server and execute loop-closing operations, such as closing and opening circuit breakers, making remote operation possible and improving system manageability. The introduction of the edge computing unit makes the IoT-based low-voltage distribution network loop-closing monitoring system more efficient, reliable, and secure, with better adaptability and economy.
[0076] 3. In this invention, by real-time monitoring of key electrical parameters such as differential pressure and phase angle difference, the system ensures that the power grid states of the two distribution areas are compatible before the loop-closing operation, thereby avoiding loop-closing failure or power grid accidents caused by mismatched electrical parameters. By automatically calculating the loop-closing inrush current, steady-state current, phase angle difference, and distribution transformer load rate difference, the system provides operators with scientific judgment criteria, making the loop-closing operation more accurate and reliable. Operators can understand the power grid status in real time through the remote online monitoring system without on-site operation, improving work efficiency and reducing personnel safety risks. With digital support, the system can quickly respond to power grid changes and adjust the loop-closing operation in a timely manner, thereby improving the operating efficiency and power supply reliability of the power grid. Real-time monitoring and rapid judgment reduce power outage time caused by loop-closing operation, improving the user's power experience. By predicting and analyzing the potential impact of the loop-closing operation, the system can detect potential faults in advance, take preventive measures, and avoid power grid accidents. Automated monitoring and judgment reduce manual intervention and lower power grid maintenance costs. Attached Figure Description
[0077] Figure 1 This is an overall system block diagram of the present invention;
[0078] Figure 2 This is a schematic diagram illustrating the principle of the edge computing unit in this invention. Detailed Implementation
[0079] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0080] Reference Figure 1-2 ,
[0081] The IoT-based low-voltage distribution network loop monitoring system includes: a sensing layer module, a communication network layer module, and an application layer module.
[0082] The perception layer module contains a sensor module and a processor module.
[0083] The communication network layer module contains an internal wireless communication network and a server;
[0084] The application layer module internally includes a client module and a data analysis module.
[0085] The sensor module is responsible for collecting electrical parameter data such as current, voltage, and phase. The sensor module has an edge computing unit inside.
[0086] The processor module processes and analyzes the data collected by the sensors and controls the loop closing operation.
[0087] Communication module: Transmits the data processed by the processor to the network layer.
[0088] The wireless communication network connects the sensing layer module and the application layer module, and is responsible for data transmission.
[0089] The server receives data from the perception layer module, stores, processes, and analyzes it, and transmits the results to the application layer module.
[0090] The client module receives data transmitted from the server and displays it to the user, allowing the user to perform loop merging operations.
[0091] The data analysis module analyzes the data transmitted from the server and provides visualizations.
[0092] The sensor module uses a Hall sensor, which is installed at the output end of the distribution transformer to collect current data in real time.
[0093] The sensor module uses a voltage transformer, which is installed at the output end of the distribution transformer to collect voltage data in real time.
[0094] The sensor module uses a digital phase measuring instrument, which is installed at the output end of the distribution transformer to collect phase data of voltage or current in real time.
[0095] The sensor module is used for sensors in the 0.4kV low-voltage distribution network through a real-time acquisition unit, including high-precision current transformers, voltage transformers and phase sensors, to acquire electrical parameters of the low-voltage outgoing lines of two transformer substations in real time, including voltage difference and phase angle difference.
[0096] The processor module uses an STM32 microcontroller and is responsible for data processing, control logic, and communication functions. It performs filtering, amplification, and conversion on current, voltage, and phase data to remove noise and improve data accuracy and reliability. Based on the loop-closing model and preset conditions, such as a voltage difference of less than 5% and a phase angle difference of less than 2°, the processor module determines whether the loop-closing conditions are met and controls the loop-closing operations, such as closing and opening the circuit breaker.
[0097] The processor module transmits the processed data to the server via the 5G module and receives control commands from the server.
[0098] The communication module transmits the data processed by the processor module to the server and receives control commands from the server.
[0099] The wireless communication network uses the mobile operator's 5G network to connect the sensing layer module and the server to achieve wireless data transmission;
[0100] The server deploys loop closure determination logic, which automatically calculates whether the loop closure conditions are met based on real-time collected data such as differential pressure, phase angle difference, loop closure inrush current, steady-state current, and load rate difference between the two transformers. When the loop closure conditions are not met, the client module can issue an early warning message to remind operators to pay attention to safety.
[0101] The sensor interface of the edge computing unit is responsible for connecting to sensors such as current, voltage, and phase sensors and receiving data collected by the sensors.
[0102] Edge computing units use analog-to-digital converters to convert analog signals collected by sensors into digital signals for subsequent processing.
[0103] Edge computing units use filters to filter digital signals, remove noise, and improve data accuracy.
[0104] The edge computing unit uses a current overload detection algorithm to perform an anomaly detection module, which specifically includes:
[0105] S1: Data Acquisition: The current sensor collects current data and transmits it to the data acquisition module.
[0106] S2: Analog-to-digital conversion: The data acquisition module converts analog current signals into digital signals.
[0107] S3: Filtering: The data processing module filters the digital current signal to remove noise.
[0108] S4: RMS value calculation: The data processing module calculates the RMS value of the current using the same formula as before.
[0109] S5: Current Change Rate Calculation: The data processing module calculates the rate of change of the effective current value between two adjacent sampling points, using the following formula:
[0110] dI_eff=(I_eff(t+Δt)-I_eff(t)) / Δt;
[0111] Where dI_eff is the rate of change of current, in amperes per second (A / s), I_eff(t+Δt) is the effective value of current at time t+Δt, I_eff(t) is the effective value of current at time t, and Δt is the sampling time interval, in seconds (s).
[0112] S6: Threshold judgment: The anomaly detection module compares the calculated current change rate with the preset current change rate threshold.
[0113] S7: Warning: If the rate of change of current exceeds the threshold, the warning module issues a red warning and transmits the warning information to the server and client through the communication module.
[0114] The edge computing unit uses the ARIMA model in time series analysis to predict loop-closed faults in IoT low-voltage distribution networks. The calculation process includes:
[0115] S1: Data Collection: Collect historical fault data of IoT low-voltage distribution network loop closure;
[0116] S2: Data preprocessing: Stabilize and remove outliers from the loop-closed data of the low-voltage distribution network;
[0117] The first-order difference formula for stabilizing and removing outliers from low-voltage distribution network loop data is as follows:
[0118] X′ t =X t -X t-1 ;where X t The value of the time series at time t, i.e., the observation value at the current time point of the loop closure data; X t-1 The value of the looped data time series at time t-1, i.e., the observation value at the previous looped data time point;
[0119] The L-lag operator, when applied to time series data with loops, L k X t =X t-k That is, L raised to the power of k acts on X t The value at time tk;
[0120] d: The order of differencing, indicating the number of times the time series of the loop data is differencing is performed to achieve stationarity.
[0121] S3: Model Identification: The parameters (p, d, q) of the ARIMA model are determined using ACF and PACF plots. ACF measures the correlation between the looped data time series and its lags. PACF measures the partial correlation between the time series and its lags, removing the influence of intermediate lags.
[0122] The formula for identifying the autocorrelation function (ACF) is:
[0123] ρ k It is the autocorrelation coefficient of lag k; γ k γ is the autocovariance of lag k; γ0 is the variance of the sequence.
[0124] The formula for identifying the partial autocorrelation function (PACF) is as follows:
[0125] in:
[0126] φk is the partial autocorrelation coefficient of lag k;
[0127] φkj is the partial autocorrelation coefficient of lag j;
[0128] S4: Model estimation: Estimate the model parameters using the maximum likelihood estimation method;
[0129] The formula for finding the maximum value of the following log-likelihood function using the ARIMA model is as follows:
[0130] Where θ is the vector of model parameters; X is the observed data; T is the number of observations; σ² is the variance of the error term; and εt is the residual at time t.
[0131] S5: Model Validation: Use residual analysis to test the effectiveness of the model;
[0132] S6: Prediction: Using models to predict future failures;
[0133] The ARIMA model is represented as ARIMA(p,d,q), where:
[0134] p is the order of the autoregressive term;
[0135] d is the degree of the difference;
[0136] q is the order of the moving average term;
[0137] The calculation formulas for the ARIMA model include:
[0138] The formula for calculating the autoregressive (AR) component is as follows: Where Yt is the observed value at time t; c is a constant term; Φi is the autoregressive coefficient; and t is the white noise error term.
[0139] The formula for calculating the difference part is:
[0140] in It is a difference operator;
[0141] B is the lag operator; BYt = Yt-1;
[0142] d is the degree of the difference;
[0143] The formula for calculating the moving average (MA) component is as follows:
[0144] Where μ is the mean of the time series;
[0145] Θi is the moving average coefficient;
[0146] t and ti are error terms.
[0147] The client module displays the data collected by the sensing layer module in the form of charts and graphs, and allows users to perform loop-closing operations according to system prompts, such as closing and opening the circuit breaker. Simultaneously, the client module also has an early warning function; when it detects that the loop-closing conditions are not met, it issues a warning message to remind the user to pay attention to safety.
[0148] The data analysis module, in conjunction with the edge computing unit, uses the ARIMA model in time series analysis to perform trend analysis on data such as current, voltage, and phase, predicting potential future problems, including loop-closing faults in the 0.4kV low-voltage distribution network. The data analysis module displays the results in the form of charts and curves, allowing users to intuitively understand the operating status of the 0.4kV low-voltage distribution network.
[0149] In this invention, the edge computing unit can process data in real time at the source of data generation, reducing the bandwidth and latency required for data transmission to the cloud or server, thereby achieving rapid response to electrical parameters such as current, voltage, and phase. Since the edge computing unit can perform preliminary data processing locally, only necessary data or analysis results need to be uploaded to the server, which greatly reduces the amount of data transmitted over the network and lowers network load. Even if the network connection is unstable or interrupted, the system can still maintain basic monitoring and control functions locally by processing data on the edge computing unit, improving system reliability. The edge computing unit can process sensitive data locally, reducing the risk of data transmission over the network and improving data security.
[0150] In this invention, the edge computing unit runs current change rate detection algorithms and ARIMA models for time series analysis. These algorithms enable local fault prediction and anomaly detection, allowing the system to make smarter decisions faster. Custom data processing and analysis algorithms provide greater flexibility. Cost-effectiveness: By processing data at the edge, reliance on a central server is reduced, thereby lowering data storage and computation costs. The edge computing unit can receive control commands from the server and execute loop-closing operations, such as closing and opening circuit breakers, making remote operation possible and improving system manageability. The introduction of the edge computing unit makes the IoT-based low-voltage distribution network loop-closing monitoring system more efficient, reliable, and secure, with better adaptability and economy.
[0151] In this invention, by monitoring key electrical parameters such as differential voltage and phase angle difference in real time, the system ensures that the power grid states of the two distribution areas are compatible before the loop-closing operation, thereby avoiding loop-closing failure or power grid accidents caused by mismatched electrical parameters. By automatically calculating the loop-closing inrush current, steady-state current, phase angle difference, and distribution transformer load rate difference, the system provides operators with scientific judgment criteria, making the loop-closing operation more accurate and reliable. Operators can understand the power grid status in real time through the remote online monitoring system without on-site operation, improving work efficiency and reducing personnel safety risks. With digital support, the system can quickly respond to changes in the power grid and adjust the loop-closing operation in a timely manner, thereby improving the operating efficiency and power supply reliability of the power grid. Real-time monitoring and rapid judgment reduce the power outage time caused by loop-closing operation and improve the user's power experience. By predicting and analyzing the potential impact of the loop-closing operation, the system can detect potential faults in advance, take preventive measures, and avoid power grid accidents. Automated monitoring and judgment reduce manual intervention and lower the maintenance cost of the power grid.
[0152] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.
[0153] The foregoing description enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A low-voltage distribution network closed-loop monitoring system based on IoT, characterized in that: The system includes: a perception layer module, a communication network layer module, and an application layer module; The perception layer module is internally equipped with a sensor module and a processor module; The communication network layer module is internally equipped with a wireless communication network and a server. The application layer module internally includes a client module and a data analysis module; The sensor module is responsible for collecting current, voltage, and phase electrical parameter data, and an edge computing unit is set inside the sensor module; The processor module processes and analyzes the data collected by the sensor and controls the loop closing operation; Communication module: transmits the data processed by the processor to the communication network layer module; The wireless communication network connects the perception layer module and the application layer module, and is responsible for data transmission; The server receives data from the perception layer module, stores, processes, and analyzes it, and transmits the results to the application layer module. The client module receives data transmitted from the server and displays it to the user, allowing the user to perform loop merging operations; The data analysis module analyzes the data transmitted from the server and provides a visual representation. The edge computing unit uses the ARIMA model in time series analysis to predict loop-closed faults in the low-voltage distribution network of the Internet of Things (IoT). The calculation process includes: S1: Data Collection: Collect historical fault data of the low-voltage distribution network loop closure of IoT; S2: Data preprocessing: Stabilize and remove outliers from the loop-closed data of the low-voltage distribution network; The first-order difference formula for stabilizing and removing outliers from low-voltage distribution network loop data is as follows: ;where X t The value of the time series at time t, i.e., the observation value at the current time point of the loop closure data; X t−1 The value of the looped data time series at time t−1, i.e., the observation value at the previous looped data time point; The L-lag operator, when applied to time series data with loops, L k X t =X t−k That is, L raised to the power of k acts on X t The value at time t−k; d: The order of differencing, indicating the number of times the time series of the loop data is differencing is performed to achieve stationarity. S3: Model Identification: The parameters (p, d, q) of the ARIMA model are determined using ACF and PACF plots, where ACF measures the correlation between the looped data time series and its lagged values; PACF measures the partial correlation between the time series and its lagged values, removing the influence of intermediate lagged terms. The formula for identifying the autocorrelation function (ACF) is: ;ρ k It is the autocorrelation coefficient of lag k; γ k γ is the autocovariance of lag k; γ0 is the variance of the sequence. The formula for identifying the partial autocorrelation function (PACF) is as follows: ;in: ϕkk is the partial autocorrelation coefficient of lag k; ϕkj is the partial autocorrelation coefficient of lag j; S4: Model estimation: Estimate the model parameters using the maximum likelihood estimation method; The formula for finding the maximum value of the following log-likelihood function using the ARIMA model is as follows: ： Where θ is a vector of model parameters; X is the observed data; T is the number of observations; σ 2 It is the variance of the error term; It is the residual at time t; S5: Model Validation: Use residual analysis to test the effectiveness of the model; S6: Prediction: Using models to predict future failures; The ARIMA model is represented as ARIMA(p, d, q), where: p is the order of the autoregressive term; d is the degree of the difference; q is the order of the moving average term; The calculation formulas for the ARIMA model include: The formula for calculating the autoregressive (AR) component is as follows: , where Yt is the observed value at time point t; c is the constant term; Φi is the autoregressive coefficient; It is a white noise error term; The formula for calculating the difference part is: ; Where ∇ is the difference operator; B is the lag operator; BY t =Y t−1 ; d is the degree of the difference; The formula for calculating the moving average (MA) is as follows: ; Where μ is the mean of the time series; Θi is the moving average coefficient; and This is the error term.
2. The IoT-based low-voltage distribution network loop monitoring system as described in claim 1, characterized in that: The sensor module uses a Hall sensor, which is installed at the output end of the distribution transformer to collect current data in real time. The sensor module uses a voltage transformer, which is installed at the output end of the distribution transformer to collect voltage data in real time. The sensor module uses a digital phase measuring instrument, which is installed at the output end of the distribution transformer to collect phase data of voltage or current in real time. The sensor module is used as a sensor for the 0.4kV low-voltage distribution network through a real-time acquisition unit, including a high-precision current transformer, a voltage transformer, and a phase sensor, to acquire electrical parameters of the low-voltage outgoing lines of the two distribution areas in real time, including voltage difference and phase angle difference.
3. The IoT-based low-voltage distribution network loop monitoring system as described in claim 1, characterized in that: The processor module uses an STM32 microcontroller to handle data processing, control logic, and communication functions. It filters, amplifies, and converts current, voltage, and phase data to remove noise and improve data accuracy and reliability. Based on the loop-closing model and preset conditions, including a voltage difference of less than 5% and a phase angle difference of less than 2°, the processor module determines whether the loop-closing conditions are met and controls the loop-closing operation, including closing and opening the circuit breaker. The processor module transmits the processed data to the server via the 5G module and receives control commands from the server.
4. The IoT-based low-voltage distribution network loop monitoring system as described in claim 1, characterized in that: The communication module transmits the data processed by the processor module to the server and receives control commands from the server.
5. The IoT-based low-voltage distribution network loop monitoring system as described in claim 1, characterized in that: The wireless communication network uses the mobile operator's 5G network to connect the perception layer module and the server, enabling wireless data transmission. The server is equipped with a loop closure determination logic. Based on real-time collected data such as differential pressure, phase angle difference, loop closure inrush current, steady-state current, and load rate difference between the two transformers, it automatically calculates whether the loop closure conditions are met. When the loop closure conditions are not met, the client module can issue an early warning message to remind the operators to pay attention to safety.
6. The IoT-based low-voltage distribution network loop monitoring system as described in claim 1, characterized in that: The sensor interface of the edge computing unit is responsible for connecting to current, voltage, and phase sensors and receiving data collected by the sensors. The edge computing unit uses an analog-to-digital converter to convert the analog signals collected by the sensors into digital signals for subsequent processing. The edge computing unit filters digital signals through filters to remove noise and improve data accuracy.
7. The IoT-based low-voltage distribution network loop monitoring system as described in claim 1, characterized in that: The edge computing unit includes an anomaly detection module based on a current overload detection algorithm, specifically comprising: S1: Data Acquisition: The current sensor collects current data and transmits it to the data acquisition module; S2: Analog-to-digital conversion: The data acquisition module converts analog current signals into digital signals; S3: Filtering: The data processing module filters the digital current signal to remove noise; S4: RMS value calculation: The data processing module calculates the RMS value of the current, using the same formula as described above; S5: Current Change Rate Calculation: The data processing module calculates the rate of change of the effective current value between two adjacent sampling points, using the following formula: dI_eff = (I_eff(t+Δt) - I_eff(t)) / Δt; Where dI_eff is the rate of change of current, in amperes per second (A / s), I_eff(t+Δt) is the effective value of current at time t+Δt, I_eff(t) is the effective value of current at time t, and Δt is the sampling time interval, in seconds (s). S6: Threshold judgment: The anomaly detection module compares the calculated current change rate with the preset current change rate threshold. S7: Warning: If the rate of change of current exceeds the threshold, the warning module issues a red warning and transmits the warning information to the server and client through the communication module.
8. The IoT-based low-voltage distribution network loop monitoring system as described in claim 1, characterized in that: The client module displays the data collected by the sensing layer module in the form of charts and curves, and allows users to perform loop-closing operations according to system prompts, including closing and opening the circuit breaker. At the same time, the client module also has an early warning function. When it detects that the loop-closing conditions are not met, it issues an early warning message to remind the user to pay attention to safety.
9. The IoT-based low-voltage distribution network loop monitoring system as described in claim 1, characterized in that: The data analysis module, in conjunction with the edge computing unit, uses the ARIMA model in time series analysis to perform trend analysis on current, voltage, and phase data, predicting potential future problems, including loop-closing faults in the 0.4kV low-voltage distribution network; the data analysis module displays the data analysis results in the form of charts and curves.