Thermal power plant electrical equipment overload self-adaptive early warning system

Abstract

The invention provides a self-adaptive early warning system for overload of electrical equipment of a thermal power plant. The self-adaptive early warning system comprises a data acquisition and fusion module, a dynamic threshold calculation module, a risk probability evaluation module, a graded early warning decision module and a feedback learning and model updating module. The data acquisition and fusion module acquires multi-dimensional operation parameters of the electrical equipment in real time and forms an equipment state vector; the dynamic threshold calculation module generates a dynamic early warning threshold through an adaptive learning algorithm based on historical data and real-time working conditions; the risk probability evaluation module calculates a quantitative risk probability in combination with the real-time parameter deviation and the equipment health degree; the grading early warning decision module executes grading response from visual prompt to load control according to the risk grade; and the feedback learning module continuously optimizes algorithm parameters. According to the method, the limitation of traditional fixed threshold early warning is broken through, dynamic threshold self-adaption, accurate risk quantification, early warning grading response and continuous model optimization are realized, the early warning accuracy is remarkably improved, and the false alarm rate is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of power equipment monitoring technology, and in particular to an adaptive early warning system for overload of electrical equipment in thermal power plants. Background Technology

[0002] In the field of power system operation and maintenance, thermal power plants, as a crucial source of electricity supply, directly impact the long-term safe and stable operation of their key electrical equipment (such as generators, transformers, and high-voltage switchgear) on the reliability and economic efficiency of the entire power grid. Overload of electrical equipment is one of the main causes of accelerated insulation aging, performance degradation, and even equipment failure and shutdown. Therefore, establishing an efficient and accurate overload early warning mechanism is of paramount importance for preventing equipment damage, avoiding unplanned shutdowns, extending equipment lifespan, and ensuring the safe and economical operation of power plants.

[0003] Currently, the overload warning systems for electrical equipment widely used in thermal power plants generally rely on preset fixed threshold judgment mechanisms. These systems typically set one or more static alarm thresholds (such as current limits, temperature limits, etc.) based on equipment nameplate parameters, design specifications, or limited historical data from typical operating conditions during the initial commissioning phase. When the real-time monitored operating parameters exceed these preset thresholds, the system triggers an alarm. While this fixed threshold-based warning method is simple to implement and logically intuitive, its inherent limitations are becoming increasingly apparent in the complex operating environment of actual thermal power plants, mainly in the following aspects: First, fixed thresholds cannot dynamically adapt to the complex and ever-changing actual operating conditions of equipment. The actual load capacity of electrical equipment in thermal power plants is not constant; it is affected by a combination of factors, including unit output plans, grid dispatch instructions, auxiliary equipment operation status, ambient temperature, humidity, and cooling system efficiency. For example, the temperature rise and thermal stress generated by the same load current for the same transformer will differ significantly under different conditions, such as high summer temperatures and suitable ambient temperatures. Its safe operating boundary is actually a dynamically changing range. Fixed threshold methods cannot perceive and respond to this dynamic change. This can lead to situations where, under relaxed operating conditions, overly conservative threshold settings may fail to provide timely warnings of potential risks, or under harsh operating conditions, overly aggressive threshold settings may generate numerous false alarms, interfering with operator judgment and even triggering a "boy who cried wolf" effect, thus reducing the authority of the alarm.

[0004] Secondly, traditional early warning methods lack consideration for the degradation process of the equipment itself. During long-term operation, electrical equipment undergoes irreversible aging and performance degradation due to electrical, thermal, mechanical, and environmental stresses, affecting its insulation materials, conductive circuits, and cooling media. The gradual deterioration of equipment health means a decrease in its overload capacity. Fixed-threshold early warning systems typically do not incorporate indicators reflecting equipment health, such as cumulative operating time, historical overload records, and recent maintenance and repair status, into their early warning models. This results in an inability to accurately assess the true risk level of aging equipment under current operating conditions, leading to insufficient accuracy and foresight in early warning.

[0005] Secondly, existing technologies have shortcomings in multi-source heterogeneous data fusion and feature mining. Modern thermal power plants are equipped with numerous sensors that collect multi-dimensional operating parameters such as current, voltage, temperature, vibration, noise, partial discharge, and ambient temperature and humidity. These parameters reflect the operating status of the equipment from different perspectives and are correlated with each other. However, traditional threshold-based early warning systems often focus only on a single or a few key parameters, failing to effectively fuse and mine the deep-seated state information contained in multi-source data (such as the fluctuation characteristics of load current, temperature rise rate, and vibration energy spectrum characteristics), making it difficult to comprehensively and accurately characterize the real-time operating status and potential overload trends of the equipment.

[0006] Furthermore, the existing system's early warning decision-making logic is relatively simple, typically relying on a binary judgment model of "alarm upon exceeding limits," lacking a quantitative assessment of risk levels and a tiered response mechanism. This can lead to unnecessary alarms triggered by minor, transient parameter fluctuations, causing operator fatigue, and when high risks actually materialize, the limited alarm format may fail to garner sufficient attention or result in untimely or inappropriate response measures.

[0007] Finally, and crucially, traditional early warning systems generally lack self-learning and adaptive optimization capabilities. Once their early warning models (i.e., threshold setting rules) are set, they typically remain unchanged for a long period, unable to utilize historical data and early warning feedback (such as false alarms and missed alarms) accumulated during system operation for self-correction and performance improvement. As equipment operating time increases, unit operating modes change, and the equipment's own state evolves, the initially set fixed thresholds may gradually deviate from the actual safety boundaries, leading to a continuous deterioration in the system's early warning performance.

[0008] In summary, existing overload early warning technologies for electrical equipment in thermal power plants, based on fixed thresholds, are inherently static, one-sided, and lack adaptability, making them insufficient to meet the higher demands of modern thermal power plants for accurate equipment status perception, early risk warning, and intelligent operation and maintenance decision-making. Developing an adaptive early warning system capable of dynamically sensing operating conditions, integrating multi-source information, quantifying risk assessment, executing tiered responses, and possessing continuous learning and optimization capabilities is an urgent need to address current technological bottlenecks and improve the safe operation of electrical equipment in thermal power plants. Summary of the Invention

[0009] The present invention aims to solve at least one of the problems existing in the prior art and provide an adaptive early warning system for overload of electrical equipment in thermal power plants.

[0010] This invention provides an adaptive early warning system for overload of electrical equipment in thermal power plants, comprising: The data acquisition and fusion module is used to collect multi-dimensional operating parameters of electrical equipment in real time, and to perform time synchronization and feature extraction on the collected heterogeneous data to form a unified format of equipment status vector. The dynamic threshold calculation module is connected to the data acquisition and fusion module. It is used to receive the device status vector and dynamically generate an overload warning threshold that matches the current device operating conditions based on the device's historical operating data, real-time load curve and environmental parameters through an embedded adaptive learning algorithm. The risk probability assessment module, connected to the dynamic threshold calculation module and the data acquisition and fusion module, is used to compare the real-time monitored operating parameters with the dynamically generated early warning thresholds, and combine them with equipment health indicators to calculate a quantified overload risk probability value. The graded early warning decision module is connected to the risk probability assessment module. It is used to execute preset graded decision logic based on the overload risk probability value and generate early warning signals or control commands of different levels. The feedback learning and model update module is connected to various modules of the system. It is used to continuously monitor the actual effect of early warning decisions and the operating status of equipment, record the actual operating status of equipment after the early warning is issued, and periodically optimize and update the parameters of the adaptive learning algorithm in the dynamic threshold calculation module and the risk probability calculation model in the risk probability assessment module using the recorded early warning accuracy and false alarm rate indicators.

[0011] Optionally, the data acquisition and fusion module specifically includes: Multi-channel signal conditioning circuit and high-precision analog-to-digital converter are used to synchronously acquire current, voltage, temperature, vibration amplitude and ambient temperature and humidity parameters of electrical equipment; The data preprocessing unit is connected to the multi-channel signal conditioning circuit and the high-precision analog-to-digital converter, and is used to perform filtering and noise reduction, outlier removal and missing data interpolation on the raw data. The feature extraction engine, connected to the data preprocessing unit, is used to extract key features characterizing the operating status of the equipment from the preprocessed time-series data. These key features include the short-term fluctuation variance of the load current, the equipment temperature rise rate, and the energy distribution of the vibration energy spectrum in a specific frequency band.

[0012] Optionally, the adaptive learning algorithm execution process in the dynamic threshold calculation module includes: Read the current load level and ambient temperature parameters from the device status vector; Access the device historical operation database stored in local non-volatile memory, which records long-term safe operation boundary data of the device under different load and different ambient temperature combinations; Based on the currently read parameter combination, perform similar operating condition matching in the device's historical operation database; Using the matched historical data points, a weighted moving average model is used to predict the upper limit of the safe operating threshold of the equipment under the current operating conditions. The predicted upper limit of the safe operation threshold is multiplied by a preset safety factor, which is dynamically adjusted according to the cumulative operating time of the equipment.

[0013] Optionally, the weighting coefficients of the weighted moving average model are jointly determined by the similarity of the working conditions and the temporal proximity of the data points.

[0014] Optionally, the execution process of the risk probability calculation model in the risk probability assessment module includes: Calculate the relative deviation between real-time operating parameters and dynamic overload warning threshold; A device health degradation factor is introduced, which is calculated by a linear regression model based on the device's cumulative runtime, historical overload counts, and recent maintenance records. The relative deviation degree and the equipment health decay factor are combined to generate an overload risk probability value between 0 and 1.

[0015] Optionally, the overload risk probability value is proportional to the product of the relative deviation and the equipment health decay factor.

[0016] Optionally, the tiered early warning decision module presets three risk probability intervals and corresponding decision actions: No warning signal is issued when the overload risk probability value is lower than the first threshold; When the overload risk probability value is between the first threshold and the second threshold, a first-level warning signal is generated, triggering a visual prompt on the system's human-machine interface and recording the event to the operation log. When the overload risk probability value exceeds the second threshold but is lower than the third threshold, a level 2 warning signal is generated, triggering enhanced visual cues, audible alarms, and sending notification information to the operator's mobile terminal; When the overload risk probability value reaches or exceeds the third threshold, a level 3 early warning signal is generated, and control commands are output to the power plant's distributed control system to execute load reduction operations.

[0017] Optionally, the various modules of the system communicate and transmit commands via an industrial Ethernet network, forming a layered distributed system architecture. The data acquisition and fusion module is deployed in the field control layer close to the electrical equipment; The dynamic threshold calculation module, risk probability assessment module, and hierarchical early warning decision-making module are deployed at the plant-level monitoring information system layer; The feedback learning and model update module is deployed at the plant-level management information system layer.

[0018] Optionally, the feedback learning and model update module is specifically used for: Record equipment operation data for 300 seconds after each warning is issued; If the current exceeds the rated value by 20% or the temperature exceeds the insulation class limit during the period, it is marked as a true overload event; The accuracy and false alarm rates of early warnings are calculated every 24 hours. If the false positive rate is higher than 5% or the accuracy is lower than 90% for three consecutive days, the model parameter optimization process is triggered.

[0019] Optionally, the model parameter optimization process uses the gradient descent algorithm to adjust the weight calculation coefficients in the adaptive learning algorithm and the scale parameters in the risk probability calculation model, with the optimization objective being to minimize the warning error function.

[0020] Compared with existing technologies, the present invention provides an adaptive early warning system for overload of electrical equipment in thermal power plants. This system replaces the traditional fixed threshold method with a dynamic threshold calculation module. This module can adaptively adjust the early warning threshold according to the real-time operating conditions and historical data of the equipment, which significantly improves the adaptability of the early warning system to complex and ever-changing operating environments and fundamentally reduces false alarms caused by unreasonable threshold settings.

[0021] Furthermore, this invention introduces a risk probability assessment mechanism, transforming the simple binary overload judgment into a continuous risk probability quantification. This probability value integrates real-time parameter deviation and long-term equipment health status, making the early warning judgment more refined and accurate, and enabling earlier identification of potential progressive overload risks.

[0022] Furthermore, this invention adopts a tiered early warning decision-making strategy, triggering differentiated response measures for different risk levels. This ensures that unnecessary interference is avoided during low-risk situations, while also ensuring that rapid and effective intervention measures can be taken during high-risk situations, thereby achieving optimal allocation of early warning resources and efficient protection of operational safety.

[0023] Furthermore, the built-in feedback learning and model update module of this invention enables the system to continuously self-optimize, and can continuously correct the internal algorithm model by using actual operation feedback, so that the early warning performance of the system can be continuously improved over time, and it has stable reliability under long-term service. Attached Figure Description

[0024] One or more embodiments are illustrated by way of example with the corresponding pictures in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0025] Figure 1 This is a schematic diagram of the architecture of the adaptive early warning system for overload of electrical equipment in thermal power plants proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the dynamic threshold calculation module in this invention; Figure 3 This is a logical flowchart of the risk probability assessment and graded early warning decision-making process in this invention. Detailed Implementation

[0026] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0027] As indicated in the specification and claims of this invention, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0028] This invention uses flowcharts to illustrate the operations performed by the system according to embodiments of the invention. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously, as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0029] Example 1: This embodiment details the specific technical implementation of an adaptive early warning system for overload of electrical equipment in a thermal power plant. Please refer to the appendix. Figure 1 The adaptive early warning system for overload of electrical equipment in thermal power plants provided in this embodiment includes a data acquisition and fusion module, a dynamic threshold calculation module, a risk probability assessment module, a graded early warning decision module, and a feedback learning and model update module. Each module is deployed in a layered, distributed manner via an industrial Ethernet network. The data acquisition and fusion module is located at the field control layer, the dynamic threshold calculation module, the risk probability assessment module, and the graded early warning decision module are deployed at the plant-level monitoring information system layer, and the feedback learning and model update module is located at the plant-level management information system layer. Through multi-source data sensing, dynamic threshold generation, risk quantification assessment, and a graded response mechanism, the system achieves accurate early warning and adaptive control of electrical equipment overload risks.

[0030] The data acquisition and fusion module is responsible for real-time acquisition of multi-dimensional operating parameters of electrical equipment. This module is equipped with a multi-channel signal conditioning circuit and a high-precision analog-to-digital converter (ADC) to simultaneously acquire parameters such as current, voltage, temperature, vibration amplitude, and ambient temperature and humidity. The signal conditioning circuit performs impedance matching and amplitude scaling on the raw analog signal, and the ADC converts the analog quantity to a digital quantity at a sampling rate of 1,000 times per second. The data preprocessing unit performs filtering and noise reduction on the acquired raw data, using a zero-phase digital filter to eliminate power frequency interference and random noise, and using a sliding window anomaly detection algorithm to remove outliers exceeding three standard deviations from the normal range. For data loss due to transmission packet loss, the preprocessing unit uses a time-series linear interpolation algorithm to reconstruct the complete data sequence. The feature extraction engine extracts key features from the preprocessed time-series data, including the short-term fluctuation variance of the load current, the equipment temperature rise rate, and the energy distribution of the vibration energy spectrum in the 100 Hz to 1 1 Hz frequency band. All features are combined into a ten-dimensional equipment state vector, timestamped to millisecond precision, and output to the dynamic threshold calculation module.

[0031] The dynamic threshold calculation module receives the device state vector and then executes an adaptive learning algorithm. Please refer to the appendix. Figure 2The algorithm first parses the current load level and ambient temperature parameters from the device state vector, and then accesses the device's historical operation database stored in local non-volatile memory. This database records the device's safe operating boundary data under different load and ambient temperature combinations over the past 365 days, including maximum allowable current, maximum withstand temperature, and vibration limits. The adaptive learning algorithm, based on the current parameter combination, performs similar operating condition matching in the historical operation database, calculates the Euclidean distance between the current parameters and historical data points, and selects the fifty historical data points with the smallest distances as a reference set. The weighted moving average model assigns weight coefficients based on the operating condition similarity and temporal proximity of each data point in the reference set. The similarity weight is inversely proportional to the Euclidean distance, and the time weight is inversely proportional to the difference in data record timestamps. The model predicts the upper limit of the device's safe operating threshold under the current operating condition through weighted calculation. The specific calculation formula is as follows: ; in, This indicates the upper limit of the predicted safe operating threshold. Indicates the first i The combined weight of each historical data point This represents the safe operating boundary value recorded at this historical data point. The dynamic threshold calculation module will... Multiply by a safety factor K, which is dynamically adjusted based on the equipment's cumulative operating time: K is 0.95 when the operating time is less than 10,000 hours, 0.9 when the operating time is between 10,000 and 20,000 hours, and 0.85 when the operating time exceeds 20,000 hours. The final output dynamic overload warning threshold is K multiplied by... The product of.

[0032] The risk probability assessment module synchronously receives real-time operating parameters from the data acquisition and fusion module and dynamic overload warning thresholds from the dynamic threshold calculation module. This module has a built-in risk probability calculation model. First, it calculates the relative deviation between the real-time operating parameters and the dynamic threshold, defined as the ratio of the absolute value of the difference between the real-time parameter value and the dynamic threshold to the dynamic threshold. The model also calculates the equipment health decay factor, which is derived from the equipment's cumulative runtime, historical overload counts, and recent maintenance records through a linear regression model. The input features of the linear regression model include the logarithmic transformation of runtime, the number of overload events in the past 30 days, and the number of days since the last maintenance. The output is a decay factor between 0.8 and 1.2. The risk probability calculation model multiplies the relative deviation by the equipment health decay factor and maps it to the zero-to-one interval using the Sigmoid function, ultimately outputting the overload risk probability value. The specific calculation formula is as follows: ; in, P This represents the probability value of overload risk. D Indicates the relative deviation.H This indicates the equipment health degradation factor. k The scale parameter is fixed at 10. θ The offset parameter is fixed at 0.5. When P When the value is higher than 0.7, the equipment is considered to be in a high-risk state.

[0033] The tiered early warning decision-making module executes tiered response strategies based on the overload risk probability value. Please refer to the appendix. Figure 3 This module presets three risk probability thresholds: the first threshold is 0.3, the second threshold is 0.6, and the third threshold is 0.8. When the overload risk probability value... P When the value is less than 0.3, no warning signal is triggered; only the device status database is updated. (The last sentence appears to be incomplete and unrelated to the preceding text.) P When the overload risk probability value is greater than or equal to 0.3 and less than 0.6, a Level 1 warning signal is generated, triggering the green indicator light on the HMI to flash. Simultaneously, the event is recorded in the operation log, with the log entry including a timestamp, device number, and risk probability value. P When the overload risk probability value is greater than or equal to 0.6 and less than 0.8, a level 2 warning signal is generated. The human-machine interface switches to a yellow warning icon and a periodic buzzer alarm is activated. Simultaneously, a warning notification is sent to the operator's mobile terminal via SMS gateway, containing the equipment location and risk level. P When the load reaches or exceeds 0.8, a Level 3 warning signal is immediately generated. The human-machine interface displays a red full-screen alarm and outputs control commands to the power plant's distributed control system. The command recommends reducing the equipment load to 80% of the rated value within 60 seconds.

[0034] The feedback learning and model update module continuously monitors the actual effectiveness of early warning decisions. This module records equipment operating data for 300 seconds after each early warning event. If the current exceeds the rated value by 20% or the temperature exceeds the insulation class limit during this period, it is marked as a true overload event. The module calculates the early warning accuracy and false alarm rate every 24 hours. When the false alarm rate is higher than 5% or the accuracy is lower than 90% for three consecutive days, the model parameter optimization process is triggered. The optimization process uses the gradient descent algorithm to adjust the weight calculation coefficients in the adaptive learning algorithm and the scale parameter k in the risk probability calculation model. The optimization objective is to minimize the early warning error function. The updated parameters are synchronized to the dynamic threshold calculation module and the risk probability assessment module after safety verification, achieving continuous improvement in system performance.

[0035] The system communication architecture is based on an industrial Ethernet network. The data acquisition and fusion module transmits device status vectors to the dynamic threshold calculation module via the Modbus TCP protocol, with a transmission cycle of one hundred milliseconds. The dynamic threshold calculation module and the risk probability assessment module exchange data using the OPC UA protocol to ensure timestamp alignment and data consistency. The hierarchical early warning decision-making module interacts with the power plant's distributed control system via the IEC 61850 protocol, and control commands include priority identifiers and execution time windows. The feedback learning and model update module accesses historical operating data through a database connection pool, and model update files are digitally signed to prevent unauthorized tampering.

[0036] This embodiment, through the specific technical solutions described above, achieves multi-dimensional perception, dynamic threshold adjustment, probabilistic assessment, and tiered decision-making response for overload risks of electrical equipment in thermal power plants. The system possesses adaptive learning capabilities, enabling it to optimize early warning strategies in real time based on the actual operating status of the equipment and environmental changes, significantly improving early warning accuracy and system reliability.

[0037] Example 2: This embodiment provides an alternative solution for an adaptive early warning system for overload of electrical equipment in thermal power plants based on a cloud-edge collaborative architecture. The dynamic threshold calculation module and risk probability assessment module are deployed on edge computing nodes, while the data acquisition and fusion module and the hierarchical early warning decision-making module remain at the field control layer. The feedback learning and model update module is migrated to the cloud analysis platform. The edge computing nodes utilize industrial gateways equipped with AI acceleration chips to achieve localized real-time inference; the cloud platform integrates a big data analysis engine responsible for historical data storage and model training optimization.

[0038] The data acquisition and fusion module, based on Example 1, adds a wireless sensor network interface to support receiving wireless data from miniature temperature and vibration sensors deployed on the device surface. The module employs a time-division multiple access protocol to coordinate wired and wireless data acquisition, ensuring time synchronization accuracy of all data within five milliseconds. The feature extraction engine adds high-frequency current harmonic distortion rate and vibration signal envelope spectrum features, expanding the device state vector to fifteen dimensions. After receiving the device state vector, the edge computing node runs a lightweight version of the adaptive learning algorithm. This algorithm uses 300 days of locally cached historical data and quickly matches similar operating conditions using the k-nearest neighbor algorithm, generating dynamic thresholds with a time delay controlled within ten milliseconds. The risk probability assessment model uses fixed parameters to calculate risk probabilities at the edge node, avoiding response lag caused by complex calculations.

[0039] The tiered early warning decision-making module incorporates an adaptive response mechanism. If a Level 1 warning fails to escalate after three consecutive warnings, the module automatically raises the first threshold from 0.3 to 0.3, reducing the disruption to operators caused by frequent low-risk warnings. If no confirmation from operators is received within five minutes of a Level 2 warning being triggered, the module automatically escalates to a Level 3 warning and implements load reduction. Edge nodes and the cloud platform establish bidirectional communication via a dedicated 4G network. Edge nodes upload compressed operational data to the cloud every ten minutes, and the cloud platform sends updated model parameters to the edge nodes every twenty-four hours.

[0040] The cloud-based feedback learning and model update module integrates data from multiple power plants to construct a cross-plant equipment health status knowledge graph. This knowledge graph includes related data such as equipment model, operating environment, and maintenance history, and uses graph neural network algorithms to uncover potential risk transmission paths. The model optimization process incorporates transfer learning technology, quickly adapting optimization parameters from highly similar equipment to newly connected equipment, shortening the system's self-learning cycle. The cloud platform also provides a visual dashboard for early warning effects, supporting statistical analysis of early warning indicators by equipment type, risk level, and time dimension, providing data support for management decisions.

[0041] This embodiment utilizes a cloud-edge collaborative architecture to expand the system's data analysis capabilities while ensuring real-time performance. Edge computing enables rapid local decision-making, while the cloud platform provides macro-level optimization and cross-system learning, making it suitable for centralized monitoring scenarios in large thermal power plant clusters. The system supports elastic expansion, allowing for easy integration of new monitoring equipment by adding edge nodes, thus meeting the flexible needs of intelligent transformation of power plants.

[0042] Example 3: This embodiment addresses the overload early warning requirements of electrical equipment in thermal power plants under special high-temperature and high-humidity environments. It proposes an enhanced system implementation plan, strengthening environmental adaptability design based on Embodiment 1. The data acquisition and fusion module utilizes wide-temperature-range electronic components, extending the operating temperature range to -40°C to 85°C. The sensor interface features a moisture-proof seal to ensure stable operation in environments with 95% relative humidity.

[0043] The dynamic threshold calculation module incorporates an environmental temperature and humidity compensation mechanism. During the historical data matching phase, the adaptive learning algorithm uses environmental temperature and humidity as independent weighting factors in similarity calculations. For historical data points under high temperature and humidity conditions, the algorithm automatically increases their weighting coefficients to ensure that the generated dynamic thresholds better meet safety requirements in harsh environments. The risk probability assessment module adds an environmental stress factor, calculated based on the difference between real-time temperature and humidity data and the equipment material's tolerance characteristics. When the ambient temperature exceeds 40 degrees Celsius and the relative humidity exceeds 80%, the risk probability calculation model automatically increases the output probability value by 10%, triggering an early warning response.

[0044] The tiered early warning decision-making module incorporates an environmental linkage strategy. When the environmental monitoring unit reports extreme weather conditions, the module automatically lowers the warning thresholds for each level: the first threshold is adjusted to 0.25, the second threshold to 0.5, and the third threshold to 0.7. Simultaneously, the second-level warning adds environmental risk alerts, and the third-level warning prioritizes the execution of environmental control commands, such as activating forced ventilation or dehumidification devices, before implementing load adjustments.

[0045] The feedback learning and model update module establishes a dedicated environmental condition database, independently storing early warning performance data under different environmental conditions. The model optimization process distinguishes between normal and extreme environment datasets, training two sets of model parameters separately. The system automatically switches between applicable models based on real-time environmental data, ensuring optimal early warning accuracy under various conditions. The module also integrates a climate prediction interface, which can load model parameters corresponding to the expected environment 24 hours in advance to achieve preventative early warning.

[0046] This embodiment addresses the performance degradation of the early warning system under special operating conditions through enhanced environmental adaptability design. The environmental compensation mechanism and linkage strategy significantly improve the system's reliability under harsh conditions, making it suitable for thermal power plants in special environments such as coastal areas and tropical climates. The system possesses operating condition identification and adaptive switching capabilities, achieving intelligent early warning and protection covering the entire environment.

[0047] Those skilled in the art will understand that the above embodiments are specific implementations of the present invention, and in practical applications, various changes can be made in form and detail without departing from the spirit and scope of the present invention.

Claims

1. An adaptive early warning system for overload of electrical equipment in a thermal power plant, characterized in that, include: The data acquisition and fusion module is used to collect multi-dimensional operating parameters of electrical equipment in real time, and to perform time synchronization and feature extraction on the collected heterogeneous data to form a unified format of equipment status vector. The dynamic threshold calculation module is connected to the data acquisition and fusion module. It is used to receive the device status vector and dynamically generate an overload warning threshold that matches the current device operating conditions based on the device's historical operating data, real-time load curve and environmental parameters through an embedded adaptive learning algorithm. The risk probability assessment module, connected to the dynamic threshold calculation module and the data acquisition and fusion module, is used to compare the real-time monitored operating parameters with the dynamically generated early warning thresholds, and combine them with equipment health indicators to calculate a quantified overload risk probability value. The graded early warning decision module is connected to the risk probability assessment module. It is used to execute preset graded decision logic based on the overload risk probability value and generate early warning signals or control commands of different levels. The feedback learning and model update module is connected to various modules of the system. It is used to continuously monitor the actual effect of early warning decisions and the operating status of equipment, record the actual operating status of equipment after the early warning is issued, and periodically optimize and update the parameters of the adaptive learning algorithm in the dynamic threshold calculation module and the risk probability calculation model in the risk probability assessment module using the recorded early warning accuracy and false alarm rate indicators.

2. The adaptive early warning system for overload of electrical equipment in thermal power plants according to claim 1, characterized in that, The data acquisition and fusion module specifically includes: Multi-channel signal conditioning circuit and high-precision analog-to-digital converter are used to synchronously acquire current, voltage, temperature, vibration amplitude and ambient temperature and humidity parameters of electrical equipment; The data preprocessing unit is connected to the multi-channel signal conditioning circuit and the high-precision analog-to-digital converter, and is used to perform filtering and noise reduction, outlier removal and missing data interpolation on the raw data. The feature extraction engine, connected to the data preprocessing unit, is used to extract key features characterizing the operating status of the equipment from the preprocessed time-series data. These key features include the short-term fluctuation variance of the load current, the equipment temperature rise rate, and the energy distribution of the vibration energy spectrum in a specific frequency band.

3. The adaptive early warning system for overload of electrical equipment in thermal power plants according to claim 1, characterized in that, The adaptive learning algorithm execution process in the dynamic threshold calculation module includes: Read the current load level and ambient temperature parameters from the device status vector; Access the device historical operation database stored in local non-volatile memory, which records long-term safe operation boundary data of the device under different load and different ambient temperature combinations; Based on the currently read parameter combination, perform similar operating condition matching in the device's historical operation database; Using the matched historical data points, a weighted moving average model is used to predict the upper limit of the safe operating threshold of the equipment under the current operating conditions. The predicted upper limit of the safe operation threshold is multiplied by a preset safety factor, which is dynamically adjusted according to the cumulative operating time of the equipment.

4. The adaptive early warning system for overload of electrical equipment in thermal power plants according to claim 3, characterized in that, The weighting coefficients of the weighted moving average model are determined by the similarity of the working conditions and the temporal proximity of the data points.

5. The adaptive early warning system for overload of electrical equipment in thermal power plants according to any one of claims 1 to 4, characterized in that, The execution process of the risk probability calculation model in the risk probability assessment module includes: Calculate the relative deviation between real-time operating parameters and dynamic overload warning threshold; A device health degradation factor is introduced, which is calculated by a linear regression model based on the device's cumulative runtime, historical overload counts, and recent maintenance records. The relative deviation degree and the equipment health decay factor are combined to generate an overload risk probability value between 0 and 1.

6. The adaptive early warning system for overload of electrical equipment in thermal power plants according to claim 5, characterized in that, The overload risk probability value is proportional to the product of the relative deviation and the equipment health decay factor.

7. The adaptive early warning system for overload of electrical equipment in thermal power plants according to any one of claims 1 to 4, characterized in that, The hierarchical early warning decision-making module pre-sets three risk probability intervals and corresponding decision actions: No warning signal is issued when the overload risk probability value is lower than the first threshold; When the overload risk probability value is between the first threshold and the second threshold, a first-level warning signal is generated, triggering a visual prompt on the system's human-machine interface and recording the event to the operation log. When the overload risk probability value exceeds the second threshold but is lower than the third threshold, a level 2 warning signal is generated, triggering enhanced visual cues, audible alarms, and sending notification information to the operator's mobile terminal; When the overload risk probability value reaches or exceeds the third threshold, a level 3 early warning signal is generated, and control commands are output to the power plant's distributed control system to execute load reduction operations.

8. The adaptive early warning system for overload of electrical equipment in thermal power plants according to any one of claims 1 to 4, characterized in that, The overload adaptive early warning system for electrical equipment in thermal power plants comprises various modules that communicate and transmit commands via an industrial Ethernet network, forming a layered distributed system architecture. The data acquisition and fusion module is deployed in the field control layer close to the electrical equipment; The dynamic threshold calculation module, risk probability assessment module, and hierarchical early warning decision-making module are deployed at the plant-level monitoring information system layer; The feedback learning and model update module is deployed at the plant-level management information system layer.

9. The adaptive early warning system for overload of electrical equipment in thermal power plants according to any one of claims 1 to 4, characterized in that, The feedback learning and model update module is specifically used for: Record equipment operation data for 300 seconds after each warning is issued; If the current exceeds the rated value by 20% or the temperature exceeds the insulation class limit during the period, it is marked as a true overload event; The accuracy and false alarm rates of early warnings are calculated every 24 hours. If the false positive rate is higher than 5% or the accuracy is lower than 90% for three consecutive days, the model parameter optimization process is triggered.

10. The adaptive early warning system for overload of electrical equipment in thermal power plants according to claim 9, characterized in that, The model parameter optimization process uses the gradient descent algorithm to adjust the weight calculation coefficients in the adaptive learning algorithm and the scale parameters in the risk probability calculation model. The optimization objective is to minimize the early warning error function.

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