Substation monitoring and early warning method based on neural network and related device
By constructing a multi-layer neural network model and using the backpropagation algorithm to train the substation monitoring system, the problem of the lack of early warning capability in substation monitoring methods is solved, the reliability and intelligence of substation operation are improved, and the stability and response speed of the power system are enhanced.
Patent Information
- Application Number
- CN202511497739.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-20
- Publication Date
- 2026-01-30
AI Technical Summary
Existing substation monitoring methods lack early warning capabilities, struggle to cope with complex operating conditions involving multi-parameter coupling and nonlinear changes, and are unable to accurately predict power system operating trends.
A neural network-based prediction method is adopted. By constructing a multi-layer neural network model and training the model using the backpropagation algorithm, the key parameters of the substation in the future are predicted and compared with the actual measured values and the safe operating domain to generate early warning signals.
It has improved the reliability and intelligence of substation operation, and has the ability to provide early warning of faults and autonomous optimization, thereby improving the stability and response speed of the power system.
Smart Images
Figure CN121440909A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power system monitoring and control technology, and relates to a substation monitoring and early warning method and related devices based on neural networks. Background Technology
[0002] With the sustained and rapid development of my country's economy and the continuous advancement of industrialization and urbanization, the demand for electricity across society has maintained a high-speed upward trend. According to relevant statistics, in July 2025, my country's monthly electricity consumption exceeded 1 trillion kilowatt-hours for the first time, roughly equivalent to Japan's total annual electricity consumption. The power system is continuously expanding in scale and becoming increasingly complex in structure, posing unprecedented challenges to the safe and stable operation of the power grid. As a key node in power conversion and distribution, the equipment status of substations directly affects the reliability and quality of regional power supply. Currently, most substations still use traditional monitoring methods based on fixed thresholds, that is, by collecting parameters such as voltage, current, temperature, and power factor in real time and comparing them with preset thresholds, an alarm is triggered once a limit is exceeded. Although this method is simple to implement, it has significant shortcomings: on the one hand, alarms often occur after the equipment status has become abnormal or even failed, lacking the ability to provide early warning; on the other hand, it is difficult to cope with complex operating conditions involving multi-parameter coupling and nonlinear changes, and cannot accurately judge the system's operating trend.
[0003] With the rapid development of big data and artificial intelligence technologies, some studies have attempted to introduce data-driven methods into power system monitoring. For example, CN118713296B proposed an online monitoring system and method for substations based on big data, which uses historical data mining to assess and regulate operational status. However, most of these methods still rely on traditional numerical calculations and statistical analysis, making it difficult to handle the dynamic behavior of high-dimensional, nonlinear, and time-varying power systems. The generalization ability and real-time performance of the models also have limitations. Therefore, there is an urgent need for an intelligent monitoring and early warning method that can integrate multi-source data and possess predictive and autonomous optimization capabilities to improve the reliability, economy, and intelligence of substation operation. Summary of the Invention
[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a substation monitoring and early warning method and related device based on neural networks. This method and related device can improve the reliability, economy and intelligence level of substation operation.
[0005] To achieve the above objectives, this invention discloses a substation monitoring and early warning method based on neural networks, comprising: Predicted values of key parameters of substations over a future period based on neural network analysis; The predicted value is compared with the actual measured value. When the deviation between the two is greater than or equal to the preset deviation, a preliminary abnormality prompt is triggered. When a preliminary abnormality is received, the preset value is compared with its preset safe operating range. If the value exceeds the preset safe operating range, a warning signal is generated.
[0006] Furthermore, before predicting the key parameters of the substation over a future period based on neural networks, the method further includes: Construct a training sample set; Constructing a multilayer neural network model The multilayer neural network model is trained using the training sample set to obtain the trained multilayer neural network model.
[0007] Furthermore, the process of constructing the training sample set is as follows: Historical operating data of substations are collected, including electrical parameters, equipment status parameters, environmental parameters, and operating event records. The historical operating data of substations is cleaned, normalized, and pre-extracted for feature extraction, and then a training sample set is constructed.
[0008] Furthermore, the multilayer neuron network model is trained using the backpropagation algorithm.
[0009] Furthermore, the output of the multilayer neural network model is:
[0010] Where y is the output and g is the activation function. Let i be the weight of the i-th input. is the input feature, and b is the bias term.
[0011] This invention discloses a substation monitoring and early warning system based on neural networks, comprising: The prediction module is used to predict the values of key parameters of the substation over a future period based on neural networks. The comparison module is used to compare the predicted value with the actual measured value. When the deviation between the two is greater than or equal to the preset deviation, a preliminary abnormality prompt is triggered. The generation module is used to compare the preset value with its preset safe operating range when a preliminary abnormality prompt is received, and to generate a warning signal when the preset safe operating range is exceeded.
[0012] Furthermore, before predicting the key parameters of the substation over a future period based on neural networks, the method further includes: Construct a training sample set; Construct a multi-layer neural network model; The multilayer neural network model is trained using the training sample set to obtain the trained multilayer neural network model.
[0013] Furthermore, the process of constructing the training sample set is as follows: Historical operating data of substations are collected, including electrical parameters, equipment status parameters, environmental parameters, and operating event records. The historical operating data of substations is cleaned, normalized, and pre-extracted for feature extraction, and then a training sample set is constructed.
[0014] This invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the substation monitoring and early warning method based on neural networks.
[0015] The present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the substation monitoring and early warning method based on neural networks.
[0016] The present invention has the following beneficial effects: In specific operation, the energy storage steam supply system and method for matching photovoltaic power generation described in this invention predicts the key parameters of the substation over a future period based on a neural network. The predicted values are then compared with the actual measured values and their preset safe operating range to generate early warning signals. This enables real-time assessment of the operating status, early warning of faults, and autonomous generation of control strategies, thereby significantly improving the stability, response speed, and adaptability of the power system. Attached Figure Description
[0017] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a structural diagram of the present invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.
[0020] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.
[0021] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.
[0022] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.
[0023] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."
[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0025] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.
[0026] Example 1 refer to Figure 1 The substation monitoring and early warning method based on neural networks described in this invention includes: 1) Construct a training sample set; Historical operating data of substations are collected, including electrical parameters (such as voltage, current, active power, reactive power, power factor, frequency, harmonic distortion rate, etc.), equipment status parameters (such as transformer oil temperature, winding temperature, circuit breaker status, insulation strength, etc.), environmental parameters (such as ambient temperature, humidity, meteorological data, etc.), and operating event records (such as load switching, fault recording, protection action, etc.). The historical operating data of substations is cleaned, normalized, and pre-extracted for feature extraction, and then a training sample set is constructed. 2) Construct and train a multi-layer neural network model; 21) Based on the characteristics of substation parameters, a deep neural network structure with multiple hidden layers is constructed. Preferably, ReLU (Rectified Linear Unit) is selected as the activation function, and its expression is:
[0027] The output of the multilayer neural network model is:
[0028] Where y is the output and g is the activation function. Let i be the weight of the i-th input. is the input feature, b is the bias term, and the model training uses the backpropagation algorithm to ensure that the model has high prediction accuracy and generalization ability.
[0029] 3) Real-time monitoring and online forecasting; The system acquires substation operating parameters in real time and inputs them into a trained neural network model. The model outputs predicted values for key substation parameters over a future period, such as load changes and equipment temperature trends within the next 5-30 minutes. These predicted values are then compared to actual measurements. If the deviation is greater than or equal to a preset deviation, a preliminary anomaly alert is triggered.
[0030] 4) Abnormal early warning and control decision-making; The predicted result is compared with the preset safe operating range. When the predicted result exceeds the preset safe operating range, an early warning signal is generated and the operation and maintenance personnel are prompted to intervene through the human-machine interface. At the same time, the control system of the substation is controlled to realize control functions such as automatic adjustment of active and reactive power, switching of operating mode, and starting of cooling device, thereby avoiding problems such as equipment overload, insulation aging, and fault expansion.
[0031] When scheduling demands require overload operation, a neural network model is used to calculate and predict changes in various parameters, and different control operations are performed in advance based on the calculation results. On the one hand, this provides data support for meeting special operating conditions, and on the other hand, it reduces damage to power plant equipment under special operating conditions.
[0032] In addition, the system continuously collects new operational data during actual operation and periodically performs incremental training or retraining on the neural network model to adapt to dynamic factors such as equipment aging, environmental changes, and load increases, thereby achieving continuous improvement in model capabilities and enhanced adaptability.
[0033] This invention has the following characteristics: This invention leverages the powerful nonlinear fitting and feature learning capabilities of deep neural networks to accurately characterize the complex coupling relationships and dynamic behaviors among multiple parameters in substations, achieving high prediction accuracy and strong adaptability. This invention realizes a shift from "post-event alarm" to "pre-event early warning."
[0034] Example 2 The substation monitoring and early warning system based on neural networks described in this invention includes: The prediction module is used to predict the values of key parameters of the substation over a future period based on neural networks. The comparison module is used to compare the predicted value with the actual measured value. When the deviation between the two is greater than or equal to the preset deviation, a preliminary abnormality prompt is triggered. The generation module is used to compare the preset value with its preset safe operating range when a preliminary abnormality prompt is received, and to generate a warning signal when the preset safe operating range is exceeded.
[0035] In this embodiment, the process of predicting the key parameters of the substation over a future period based on neural networks also includes: Construct a training sample set; Construct a multi-layer neural network model; The multilayer neural network model is trained using the training sample set to obtain the trained multilayer neural network model.
[0036] In this embodiment, the process of constructing the training sample set is as follows: Historical operating data of substations are collected, including electrical parameters, equipment status parameters, environmental parameters, and operating event records. The historical operating data of substations is cleaned, normalized, and pre-extracted for feature extraction, and then a training sample set is constructed.
[0037] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.
[0038] Example 3 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the substation monitoring and early warning method based on a neural network. For example, the method includes: predicting the predicted values of key substation parameters for a future period based on a neural network; comparing the predicted values with actual measured values, and triggering and issuing a preliminary anomaly alert when the deviation between the two is greater than or equal to a preset deviation; upon receiving the preliminary anomaly alert, comparing the preset value with its preset safe operating range, and generating an early warning signal when the preset safe operating range is exceeded. Prior to predicting the predicted values of key substation parameters for a future period based on a neural network, the method further includes: constructing a training sample set; constructing a multi-layer neural network model and training the multi-layer neural network model using the training sample set to obtain a trained multi-layer neural network model. The memory may include main memory, such as high-speed random access memory (RAM), or non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, or an extended industry-standard architecture bus. The bus can be categorized as an address bus, data bus, or control bus. The memory stores programs; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.
[0039] Example 4 A computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the substation monitoring and early warning method based on a neural network. For example, the method includes: predicting the predicted values of key substation parameters for a future period based on a neural network; comparing the predicted values with actual measured values, and triggering and issuing a preliminary anomaly alert when the deviation is greater than or equal to a preset deviation; upon receiving the preliminary anomaly alert, comparing the preset value with a preset safe operating range, and generating an early warning signal when the preset safe operating range is exceeded. Prior to predicting the predicted values of key substation parameters for a future period based on a neural network, the method further includes: constructing a training sample set; constructing a multi-layer neural network model and training the multi-layer neural network model using the training sample set to obtain a trained multi-layer neural network model. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.
[0040] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0041] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0042] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0043] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0044] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.
[0045] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.
[0046] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.
Claims
1. A substation monitoring and early warning method based on a neuron network, characterized in that, The method comprises the following steps: predicting a predicted value of a key parameter of a substation in a future period of time based on a neuron network; comparing the predicted value with an actual measured value, and triggering and issuing a preliminary abnormality prompt when a deviation between the two is greater than or equal to a preset deviation; comparing the preset value with a preset safe operation domain thereof when the preliminary abnormality prompt is received, and generating a pre-warning signal when the preset safe operation domain is exceeded.
2. The neuron network based substation monitoring and early warning method according to claim 1, characterized in that, Before the step of predicting the predicted value of the key parameter of the substation in the future period of time based on the neuron network, the method further comprises the following steps: constructing a training sample set; constructing a multi-layer neuron network model; training the multi-layer neuron network model by using the training sample set to obtain a trained multi-layer neuron network model.
3. The neuron network based substation monitoring and early warning method according to claim 2, characterized in that, The process of constructing the training sample set comprises the following steps: collecting historical operation data of the substation, wherein the historical operation data of the substation comprises electrical parameters, equipment state parameters, environmental parameters and operation event records, and the historical operation data of the substation is cleaned, normalized and feature-extracted to construct the training sample set.
4. The neuron network based substation monitoring and early warning method according to claim 2, characterized in that, The multi-layer neuron network model is trained by using an error back propagation algorithm.
5. The neuron network based substation monitoring and early warning method according to claim 2, characterized in that, The output of the multi-layer neuron network model comprises: where y is the output, g is the activation function, is the weight for the ith input, is the input feature, and b is the bias term.
6. A neuron network based substation monitoring and early warning system, characterized in that, The method comprises the following steps: a prediction module configured to predict a predicted value of a key parameter of a substation in a future period of time based on a neuron network; a comparison module configured to compare the predicted value with an actual measured value, and trigger and issue a preliminary abnormality prompt when a deviation between the two is greater than or equal to a preset deviation; a generation module configured to compare the preset value with a preset safe operation domain thereof when the preliminary abnormality prompt is received, and generate a pre-warning signal when the preset safe operation domain is exceeded.
7. The neuron network based substation monitoring and warning system according to claim 6, wherein, Before the step of predicting the predicted value of the key parameter of the substation in the future period of time based on the neuron network, the method further comprises the following steps: constructing a training sample set; constructing a multi-layer neuron network model; training the multi-layer neuron network model by using the training sample set to obtain a trained multi-layer neuron network model.
8. The neuron network based substation monitoring and warning system according to claim 7, wherein, The process of constructing the training sample set comprises the following steps: collecting historical operation data of the substation, wherein the historical operation data of the substation comprises electrical parameters, equipment state parameters, environmental parameters and operation event records, and the historical operation data of the substation is cleaned, normalized and feature-extracted to construct the training sample set.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the substation monitoring and pre-warning method based on the neuron network according to any one of claims 1-5.
10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the steps of the substation monitoring and pre-warning method based on the neuron network according to any one of claims 1-5.