Intelligent power control system based on Internet of Things

Abstract

The invention relates to the technical field of intelligent power control, and discloses an intelligent power control system based on the Internet of Things, and the system comprises a monitoring terminal, a power execution device and a cloud platform control center which are in communication connection. The monitoring terminal collects current and voltage signals; the power execution equipment receives an instruction to switch the switch state; a load monitoring unit of the cloud platform control center determines a fault section and generates and maps a load characteristic curve, a prediction processing unit corrects a power supply path to generate an execution strategy, and a steady state regulation and control unit and a transient state regulation and control unit of the power control unit control execution operation under normal and abnormal working conditions respectively. The monitoring terminal comprises a data acquisition device and the like, and the power execution equipment comprises a circuit breaker and other modules. The system realizes intelligent monitoring, fault positioning and load management and protection of a power system, and improves reliability and efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power control, and in particular to an intelligent power control system based on the Internet of Things. Background Art

[0002] With the rapid development of IoT technology and the increasing demand for intelligent power systems, traditional power control systems are no longer able to meet the reliability, efficiency, and intelligent management requirements of modern power grids. In traditional power control systems, power line monitoring often suffers from a lack of real-time performance. For example, the inability to accurately and promptly collect current and voltage signals leads to delayed judgment of the operating status of power equipment and difficulty in promptly identifying potential faults. Furthermore, there is a lack of effective methods for identifying faulty sections. Relying solely on manual experience or simple electrical parameter analysis is not only inefficient but also prone to misjudgment, impacting the normal operation of the power system.

[0003] Traditional systems struggle to effectively control and regulate abnormal loads. When load anomalies occur, they can't accurately keep them within safe thresholds, potentially leading to equipment overload, shortening equipment lifespan, or even causing more serious power failures. Furthermore, the lack of in-depth analysis and prediction of load characteristics prevents timely adjustments to power supply paths based on load fluctuations, compromising the stability and reliability of power supply.

[0004] Furthermore, the communication connections between devices in traditional power control systems are not flexible and efficient enough. Limitations in communication protocols lead to delayed and unstable data transmission, hindering the cloud platform control center's real-time control and command transmission of power actuators. Furthermore, when faced with abnormal operating conditions, traditional systems' protection operations lack intelligent strategies and optimization algorithms, making it impossible to quickly and accurately implement effective protection measures, making it difficult to ensure the safe and stable operation of the power system.

[0005] As power grids continue to expand and become increasingly complex, higher demands are placed on the intelligence, automation, and reliability of power control systems. Therefore, there is an urgent need for an intelligent power control system based on IoT technology to address the many issues existing in traditional systems and enable comprehensive, real-time monitoring of the power system, precise fault location and load management, as well as efficient, intelligent control and protection operations. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent power control system based on the Internet of Things to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent power control system based on the Internet of Things, the system comprising:

[0008] A monitoring terminal, a power execution device, and a cloud platform control center, wherein the monitoring terminal and the power execution device are respectively connected to the cloud platform control center for communication, and the cloud platform control center includes a load monitoring unit, a prediction processing unit, and a power control unit;

[0009] The monitoring terminal is used to collect current signals and voltage signals in the power line in real time;

[0010] The power execution device is used to receive the command signal from the cloud platform control center to switch the switch state;

[0011] The load monitoring unit is used to record the current mutation location and voltage recovery location of the power execution device during operation, determine the fault section based on the current mutation location and voltage recovery location, control the power execution device to maintain a preset safety threshold for abnormal load and record fluctuation parameters, generate a load characteristic curve based on the fluctuation parameters, and perform load prediction mapping on the power grid topology;

[0012] The prediction processing unit is used to modify the power supply path of the power grid topology map that completes the load prediction mapping and generate an execution strategy;

[0013] The power control unit includes a steady-state control unit and a transient control unit. The steady-state control unit is used to control the power execution device to execute a reference power output when the power execution device is in normal working conditions; the transient control unit is used to control the power execution device to execute a protection operation within the fault section according to the execution strategy when the power execution device detects an abnormal working condition.

[0014] Preferably, the monitoring terminal includes a terminal housing and a data acquisition device, a waveform analysis device, a diagnostic unit and a communication unit arranged in the housing;

[0015] The data acquisition device is used to synchronously acquire three-phase current waveform and voltage waveform data;

[0016] The waveform analysis device is used to extract the harmonic characteristic components of the current waveform;

[0017] The diagnostic unit is used to calculate the similarity between the current harmonic characteristic component and the historical standard waveform based on the LSTM neural network, determine the deviation of the operating state of the power equipment, and trigger an early warning signal according to the calculation result;

[0018] The communication unit is used to upload the collected data to the cloud platform control center via the LoRaWAN protocol.

[0019] Preferably, the power execution equipment includes a cabinet and a circuit breaker module, a voltage detection module, a command execution module and a communication relay module arranged in the cabinet.

[0020] Preferably, recording the current mutation position and voltage recovery position of the power execution device during operation includes:

[0021] When the power execution device detects a sudden change in current, it records the phase angle data at the time the sudden change occurs, continuously monitors the voltage recovery critical point through the voltage detection module, and records the cycle count at the recovery time.

[0022] Preferably, determining the fault section according to the current mutation position and the voltage recovery position includes:

[0023] Generate mutation coordinates and recovery coordinates based on phase angle data and cycle counts;

[0024] The mutation coordinates, recovery coordinates and monitoring terminal positions are topologically connected to form a closed interval, which is marked as a fault section.

[0025] Preferably, the controlling of the power execution device to maintain a preset safety threshold for abnormal load and record fluctuation parameters, generating a load characteristic curve according to the fluctuation parameters and performing load prediction mapping on the power grid topology diagram comprises the following steps:

[0026] Controlling the power execution equipment to adjust the output power of the fault section and obtaining amplitude parameters in real time through the voltage detection module;

[0027] A preset safety threshold range is set. If the amplitude parameter exceeds the preset safety threshold range, the power execution equipment is controlled to maintain the preset safety threshold operation;

[0028] Continuously record the power output value of the power execution equipment to form a set of fluctuation parameters;

[0029] Based on the fluctuation parameter set, the cubic spline interpolation algorithm is used to generate the load dynamic characteristic curve;

[0030] Extracting a number of key feature points at equal time intervals from the load dynamic characteristic curve, wherein the number of the key feature points is proportional to the duration of the curve;

[0031] The load distribution mapping of the power grid topology is annotated based on the extracted key feature points.

[0032] Preferably, the power supply path correction of the power grid topology map that has completed the load forecast mapping includes logically shielding the preset power supply path in the power grid topology map that overlaps with the overload section after marking the load distribution mapping of the power grid topology map.

[0033] Preferably, when the power execution device detects an abnormal working condition, controlling the power execution device to perform a protection operation in the fault section according to the execution strategy includes:

[0034] S1. Select the access point of the fault section with the least operation steps based on the next power supply node to be switched;

[0035] S2. Control the power execution device to switch to the access point and perform phase synchronization;

[0036] S3, controlling the power execution device to switch into the fault section from the access point and perform a switching action according to the power supply path in the execution strategy;

[0037] S4. Obtain the output parameters of the power execution equipment in real time and optimize and compensate the output parameters using a dynamic programming algorithm;

[0038] S5. After each continuous power supply operation is completed in the fault section, the power execution device is controlled to suspend output and exit the fault section, and S1 is re-executed.

[0039] Preferably, said S1 comprises the following steps:

[0040] The A* algorithm is used to calculate the feasible access points for each fault section and to provide a comprehensive score based on the operation steps and electrical distance parameters.

[0041] Based on the comprehensive scoring results, select the access point to the fault section with the least operation steps and the shortest electrical distance.

[0042] Preferably, the comprehensive scoring based on the operation steps and electrical distance parameters includes:

[0043] Set the operation step weight coefficient to α and the electrical distance weight coefficient to β;

[0044] Calculate the score value S = α × the inverse of the operation steps + β × the inverse of the electrical distance;

[0045] The power control unit is used to select the access point of the fault section with the highest score S.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The IoT-based intelligent power control system provided by this invention achieves comprehensive intelligent control of the power system through the collaborative operation of monitoring terminals, power execution equipment, and a cloud platform control center. The monitoring terminals collect current and voltage signals from power lines in real time, providing accurate data support for system analysis and decision-making. The load monitoring unit in the cloud platform control center can accurately determine the fault section by recording the location of current mutations and voltage recovery. This is more efficient and accurate than traditional methods, avoiding the errors and delays associated with manual judgment.

[0048] In terms of abnormal load handling, the system controls power actuators to maintain preset safety thresholds for abnormal loads, effectively preventing equipment overload and extending its service life. Furthermore, it generates load characteristic curves based on fluctuation parameters and performs load forecasting mapping on the grid topology, enabling the system to gain a deeper understanding of load fluctuation patterns and providing a reliable basis for subsequent power supply path corrections.

[0049] The prediction processing unit corrects the power supply path of the power grid topology map that completes the load prediction mapping and generates an execution strategy. It can adjust the power supply path in time according to the load distribution, improve the stability and reliability of the power supply, and avoid power supply problems caused by unreasonable power supply paths.

[0050] The steady-state control unit and transient control unit of the power control unit function under normal and abnormal operating conditions, respectively. The steady-state control unit ensures that the power actuators stably output the reference power under normal operating conditions, thus guaranteeing the quality of power supply. When an abnormal operating condition is detected, the transient control unit performs protection operations within the fault section according to the execution strategy. Through a series of optimization steps, such as selecting the access point for the fault section with the fewest operation steps, performing phase synchronization, and optimizing compensation using a dynamic programming algorithm, it achieves rapid response and effective handling of the fault, minimizing the impact of the fault on the power system.

[0051] The LSTM neural network in the monitoring terminal is used to determine the degree of deviation in the operating status of power equipment. This can detect potential equipment failures in advance and trigger early warning signals, improving the safety and reliability of the system. The communication unit uploads collected data via the LoRaWAN protocol, ensuring stable and efficient data transmission.

[0052] The various modules within the power execution equipment work together to accurately record current fluctuations and voltage recovery, providing critical data for fault identification. During protection operations, the A* algorithm calculates access points and comprehensively scores them, selecting the optimal access point, improving the efficiency and accuracy of protection operations. A dynamic programming algorithm optimizes and compensates for output parameters, further ensuring the stability and reliability of power output. Through the coordinated operation and intelligent design of its various components, this system improves the reliability, efficiency, and safety of the power system, enabling comprehensive intelligent management and control of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a working principle diagram of the intelligent power control system based on the Internet of Things according to the present invention;

[0054] Figure 2 This is the working principle diagram of the monitoring terminal;

[0055] Figure 3Flowchart for the generation of load prediction maps. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] See also Figure 1-Figure 3 The present invention relates to an intelligent power control system based on the Internet of Things. The system includes a monitoring terminal, a power execution device, and a cloud platform control center. The monitoring terminal and the power execution device are respectively connected to the cloud platform control center for communication. The cloud platform control center includes a load monitoring unit, a prediction processing unit, and a power control unit. Specifically, the system includes the following steps:

[0058] The monitoring terminal is used to collect the current signal and voltage signal in the power line in real time. The power execution device is used to receive the command signal from the cloud platform control center to switch the switch state. The load monitoring unit is used to record the current mutation position and voltage recovery position of the power execution device during operation, determine the fault section according to the current mutation position and voltage recovery position, control the power execution device to maintain the preset safety threshold operation for abnormal loads and record the fluctuation parameters, generate the load characteristic curve according to the fluctuation parameters and perform load prediction mapping on the power grid topology. The prediction processing unit is used to correct the power supply path of the power grid topology map that has completed the load prediction mapping and generate an execution strategy. The power control unit includes a steady-state control unit and a transient control unit. The steady-state control unit is used to control the power execution device to execute the reference power output when it is in normal working conditions; the transient control unit is used to control the power execution device to execute protection operations in the fault section according to the execution strategy when the power execution device detects an abnormal working condition.

[0059] The present invention will be further described below in conjunction with Examples 1 to 5:

[0060] Embodiment 1:

[0061] In this embodiment, the monitoring terminal serves as the system's front-end data acquisition device. Its structure and functionality are crucial to the operation of the entire intelligent power control system. The monitoring terminal includes a terminal housing, which provides physical protection and a mounting platform for the internal functional modules. Its material and structural design must meet the protection requirements of the power environment, such as dustproofing, moisture-proofing, and electromagnetic interference resistance, to ensure stable operation of the internal modules in the complex power field environment.

[0062] A data acquisition device is installed inside the shell. The core function of this device is to synchronously acquire three-phase current waveform and voltage waveform data. Specifically, it realizes the real-time acquisition of current and voltage signals in three-phase power lines by deploying high-precision current sensors and voltage sensors. These sensors must have high sensitivity and precise linearity to capture subtle changes in power signals and ensure that the collected waveform data truly reflects the actual operating status of the power line. The data acquisition device must also be equipped with corresponding signal conditioning circuits to amplify, filter and other pre-processing of the weak electrical signals output by the sensors to eliminate noise interference and improve data reliability. At the same time, in order to achieve synchronous acquisition of three-phase current and voltage waveforms, a high-precision clock synchronization circuit is installed inside the device to ensure the time consistency of data acquisition of each phase, which is of great significance for subsequent waveform analysis and fault diagnosis.

[0063] The waveform analysis device is responsible for in-depth processing of the current waveform output by the data acquisition device. Its main task is to extract the harmonic characteristic components of the current waveform. The device uses digital signal processing technology to first perform analog-to-digital conversion on the current waveform, converting the analog signal into a digital signal for easy computer processing. Then, it uses algorithms such as the Fast Fourier Transform (FFT) to perform spectral analysis on the digital signal, converting the current waveform in the time domain into a spectral distribution in the frequency domain, thereby separating the various harmonic components. By calculating the characteristic parameters such as the amplitude and phase of each harmonic, a set of harmonic characteristic components is formed. During the extraction process, the waveform analysis device must also have anti-interference capabilities, and be able to identify and eliminate false harmonic components caused by external interference or equipment abnormalities, to ensure that the extracted harmonic characteristic components accurately reflect the actual operating conditions of the power equipment.

[0064] The diagnostic unit is the core decision-making module of the monitoring terminal. Based on an LSTM neural network, it calculates the similarity between the current harmonic characteristic components and historical standard waveforms to determine the degree of deviation in the operating status of power equipment. As a special type of recurrent neural network, the LSTM neural network effectively processes time series data and captures long-term dependencies within the data. Before implementation, the LSTM neural network must be trained. A large amount of historical standard waveform data is fed into the network. The network parameters are adjusted using an optimization algorithm to enable the network to learn the distribution patterns of harmonic characteristics under normal operating conditions. When the current harmonic characteristic component is detected, the diagnostic unit inputs it into the trained LSTM neural network and calculates the similarity between the current characteristic and the historical standard characteristic. This similarity calculation can use methods such as Euclidean distance and cosine similarity. By setting a reasonable threshold, when the similarity falls below the threshold, it indicates a deviation in the operating status of the power equipment, and the diagnostic unit triggers a corresponding warning signal. This warning signal can be in the form of an audible or visual alarm or a digital output for linkage with other systems.

[0065] The communication unit uploads the data collected and processed by the monitoring terminals to the cloud platform control center. It uses the LoRaWAN protocol for data transmission. The LoRaWAN protocol is a low-power wide area network communication technology with advantages such as long transmission distance, low power consumption, and large network capacity. It is suitable for data communication between decentralized monitoring terminals and a centralized control center in power monitoring scenarios. The communication unit includes a LoRaWAN wireless transceiver module, an antenna, and related signal processing circuitry. During data transmission, the communication unit first packages and encapsulates the collected three-phase current and voltage waveform data, harmonic characteristic components, and diagnostic results. This information is then transmitted via the LoRaWAN wireless link to the base station, which then forwards it to the cloud platform control center. To ensure data transmission reliability, the communication unit also requires a data verification and retransmission mechanism. If a data transmission error is detected, it can automatically request a retransmission to ensure that the data received by the cloud platform control center is complete and accurate.

[0066] Furthermore, the monitoring terminal's functional modules exchange data and transmit control commands via an internal bus, ensuring coordinated operation of the entire terminal system. For example, the data acquisition device transmits collected data to the waveform analysis device for processing. The harmonic characteristic components processed by the waveform analysis device are then transmitted to the diagnostic unit for status assessment. The diagnostic unit's results are uploaded to the cloud platform control center via the communication unit. Simultaneously, the cloud platform control center can also send control commands to the monitoring terminal through the communication unit to adjust the monitoring terminal's operating mode.

[0067] In practical applications, monitoring terminals can be installed at key nodes of power lines, such as substation outlets, the low-voltage side of distribution transformers, and critical load branches, enabling real-time monitoring of different levels of the power system. By collecting current and voltage signals in real time, extracting harmonic characteristics, and combining them with intelligent diagnosis using LSTM neural networks, they can promptly detect potential faults in power equipment, such as transformer core saturation, motor winding faults, and abnormalities in power electronics. This provides strong support for condition-based maintenance and fault warnings for the power system. Furthermore, monitoring data is uploaded to the cloud platform control center via the LoRaWAN protocol, enabling operations and maintenance personnel to remotely monitor the power system's operating status in real time, enhancing the system's intelligent management capabilities.

[0068] Example 2:

[0069] In this embodiment, the power actuator device, as the execution unit of the intelligent power control system, undertakes the critical task of receiving commands and implementing power control. Its structure and functionality directly impact the system's ability to regulate power lines. The power actuator device comprises a cabinet, which provides physical support and protection for the various functional modules within. Its design must comply with power equipment installation specifications, possessing excellent mechanical strength and protective properties, and be adaptable to various installation environments, such as indoor distribution cabinets and outdoor box-type substations. The cabinet's internal layout must be rationally planned to ensure secure and reliable electrical connections between modules while facilitating maintenance and repair.

[0070] A circuit breaker module is installed inside the cabinet. This module is one of the core components of the power execution equipment and is mainly used to realize the on-off control of power lines. The circuit breaker module uses high-performance circuit breaker components with rapid opening and closing capabilities and reliable arc extinguishing performance. It can cut off the fault current in a short time and protect the power system equipment. The drive mechanism of the circuit breaker module adopts electric or electromagnetic drive mode, and realizes the switching action of the circuit breaker by receiving the control signal from the instruction execution module. To ensure the accuracy and reliability of the circuit breaker operation, a position feedback device is installed inside the module, which can monitor the opening and closing status of the circuit breaker in real time and feedback the status information to the instruction execution module.

[0071] The voltage detection module monitors voltage conditions in power lines in real time, providing data support for recording the locations of current surges and voltage recovery. This module consists of a voltage sensor and a signal processing circuit. The voltage sensor utilizes a high-precision voltage transformer, which accurately reflects voltage changes in the power lines. The signal processing circuit conditions the voltage signal output by the sensor, including amplification, filtering, and analog-to-digital conversion, converting the analog voltage signal into a digital signal for subsequent processing and analysis. The sampling frequency of the voltage detection module must meet the requirements of dynamic power system monitoring and be able to capture instantaneous voltage changes, laying the foundation for accurate recording of current surges and voltage recovery.

[0072] The instruction execution module is the bridge between the power execution equipment and the cloud platform control center, and is used to receive and execute the instruction signals sent by the cloud platform control center. This module contains a communication interface circuit and an instruction parsing and processing unit. The communication interface circuit supports the communication protocol with the cloud platform control center and can accurately receive instruction signals. The instruction parsing and processing unit parses and verifies the received instruction signals, and generates corresponding control signals based on the type and content of the instructions to control the operation of components such as the circuit breaker module and the voltage detection module. At the same time, the instruction execution module is also responsible for collecting the operating status information of the power execution equipment, such as the circuit breaker status and voltage detection data, and packaging this information and uploading it to the cloud platform control center through the communication relay module.

[0073] The communication relay module is used to establish communication connections between power execution devices, realize data relay transmission, and improve the communication reliability and coverage of the system. This module adopts wireless or wired communication methods, such as ZigBee, WiFi, Ethernet, etc., and can select the appropriate communication method according to the actual application scenario. The communication relay module contains a transceiver, an antenna, and a relay processing unit. The transceiver is responsible for sending and receiving signals, and the relay processing unit amplifies, shapes, and forwards the received signals to ensure the quality of the signal during transmission. When a power execution device receives instructions from the cloud platform control center or data from other devices, the communication relay module can relay this information to other related devices to realize two-way transmission and sharing of data.

[0074] When a power actuator detects a sudden change in current, its internal detection mechanism activates and records the phase angle data at the moment of the change. This detection is typically achieved through real-time monitoring and analysis of the current signal. A sudden change is identified when parameters such as the current amplitude and rate of change exceed a set threshold. The recording of phase angle data requires integration with the power system's synchronized clock to ensure accurate phase information at the moment of the change. This is crucial for subsequently determining the location of the faulty section within the power line.

[0075] After recording the phase angle data at the moment of the current mutation, the voltage detection module continuously monitors the voltage recovery critical point and records the cycle count at the moment of recovery. The voltage recovery critical point is determined based on the changing characteristics of the voltage signal. When the voltage recovers from the fault state and reaches the set recovery threshold, it is determined to be the voltage recovery critical point. The cycle count is recorded based on the power system's power frequency cycle. By recording the cycle count at the moment of recovery, the position of voltage recovery within the power frequency cycle can be accurately determined. Combined with the phase angle data at the moment of the current mutation, this provides a precise time reference for generating mutation coordinates and recovery coordinates.

[0076] The functional modules of the power execution equipment exchange data and transmit control commands via an internal communication bus, forming a coordinated whole. For example, the voltage detection module transmits real-time voltage data to the command execution module, which controls the circuit breaker module based on commands and voltage data from the cloud platform control center. The communication relay module is responsible for transmitting status information and data from each module to the external communication network, enabling information exchange with the cloud platform control center.

[0077] In actual applications, power execution equipment can be installed at various nodes of the power line, such as substations, distribution lines, user terminals, etc., to achieve hierarchical control of the power system. Through the switching action of the circuit breaker module, the power line can be switched on and off according to the instructions of the cloud platform control center to achieve load transfer and isolation of the fault section. The real-time monitoring function of the voltage detection module can provide the system with accurate voltage data, support the detection and recording of current mutations and voltage recovery, and provide data support for the location of the fault section and load characteristic analysis. The setting of the communication relay module enables the power execution equipment to form an interconnected network, improve the communication reliability and response speed of the system, ensure that when an abnormal situation occurs in the power system, the protection operation can be quickly executed, and ensure the safe and stable operation of the power system.

[0078] Example 3:

[0079] In this embodiment, determining the fault section based on the current mutation location and voltage recovery location and handling abnormal loads are key steps in the intelligent power control system to achieve accurate fault location and load management. Taking a certain city's power distribution network as an example, when a 10kV distribution line experiences a transient short circuit fault, the power execution equipment detects a sudden increase in current and begins recording the current mutation location and voltage recovery location. The specific process is as follows:

[0080] The current detection unit in the power execution equipment monitors the line current in real time. When the current value suddenly rises from about 200A during normal operation to 1500A, the current mutation detection mechanism is triggered. At this time, the device immediately records the phase angle data at the moment of the mutation, assuming that this moment corresponds to a 30° phase angle of the power frequency voltage. At the same time, the voltage detection module continuously monitors the line voltage. After the short-circuit fault is removed, the voltage begins to recover from near zero. When the voltage recovers to 90% of the rated voltage, it is determined to be the voltage recovery critical point, and the cycle count at the recovery moment is recorded at this time. Assuming that the power system frequency is 50Hz and each cycle is 20ms, it takes 3 complete cycles from the current mutation to the voltage recovery, that is, the cycle count at the recovery moment is 3.

[0081] Based on the aforementioned phase angle data and cycle counts, mutation coordinates and recovery coordinates are generated. The mutation coordinates are determined by integrating the power line topology into the phase angle data, converting it into a physical location on the line. For example, in this distribution line, the phase angle at the time of the current mutation is known to be 30°. Combining the line length and phase propagation characteristics, the mutation location can be determined to be 2.5 km from the substation on Line A. The recovery coordinates, based on the cycle count and the phase at the time of voltage recovery, determine that the voltage recovery location is 2.7 km from the substation on the same Line A.

[0082] Next, the mutation coordinates, recovery coordinates, and monitoring terminal locations are topologically connected to form a closed interval. In this example, the monitoring terminals are installed at the outgoing line end of the substation and at two branch points in the middle of the line. The monitoring terminal 2 km away from the substation detects a current mutation signal, and the monitoring terminal 3 km away from the substation detects a voltage recovery signal. The mutation coordinates (at 2.5 km), the recovery coordinates (at 2.7 km), and the locations of the two monitoring terminals (at 2 km and 3 km) are topologically connected to form a closed interval from 2 km to 3 km. This interval is the fault section, that is, it is determined that the fault occurred in the line section between 2 km and 3 km from the substation.

[0083] After determining the fault section, the power execution equipment is controlled to maintain the preset safety threshold for abnormal loads and record the fluctuation parameters. Assume that there is a commercial complex load in the fault section, the power is 500kW during normal operation, and the preset safety threshold is 600kW. The power execution equipment is controlled to adjust the output power of the fault section and obtain the amplitude parameters in real time through the voltage detection module. When the power of the commercial complex load instantly rises to 650kW due to reasons such as the start-up of the air conditioner, exceeding the preset safety threshold range, the power execution equipment immediately adjusts the output to maintain the preset safety threshold of 600kW to avoid overload.

[0084] Continuously record the power output of power actuators to form a set of fluctuation parameters. In this example, the power output is recorded every 100ms for 10 minutes, resulting in 6,000 power data points, which form the set of fluctuation parameters. These data points reflect the power variations of the load at different times, such as the power increase during the startup phase of a commercial complex between 8:00 and 9:00 a.m. and the power plateau between 12:00 and 1:00 p.m.

[0085] Based on the set of fluctuation parameters, a cubic spline interpolation algorithm is used to generate a load dynamic characteristic curve. The cubic spline interpolation algorithm can construct a smooth curve based on a discrete set of fluctuation parameters, accurately reflecting the changing trend of load power over time. In this example, 6,000 data points are fitted into a continuous curve using the interpolation algorithm. The curve shows that the power changes of the commercial complex load on weekdays show obvious periodicity. The power rises from 8:00 to 10:00 in the morning, and stabilizes from 10:00 to 12:00. There is a slight decrease from 12:00 to 14:00, and then rises again from 14:00 to 18:00, and then gradually decreases after 18:00.

[0086] Key feature points are extracted from the load dynamic characteristic curve at equal intervals. The number of key feature points is proportional to the curve duration. For example, in a 10-minute curve, one key feature point is extracted every minute, for a total of 10 key feature points. Each feature point contains a timestamp and corresponding power value, such as (8:00, 300kW) and (8:10, 450kW). These key feature points can concisely summarize the dynamic characteristics of the load, facilitating subsequent load forecasting and mapping of the power grid topology.

[0087] The load distribution map of the power grid topology is annotated based on the extracted key feature points. The power grid topology intuitively displays the line connections and equipment distribution of the power system. In this example, the extracted key feature points are mapped to the location of the commercial complex in the power grid topology map, and the load power values ​​at different times are annotated with different colors or symbols to form a load distribution map. For example, next to the icon of the commercial complex on the topology map, the power at 8:00 is marked as 300kW, and at 8:10 it is marked as 450kW, so that operation and maintenance personnel can intuitively understand the distribution and changes of the load in the area.

[0088] Through the above steps, the fault section is accurately identified and abnormal loads are effectively managed. In practical applications, this method can be widely used in scenarios such as urban distribution networks and industrial park power supply systems. For example, in an urban distribution network, when a distribution line fails, this method can quickly locate the faulty section, shortening troubleshooting time. In industrial parks, real-time monitoring and load characteristic analysis of important loads can adjust power supply strategies based on load changes, improving power supply reliability and economic efficiency. At the same time, by performing load forecasting mapping on the power grid topology, data support is provided for power system planning and expansion, ensuring the stable operation and sustainable development of the power system.

[0089] Embodiment 4:

[0090] In this embodiment, power supply path correction and protection operations under abnormal conditions based on the grid topology map that has completed load prediction mapping are the core links for the intelligent power control system to achieve optimized power supply and fault protection. Taking the power supply system of an industrial park as an example, the system includes multiple power supply paths and multiple load areas. After the load prediction mapping is completed for the grid topology map, power supply path correction and protection operations under abnormal conditions are required. The specific process is as follows:

[0091] At 3 p.m. on a weekday, large equipment was simultaneously started up in multiple production workshops within the industrial park. The cloud platform control center collected real-time data from each load area through monitoring terminals and power execution equipment. The load monitoring unit analyzed the data and generated a load dynamic characteristic curve. The load distribution was mapped and annotated on the power grid topology based on key characteristic points. At this point, the topology showed that the load power in the section where Workshop 3 was located had reached 800kW, while the rated capacity of this section was 700kW, indicating an overload. At the same time, one of the system's preset power supply paths passed through this overloaded section. To ensure power supply safety, the power supply path needed to be corrected.

[0092] Logically shield the preset power supply paths that overlap with the overloaded sections in the power grid topology. In this example, the original power supply path P1 starts from the substation and reaches Workshop 3 via lines L1 and L2. The L2 section is located in the overloaded Workshop 3 area. When the L2 section is detected to be overloaded, the prediction processing unit of the cloud platform control center immediately logically shields the power supply path P1, making it no longer a feasible power supply path. At this point, the system needs to re-plan the power supply path and select other paths that do not pass through the overloaded section, such as the power supply path P2, which starts from the substation and reaches Workshop 3 via lines L3 and L4 to ensure the reliability and safety of the power supply.

[0093] When power execution equipment detects an abnormal operating condition, it must perform protection operations within the fault section according to the execution strategy. Suppose at a certain moment, a short circuit occurs on line L5 in the industrial park's power supply system, causing a sudden increase in current and a sudden decrease in voltage. Nearby power execution equipment detects the abnormal condition and immediately initiates the protection operation process:

[0094] S1. Select the access point for the faulty section with the fewest steps based on the next power supply node to be switched. In this case, the next power supply node to be switched is Workshop 4 at the end of line L5. The system uses the A* algorithm to calculate feasible access points for each faulty section. In this example, the feasible access points for faulty section L5 are points A, B, and C. Point A is located near the substation, point B is in the middle of the section, and point C is located near Workshop 4. The algorithm performs a comprehensive scoring based on the number of steps and electrical distance parameters, with the step weighting factor α set to 0.6 and the electrical distance weighting factor β set to 0.4. The calculation shows that point A has five steps and an electrical distance of 1.2 km; point B has three steps and an electrical distance of 0.8 km; and point C has two steps and an electrical distance of 0.5 km. Point C has the highest score according to the scoring formula, so it is selected as the access point for the faulty section.

[0095] S2: Control the power actuator to switch to the access point and perform phase synchronization. Upon receiving the command, the power actuator activates the circuit breaker module, switching from its original operating position to the access point C. During the switching process, the synchronization device synchronizes with the power system phase to ensure phase consistency upon access, preventing damage to the equipment caused by surge currents due to phase differences.

[0096] S3: Control the power actuator to switch into the faulty section from the access point and execute switching according to the power supply path specified in the execution strategy. After synchronization of the access points, the power actuator switches into the faulty section L5 from point C. According to the pre-generated execution strategy, the circuit breaker on line L5 must be opened to isolate the faulty section. Upon receiving the command, the circuit breaker module of the power actuator quickly opens, interrupting the fault current and preventing further expansion of the fault.

[0097] S4. The system obtains the output parameters of the power actuator equipment in real time and uses a dynamic programming algorithm to optimize and compensate for these output parameters. After shutting off the fault current, the power actuator equipment continuously monitors output parameters such as current, voltage, and power. If a voltage sag is detected, causing unstable operation of some equipment in Workshop 4, the system calculates the optimal compensation strategy using a dynamic programming algorithm, adjusts the output of the power actuator equipment, and dynamically compensates for the voltage to ensure normal operation of the equipment.

[0098] S5: After each successive power supply operation in the faulty section, the power execution device is controlled to suspend output and exit the faulty section, re-executing S1. When the circuit breaker in the faulty section L5 is tripped and the fault is isolated, the power execution device suspends output, exits the faulty section, and awaits the next instruction. At this point, the system re-evaluates the next power supply node to be switched and re-executes S1 to find a new access point in preparation for restoring power to the non-faulty section.

[0099] In practical applications, this implementation can effectively deal with various abnormal situations in the power system. For example, in an urban distribution network, when a feeder is overloaded, the power supply path of the overloaded section can be logically shielded to automatically adjust the power flow distribution of the power supply network and avoid power outages caused by overloads. In the event of a transient fault, the power execution equipment can quickly isolate the fault and restore power supply to the non-faulty section through rapid protection operations, thereby improving power supply reliability. At the same time, the optimization and compensation of output parameters through dynamic programming algorithms can effectively improve the power quality and meet the high requirements of sensitive loads for power supply reliability and power quality. In addition, the phase synchronization and access point optimization selection in this process ensure the safety and efficiency of protection operations, reduce the impact on the power system, and extend the service life of the equipment.

[0100] Example 5:

[0101] In this embodiment, when the power actuator detects an abnormal operating condition and performs a protection operation, step S1 is a key step in comprehensively scoring and selecting the access point for the fault section using the A* algorithm combined with the operation steps and electrical distance parameters. For example, when a short circuit occurs on line L in a certain regional distribution network, this method is used to determine the optimal access point. The specific implementation is as follows:

[0102] Algorithm A calculates feasible access points for each faulty segment. Assuming there are three feasible access points, M, N, and P, on faulty segment L, Algorithm A considers the actual cost from the starting point to each access point and the estimated cost from each access point to the target node. For each access point, the algorithm generates a total cost function that evaluates the merits of that point.

[0103] Comprehensive scoring is performed based on the operation steps and electrical distance parameters. The operation step weight coefficient is set to α, and the electrical distance weight coefficient is set to β, where the value range of α and β is 0 to 1, and α+β=1, which respectively represent the importance of the operation steps and electrical distance in the comprehensive scoring. The formula for calculating the scoring value is:

[0104]

[0105] Where S is the score value, T is the number of operation steps, and D is the electrical distance (unit: km). Indicates the reciprocal of the operation steps. The fewer the operation steps, the larger the value. This value represents the reciprocal of the electrical distance. The shorter the electrical distance, the larger the value. α and β are manually set, and their weights can be adjusted based on actual needs. For example, in scenarios with high operational efficiency requirements, the value of α can be appropriately increased, while in scenarios with high electrical loss requirements, the value of β can be appropriately increased.

[0106] In this example, assume that α is 0.6 and β is 0.4. The number of operation steps T for access point M is 4, and the electrical distance D is 1.5 km. Its score is:

[0107]

[0108] The number of operation steps T for access point N is 3, and the electrical distance D is 1.0 km. Its score is:

[0109]

[0110] The number of operation steps T for access point P is 2, and the electrical distance D is 0.8 km. Its score is:

[0111]

[0112] According to the comprehensive scoring results, the access point of the fault section with the least operation steps and the shortest electrical distance is selected. P It is 0.8, the highest among the three access points, so point P is selected as the access point of the fault section.

[0113] After determining the access point, the power actuator is controlled to switch to that access point and perform phase synchronization. The power actuator adjusts the position of the circuit breaker through its internal drive mechanism to reach the access point P. Simultaneously, the power actuator uses a phase detection device to monitor the power system phase in real time. By adjusting its own phase parameters, the device's phase is aligned with the system phase, ensuring electrical stability during access.

[0114] The power actuator controls the power supply to enter the faulty section from the access point and executes the switching action according to the power supply path specified in the execution strategy. After the power actuator completes phase synchronization at point P, it controls the circuit breaker module to open or close according to the execution strategy generated by the cloud platform control center. For example, if the execution strategy requires isolating the faulty section L, the power actuator controls the circuit breaker at point P to open, severing the electrical connection between the faulty section and other normal sections.

[0115] During switching operations, the output parameters of power actuators are acquired in real time and optimized using a dynamic programming algorithm. Voltage and current detection modules collect real-time data on the output voltage, current, power, and other parameters of the devices, which are then fed into the dynamic programming algorithm. The dynamic programming algorithm, with system stability and economic efficiency as its goals, calculates the optimal compensation solution, such as adjusting transformer taps and switching reactive power compensation devices. This real-time optimization of output parameters ensures power quality in non-faulty sections.

[0116] After each continuous power supply operation in the faulty section, the power execution device is controlled to suspend output and exit the faulty section, re-executing S1. For example, after the faulty section L is successfully isolated, the power execution device stops power output, the circuit breaker is in the open state, and it exits the faulty section. Then, based on the next power supply node to be switched, the A* algorithm is re-calculated as a feasible access point, and the above steps are repeated until all necessary protection operations and power restoration work are completed.

[0117] In practical applications, this implementation can be applied to power systems of various sizes. For example, in large urban power grids, when a high-voltage line fails, this method can quickly determine the optimal access point, reducing the time and impact of protection operations on the system. In rural distribution networks, faced with complex line topologies, this method can effectively optimize the selection of access points and improve the efficiency of fault handling. By properly setting the weight coefficients for operation steps and electrical distances, it can adapt to the needs of different power systems, ensuring that power execution equipment can efficiently and safely perform protection operations under abnormal operating conditions, thereby ensuring the stable operation of the power system.

[0118] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0119] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent power control system based on the Internet of Things, characterized in that: include: A monitoring terminal, a power execution device, and a cloud platform control center, wherein the monitoring terminal and the power execution device are respectively connected to the cloud platform control center for communication, and the cloud platform control center includes a load monitoring unit, a prediction processing unit, and a power control unit; The monitoring terminal is used to collect current signals and voltage signals in the power line in real time; The power execution device is used to receive the command signal from the cloud platform control center to switch the switch state; The load monitoring unit is used to record the current mutation location and voltage recovery location of the power execution device during operation, determine the fault section based on the current mutation location and voltage recovery location, control the power execution device to maintain a preset safety threshold for abnormal load and record fluctuation parameters, generate a load characteristic curve based on the fluctuation parameters, and perform load prediction mapping on the power grid topology; The prediction processing unit is used to modify the power supply path of the power grid topology map that completes the load prediction mapping and generate an execution strategy; The power control unit includes a steady-state control unit and a transient control unit. The steady-state control unit is used to control the power execution device to execute a reference power output when the power execution device is in normal working conditions; the transient control unit is used to control the power execution device to execute a protection operation within the fault section according to the execution strategy when the power execution device detects an abnormal working condition.

2. The intelligent power control system based on the Internet of Things according to claim 1, characterized in that: The monitoring terminal includes a terminal housing and a data acquisition device, a waveform analysis device, a diagnostic unit and a communication unit arranged in the housing; The data acquisition device is used to synchronously acquire three-phase current waveform and voltage waveform data; The waveform analysis device is used to extract the harmonic characteristic components of the current waveform; The diagnostic unit is used to calculate the similarity between the current harmonic characteristic component and the historical standard waveform based on the LSTM neural network, determine the deviation of the operating state of the power equipment, and trigger an early warning signal according to the calculation result; The communication unit is used to upload the collected data to the cloud platform control center via the LoRaWAN protocol.

3. The intelligent power control system based on the Internet of Things according to claim 1, characterized in that: The electric power execution equipment includes a cabinet and a circuit breaker module, a voltage detection module, an instruction execution module and a communication relay module arranged in the cabinet.

4. The intelligent power control system based on the Internet of Things according to claim 3 is characterized in that: The recording of the current mutation position and voltage recovery position of the power execution equipment during operation includes: When the power execution device detects a sudden change in current, it records the phase angle data at the time the sudden change occurs, continuously monitors the voltage recovery critical point through the voltage detection module, and records the cycle count at the recovery time.

5. The intelligent power control system based on the Internet of Things according to claim 4 is characterized in that: Determining the fault section according to the current mutation position and the voltage recovery position includes: Generate mutation coordinates and recovery coordinates based on phase angle data and cycle counts; The mutation coordinates, recovery coordinates and monitoring terminal positions are topologically connected to form a closed interval, which is marked as a fault section.

6. The intelligent power control system based on the Internet of Things according to claim 5, characterized in that: The control of the power execution device to maintain a preset safety threshold for abnormal load and record the fluctuation parameters, generate a load characteristic curve according to the fluctuation parameters and perform load prediction mapping on the power grid topology includes the following steps: Controlling the power execution equipment to adjust the output power of the fault section and obtaining amplitude parameters in real time through the voltage detection module; A preset safety threshold range is set. If the amplitude parameter exceeds the preset safety threshold range, the power execution equipment is controlled to maintain the preset safety threshold operation; Continuously record the power output value of the power execution equipment to form a set of fluctuation parameters; Based on the fluctuation parameter set, the cubic spline interpolation algorithm is used to generate the load dynamic characteristic curve; Extracting a number of key feature points at equal time intervals from the load dynamic characteristic curve, wherein the number of the key feature points is proportional to the duration of the curve; The load distribution mapping of the power grid topology is annotated based on the extracted key feature points.

7. The intelligent power control system based on the Internet of Things according to claim 6, characterized in that: The power supply path correction of the power grid topology map that has completed the load prediction mapping includes performing load distribution mapping marking on the power grid topology map and then logically shielding the preset power supply path overlapping with the overload section in the power grid topology map.

8. The intelligent power control system based on the Internet of Things according to claim 1, characterized in that: When the power execution device detects an abnormal working condition, controlling the power execution device to perform a protection operation in the fault section according to the execution strategy includes: S1. Select the access point of the fault section with the least operation steps based on the next power supply node to be switched; S2. Control the power execution device to switch to the access point and perform phase synchronization; S3, controlling the power execution device to switch into the fault section from the access point and perform a switching action according to the power supply path in the execution strategy; S4. Obtain the output parameters of the power execution equipment in real time and optimize and compensate the output parameters using a dynamic programming algorithm; S5. After each continuous power supply operation is completed in the fault section, the power execution device is controlled to suspend output and exit the fault section, and S1 is re-executed.

9. The intelligent power control system based on the Internet of Things according to claim 8, characterized in that: Said S1 comprises the following steps: The A* algorithm is used to calculate the feasible access points for each fault section and to provide a comprehensive score based on the operation steps and electrical distance parameters. Based on the comprehensive scoring results, select the access point to the fault section with the least operation steps and the shortest electrical distance.

10. The intelligent power control system based on the Internet of Things according to claim 9, characterized in that: The comprehensive scoring based on the operation steps and electrical distance parameters includes: Set the operation step weight coefficient to α and the electrical distance weight coefficient to β; Calculate the score value S = α × the inverse of the operation steps + β × the inverse of the electrical distance; The power control unit is used to select the access point of the fault section with the highest score S.

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