Analysis method and system for energy conservation and loss reduction of transformer

Abstract

The invention discloses an analysis method and system for energy conservation and loss reduction of transformers, and the method comprises the steps: collecting the standby state data of a transformer group, recognizing a high-loss unit, and predicting the parameter drift trend of the high-loss unit; acquiring an electromagnetic impact strength signal in a working state; the parameter drift trend and the electromagnetic impact strength signal are fused, and the collaborative imbalance risk is evaluated; and according to a risk assessment result and a risk cause, dynamic voltage regulation and load balancing optimization are adaptively executed. The cross-working-condition correlation analysis and closed-loop control are carried out on the transformer group serving as the power supply core, so that the standby no-load loss can be effectively reduced, the equipment aging can be delayed, the operation cost can be reduced, the long-term stability and reliability of the power supply system can be ensured, and the method can be widely applied to the industrial fields of pulsed electric fields, precision manufacturing and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power transmission and distribution automatic control, and in particular to an analysis method and system for transformer energy saving and loss reduction. BACKGROUND

[0002] Pulsed electric field (PEF) technology is a non-thermal food processing technology that can achieve sterilization or improve product quality without significantly increasing the temperature of the food, thus having a significant advantage in preserving the original flavor, color and nutritional ingredients of the food, and is gradually being applied in the food industry. When PEF technology is applied on a large scale in industrial production, the long-term operation cost and stability of the high-voltage pulse power supply system, which is the core equipment of PEF technology, become a technical bottleneck for its widespread application. Industrial production lines usually have the characteristics of intermittent work, and during the production interval of changing food batches, pipe cleaning or equipment maintenance, the PEF power supply system is often in a long standby state. When processing food, the power supply system needs to output high-power electric pulses of microseconds with high frequency and high stability.

[0003] This coexistence of "long standby" and "instant impact" working conditions raises two major technical problems. First, the energy efficiency and cost problem. During standby, the transformer group inside the power supply system has a continuous no-load loss, which directly translates into operating electricity expenses. At the same time, during production, the equipment ages due to repeated high-power electromagnetic shocks, which can lead to a decrease in energy conversion efficiency, further increasing the processing cost per product. Second, the stability and quality problem. The food processing industry has very high requirements for the uniformity of product quality, and the electrical parameters of each transformer unit in the power supply system will gradually drift under repeated shocks, causing the coordinated work of each unit to be out of sync. This imbalance can cause the pulse electric field strength output by the PEF device to be unstable, directly affecting the consistency of food processing results, which can lead to incomplete sterilization or over-processing, and thus cause food safety risks or product scrap.

[0004] The energy efficiency and stability problems caused by the coexistence of "long standby" and "instant impact" working conditions are common in many industries that use transformer groups for power supply, and are a common technical bottleneck that restricts the operation cost and reliability of such systems. SUMMARY The technical problem to be solved by the present application is the energy efficiency decay and stability degradation of the transformer group as the power supply core under complex working conditions with intermittent and impact loads.

[0005] To achieve the above object, the application provides an analysis method for energy saving and loss reduction of a transformer, which is applied to a power supply system comprising a plurality of transformer units, and comprises the following steps: Step one, collecting and processing standby state data to obtain the no-load loss distribution characteristics of the power supply system. Specifically, the current, voltage data and temperature indicators of each transformer unit in the standby state of the power supply system during production intervals are collected in real time, and the Fourier transform method is used to process the collected raw data, so as to obtain the no-load loss distribution characteristics capable of representing the energy consumption level of each transformer unit.

[0006] Step two, identifying high-loss units and fitting their parameter drift trends. According to the no-load loss distribution characteristics, a high-loss unit set is selected from the transformer group constituting the power supply system, and the electrical parameter history records of each unit in the set are extracted. If the extracted records show that the parameter drift trend exceeds the preset degradation threshold, the least square method is used to fit the history records to obtain the parameter drift trend curve representing the performance degradation law. The application uses the no-load loss data generated by the power supply system in the standby state as the basis for diagnosing the health status and predicting future performance degradation, thereby performing cross-condition correlation diagnosis on the state of the power supply system.

[0007] Step three, obtaining the electromagnetic shock intensity signal in the working state. For the transformer units that have been identified to have significant parameter drift trends, the electromagnetic shock intensity signals acting on these units when the power supply system is in high-power output are further obtained, which reflect the electrical stress level of the equipment in the working state.

[0008] Step four, fusion of multi-source information and collaborative disorder risk level evaluation. The Kalman filtering algorithm is used to fuse and process the parameter drift trend curve and the electromagnetic shock intensity signal. This step combines the inherent aging trend of the equipment with the impact of the actual working condition to dynamically judge and quantitatively evaluate the collaborative disorder risk level that may be caused by the inconsistent performance of each unit in the power supply system.

[0009] Step five, adaptively assigning weight to risk source and determining adjustment strategy. After judging the level of synergy disorder risk, further dynamically assess the main factors leading to parameter drift. Specifically, analyze the correlation between the recent fluctuation characteristics (e.g. frequency, amplitude change rate) of the electromagnetic impact intensity signal and the slope change of the parameter drift trend curve. If the correlation is high, it indicates that the recent high-intensity processing task is the main cause of the accelerated parameter drift, at which time a higher adjustment weight will be given to the "suppress aging" goal; otherwise, if the correlation is low, it indicates that the parameter drift is more due to device inherent aging or environmental factors, at which time a higher adjustment weight will be given to the "standby energy saving" goal. The present application can further trace the causes of the risk on the basis of predicting the risk, and dynamically adjust the focus of the subsequent control strategy according to different causes, so as to achieve more targeted adaptive adjustment.

[0010] Step six, performing dynamic voltage adjustment and optimizing energy distribution. If the level of synergy disorder risk judged in step four is higher than the preset risk threshold, then according to the adjustment strategy weight determined in step five, the input voltage of the low-loss unit set with relatively stable performance in the power supply system is adjusted through the dynamic voltage adjustment protocol, so as to obtain an optimized energy distribution matrix, which is used to balance the energy saving and anti-aging needs of the entire system.

[0011] Step seven, updating load balancing configuration and determining device aging suppression threshold. According to the optimized energy distribution matrix, when the power supply system is instantaneously switched from standby state to high-load working state, the load balancing configuration of the transformer group in the power supply system is updated using the matrix. This configuration transfers more load and impact stress to the units in good health, and at the same time determines a device aging suppression threshold for the units whose performance has appeared to drift, so as to slow down the further decline of their performance.

[0012] Step eight, calibrating energy efficiency evaluation value and outputting final running parameters. From the device aging suppression threshold, a correction coefficient is derived for representing the difference in system stability. The correction coefficient is used to calibrate the energy efficiency evaluation value of the entire production line, so that the energy efficiency evaluation not only considers the instantaneous power consumption, but also takes into account the long-term impact due to the decline in stability, and finally obtains a set of final running parameters for guiding the operation of the power supply system.

[0013] Another aspect of the present application also provides an analysis system for transformer energy saving and loss reduction, which is configured to perform the above method, comprising: a data acquisition module for acquiring current-voltage data and temperature indicators of each transformer unit in the power supply system in standby state during production intermittence in real time; a loss analysis module for processing the data using the Fourier transform method to obtain the no-load loss distribution characteristics of the transformer group, and identifying high-loss units according to the characteristics; A trend prediction module is used to extract the electrical parameter history records of the high-loss unit and fit the records by the least square method to obtain a parameter drift trend curve; a risk assessment module, configured to obtain an electromagnetic shock intensity signal and fuse the parameter drift trend curve and the electromagnetic shock intensity signal using a Kalman filter algorithm to determine a level of synergy disorder risk within the power supply system; Adaptive adjustment module, used to adaptively assign adjustment weights based on attribution analysis of risk sources; an optimization control module, configured to adjust the input voltage of the low-loss unit set through a dynamic voltage regulation protocol if the determined risk level of decoordination is higher than a preset threshold, thereby obtaining an optimized energy distribution matrix and updating the load balancing configuration accordingly; The parameter calibration module is used to derive a correction coefficient for the poor stability index from the equipment aging suppression threshold, and use the coefficient to calibrate the overall production line energy efficiency evaluation value to obtain the final power system operating parameter set.

[0014] Compared with the prior art, this application has the following beneficial effects: This application helps ensure the long-term stability and accuracy of the output pulses of PEF and equipment with similar working characteristics by performing correlation analysis and predictive closed-loop control on the data in the standby and pulse working states of the power system, thereby ensuring the consistency of working results and reducing the risk of product quality fluctuations caused by uneven processing. At the same time, through real-time analysis of no-load losses in the standby state of the power system and identification of high-loss units, it is possible to optimize energy distribution and reduce energy waste during non-production time; by actively suppressing equipment aging, it helps maintain the high energy conversion efficiency of the system during the production process, thereby reducing operating costs. In addition, the closed-loop regulation and load balancing strategies adopted in this application can actively slow down the aging process of key electrical components, help improve the overall reliability and operating rate of the PEF production line, and extend the service life of the power system. BRIEF DESCRIPTION OF THE DRAWINGS The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0015] Figure 1 This is a flow chart of an energy-saving and loss-reduction analysis method for a pulsed electric field power supply system provided in an embodiment of the present application.

[0016] Figure 2 This is a structural block diagram of an energy-saving and loss-reduction analysis system for a pulsed electric field power supply system provided in an embodiment of the present application.

[0017] Figure 3 It is a schematic diagram used to illustrate parameter drift trends and electromagnetic shock intensity signals in the embodiments of the present application.

[0018] Figure 4 FIG. 1 is a schematic diagram of the principle of multi-source information fusion using Kalman filtering algorithm in an embodiment of the present application.

[0019] Figure 5 FIG. 2 is a schematic diagram of adaptive allocation of adjustment strategy weights in an embodiment of the present application.

[0020] Figure 6 FIG. 3 is a schematic diagram of transformer group no-load loss distribution and high-loss unit identification in a specific application scenario in an embodiment of the present application.

[0021] Figure 7 FIG. 4 is a comparative schematic diagram of the impact of load balancing strategies before and after optimization on unit inrush current in an embodiment of the present application.

[0022] Figure 8 FIG. 5 is a comparative curve diagram of traditional energy efficiency evaluation and calibrated energy efficiency evaluation in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the present application clearer, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments herein are only used to explain the present application and not to limit the present application.

[0024] In order to make those skilled in the art better understand the technical solutions of the present application, the present application will be described in detail below taking a pulsed electric field (PEF) power supply system as a typical application scenario. It should be understood that the inventive concept of the present application is not limited to this specific scenario, and any system that uses a transformer group for power supply and has similar "long standby" and "instantaneous impact" working conditions can apply the technical solutions of the present application to achieve the purpose of energy saving and loss reduction and improving stability.

[0025] Embodiment 1 The present embodiment provides an energy saving and loss reduction analysis method for a pulsed electric field (PEF) power supply system, which is applied to scenarios such as food processing production lines. The power supply system is usually composed of a transformer group composed of multiple transformer units in parallel or series, which provides high-voltage power supply for PEF equipment. As shown in FIG. 1, the method comprises the following steps: Figure 1 Step S1, collect and process standby state data to obtain the no-load loss distribution characteristics of the power supply system.

[0026] ​When the PEF production line enters the production interval, such as replacing batch materials or cleaning the equipment, the power supply system is in standby state. This step uses this standby window to make a preliminary diagnosis of the health status of the system. Specifically, by deploying high-precision current sensors (such as Hall effect sensors) and voltage sensors (such as voltage dividers) at the input of each transformer unit, real-time acquisition of current and voltage waveform data in standby state is carried out at a sampling frequency of not less than 10 kHz. At the same time, temperature data is collected by temperature sensors (such as PT100 thermal resistance) attached to the core or winding surface of each transformer unit. Since the load current in standby state is very small, the input power at this time mainly reflects the no-load loss, including the hysteresis loss and eddy current loss in the core. Due to device aging or insulation performance degradation, the standby current waveform may be distorted. Therefore, this step preferably uses the Fast Fourier Transform (FFT) algorithm to process the collected non-sinusoidal current and voltage waveform data, and decomposes it into fundamental and harmonic components. The no-load loss (P_loss) can be calculated according to the following formula: where n is the harmonic number, V_n and I_n are the effective values of the n-th harmonic voltage and current, and φ_n is the phase difference between the n-th harmonic voltage and current. By calculating all units in the transformer group, a no-load loss distribution characteristic that can quantitatively represent the energy consumption level of each unit is obtained.

[0027] Step S2, identify high-loss units and fit their parameter drift trend.

[0028] Based on the no-load loss distribution characteristic obtained in step S1, a high-loss screening threshold is set to identify abnormal performance units from the transformer group. Preferably, the threshold can be set to 1.5 times the average no-load loss of all units, or 120% higher than the factory calibration value. For the high-loss units screened out, the historical records of their key electrical parameters, such as no-load current, winding resistance, insulation resistance, etc., are retrieved from the system's historical database. If the records show that the parameters exhibit a monotonic or fluctuating drift trend over time, and the cumulative drift exceeds a pre-set degradation threshold (e.g., winding resistance change exceeds 5%), it is considered that the unit has obvious performance degradation. At this time, the least squares method is used to fit the historical data points of the parameter to obtain a parameter drift trend curve that can represent the performance degradation law. For example, it can be fitted as a quadratic polynomial where P(t) is the parameter value, t is the running time, a, b, c are coefficients determined by fitting the historical data points by the least squares method. The curve can be used to predict the performance status of the unit in the future period of time. For example, Figure 3As shown, the parameter drift trend 301 is the drift trend curve of the electrical parameter (such as no-load current) of a certain high-loss unit changing over time. This step associates the standby state energy consumption data with the historical electrical parameters, realizing cross-condition, predictive diagnosis of the device health status.

[0029] Step S3, acquire the electromagnetic impact strength signal in the working state.

[0030] For the transformer unit identified in step S2 as having a significant parameter drift trend, further evaluation of the stress level it is subjected to in the actual working state is needed. When the PEF device is processing food, i.e. the power supply system is outputting high-voltage pulses at high frequency, through high-bandwidth Rogowski coil sensors, the pulse current waveform acting on these units is collected. The electromagnetic impact strength signal can be quantified by calculating the rise rate (di / dt) or peak amplitude of the pulse current. For example, the di / dt peak value of each pulse is taken as a data point to form a time series signal. As shown, 302 is a series of electromagnetic impact strength pulses that the unit is subjected to in the working state. This signal directly reflects the electrical stress the device is subjected to when working, and is a key external factor for analyzing performance accelerated degradation. Figure 3

[0031] Step S4, fuse multi-source information and perform collaborative misadjustment risk assessment.

[0032] To realize accurate assessment of the overall stability of the power supply system, this step uses the Kalman filter algorithm to fuse the parameter drift trend curve (from step S2) representing the internal aging law of the device and the electromagnetic impact strength signal (from step S3) reflecting the external working condition stress. The state prediction model of the Kalman filter is based on the parameter drift trend curve 401, predicting the electrical parameter state at the next time. The state update part, however, takes the real-time acquired electromagnetic impact strength signal 402 as an external input to correct the prediction result. Specifically, the size of the impact strength signal can be associated with the process noise covariance matrix Q, i.e. when the impact strength increases, the Q value increases accordingly, indicating an increase in the uncertainty of the system state. Through the iterative operation of the filter, a more accurate parameter state estimate value 403 is obtained, which fuses the long-term trend and instantaneous impact. Based on this estimate value, the parameter difference between this unit and other healthy units is assessed. When the difference exceeds the allowable range for collaborative work, it is determined that there is a risk of collaborative misadjustment. This risk level can be quantified, for example, defined as (estimated parameter value - average parameter value of healthy units) / average parameter value of healthy units. This fusion processing method can dynamically and quantitatively judge the risk of instability of the entire power supply system caused by the performance degradation of individual units.

[0033] ​Step S5, adaptively assign risk source weight and determine adjustment strategy.

[0034] After determining the level of synergy disorder risk, this step further traces the main cause of the risk in order to take more targeted adjustment strategies. Specifically, by calculating the Pearson correlation coefficient between the fluctuation characteristics (such as standard deviation or change rate) of the electromagnetic impact intensity signal in the recent time window (for example, the past week) and the slope change of the parameter drift trend curve (i.e. the parameter degradation acceleration). If the correlation is high (for example, the correlation coefficient is greater than 0.7), it indicates that recent heavy load, high intensity processing task is the main reason for the accelerated drift of the parameter, at this time the focus of the system adjustment strategy should be placed on “suppressing aging”, giving this goal a higher adjustment weight, such as the weight W_aging in Figure 5 . On the contrary, if the correlation is low (for example, the correlation coefficient is less than 0.3), it indicates that the parameter drift is more due to the natural aging of the equipment or environmental temperature and other factors, at this time the focus of the system adjustment should be placed on “standby energy saving”, that is, to prioritize the high loss problem in standby state, give this goal a higher adjustment weight, such as the weight W_standby in Figure 5 . This step realizes the optimal allocation of control resources by intelligently attributing the problem to its root cause, so that the subsequent control adjustment can be adaptively adjusted according to the root cause. In another embodiment, more complex statistical methods such as Granger causality test can also be used to analyze the causal relationship between the two.

[0035] Step S6, execute dynamic voltage adjustment and optimize energy distribution.

[0036] If the level of synergy disorder risk determined in step S4 is higher than the preset risk threshold (for example, the risk quantization value is greater than 0.1), the optimization control is started according to the adjustment strategy weight determined in step S5. This step adjusts the input voltage of the low-loss unit set with relatively stable performance in the power supply system through a dynamic voltage adjustment protocol. For example, if the “aging suppression” weight is high, the protocol will instruct the solid-state voltage regulator or silicon-controlled voltage regulator connected to the front end of the power supply system to slightly reduce the input voltage of the high-loss unit, while slightly increasing the input voltage of some healthy units. The goal of this operation is to generate an optimized energy distribution matrix. This matrix is a diagonal matrix, and the diagonal elements represent the adjustment coefficients of the voltage (or power) of each transformer unit. For high-loss units that need to be protected, the coefficient is less than 1; for healthy units that need to bear more load, the coefficient is greater than 1, and the adjustment of all coefficients needs to ensure that the total output power of the system remains unchanged.

[0037] Step S7, update load balancing configuration and determine aging suppression threshold.

[0038] Based on the optimized energy allocation matrix generated in step S6, the power system's central controller applies this matrix to update the load balancing configuration of the transformer group when the production line switches instantaneously from standby mode to PEF pulse processing. Specifically, when allocating pulse power tasks, more load (i.e., higher pulse current) is directed to healthy units with a regulation coefficient greater than 1, while the load allocated to high-loss units is reduced accordingly. Simultaneously, a dynamic device aging suppression threshold is determined for these high-loss units. This threshold is a specific upper limit on an operating parameter, for example, limiting their peak operating current to 80% of their rated value or controlling their maximum operating temperature to 5°C below normal. This proactively mitigates further performance degradation through "derating" and extends their effective service life.

[0039] Step S8: calibrate the energy efficiency evaluation value and output the final operating parameters.

[0040] The traditional energy efficiency evaluation value only focuses on the instantaneous input-output power ratio, ignoring the long-term stability and reliability costs caused by inconsistent equipment performance. This step is used to make up for this shortcoming. From the equipment aging suppression threshold determined for the high-loss unit in step S7, a correction factor is derived to characterize the difference in system stability. For example, the correction factor C = 1- (unit rated peak current-equipment aging suppression threshold) / unit rated peak current. For healthy units that are not restricted, the correction factor is 1. Then, the product of the correction factors of all units is used to calibrate the energy efficiency evaluation value of the entire production line. The calibrated energy efficiency value , where η_original is the original energy efficiency assessment value, and Π(C_i) is the product of all unit correction coefficients. This calibrated energy efficiency metric not only measures immediate power consumption but also considers the system's long-term stable operation. Ultimately, based on this comprehensive assessment, the system outputs a final set of operating parameters to guide the PEF power system. This parameter set includes an updated load balancing strategy, aging suppression thresholds for each unit, and an optimized standby management scheme, forming a complete closed-loop optimization control system.

[0041] Example 2 The present application also provides an energy-saving and loss-reduction analysis system for a pulsed electric field power supply system. Figure 2 As shown, the system is configured to perform the method described in Example 1. The system can be embedded in the central controller of the PEF power system or exist as an independent monitoring and analysis unit. The system includes: The data acquisition module 201 is used to collect the current, voltage and temperature data in the standby state during production breaks in real time through sensors deployed on each transformer unit in the power supply system, and transmit the data to subsequent modules.

[0042] a loss analysis module 202, which internally integrates a Fourier transform processing unit, is used to process the raw data sent by the data acquisition module 201, calculate the no-load loss of each transformer unit, form the no-load loss distribution characteristics of the power supply system, and identify high-loss units according to preset rules.

[0043] a trend prediction module 203, which is used to connect a historical database, extract the electrical parameter history records of the high-loss units identified by the loss analysis module 202, and through the logic unit of the built-in least squares method or other fitting algorithms, fit the historical records to obtain a parameter drift trend curve representing performance degradation.

[0044] a risk assessment module 204, which is used to obtain electromagnetic shock intensity signals in the working state from a sensor network, and internally integrates a Kalman filter algorithm processor. The core function of this module is to fuse the parameter drift trend curve output by the trend prediction module 203 and the real-time electromagnetic shock intensity signals, dynamically judge and quantitatively evaluate the risk level of the internal coordination disorder of the power supply system.

[0045] an adaptive adjustment module 205, which is used to perform attribution analysis on the risks output by the risk assessment module 204. This module adaptively allocates adjustment weights for the two goals of “suppressing aging” and “standby energy saving” by analyzing the correlation between the electromagnetic shock intensity signals and the parameter drift changes, in order to determine the subsequent control strategy.

[0046] an optimal control module 206, which is activated when it receives a signal that the risk level exceeds the threshold. It generates and executes dynamic voltage regulation instructions according to the weights determined by the adaptive adjustment module 205, adjusts the input voltage of the low-loss unit set, obtains an optimized energy distribution matrix, and updates the load balancing configuration of the system according to the matrix.

[0047] a parameter calibration module 207, which is used to derive a stability difference correction coefficient from the device aging suppression threshold determined by the optimal control module 206, and calibrate the energy efficiency evaluation value of the overall production line using the coefficient, and finally generate and output a final set of operating parameters for guiding the operation of the PEF power supply system.

[0048] The above-mentioned method and system are used to perform correlation analysis and closed-loop control on the power supply system, thereby saving energy and reducing consumption, and maintaining the stability and high efficiency of the system during long-term operation.

[0049] Embodiment 3 This embodiment will take a specific application scenario to further illustrate the actual operation process and effect of the method described in this application. In a specific application scenario, a pulse electric field (PEF) system for juice sterilization is deployed on a certain juice processing production line, and its high-voltage pulse power supply system is composed of 12 parallel transformer units (Unit 1 to Unit 12). After the system has been running for two years, the energy-saving loss analysis system described in this application is introduced.

[0050] During the initial application of the system, the method of this application starts to execute within the 30-minute standby window of a production batch change. First, the data acquisition module collects standby current, voltage, and temperature data for all 12 transformer units. After Fourier transform processing by the loss analysis module, it is found that the no-load loss of most units is stable at about 80 watts, while the no-load loss of Unit 4 and Unit 7 is as high as 110 watts and 135 watts, respectively, which are identified as high-loss units by the system. Figure 6

[0051] Subsequently, the trend prediction module automatically retrieves the running history database of Unit 4 and Unit 7 units for the past two years. The data shows that the winding resistance of Unit 7 unit has increased by 6.2% compared to the factory value, exceeding the preset degradation threshold of 5%. The system uses the least squares method to fit the historical data points of the resistance of Unit 7 unit, obtaining a quadratic polynomial parameter drift trend curve: where t is the number of months of operation, and R(t) is the predicted resistance value (ohms). This indicates that the aging process of Unit 7 unit has shown an accelerating trend.

[0052] In the next production cycle, when the PEF system is processing juice, the risk assessment module starts to focus on monitoring Unit 7 unit. Through the deployment of a Rogowski coil at the output side of Unit 7 unit, the real-time electromagnetic shock intensity signal it receives is obtained, and it is found that when processing high-viscosity fruit pulp, the peak value of the rise rate (di / dt) of the pulse current frequently reaches 1.5 kA / μs. The Kalman filter algorithm in the risk assessment module takes this high shock intensity signal as an external input to real-time correct the resistance state prediction value based on the aforementioned drift curve. After fusion processing, the system obtains a more accurate current equivalent resistance estimate value of Unit 7 unit, and based on this, calculates the parameter difference with healthy units, and quantitatively obtains the synergistic disorder risk level as 0.13, which exceeds the system's set risk threshold of 0.1.

[0053] ​At this time, the adaptive adjustment module initiates the risk attribution analysis. By calculating the Pearson correlation coefficient between the average daily electromagnetic impact intensity and the slope change of the Unit 7 cell resistance drift curve in the past month, a high correlation result of 0.85 is obtained. This indicates that the recent high-intensity production task is the main cause of the accelerated degradation of the performance of Unit 7 cell. Therefore, the system adaptively tilts the adjustment strategy weight to "suppress aging" and gives it a weight of 0.8, while the weight of "standby energy saving" is 0.2.

[0054] Since the risk level of coordination imbalance has exceeded the threshold, the optimization control module generates an optimized energy distribution matrix according to the "suppress aging" priority strategy through the dynamic voltage adjustment protocol. The matrix instructs the solid-state voltage regulator connected to the front end of the power system to adjust the input voltage coefficient of Unit 4 and Unit 7 cells to 0.9, while setting the voltage coefficient of the four cells with the optimal performance state to 1.05 to compensate for the total power output. When the production line switches from standby to working state, the matrix is used to update the load balancing configuration, transferring more pulse load tasks to the cells with better state. At the same time, the system determines an aging suppression threshold for Unit 7 cell, i.e. limiting its peak working current within 85% of its rated value. As shown in Figure 7 , the optimized load balancing strategy significantly reduces the impact current that the high-loss Unit 4 and Unit 7 cells bear when working, while allowing the healthy Unit 2 and Unit 5 cells to bear more load.

[0055] Finally, the parameter calibration module derives a stability difference correction coefficient C_T6=0.85 from the 85% current limit and uses it to calibrate the energy efficiency evaluation value of the overall production line, so that the energy efficiency evaluation result can fully reflect the long-term health status of the system. Finally, the system outputs a final set of operating parameters including the latest load balancing scheme, Unit 4 and Unit 7 cell operating limits, and optimized standby strategy, and issues them to the central controller of the PEF power system for execution. After three months of continuous optimization and operation, the monitoring data shows that the resistance drift rate of Unit 7 cell has slowed down by about 40%, the unit energy consumption of the production line per ton of juice has decreased by 3.5%, and the uniformity of the product sterilization effect has been improved. As shown in Figure 8 , the calibrated energy efficiency evaluation curve can reflect the long-term operation benefit of the system compared with the traditional energy efficiency evaluation.

[0056] The above is only a preferred embodiment of the present application, and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A transformer energy saving and loss reduction analysis method, which is applied to a power supply system including multiple transformer units, characterized in that: The method comprises: Collecting current data and voltage data of each transformer unit in the power supply system in a standby state, and processing the collected current data and voltage data using a Fourier transform method to determine a no-load loss distribution feature that can characterize the energy consumption level of each transformer unit; identifying high-loss units and low-loss units from the plurality of transformer units according to the no-load loss distribution characteristics; Extracting the electrical parameter history records of the high-loss unit, and fitting the electrical parameter history records by the least square method to obtain a parameter drift trend curve that characterizes the performance degradation law of the high-loss unit; For the high-loss unit, obtaining an electromagnetic shock intensity signal acting on the high-loss unit when the power supply system is in a high-power output working state, wherein the electromagnetic shock intensity signal reflects the electrical stress level borne by the high-loss unit in the working state; The Kalman filter algorithm is used to fuse the parameter drift trend curve and the electromagnetic shock intensity signal to dynamically and quantitatively evaluate the risk level of synergy disorder that may be caused by the performance inconsistency between the high-loss unit and other transformer units.

2. The method according to claim 1, characterized in that The step of identifying a high-loss unit from the plurality of transformer units according to the no-load loss distribution characteristics specifically includes: setting a high loss screening threshold; and, The no-load loss value of each transformer unit is compared with the high-loss screening threshold, and the transformer unit with a no-load loss value higher than the high-loss screening threshold is identified as a high-loss unit.

3. The method according to claim 1, characterized in that The method further comprises: After dynamically quantifying the dyssynergia risk level, calculating the Pearson correlation coefficient between the fluctuation characteristics of the electromagnetic shock intensity signal and the slope change of the parameter drift trend curve within a preset time window to obtain a correlation between the two; and Based on the Pearson correlation coefficient, adaptively assigning adjustment weights to the aging suppression target and the standby energy saving target, wherein the assignment of the adjustment weights includes: If the Pearson correlation coefficient is higher than a preset first correlation threshold, assigning a higher adjustment weight to the aging suppression target than to the standby energy saving target; and If the Pearson correlation coefficient is lower than a preset second correlation threshold, a higher adjustment weight is given to the standby energy saving target than to the aging suppression target.

4. The method according to claim 3, characterized in that The method further comprises: If the risk level of the decoordination is higher than a preset risk threshold, adjusting the input voltage of the low-loss unit with stable performance among the plurality of transformer units through a dynamic voltage regulation protocol to obtain an optimized energy allocation matrix; and According to the optimized energy distribution matrix, when the power supply system switches from a standby state to a high-load working state, the load balancing configuration of the plurality of transformer units is updated.

5. The method according to claim 4, characterized in that The step of updating the load balancing configuration of the plurality of transformer units comprises: The load and impact stress are transferred to the low-loss unit, and an equipment aging suppression threshold is determined for the high-loss unit, wherein the equipment aging suppression threshold is the peak operating current upper limit or the maximum operating temperature upper limit of the high-loss unit.

6. The method according to claim 5, characterized in that The method further comprises: deriving a correction factor for characterizing system stability differences from the device aging suppression threshold; Calibrate an original energy efficiency evaluation value using the correction factor to obtain a calibrated energy efficiency value that takes into account the long-term impact of the stability degradation; and Based on the calibrated energy efficiency value, a final operating parameter set for controlling the operation of the power system is output.

7. The method according to claim 6, wherein The correction coefficient is calculated as follows: correction coefficient C = 1 - (unit rated peak current - device aging suppression threshold) / unit rated peak current; the calibrated energy efficiency value η_calibrated is calculated as follows: , where η_original is the original energy efficiency evaluation value, Π(C_i) is the product of all transformer unit correction coefficients, and i is the index of the transformer unit.

8. The method according to claim 1, characterized in that The data collected in the standby state also includes the temperature index of each transformer unit.

9. The method according to claim 1, characterized in that The electromagnetic shock intensity signal is quantified by calculating the rise rate di / dt or peak amplitude of the pulse current acting on the high-loss unit, where d represents the differential operator, i represents the current, and t represents the time.

10. An analysis system for transformer energy saving and loss reduction, which is applied to a power supply system including multiple transformer units, characterized in that: The system comprises: A data acquisition module, used to collect current data and voltage data of each transformer unit in the power supply system in a standby state; a loss analysis module, configured to process the current data and voltage data collected by the data acquisition module using a Fourier transform method, determine a no-load loss distribution characteristic that can characterize the energy consumption level of each transformer unit, and identify high-loss units and low-loss units based on the no-load loss distribution characteristic; a trend prediction module, configured to extract the electrical parameter history records of the high-loss unit and fit the electrical parameter history records by the least squares method to obtain a parameter drift trend curve characterizing the performance degradation law of the high-loss unit; The risk assessment module is used to obtain the electromagnetic shock intensity signal acting on the high-loss unit when the power supply system is in a high-power output working state, and adopts a Kalman filtering algorithm to fuse the parameter drift trend curve and the electromagnetic shock intensity signal to dynamically quantitatively assess the risk level of synergy disorder that may be caused by the inconsistent performance of the high-loss unit and other transformer units.

Images