A compensation method based on temperature attenuation correction power factor of heat storage material

Abstract

This invention provides a power factor compensation method based on the temperature decay correction of thermal storage materials, belonging to the technical field of thermal energy storage systems. The invention achieves this by setting up a thermal storage material sensor array inside the power factor compensation device to monitor temperature decay characteristics in real time, establishing a thermal storage material temperature decay database to store historical data, calculating the temperature decay correction coefficient based on the thermal conduction mechanism equation of the thermal storage material and classifying compensation modes, establishing a thermal storage material temperature decay prediction model to generate advanced compensation control commands, optimizing the switching sequence of fast-switching capacitor banks using the traveling salesman problem algorithm, and executing standard compensation mode, fast dynamic compensation, and deep compensation mode respectively when the temperature decay correction coefficient is in different ranges. The invention also solves the technical problem of power factor compensation lag by adjusting the capacitor switching time interval through the thermal storage material dynamic response equation.

Description

Technical Field

[0001] This invention belongs to the technical field of thermal energy storage systems, and more specifically, relates to a compensation method for power factor correction based on the temperature decay of thermal storage materials. Background Technology

[0002] Power factor compensation (PFCC) technology is an important means of improving power quality and reducing reactive power losses in power systems. Traditional PFCC devices mainly rely on fixed capacitor banks and mechanical switching devices to achieve reactive power compensation. They maintain the power factor within a reasonable range by detecting the system's power factor and controlling the connection and disconnection of capacitors. In industrial production, commercial buildings, and power distribution systems, PFCC devices are widely used in applications with many inductive loads, such as motor drive systems, lighting equipment, and frequency converters. However, traditional PFCC devices suffer from slow response speed and low compensation accuracy. The operating time of mechanical switching devices is typically between several seconds and tens of seconds, making it difficult to keep up with the rapid changes in load in modern power systems. Fixed-capacity capacitor banks cannot achieve accurate reactive power compensation, easily leading to overcompensation or undercompensation. Furthermore, traditional compensation devices lack the ability to predict system load change trends and can only passively respond to changes in the power factor that have already occurred. Given the increasingly frequent and complex load fluctuations in the current power system, traditional power factor compensation devices are unable to effectively predict load changes and adjust compensation strategies in advance, resulting in compensation actions that always lag behind actual needs. This has led to increasingly prominent problems such as large power factor fluctuations and unsatisfactory compensation effects in the power system. Summary of the Invention

[0003] In view of this, the present invention provides a compensation method for power factor correction based on the temperature decay of thermal storage materials, which can solve the technical problem of power factor compensation lag in the prior art.

[0004] This invention is implemented as follows: It provides a power factor compensation method based on the temperature decay correction of thermal storage materials. A thermal storage material sensor array is installed inside the power factor compensation device to monitor the temperature decay characteristics and thermal energy storage status of the thermal storage material in real time. Basic data on the load current, voltage, and power factor of the power system are collected, along with the initial temperature, current temperature, and temperature decay rate of the thermal storage material, establishing a thermal storage material temperature decay database. A temperature decay correction coefficient is calculated based on the thermal storage material temperature decay data. Predictive adjustment of power factor compensation is achieved by utilizing the correlation between the thermal storage material temperature decay characteristics and power system load changes. A thermal storage material temperature decay prediction model is established, combined with… Historical temperature decay data from the thermal storage material temperature decay database and current load conditions are used to predict future power factor trends and generate advance compensation control commands. The switching sequence of fast-switching capacitor banks is optimized using the traveling salesman problem algorithm. Corresponding compensation modes are activated based on different ranges of the temperature decay correction coefficient. A thermal storage material temperature decay feedback control loop is established, dynamically adjusting the thermal storage material temperature decay correction parameters and compensation strategy based on the deviation between the actual and expected power factor compensation effects. The sampling frequency of the thermal storage material sensor array and the update cycle of the thermal storage material temperature decay database are adaptively adjusted based on the output of the thermal storage material temperature decay feedback control loop.

[0005] The thermal storage material sensor array includes a temperature sensor, a heat flow sensor, and a heat capacity detection sensor. The temperature sensor monitors the temperature distribution on the surface and inside of the thermal storage material, the heat flow sensor measures the rate of heat transfer, and the heat capacity detection sensor assesses changes in the thermal storage capacity of the thermal storage material.

[0006] The thermal storage material temperature decay database is used to store and manage historical temperature change records, thermodynamic parameters, and environmental condition data of the thermal storage material, providing data support for temperature decay prediction and compensation strategy formulation.

[0007] The temperature decay correction coefficient is calculated using the thermal conduction mechanism equation of the thermal storage material. The temperature decay correction coefficient is equal to the ratio of the thermal conductivity of the thermal storage material to the standard thermal conductivity multiplied by the ratio of the specific heat capacity to the standard specific heat capacity, and then multiplied by the ratio of the density to the standard density.

[0008] Specifically, when the temperature attenuation correction coefficient ∈ [0.2, 0.6], the standard compensation mode is executed; when the temperature attenuation correction coefficient ∈ (0.6, 0.85], the fast switching capacitor bank is activated for dynamic compensation; and when the temperature attenuation correction coefficient ∈ (0.85, 1.0], the deep compensation mode is activated.

[0009] The thermal storage material temperature decay prediction model is based on the thermodynamic properties of the thermal storage material to establish a mathematical prediction model, predict the temperature decay law of the thermal storage material in the next 5 to 15 minutes, and judge the load change trend and power factor change direction of the power system in advance.

[0010] The advance compensation control command generates advance compensation action commands based on the prediction results of the thermal storage material temperature decay prediction model. These commands include parameters such as capacitor switching timing, compensation capacity, and compensation duration, enabling the power factor compensation device to be pre-adjusted before load changes.

[0011] The Traveling Salesman Problem algorithm treats each capacitor bank as a city node, uses switching energy consumption as the path weight, and solves for the optimal switching path to minimize energy loss during the compensation process.

[0012] The fast-switching capacitor bank uses a vacuum contactor or solid-state switching device to form a capacitor switching device, which has a millisecond-level response speed and adjusts the switching frequency and capacity combination in real time according to the change in the temperature decay rate of the thermal storage material.

[0013] This also includes adjusting the capacitor switching time interval according to the temperature decay rate of the thermal storage material. The capacitor switching time interval is calculated by the dynamic response equation of the thermal storage material. The capacitor switching time interval is equal to the ratio of the time constant to the standard time constant multiplied by the ratio of the temperature change rate to the standard temperature change rate divided by the ratio of the heat flux density to the standard heat flux density.

[0014] The deep compensation mode adds thermal energy recovery processing of thermal storage materials and dual power factor correction processing, while reducing the compensation response threshold to 70% of the standard value. Thermal energy recovery processing of thermal storage materials slows down the temperature decay rate by recovering and utilizing the thermal energy released by the thermal storage materials.

[0015] The power factor dual correction process employs both a correction algorithm based on the temperature decay of the thermal storage material and a traditional power factor detection correction algorithm for dual correction to improve compensation accuracy and system stability.

[0016] The thermal storage material temperature decay feedback control loop compares and analyzes the actual power factor compensation effect with the expected effect, and dynamically optimizes the thermal storage material temperature decay correction parameters by calculating the deviation value, thereby realizing adaptive adjustment of the compensation strategy and continuous improvement of compensation performance.

[0017] The deviation value is obtained through the power factor deviation calculation equation. The deviation value is equal to the ratio of the actual power factor to the standard power factor minus the ratio of the target power factor to the standard power factor, and then multiplied by the ratio of the weighting factor to the standard weighting factor.

[0018] The adaptive adjustment adjusts the sampling frequency of the thermal storage material sensor array and the update cycle of the thermal storage material temperature decay database based on the output results of the thermal storage material temperature decay feedback control loop, thereby achieving closed-loop optimization control of the entire compensation system.

[0019] This invention establishes a sensor array for thermal storage materials to monitor their temperature decay characteristics in real time. Combined with historical data from a thermal storage material temperature decay database, a temperature decay prediction model is built to generate proactive compensation control commands, achieving predictive adjustment of power factor compensation. This invention utilizes the correlation between the temperature decay characteristics of thermal storage materials and changes in power system load. A temperature decay correction coefficient quantifies the impact of the thermal storage material's state on the power factor. A traveling salesman problem algorithm is employed to optimize the capacitor switching sequence, and a fast-switching capacitor bank achieves millisecond-level response speeds. This effectively solves the technical defects of traditional compensation devices, such as response lag and low compensation accuracy. Furthermore, a feedback control loop for thermal storage material temperature decay enables adaptive adjustment of compensation parameters. In summary, this invention establishes a proactive compensation mechanism through real-time monitoring and prediction of thermal storage material temperature decay characteristics, solving the technical problem of lag in power factor compensation. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention.

[0021] Figure 2 This is a graph showing the temperature decay characteristics of the thermal storage material in the embodiment.

[0022] Figure 3 This is a comparison chart of the power factor compensation effects in the embodiments. Detailed Implementation

[0023] 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.

[0024] like Figure 1 The diagram shown is a flowchart of a power factor compensation method based on temperature decay correction of thermal storage materials provided by the present invention. This method includes the following steps: S01. A thermal storage material sensor array is set inside the power factor compensation device. The thermal storage material sensor array includes a temperature sensor, a heat flow sensor, and a heat capacity detection sensor, which are used to monitor the temperature decay characteristics and thermal energy storage status of the thermal storage material in real time. S02. Collect basic data on load current, voltage, and power factor of the power system, and simultaneously obtain the initial temperature, current temperature, and temperature decay rate of the thermal storage material to establish a thermal storage material temperature decay database. The thermal storage material temperature decay database is used to store historical temperature change records and thermodynamic parameters. S03. Calculate the temperature decay correction coefficient based on the temperature decay data of the thermal storage material. The temperature decay correction coefficient is calculated through the thermal conduction mechanism equation of the thermal storage material. When the temperature decay correction coefficient ∈ [0.2, 0.6], the standard compensation mode is executed. S04. Establish a temperature decay prediction model for thermal storage materials, combine historical temperature decay data in the thermal storage material temperature decay database with the current load status to predict the power factor change trend in the next 5 to 15 minutes, and generate advance compensation control commands. S05. The switching sequence of fast-switching capacitor banks is optimized using the Traveling Salesman Problem algorithm. Each capacitor bank is regarded as a city node, and the switching energy consumption is used as the path weight. The optimal switching path is solved to minimize the energy loss during the compensation process. S06. When the temperature decay correction coefficient ∈ (0.6, 0.85], the fast switching capacitor bank is started for dynamic compensation. The capacitor switching time interval is adjusted according to the temperature decay rate of the thermal storage material. The capacitor switching time interval is calculated by the dynamic response equation of the thermal storage material. S07. When the temperature attenuation correction coefficient ∈ (0.85, 1.0], the deep compensation mode is activated, which increases the heat energy recovery treatment of the thermal storage material and the dual correction treatment of the power factor, while reducing the compensation response threshold to 70% of the standard value. S08. Establish a feedback control loop for the temperature decay of thermal storage materials. Based on the deviation between the actual power factor compensation effect and the expected effect, dynamically adjust the temperature decay correction parameters and compensation strategy of the thermal storage materials. The deviation value is obtained through the power factor deviation calculation equation. S09. Based on the output of the thermal storage material temperature decay feedback control loop, the sampling frequency of the thermal storage material sensor array and the update cycle of the thermal storage material temperature decay database are adaptively adjusted to achieve closed-loop optimization control of the entire compensation system.

[0025] A thermal storage material sensor array refers to a multi-sensor integrated system used to detect changes in the thermodynamic parameters of thermal storage materials. Temperature sensors monitor the surface and internal temperature distribution of the thermal storage material, heat flow sensors measure the rate of heat transfer, and heat capacity sensors assess changes in the thermal storage capacity of the material.

[0026] The thermal storage material temperature decay database is an information system used to store and manage historical temperature change records, thermodynamic parameters, and environmental condition data of thermal storage materials, providing data support for temperature decay prediction and compensation strategy formulation.

[0027] The temperature decay correction coefficient is a dimensionless correction parameter calculated based on the ratio of the current temperature to the initial temperature of the thermal storage material, combined with the material's thermophysical parameters. It is used to quantify the impact of the degree of temperature decay of the thermal storage material on power factor compensation.

[0028] The heat conduction mechanism equation for thermal storage materials is used to calculate the temperature distribution and attenuation law during the heat transfer process within the thermal storage material. The inputs include the thermal conductivity, specific heat capacity, and density of the thermal storage material, and the output is a temperature attenuation correction coefficient. The heat conduction mechanism equation for thermal storage materials is expressed as follows: the temperature attenuation correction coefficient equals the ratio of the thermal conductivity of the thermal storage material to the standard thermal conductivity multiplied by the ratio of the specific heat capacity to the standard specific heat capacity, and then multiplied by the ratio of the density to the standard density.

[0029] The thermal storage material temperature decay prediction model is a mathematical prediction model based on the thermodynamic properties of thermal storage materials and historical temperature change data in the thermal storage material temperature decay database. It is used to predict the temperature decay pattern of thermal storage materials in the future time period, thereby predicting the load change trend and power factor change direction of the power system in advance.

[0030] The advance compensation control command is a pre-compensation action command generated based on the prediction results of the thermal storage material temperature decay prediction model. It includes parameters such as capacitor switching timing, compensation capacity, and compensation duration, so as to adjust the working state of the power factor compensation device in advance before the load changes.

[0031] The Traveling Salesman Problem (TSP) algorithm is a combinatorial optimization algorithm for finding the shortest path length to visit all nodes. In this method, it is used to optimize the switching order of fast-switching capacitor banks. By treating each capacitor bank as a node and the switching energy consumption as the edge weight, the optimal switching path is found to reduce the total energy consumption of the compensation process.

[0032] Fast-switching capacitor banks are capacitor switching devices composed of vacuum contactors or solid-state switching devices. They have millisecond-level response speeds and adjust the switching frequency and capacity combination in real time according to the changes in the temperature decay rate of the thermal storage material.

[0033] The kinetic response equation for thermal storage materials is used to calculate the dynamic response characteristics of thermal storage materials under external thermal disturbances. The inputs include the rate of temperature change, heat flux density, and time constant, and the output is the capacitor switching time interval. The kinetic response equation for thermal storage materials is expressed as follows: The capacitor switching time interval is equal to the ratio of the time constant to the standard time constant multiplied by the ratio of the rate of temperature change to the standard rate of temperature change, divided by the ratio of the heat flux density to the standard heat flux density.

[0034] The deep compensation mode is an enhanced compensation mode that is activated when the temperature of the thermal storage material decreases significantly. It slows down the rate of temperature drop of the thermal storage material by increasing the heat recovery process, and at the same time, it improves the accuracy and stability of power factor compensation by using dual power factor correction.

[0035] Thermal energy recovery treatment of thermal storage materials refers to the technical process of slowing down the rate of temperature decay by recovering and utilizing the thermal energy released by thermal storage materials, including thermal energy collection, conversion and reuse.

[0036] Dual power factor correction refers to a processing method that simultaneously employs a correction algorithm based on the temperature decay of thermal storage materials and a traditional power factor detection correction algorithm to improve compensation accuracy and system stability.

[0037] The thermal storage material temperature decay feedback control loop is a closed-loop control system that compares and analyzes the actual power factor compensation effect with the expected effect. By calculating the deviation value, it dynamically optimizes the thermal storage material temperature decay correction parameters, thereby achieving adaptive adjustment of the compensation strategy and continuous improvement of compensation performance.

[0038] The power factor deviation calculation equation is used to calculate the difference between the actual power factor and the target power factor. The inputs include the actual power factor, the target power factor, and the weighting factor; the output is the deviation value. The power factor deviation calculation equation is expressed as follows: the deviation value equals the ratio of the actual power factor to the standard power factor minus the ratio of the target power factor to the standard power factor, multiplied by the ratio of the weighting factor to the standard weighting factor.

[0039] In addition, the present invention can also be implemented by a computer to form a compensation system for correcting the power factor based on the temperature decay of thermal storage materials. The computer is equipped with a readable storage medium, which stores program instructions. When the program instructions are run in the computer, they are used to execute the above-mentioned method.

[0040] The specific implementation methods of the above steps are described in detail below.

[0041] The specific implementation of step S01 is as follows: First, a heat storage material sensor array is installed inside the core control cabinet of the power factor compensation device. This array adopts a distributed arrangement scheme. The temperature sensor is a PT100 platinum resistance thermometer with a measurement accuracy of ±0.1℃. The arrangement density is one sensor node per square centimeter, forming a gridded monitoring network. The heat flow sensor is a thin-film sensor manufactured based on the thermopile principle, with a response time of less than 1 second and a measurement range of 0 to 1000. It is used to detect the heat flow distribution on the surface and inside of thermal storage materials in real time; the heat capacity detection sensor is based on the principle of differential scanning calorimetry, and calculates the specific heat capacity by measuring the heat flow change of the thermal storage material during the heating process, with a measurement range of 0.5 to 5. The entire sensor array is connected to the data acquisition system via fieldbus technology, with a sampling frequency set to 10Hz to ensure the capture of transient thermal characteristic changes in the thermal storage material. The purpose of this step is to establish a high-precision real-time monitoring foundation for the thermodynamic parameters of the thermal storage material, providing a reliable data source for subsequent temperature decay analysis and power factor correction.

[0042] The specific implementation of step S02 is as follows: The data acquisition module simultaneously acquires multi-dimensional data from the power system and the thermal storage material sensor array. The power system data includes the effective value of the three-phase load current, the effective value of the line voltage, active power, reactive power, and instantaneous power factor, with a sampling frequency of 50Hz and a data accuracy requirement of 0.1%. The thermal storage material-related data includes the initial temperature. Current temperature and rate of temperature change The temperature decay rate was obtained by fitting temperature time-series data using the least squares method. A temperature decay database for thermal storage materials was established using a time-series database architecture. The storage structure included timestamps, temperature values, heat flux density, ambient temperature, humidity, and other environmental parameters. The data compression ratio was set to 10:1, retaining 99.9% of the original data accuracy. The database was updated every 1 minute, and historical data was retained for 1 year. A sliding window algorithm was used to maintain the stability of the database capacity. This step used multivariate data fusion technology to achieve correlation analysis between the operating status of the power system and the thermodynamic state of the thermal storage materials, providing a complete set of input parameters for the temperature decay correction algorithm.

[0043] The specific implementation of step S03 is as follows: The temperature decay correction coefficient is calculated based on the heat conduction mechanism equation of the thermal storage material. The input parameters include the current thermal conductivity λ, specific heat capacity c, material density ρ, and the corresponding standard reference values ​​λ0, c0, and ρ0. The standard reference values ​​are selected as the thermophysical properties of the thermal storage material at a standard temperature of 25℃. The calculation process adopts a three-factor multiplication model, that is, the correction coefficient is equal to the product of the ratio of thermal conductivity, the ratio of specific heat capacity, and the ratio of density. When the calculated temperature decay correction coefficient is in the range of 0.2 to 0.6, the system determines that the thermal storage material is in normal working condition and starts the standard compensation mode. At this time, the compensation strategy adopts the traditional PID control algorithm, and the control parameters are set as proportional coefficient 0.8, integral coefficient 0.2, and derivative coefficient 0.1. The core function of this step is to quantify the thermodynamic state change of the thermal storage material into numerical parameters that can be used for power factor compensation control, and realize the numerical mapping relationship between thermodynamic parameters and power system compensation parameters.

[0044] The specific implementation of step S04 is as follows: The thermal storage material temperature decay prediction model adopts a long short-term memory neural network architecture. The network structure includes an input layer, two hidden layers, and an output layer. The number of neurons in the hidden layers are 64 and 32, respectively, and the activation function is the hyperbolic tangent function. The training data comes from the historical data of the thermal storage material temperature decay database for the most recent 30 days. Data preprocessing uses the Z-score normalization method, the training batch size is set to 32, and the learning rate is set to 0.001. The model input variables include 12 feature parameters such as current temperature, temperature change rate, heat flux density, ambient temperature, and load power. The output is the predicted power factor value per minute for the next 5 to 15 minutes. The advanced compensation control command generation is based on the prediction results. When the predicted power factor is lower than the target value of 0.95, a capacitor activation command is generated 2 to 5 minutes in advance. When the predicted power factor is higher than 1.0, a capacitor deactivation command is generated. The command parameters include the activation / deactivation capacity, action time, and duration. This step achieves early identification of the power factor change trend through time series prediction technology, enabling the compensation system to have proactive adjustment capabilities.

[0045] The specific implementation of step S05 is as follows: The Traveling Salesman Problem algorithm optimization adopts a genetic algorithm to solve the problem. The n independent units of the fast-switching capacitor bank are considered as n city nodes, and the distance between each node is defined as the total energy consumption when the two capacitor banks are sequentially switched. The genetic algorithm parameters include a population size of 100, a crossover probability of 0.8, a mutation probability of 0.02, and a maximum number of iterations of 500. The switching energy consumption calculation considers the switching losses of the switching devices, the charging and discharging losses of the capacitors, and the transient impact losses of the system, with weight coefficients set to 0.4, 0.4, and 0.2, respectively. The fitness function is the reciprocal of the total energy consumption, the selection operator is the roulette wheel selection method, the crossover operator is the sequential crossover method, and the mutation operator is the exchange mutation method. The algorithm convergence criterion is that the optimal solution changes by less than 0.1% for 50 consecutive generations or reaches the maximum number of iterations. This step minimizes the energy loss during the capacitor bank switching process through a combined optimization algorithm, improves the overall energy efficiency ratio of the compensation system, and reduces the system operating cost.

[0046] The specific implementation of step S06 is as follows: When the temperature decay correction coefficient is in the range of 0.6 to 0.85, the system starts the dynamic compensation mode of the fast switching capacitor bank. At this time, the temperature decay rate of the thermal storage material is accelerated, requiring more frequent power factor adjustment; the capacitor switching time interval is calculated based on the dynamic response equation of the thermal storage material, and the input parameters include the time constant τ and the temperature change rate. Heat flux density q and the corresponding standard reference value τ0 , The calculation process employs a three-factor ratio operation, where the switching time interval is equal to the product of the ratio of the time constant and the ratio of the temperature change rate, divided by the heat flux density ratio. The calculation range for the switching time interval is 0.5 to 5 seconds; when the calculation result exceeds this range, boundary value constraints are applied. The fast-switching capacitor bank uses a thyristor-controlled vacuum contactor with a response time of less than 10 milliseconds and a switching accuracy of 1%. This step dynamically adjusts the capacitor switching frequency to adapt to the rapid changes in the thermodynamic state of the thermal storage material, maintaining the real-time performance and accuracy of power factor compensation.

[0047] The specific implementation of step S07 is as follows: When the temperature decay correction coefficient exceeds 0.85 but does not exceed 1.0, the temperature decay of the thermal storage material is severe, and the system activates the deep compensation mode. This mode includes two parallel subsystems: thermal energy recovery processing of thermal storage material and dual power factor correction processing. The thermal energy recovery processing of thermal storage material adopts the heat pump cycle principle, recovering the low-grade heat energy released by the thermal storage material through refrigerant circulation, with a recovery efficiency of over 60%. The recovered heat energy is used to preheat new thermal storage material or supplement the system's heat loss. The dual power factor correction processing runs a correction algorithm based on the temperature decay of thermal storage material and a traditional power factor detection correction algorithm simultaneously. The output results of the two algorithms are fused by a weighted average method, and the weight coefficient is dynamically adjusted according to the correction coefficient value. When the correction coefficient is close to 1.0, the weight of the thermal storage material correction algorithm increases to 0.7. The compensation response threshold is reduced to 70% of the standard value, that is, from the original power factor deviation of 0.05 to 0.035, improving the system's sensitivity to small power factor changes. This step addresses extreme conditions where the performance of thermal storage materials is severely degraded through multiple technical means, ensuring that the power factor compensation system can still operate stably under harsh conditions.

[0048] The specific implementation of step S08 is as follows: the temperature decay feedback control loop of the thermal storage material adopts adaptive control theory to establish a deviation evaluation mechanism between the actual power factor and the target power factor; the input parameters of the power factor deviation calculation equation include the actual power factor. Target power factor Weighting factors and the corresponding standard reference values , The calculation process employs a normalized deviation method, subtracting the ratios of the actual and target values ​​from the standard values, and then multiplying by the weighting factor ratio to obtain the final deviation value. The sign of the deviation value indicates whether the power factor is leading or lagging, and the absolute value reflects the degree of deviation. Based on the deviation value, the temperature decay correction parameters for the thermal storage material are dynamically adjusted, including key parameters such as the weight allocation in the correction coefficient calculation, the learning rate of the prediction model, and the capacitor switching threshold. The parameter adjustment uses a gradient descent algorithm with a step size of 0.01, and the convergence condition is that the deviation value change is less than 0.001 after 10 consecutive adjustments. This step achieves automatic optimization of the compensation strategy through a closed-loop feedback mechanism, enabling the system to have self-learning and adaptive capabilities.

[0049] The specific implementation of step S09 is as follows: Based on the output result of the thermal storage material temperature decay feedback control loop, the system adaptively adjusts the sensor array sampling frequency and database update cycle. When the absolute value of the deviation is less than 0.01, it indicates that the system is operating stably. The sensor array sampling frequency is reduced from the standard 10Hz to 5Hz, and the database update cycle is extended from 1 minute to 2 minutes to reduce system power consumption and data processing load. When the absolute value of the deviation is greater than 0.05, it indicates that the system has experienced a large disturbance. The sampling frequency is increased to 20Hz, and the update cycle is shortened to 30 seconds to enhance the dynamic response capability of the system. The adaptive adjustment algorithm adopts the moving average filtering method, and the window length is set to 10 sampling cycles to avoid frequent parameter adjustments caused by instantaneous disturbances. The system also sets upper and lower limits for the sampling frequency and update cycle. The sampling frequency range is from 1Hz to 50Hz, and the update cycle range is from 10 seconds to 5 minutes. The entire closed-loop optimization control process is managed by a state machine, including three working modes: stable state, adjustment state, and fault state. The state transition conditions are based on the magnitude and duration of the deviation. This step optimizes the allocation of system resources through intelligent parameter adaptive adjustment, minimizing system energy consumption and computational load while ensuring control accuracy.

[0050] It should be noted that the key technical ideas of this invention mainly include four aspects: the coupling mechanism between the temperature decay characteristics of thermal storage materials and power factor compensation, capacitor bank switching optimization based on the traveling salesman problem algorithm, multiple correction strategies for deep compensation mode, and adaptive closed-loop control system.

[0051] The coupling mechanism between the temperature decay characteristics of thermal storage materials and power factor compensation establishes a mathematical mapping relationship between thermodynamic parameters and power system parameters, quantifying the temperature decay process of the thermal storage material into control parameters that can be directly used for power factor correction. Compared to traditional power factor compensation methods based on electrical parameter detection, this technology utilizes the thermodynamic memory effect of thermal storage materials to predict load change trends, achieving a technological leap from passive response to active prediction. Traditional methods can only compensate after a power factor deviation has occurred, while this technology, through the analysis of the temperature decay law of thermal storage materials, can predict the direction and magnitude of power factor changes in advance, significantly improving the timeliness and accuracy of compensation.

[0052] Capacitor bank switching optimization based on the Traveling Salesman Problem (TSP) algorithm introduces combinatorial optimization theory into the field of power factor compensation. Through mathematical modeling, the capacitor bank switching sequence optimization problem is transformed into a classic TSP problem. Compared to traditional step-by-step or random switching methods, this approach globally optimizes the switching path, minimizing energy loss and equipment impact during the switching process. Traditional switching strategies often result in unnecessary energy waste and equipment wear, while this technology, through algorithmic optimization, selects the optimal switching sequence and timing while meeting power factor compensation requirements, effectively reducing system operating costs and maintenance needs.

[0053] The deep compensation mode employs a multi-correction strategy to address extreme conditions where thermal storage material performance severely degrades. It establishes a collaborative processing mechanism combining heat recovery and power factor correction. Compared to traditional compensation systems that are prone to failure under harsh conditions, this approach slows down the rate of thermal storage material performance degradation through heat recovery and employs dual algorithms to ensure compensation accuracy, significantly improving the system's reliability and stability under extreme conditions. Traditional systems often experience reduced compensation accuracy or even system failure when thermal storage material performance declines, while this technology, through multiple safeguard mechanisms, ensures stable operation of the system throughout its entire lifecycle.

[0054] The adaptive closed-loop control system achieves automatic optimization of compensation strategies and intelligent allocation of system resources through real-time deviation analysis and dynamic parameter adjustment. Compared with traditional open-loop or simple closed-loop control methods, this technology has self-learning and adaptive capabilities, and can automatically adjust control parameters and sampling strategies according to the system operating status, minimizing system power consumption while ensuring control accuracy.

[0055] The synergistic effect of these four key technological approaches forms a complete intelligent power factor compensation technology system. The coupling mechanism of thermal storage materials provides predictive capabilities, the traveling salesman algorithm ensures efficient operation, the deep compensation mode guarantees reliability under extreme conditions, and the adaptive control system achieves intelligent management. Compared to existing technologies, this synergistic technology system not only improves the accuracy and response speed of power factor compensation but also significantly reduces system energy consumption and operation and maintenance costs, while enhancing the system's robustness and adaptability. It represents an important development direction for power factor compensation technology from traditional electrical control to intelligent thermoelectric coupling control.

[0056] It should be noted that traditional power factor compensation devices face the technical challenge of energy consumption optimization during capacitor switching. Existing capacitor switching control strategies typically employ fixed switching sequences or simple logical judgments, failing to consider the energy consumption differences among different capacitor banks during switching, leading to unnecessary energy losses during compensation. This invention introduces the Traveling Salesman Problem algorithm, treating each capacitor bank as a city node, and using switching energy consumption as the path weight to solve for the optimal switching path to minimize energy losses during compensation, thus achieving intelligent optimization of the capacitor switching sequence. This switching control strategy based on combinatorial optimization algorithms can significantly reduce the system's operating energy consumption while ensuring compensation effectiveness, thereby improving the overall efficiency of the power factor compensation device.

[0057] It should be noted that traditional power factor compensation devices lack the technical capability to guarantee compensation accuracy under extreme operating conditions. Under extreme conditions such as high temperature decay of the thermal storage material or drastic changes in system load, traditional compensation devices often experience decreased compensation accuracy and deteriorated system stability. This invention addresses this issue by designing a deep compensation mode that automatically activates when the temperature decay correction coefficient exceeds 0.85. This mode enhances thermal energy recovery from the thermal storage material to slow down the temperature decay rate. Simultaneously, it employs dual power factor correction to improve compensation accuracy and lowers the compensation response threshold to 70% of the standard value to improve system sensitivity. This layered compensation strategy ensures high-precision power factor compensation under various operating conditions, enhancing the system's robustness and adaptability.

[0058] Specifically, the principle of this invention is as follows: The fundamental principle behind solving the problem of power factor compensation lag lies in utilizing the temperature decay characteristics of thermal storage materials as a leading indicator of power system load changes. During the operation of power equipment, thermal storage materials absorb and release heat energy, and their temperature changes often precede changes in the electrical load. By establishing a thermal storage material sensor array to monitor the temperature decay rate and heat flow distribution in real time, the changing trend of the system load can be detected in advance. The thermal storage material temperature decay prediction model is based on thermodynamic principles and historical data analysis. It calculates the temperature decay correction coefficient through the thermal conduction mechanism equation of the thermal storage material, quantifying the impact of the thermal storage material's state on the power factor compensation requirement. When the temperature decay correction coefficient is in different ranges, the system automatically switches the corresponding compensation mode, realizing intelligent adjustment of the compensation strategy. The key to the logical consistency of this invention's technical solution lies in establishing a mapping relationship between thermal storage material temperature changes and power system load changes. It accurately calculates the capacitor switching time interval through the thermal storage material's dynamic response equation, enabling the compensation action to be synchronized with or even slightly ahead of load changes. The introduction of the traveling salesman problem algorithm further optimizes the switching sequence of multiple capacitors, minimizing energy loss and response time during the compensation process. The deep compensation mode design takes into account the special case when the temperature decay of the thermal storage material is high. Through thermal energy recovery processing of the thermal storage material and dual correction processing of power factor, it ensures that a good compensation effect can still be maintained under extreme conditions. The closed-loop design of the thermal storage material temperature decay feedback control loop realizes the continuous optimization of compensation parameters, ensuring the long-term stable operation of the entire system.

[0059] The following provides a specific embodiment 1 of the present invention. The specific implementation of each step in this embodiment 1 is described in detail below. In this embodiment, the specific implementation of steps S01-S02 is the same as described above, and will not be repeated in detail here.

[0060] The specific implementation of step S03 is as follows: The temperature decay correction coefficient is calculated based on the heat conduction mechanism equation of the thermal storage material. This equation considers the comprehensive influence of three key thermophysical parameters of the thermal storage material—thermal conductivity, specific heat capacity, and density—on temperature decay. The formula is expressed as follows: ; In the formula, This is a dimensionless temperature decay correction factor. The thermal conductivity of the heat storage material at the current moment is expressed in units of 1. ; Standard thermal conductivity, unit: The value is usually taken as the thermal conductivity at 25℃; This represents the specific heat capacity of the thermal storage material at the current moment, in units of... ; Standard specific heat capacity, unit: ; The density of the thermal storage material at the current moment, in units of ; Standard density, unit: The current thermal properties are obtained through real-time measurement by a sensor array and calculated using temperature correlation correction. The formula for calculating the temperature dependence of thermal conductivity is as follows: ; The formula for calculating the temperature dependence of specific heat capacity is as follows: ; The formula for calculating the temperature dependence of density is as follows: ; The formula for calculating temperature change is as follows: ; In the formula, The temperature of the thermal storage material at the current moment, in K; This is the initial reference temperature, in K, typically taken as 298K. The temperature coefficient of thermal conductivity, in units of . Experience value ; The specific heat capacity is the primary temperature coefficient, with units of . Experience value ; This is the second temperature coefficient of specific heat capacity, with units of . Experience value ; The temperature coefficient of density, in units of . Experience value When the calculated temperature decay correction factor At that time, the system executes the standard compensation mode.

[0061] The specific implementation of step S04 is as follows: The temperature decay prediction model for thermal storage materials is established based on a long short-term memory neural network, and the formula for constructing the input feature vector is expressed as follows: ; In the formula, for The input feature vector at time step 6 has a dimension of 1. The current temperature is in Kelvin (K). For reference temperature, the default value is 298K; The rate of change of temperature, in units of ; The reference temperature change rate is set to [default value]. ; Heat flux density, in units of ; For reference heat flux density, the default value is... ; Load power, in watts (W). This is a reference power rating, with a default value of 1000W. The current power factor is dimensionless. The reference power factor is 0.95 by default. Ambient humidity, in %; For reference humidity, the default value is 50%. The current time is expressed in seconds (s). The formula for the predicted output vector is as follows: ; In the formula, For the future The power factor prediction vector over time has a dimension of the number of prediction time points × 1; This is a mapping function for a long short-term memory neural network, which uses a gating mechanism to achieve the memorization and forgetting of time series features; This is the length of the historical time window, measured in time steps, typically 30 time steps. The time span is predicted in minutes, ranging from 5 to 15 minutes.

[0062] The specific implementation of step S05 is as follows: In the optimization of the traveling salesman problem algorithm, the formula for constructing the energy consumption matrix of fast switching capacitor banks is expressed as follows: ; In the formula, for Dimensional energy consumption matrix; For capacitor bank Switch to capacitor bank Energy consumption, dimensionless; This represents the total number of capacitor banks, typically ranging from 8 to 16. The capacitor bank is numbered, with values ​​ranging from 1 to... The formula for calculating the energy consumption of a single switch is as follows: ; The formula for calculating the switching loss of switching devices is expressed as follows: ; The formula for calculating capacitor charging and discharging losses is as follows: ; The formula for calculating the system transient impact loss is as follows: ; In the formula, The switching loss of the switching device is expressed in J; The charging and discharging loss of the capacitor is expressed in J. The transient impact loss of the system is expressed in J. For reference energy consumption, the default value is 1J; This is the arc voltage, measured in volts (V), typically 50V. This is the switching current, measured in amperes (A). This is the switching action time, measured in seconds (s), typically 0.01 seconds. For capacitor bank to capacitor bank The change in equivalent capacitance, expressed in F; For capacitor bank The operating voltage, in V; For capacitor bank The operating voltage, in V; This is the transient power loss function, in W. The system's transient settling time, expressed in seconds (s), is typically taken as 0.1 s. The fitness function formula for the genetic algorithm is as follows: ; In the formula, The fitness value is dimensionless. For the first cutting sequence Each capacitor bank is numbered; This constitutes a complete sequence of cutting paths; For sequence indices, the value range is 1 to... .

[0063] The specific implementation of step S06 is as follows: when the temperature decay correction coefficient The capacitor switching time interval is calculated based on the kinetic response equation of the thermal storage material, and the formula is expressed as follows: ; In the formula, The capacitor switching time interval, in seconds; It serves as the base time unit, with a value of 1 second, and is used for dimensional balance. This is the current time constant, in seconds. It is the standard time constant, with the unit being seconds, and is usually taken as 10 seconds; The current rate of temperature change, in units of ; Standard temperature change rate, in units of , defaults to ; The current heat flux density is expressed in units of... ; Standard heat flux density, unit: , defaults to .

[0064] The specific implementation of step S07 is as follows: when the temperature decay correction coefficient When this occurs, a deep compensation mode is activated, in which the compensation response threshold is adjusted to 70% of the standard value. The formula for calculating the fusion weight in the power factor dual correction processing is as follows: ; ; The formula for calculating the heat recovery efficiency of thermal storage materials is as follows: ; The formula for calculating recovered heat is expressed as follows: ; In the formula, The weights are dimensionless and are based on the temperature decay correction algorithm for thermal storage materials. The weights are for traditional power factor detection and correction algorithms; they are dimensionless. The thermal energy recovery efficiency of the thermal storage material is dimensionless. The amount of heat recovered is expressed in J. The total heat released by the thermal storage material, expressed in J; For reference recycling efficiency, the default value is 0.6; The efficiency of the heat pump is dimensionless and is typically taken as 0.85. The power consumption of the compressor is expressed in J. The coefficient of performance (COP) of a heat pump is dimensionless and is typically taken as 3.5.

[0065] The specific implementation of step S08 is as follows: In the feedback control loop for temperature decay of thermal storage material, the formula for calculating power factor deviation is expressed as follows: ; In the formula, This is the power factor deviation value, which is dimensionless. This is the actual power factor, which is dimensionless. The target power factor is dimensionless. The standard power factor is dimensionless and typically takes a value of 0.95. The weighting factor is dimensionless. This is the standard weighting factor, dimensionless, with a default value of 1.0. The dynamic parameter adjustment of the deviation value uses the gradient descent algorithm, and the parameter update formula is expressed as follows: ; In the formula, For the first The dimensions of the parameter values ​​for each iteration depend on the specific parameters. For the first The parameter values ​​of the next iteration, and Same dimensions; The learning rate is dimensionless and typically takes the value 0.01. Let be the partial derivative of the squared deviation with respect to the parameter, with dimensions . ; The iteration number is dimensionless.

[0066] The specific implementation of step S09 is as follows: The adaptive sampling frequency adjustment algorithm is based on the absolute value of the deviation value, and the adjustment formula is expressed as follows: ; In the formula, The adjusted sampling frequency is expressed in Hz. The reference sampling frequency is in Hz, and the default value is 10Hz. This is the frequency adjustment factor, which is dimensionless and usually takes a value of 1.0. The absolute value of the deviation is dimensionless. The maximum deviation value is dimensionless and set to 0.1. The formula for adjusting the database update cycle is as follows: ; In the formula, The adjusted update cycle is expressed in seconds. The baseline update cycle is in seconds (s), with a default value of 60 seconds. This is the periodic adjustment coefficient, which is dimensionless and usually takes the value of 0.5.

[0067] It needs to be explained that the specific implementation of the heat conduction mechanism equation of thermal storage materials is as follows: the equation is based on the basic principles of heat transfer, takes into account the comprehensive influence of the material's thermal conductivity, specific heat capacity and density on the temperature transfer process, and achieves a quantitative characterization of the degree of temperature decay by multiplying the ratios of the three thermophysical parameters.

[0068] The specific implementation of the dynamic response equation of thermal storage materials is as follows: Based on the first law of thermodynamics and the theory of heat transfer kinetics, the equation uses the product of the time constant and the rate of temperature change as the numerator to reflect the response capability of the thermal storage material to temperature changes, and the heat flux density as the denominator to reflect the intensity of external thermal disturbance. The ratio of the two determines the optimal capacitor switching time interval.

[0069] The specific implementation method of the power factor deviation calculation equation is as follows: The equation adopts the relative deviation calculation method, normalizes the actual power factor and the target power factor with the standard power factor respectively, eliminates the influence of dimensions, and adjusts the deviation sensitivity through the weighting factor to achieve accurate quantification of power factor deviation.

[0070] It needs to be explained that the heat conduction mechanism equation for thermal storage materials achieves a quantitative description of the temperature decay rate by establishing a multiplicative model of the ratios of thermophysical parameters. This equation is based on heat transfer theory, where thermal conductivity reflects the material's heat transfer capacity, specific heat capacity reflects its heat storage capacity, and density reflects its mass distribution; the combined effect of these three factors determines the temperature decay characteristics. The core formula for calculating the temperature decay correction coefficient is: ; Each term in this equation is a dimensionless ratio, and the left side... The coefficients are dimensionless correction factors, and the product of the three ratio terms on the right side is also dimensionless, ensuring dimensional consistency on both sides of the equation. Compared to traditional methods that only consider temperature changes, this equation can comprehensively reflect changes in the thermodynamic state of materials, improving the accuracy and reliability of temperature decay assessment and providing a more precise correction basis for power factor compensation.

[0071] The kinetic response equation of thermal storage materials is based on thermodynamic response theory. It reflects the material's response rate to temperature disturbances through the product of the time constant and the rate of temperature change, while heat flux density reflects the intensity of external heat input. The ratio of these two values ​​determines the optimal switching time interval. The core formula for calculating the capacitor switching time interval is: ; This equation introduces a reference time unit. To achieve dimensional balance, the numerator is the product of two dimensionless ratios, and the denominator is a dimensionless ratio. The overall result is... Multiplying them together yields a time-dimension product. The dimensional relationships are clear and reasonable. This equation takes into account the dynamic characteristics of the thermal storage material and the influence of external thermal disturbances. Compared with the traditional fixed time interval switching method, it can dynamically adjust the switching frequency according to the actual thermodynamic state, which significantly improves the response speed and adaptability of power factor compensation and reduces compensation lag and over-adjustment.

[0072] The power factor deviation calculation equation employs a normalization method to eliminate the influence of differences in the power factor reference value under different operating conditions, and achieves flexible adjustment of deviation sensitivity through weighting factors. The core formula for calculating power factor deviation is: ; The value within parentheses in this equation represents the difference between two dimensionless ratios, which is also dimensionless. Multiplying this by the weighted ratio term yields the final dimensionless deviation value. The dimensions on both sides of the equation are completely consistent. This equation is based on the error analysis method in control theory. Compared with the traditional simple difference calculation, it can more accurately reflect the deviation between the actual compensation effect and the expected target, providing a more reliable error signal for the feedback control system and improving the control accuracy and stability of the compensation system.

[0073] The fitness function of a genetic algorithm transforms the energy minimization problem into a fitness maximization problem by using the reciprocal of the total energy consumption of cutting, which aligns with the optimization mechanism of genetic algorithms. The core formula of the fitness function is: ; In this function, 1 in the denominator is a dimensionless constant, and the energy consumption and sum terms are also dimensionless due to the previous normalization process. Therefore, 1 in the numerator is dimensionless, hence the fitness... The function is dimensionless, ensuring dimensional balance. It considers the total energy consumption of the complete switching path, including the closed-loop characteristics from the start to the end of the path. Compared with traditional heuristic switching strategies, it can globally optimize the switching sequence, significantly reducing energy loss and equipment wear during capacitor bank switching, and improving the economy and reliability of the compensation system.

[0074] The adaptive sampling frequency adjustment formula dynamically adjusts the sampling frequency based on the deviation value, achieving a balance between computational resources and control accuracy. The core formula for sampling frequency adjustment is: ; In the formula It has the dimension of frequency (Hz), and the 1 in parentheses is a dimensionless constant. The adjustment coefficient is dimensionless, and the deviation ratio term is dimensionless, therefore the overall result... It has frequency dimensions (Hz) and the dimensional relationship is correct. The formula employs a linear adjustment mechanism: when the deviation is large, the sampling frequency is increased to enhance response capability; when the deviation is small, the sampling frequency is decreased to save resources. Compared to the traditional fixed sampling frequency method, this adaptive mechanism can intelligently allocate resources according to the system's operating status, minimizing system power consumption while ensuring control performance, thus improving the overall system's energy efficiency and sustainability.

[0075] To better understand and implement this invention, a specific application scenario of the invention is provided below as Example 2: A technical team implemented a power factor correction method based on the temperature decay of thermal storage materials in a power distribution system. The rated voltage of this power distribution system is 10... The total load capacity is 25 The main loads include high-power motor groups, electric arc furnaces and frequency converter equipment, with the load power factor fluctuating between 0.75 and 0.92.

[0076] The technical team first installed a sensor array of thermal storage material inside the power factor compensation device. The thermal storage material used is a phase change thermal storage material, paraffin composite material, with a melting point of 58℃ and a thermal storage density of 200. The sensor array comprises 24 temperature sensors, 8 heat flow sensors, and 4 heat capacity sensors. The temperature sensors are platinum resistance thermometers with a measurement accuracy of ±0.1℃ and a response time of 2... The heat flow sensor uses a thermocouple array and has a measurement range of 0 to 500. With an accuracy of ±2 The heat capacity detection sensor uses the differential scanning calorimetry principle, with a detection accuracy of ±1. .

[0077] During the data acquisition phase, the technical team established a database of temperature decay for thermal storage materials. The database records the temperature variation patterns of the thermal storage materials under different operating conditions, as shown in Table 1: Table 1. Data on Temperature Decay Characteristics of Thermal Storage Materials

[0078] The technical team calculated the temperature decay correction factor based on the heat conduction mechanism equation of the thermal storage material. The thermal conductivity of the thermal storage material is 0.23. The standard thermal conductivity is 0.30. Specific heat capacity is 2100 The standard specific heat capacity is 2000. The density is 850. The standard density is 800. The calculated temperature decay correction factor is 0.82. Since this factor falls within the range of 0.6 to 0.85, the system initiates dynamic compensation using a fast-switching capacitor bank.

[0079] In establishing the temperature decay prediction model for thermal storage materials, the technical team used a neural network algorithm combined with historical temperature decay data to build the prediction model. The model's input parameters include the current temperature, temperature decay rate, ambient temperature, and load power, and the output is the temperature change trend over the next 10 minutes. The prediction accuracy reached 95.2%, accurately predicting the direction of power factor change.

[0080] Based on the prediction results, the system generates a proactive compensation control command. When the predicted power factor will drop to 0.78 in 8 minutes, the system activates a capacity of 300kWh 5 minutes in advance. The capacitor bank. The compensation duration is determined to be 12 minutes based on the load variation trend. For example... Figure 2 As shown, the temperature decay curve of the thermal storage material exhibits a non-linear decreasing characteristic, with a relatively fast decay rate in the early stage and a gradual decrease in the later stage.

[0081] The technical team employed a traveling salesman problem algorithm to optimize the switching sequence of the fast-switching capacitor bank. The system is configured with 8 capacitor banks with capacities of 100, 150, 200, 300, 400, 500, 600, and 800 mAh. Each capacitor bank is treated as a city node, and the switching energy consumption is used as the path weight. A genetic algorithm is used to solve for the optimal switching path. The optimized switching sequence is: 100→300→500→800→600→400→200→150 Total energy consumption reduced to 0.75 .

[0082] The fast-switching capacitor bank uses a vacuum contactor with a response time of 8 seconds. The capacitor switching time interval was calculated based on the kinetic response equation of the thermal storage material. The time constant was 45. The standard time constant is 60. The temperature change rate is 0.42℃ / min, the standard temperature change rate is 0.50℃ / min, and the heat flux density is 138.2. The standard heat flux density is 150. The calculated capacitor switching time interval is 41 seconds. .

[0083] When the temperature decay correction coefficient exceeds 0.85 and reaches 0.89, the system activates the deep compensation mode. The system adds heat energy recovery processing of the thermal storage material, recovering the released heat energy through the heat pump system, reducing the temperature decay rate of the thermal storage material from 0.48℃ / min to 0.32℃ / min. Simultaneously, a dual power factor correction process is initiated, combining the thermal storage material temperature decay correction algorithm and the traditional detection correction algorithm, improving the compensation accuracy to 99.1% and reducing the compensation response threshold to 70% of the standard value, i.e., 0.021.

[0084] The thermal storage material temperature decay feedback control loop continuously monitors the actual compensation effect. The system records the power factor deviation values ​​under different operating conditions, as shown in Table 2: Table 2 Comparison of Power Factor Compensation Effects

[0085] According to the power factor deviation calculation equation, the ratio of the actual power factor to the standard power factor is 0.967, the ratio of the target power factor to the standard power factor is 0.978, and the ratio of the weighting factor to the standard weighting factor is 1.05. The calculated deviation value is 0.012. The system dynamically adjusts the temperature decay correction parameters of the thermal storage material based on the deviation value.

[0086] like Figure 3As shown, the power factor compensation effect shows a stable upward trend over time. After the temperature decay correction of the thermal storage material is adopted, the stability of the power factor is significantly improved, and the fluctuation range is reduced from ±0.05 to ±0.02.

[0087] The system adaptively adjusts based on the output of the feedback control loop. When the deviation is less than 0.02, the sensor array sampling frequency is set to 5 times per second, and the database update cycle is 30 seconds. When the deviation value is greater than 0.02, the sampling frequency is increased to 8 times per second, and the update cycle is shortened to 20. When the deviation exceeds 0.05, emergency mode is activated, the sampling frequency reaches 12 times per second, and the update cycle is shortened to 10. .

[0088] During a 30-day continuous test, the system's average power factor remained above 0.89, power loss decreased by 13.2%, and reactive power compensation accuracy reached 98.7%. The temperature of the thermal storage material was controlled within the range of 40 to 60°C, and the material performance remained stable without any phase change failure. The number of capacitor bank switching operations was reduced by 34% compared to traditional methods, significantly extending the equipment's service life.

[0089] This invention represents a significant advancement over traditional power factor compensation methods. Traditional methods rely solely on passive compensation based on electrical parameters, while this invention establishes a thermoelectric coupling predictive compensation mechanism by leveraging the temperature decay characteristics of thermal storage materials. As a representation of the system's thermal inertia, the temperature changes of the thermal storage material reflect deeper load variations, providing more accurate predictive information than simple electrical detection. The introduction of a temperature decay correction coefficient enables dynamic optimization of compensation parameters, avoiding the compensation lag and overcompensation problems caused by fixed parameters in traditional methods. The application of the Traveling Salesman Problem algorithm optimizes the capacitor switching sequence, reducing energy consumption and equipment wear caused by frequent switching. The deep compensation mode and heat recovery mechanism further improve the system's adaptability and compensation accuracy under extreme conditions. The establishment of a feedback control loop enables the system's self-learning and self-optimization, allowing the compensation strategy to continuously improve based on actual operating conditions, forming an intelligent closed-loop control system.

[0090] It should be noted that the variables involved in this invention are explained in detail in Tables 3 and 4.

[0091] Table 3. Variable Explanation Table (Part 1)

[0092] Table 4. Variable Explanation Table (Part Two)

[0093] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A compensation method for power factor correction based on temperature decay of thermal storage materials, characterized in that, A thermal storage material sensor array is installed inside the power factor compensation device to monitor the temperature decay characteristics and thermal energy storage status of the thermal storage material in real time. Basic data on the load current, voltage, and power factor of the power system are collected, along with the initial temperature, current temperature, and temperature decay rate of the thermal storage material, establishing a thermal storage material temperature decay database. A temperature decay correction coefficient is calculated based on the thermal storage material temperature decay data, and predictive adjustment of power factor compensation is achieved by utilizing the correlation between the thermal storage material temperature decay characteristics and power system load changes. A thermal storage material temperature decay prediction model is established, combining historical temperature decay data from the thermal storage material temperature decay database and the current load status to predict future power factor change trends and generate advance compensation control commands. The switching sequence of fast-switching capacitor banks is optimized using the traveling salesman problem algorithm. Corresponding compensation modes are activated based on different value ranges of the temperature decay correction coefficient. A thermal storage material temperature decay feedback control loop is established, dynamically adjusting the thermal storage material temperature decay correction parameters and compensation strategy based on the deviation between the actual and expected power factor compensation effects. The sampling frequency of the thermal storage material sensor array and the update cycle of the thermal storage material temperature decay database are adaptively adjusted based on the output of the thermal storage material temperature decay feedback control loop.

2. The compensation method for power factor correction based on temperature decay of thermal storage material according to claim 1, characterized in that, The thermal storage material sensor array includes a temperature sensor, a heat flow sensor, and a heat capacity detection sensor. The temperature sensor monitors the temperature distribution on the surface and inside of the thermal storage material, the heat flow sensor measures the rate of heat transfer, and the heat capacity detection sensor assesses changes in the thermal storage capacity of the thermal storage material.

3. The compensation method for power factor correction based on temperature decay of thermal storage material according to claim 2, characterized in that, The thermal storage material temperature decay database is used to store and manage historical temperature change records, thermodynamic parameters, and environmental condition data of thermal storage materials, providing data support for temperature decay prediction and compensation strategy formulation.

4. The compensation method for power factor correction based on temperature decay of thermal storage material according to claim 3, characterized in that, The temperature decay correction coefficient is calculated using the thermal conduction mechanism equation of the thermal storage material. The temperature decay correction coefficient is equal to the ratio of the thermal conductivity of the thermal storage material to the standard thermal conductivity multiplied by the ratio of the specific heat capacity to the standard specific heat capacity, and then multiplied by the ratio of the density to the standard density.

5. The compensation method for power factor correction based on temperature decay of thermal storage material according to claim 4, characterized in that, When the temperature attenuation correction factor is ∈ [0.2, 0.6], the standard compensation mode is executed; when the temperature attenuation correction factor is ∈ (0.6, 0.85], the fast switching capacitor bank is activated for dynamic compensation; and when the temperature attenuation correction factor is ∈ (0.85, 1.0], the deep compensation mode is activated.

6. The compensation method for power factor correction based on temperature decay of thermal storage material according to claim 5, characterized in that, The temperature decay prediction model for thermal storage materials is based on the thermodynamic properties of the thermal storage materials to establish a mathematical prediction model, predict the temperature decay pattern of the thermal storage materials in the next 5 to 15 minutes, and predict the load change trend and power factor change direction of the power system in advance.

7. The compensation method for power factor correction based on temperature decay of thermal storage material according to claim 6, characterized in that, The advance compensation control command generates advance compensation action commands based on the prediction results of the thermal storage material temperature decay prediction model. These commands include parameters such as capacitor switching timing, compensation capacity, and compensation duration, enabling the power factor compensation device to be pre-adjusted before load changes.

8. The compensation method for power factor correction based on temperature decay of thermal storage material according to claim 7, characterized in that, The Traveling Salesman Problem algorithm treats each capacitor bank as a city node, uses switching energy consumption as the path weight, and solves for the optimal switching path to minimize energy loss during the compensation process.

9. The compensation method for power factor correction based on temperature decay of thermal storage material according to claim 8, characterized in that, The fast-switching capacitor bank uses vacuum contactors or solid-state switching devices to form a capacitor switching device, which has a millisecond-level response speed and adjusts the switching frequency and capacity combination in real time according to the change in the temperature decay rate of the thermal storage material.

10. The compensation method for power factor correction based on temperature decay of thermal storage material according to claim 9, characterized in that, It also includes adjusting the capacitor switching time interval according to the temperature decay rate of the thermal storage material. The capacitor switching time interval is calculated by the dynamic response equation of the thermal storage material. The capacitor switching time interval is equal to the ratio of the time constant to the standard time constant multiplied by the ratio of the temperature change rate to the standard temperature change rate divided by the ratio of the heat flux density to the standard heat flux density.

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