Efficient capacitor charging and discharging energy optimization management system
By constructing an energy topology and parameter acquisition module for capacitors, configuring control nodes and performing active energy regulation, and combining this with signal analysis by a decision-making unit, the inefficiency of energy optimization and anomaly handling in existing capacitor charging and discharging management systems is solved, achieving efficient capacitor energy management and operation and maintenance decision-making.
Patent Information
- Application Number
- CN202511115539.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-11
- Publication Date
- 2025-10-31
AI Technical Summary
Existing capacitor charging and discharging management systems are inefficient in terms of energy optimization, status monitoring, and anomaly handling. They cannot fully acquire capacitor distribution information, lack in-depth mining and correlation analysis of key parameters, and are difficult to achieve accurate energy management decisions and anomaly location.
The energy topology construction module acquires capacitor distribution information and constructs a circuit topology that includes multiple capacitor types; the energy parameter acquisition module mines and marks key parameters; the optimization strategy generation module configures control nodes and sets adjustment cycles; the charge and discharge execution module performs passive energy acquisition and active energy adjustment; the state assessment module, in conjunction with the decision-making unit, performs signal analysis and anomaly location; and the early warning feedback module provides terminal visualization feedback.
It achieves improved accuracy and efficiency in capacitor energy management, accurately identifies operational status characteristics and anomaly locations, provides intuitive operation and maintenance management decision support, and improves system reliability and operation and maintenance efficiency.
Smart Images
Figure CN120879870A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of capacitor energy management technology, specifically to a high-efficiency capacitor charging and discharging energy optimization management system. Background Technology
[0002] Existing capacitor charging and discharging management technologies generally suffer from low energy management efficiency. Traditional systems often fail to comprehensively acquire capacitor distribution information, making it difficult to construct an energy topology that accurately reflects the actual circuit connections. This results in a lack of clear understanding of the energy flow in a pre-defined circuit composed of multiple capacitor types. Regarding energy parameter acquisition, most existing technologies can only retrieve capacitor operation data for a single time period, failing to delve into key parameters such as location, type, level, and frequency. Furthermore, they struggle to identify the upstream and downstream relationships between key parameters, leading to a lack of correlation and temporal logic in parameter analysis, hindering accurate energy management decisions.
[0003] Traditional optimization strategies are generated in a relatively fixed manner, failing to determine effective adjustment positions based on the characteristic patterns of energy fluctuations in the energy topology. This leads to unreasonable control node configurations, a single adjustment cycle setting, and difficulty in adapting to dynamic changes in energy fluctuations within the circuit. During charging and discharging, passive energy harvesting methods cannot be combined with active adjustment strategies to achieve accurate energy compensation, resulting in deviations between the baseline energy signal and actual requirements. Furthermore, existing state assessment modules lack independent division between active and passive decision-making regions, making it difficult to consider the characteristics of different types of energy signals during signal analysis and anomaly localization. This results in inaccurate identification of operating state characteristics and low anomaly localization efficiency.
[0004] In the early warning and feedback phase, traditional systems cannot prioritize capacitor monitoring records by parameter level and impact range, lack homogeneous classification processing of operational status characteristics, and offer limited terminal visualization feedback that fails to intuitively present anomalies of different risk levels, severely impacting the efficiency and accuracy of capacitor operation and maintenance management. These technical deficiencies make existing capacitor charging and discharging management systems inadequate for meeting the demands of efficient operation in energy optimization, status monitoring, and anomaly handling. Therefore, an optimized management system that comprehensively integrates topology construction, parameter analysis, dynamic adjustment, and intelligent evaluation is needed. Summary of the Invention
[0005] The purpose of this invention is to provide a high-efficiency capacitor charging and discharging energy optimization management system to solve the problems mentioned in the background art.
[0006] To achieve the above objectives, the present invention provides the following technical solution: a high-efficiency capacitor charging and discharging energy optimization management system, the system comprising:
[0007] An energy topology construction module is used to acquire capacitor distribution information and construct an energy topology, wherein the energy topology is a circuit topology and includes multiple capacitor types of preset circuits;
[0008] An energy parameter acquisition module is used to retrieve capacitor operation data for a preset time period, mine key parameters, and mark the energy topology. The key parameters include parameter location, parameter type, parameter level, and parameter frequency, and the key parameters have a front-end and back-end relationship.
[0009] An optimization strategy generation module is used to traverse the energy topology, combine the key parameters, configure control nodes and set adjustment cycles, wherein the control nodes are energy adjustment nodes.
[0010] A charge / discharge execution module is used to perform passive energy acquisition and determine the basic energy signal based on the control node;
[0011] An active adjustment module is used to perform active energy adjustment based on the adjustment period and determine a compensation energy signal;
[0012] The status assessment module is used to combine the basic energy signal and the compensation energy signal with the decision-making unit to perform signal analysis and anomaly localization, and determine the operating status characteristics.
[0013] The early warning feedback module is used to generate capacitance monitoring records and provide terminal visualization feedback based on the operating status characteristics.
[0014] Preferably, the energy parameter acquisition module is used for:
[0015] The key parameters are traversed, and multiple parameter groups are formed based on their correlations, where the correlations include numerical correlations and temporal correlations.
[0016] Based on the capacitor location, the multiple parameter groups are associated with each other from the front end to the back end, and the association rules are determined to establish the relationship between the front end and the back end.
[0017] Preferably, the optimization strategy generation module is used for:
[0018] By traversing the key parameters, the characteristic patterns of energy fluctuations are determined, including the propagation path of the fluctuations along the circuit.
[0019] Based on the aforementioned characteristics, the effective adjustment position is determined;
[0020] Traverse the effective adjustment positions and configure the control node.
[0021] Preferably, the active adjustment module is used for:
[0022] The adjustment cycles of the key parameters of the energy topology markers vary;
[0023] Identify the adjustment cycle and determine the position of the pre-adjustment capacitor;
[0024] Based on the energy regulator, the active energy is output in a directional manner to regulate the energy at the position of the pre-adjustment capacitor.
[0025] Preferably, the active adjustment module is further used for:
[0026] Determine the initial conditioning energy;
[0027] The initial adjustment energy is corrected and used as the active adjustment energy;
[0028] The energy correction methods include:
[0029] Determine the multi-cycle amplitude, correct and label the initial adjustment energy as a type of active adjustment energy;
[0030] A tracking medium is introduced, which, combined with the initial conditioning energy, serves as a type II active conditioning energy.
[0031] Preferably, the state assessment module is used for:
[0032] The decision-making unit includes an active decision-making area and a passive decision-making area;
[0033] Receive the basic energy signal and activate the active decision-making area to perform signal analysis and anomaly localization;
[0034] The compensation energy signal is received, and the passive decision-making area is activated to perform signal analysis and anomaly localization. The active decision-making area and the passive decision-making area are relatively independent.
[0035] Preferably, the early warning feedback module is used for:
[0036] The capacitor monitoring records are prioritized based on parameter level as the first priority feature and the scope of influence as the second priority feature to determine the risk sequence.
[0037] The operational status characteristics are categorized based on their common origin to determine the categorization result;
[0038] Based on the classification results, the risk sequences are marked, and terminal visualization feedback and capacitor operation and maintenance management are performed.
[0039] Preferably, the energy topology building module is used for:
[0040] Obtain capacitor model information, installation location information, and circuit connection information as the capacitor distribution information;
[0041] Based on the capacitor distribution information, the energy topology is constructed using a node connection method, wherein the node connection method includes the definition of capacitor nodes and the mapping of connection relationships.
[0042] Preferably, the energy parameter acquisition module is further used for:
[0043] Perform timing verification on the multiple parameter groups and determine the verification rules;
[0044] Based on the verification rules, the association rules are modified to form the final association rules.
[0045] Preferably, the capacitance monitoring record includes parameter location, parameter type, parameter level, anomaly type, and anomaly location information;
[0046] The terminal's visual feedback is displayed through interface partitions, where high-parameter-level anomalies are shown in red, medium-parameter-level anomalies in yellow, and low-parameter-level anomalies in green.
[0047] Compared with the prior art, the beneficial effects of the present invention are:
[0048] The high-efficiency capacitor charging and discharging energy optimization management system provided by this invention acquires distribution information such as capacitor model, installation location, and circuit connection relationships through an energy topology construction module. It then uses a node connection method to construct a circuit topology that includes multiple capacitor types, ensuring a high degree of matching between the energy management infrastructure and the actual circuit, providing precise structural support for subsequent energy optimization. The energy parameter acquisition module traverses key parameters and divides parameter groups based on numerical and temporal correlations. It determines the front-end and back-end relationships and correlation rules by combining capacitor locations, and corrects these rules through temporal verification, achieving in-depth mining and correlation analysis of capacitor operating data, providing comprehensive data support for energy optimization strategies.
[0049] The optimization strategy generation module iterates through key parameters to determine the characteristic patterns and transmission paths of energy fluctuations. Based on this, it identifies effective adjustment locations and configures control nodes, enabling the adjustment strategy to precisely target energy fluctuation nodes and improve the effectiveness of energy regulation. The active adjustment module identifies the location of the pre-adjustment capacitor based on the differentiated adjustment cycle of the energy topology markers. By performing multi-cycle amplitude correction on the initial adjustment energy and introducing tracking medium correction, it generates precise active adjustment energy, achieving directional energy compensation for the pre-adjustment location and improving energy utilization efficiency.
[0050] The status assessment module processes basic energy signals and compensation energy signals through independent active and passive decision-making zones, enabling targeted analysis and anomaly localization for different signal types. This improves the accuracy of operational status feature identification and the efficiency of anomaly localization. The early warning feedback module prioritizes capacitor monitoring records by parameter level and then by the scope of impact. It categorizes operational status characteristics based on their common origins and marks risk sequences. The interface provides visual feedback on anomalies at different parameter levels, offering intuitive and efficient decision-making support for capacitor operation and maintenance management, thus improving system reliability and operational efficiency. Attached Figure Description
[0051] Figure 1 This is a schematic diagram illustrating the working principle of the high-efficiency capacitor charge and discharge energy optimization management system described in this invention.
[0052] Figure 2 A flowchart illustrating the parameter association for the energy parameter acquisition module;
[0053] Figure 3 A flowchart for decision-making in the status assessment module;
[0054] Figure 4 This is a flowchart illustrating the priority processing for the early warning feedback module. Detailed Implementation
[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0056] Please see Figures 1-4 The present invention relates to a high-efficiency capacitor charging and discharging energy optimization management system, the specific implementation steps of which are as follows:
[0057] An energy topology construction module is used to acquire capacitor distribution information and construct an energy topology, wherein the energy topology is a circuit topology and includes multiple capacitor types of preset circuits;
[0058] An energy parameter acquisition module is used to retrieve capacitor operation data for a preset time period, mine key parameters, and mark the energy topology. The key parameters include parameter location, parameter type, parameter level, and parameter frequency, and the key parameters have a front-end and back-end relationship.
[0059] An optimization strategy generation module is used to traverse the energy topology, combine the key parameters, configure control nodes and set adjustment cycles, wherein the control nodes are energy adjustment nodes.
[0060] A charge / discharge execution module is used to perform passive energy acquisition and determine the basic energy signal based on the control node;
[0061] An active adjustment module is used to perform active energy adjustment based on the adjustment period and determine a compensation energy signal;
[0062] The status assessment module is used to combine the basic energy signal and the compensation energy signal with the decision-making unit to perform signal analysis and anomaly localization, and determine the operating status characteristics.
[0063] The early warning feedback module is used to generate capacitance monitoring records and provide terminal visualization feedback based on the operating status characteristics.
[0064] Example 1: In the specific implementation of the energy parameter acquisition module, the first step is to traverse the key parameters. These key parameters have a specific structure, including information such as parameter location, parameter type, parameter level, and parameter frequency, and there are front-end and back-end relationships between these key parameters. When traversing these key parameters, they need to be processed based on correlations, which mainly include numerical correlations and temporal correlations.
[0065] For numerical correlation analysis, it is necessary to analyze the numerical relationships between different key parameters. For example, if the parameter type at a certain location is voltage, is there a numerical correspondence between the changes in its parameter level and frequency and the changes in the level and frequency of the current parameter at another location? By analyzing such numerical correlations, key parameters with similar numerical variation patterns or interrelationships can be grouped into the same parameter group.
[0066] Time series correlation focuses on the chronological order and mutual influence of key parameters over time. For example, during capacitor operation, if the parameter type at a certain location is temperature, will an increase in its parameter level lead to a change in the capacitance parameter level at another location after a certain period of time? This temporal sequence and causal relationship is what time series correlation needs to consider. Based on the analysis results of time series correlation, key parameters that are correlated over time can also be divided into corresponding parameter groups.
[0067] By dividing the key parameters based on numerical and temporal correlations, multiple parameter groups are obtained. Next, front-end and back-end correlation processing needs to be performed on these parameter groups based on capacitor locations. The connection relationships between the front and back ends of capacitors differ depending on their position in the circuit, which leads to specific front-end and back-end relationships between different parameter groups.
[0068] Specifically, it is necessary to determine the upstream and downstream nodes of each capacitor location in the circuit, and then analyze the signal transmission or influence relationships between the parameter group at that location and the parameter groups at other capacitor locations. For example, changes in the parameter group of a capacitor located at the front end of the circuit may affect the parameter performance of the capacitor group at the downstream end. This influence relationship needs to be identified and determined, and then association rules can be formed.
[0069] In determining the association rules, it is necessary to comprehensively consider the physical connections of capacitor locations and the logical influence relationships between parameter groups. By sorting out and analyzing these relationships, the front-end and back-end relationships between each parameter group are clarified, so that the position and interrelationships of each parameter group in the energy topology can be accurately defined.
[0070] In addition, the energy parameter acquisition module also needs to perform time-series verification on multiple parameter groups. The purpose of time-series verification is to ensure the consistency and rationality of the parameter groups over time. In capacitor operation data, the changes of each parameter should follow a certain time pattern. If the changes of a parameter group are abnormal in time, it may affect the analysis and judgment of the entire system.
[0071] When performing timing verification, it is necessary to establish corresponding verification rules. These rules can be based on the normal operating characteristics of the capacitor, the circuit design principles, and historical operating data. For example, for a specific capacitor location, the changes in its voltage parameters should have a certain sequence and time interval with the changes in its current parameters. The verification rule could then be set to check whether the timing sequence of these two parameter changes conforms to a normal pattern.
[0072] Based on the established validation rules, the previously formed association rules are revised. If, during time-series validation, it is found that the relationship between parameter groups described by a certain association rule does not conform to the validation rules in terms of time series, the association rule needs to be adjusted and modified to ensure its accuracy and reliability. Through this revision process, the final association rules that accurately reflect the true relationship between parameter groups are ultimately formed.
[0073] Example 2: In actual operation, the optimization strategy generation module needs to perform a comprehensive traversal of key parameters. These key parameters include information such as parameter location, parameter type, parameter level, and parameter frequency, and there are front-end and back-end relationships between them. During the traversal, the module needs to extract energy fluctuation-related features from a large amount of parameter data, focusing on analyzing the manifestation of energy fluctuations in the circuit, such as the fluctuation patterns of voltage and current, and the evolution trend of these fluctuations over time.
[0074] When determining the characteristics of energy fluctuations, it is crucial to focus on the propagation path of the fluctuations along the circuit. Since the capacitor nodes in a circuit are interconnected, energy fluctuations originate from one node and propagate to other nodes through the circuit. For example, when an energy fluctuation occurs in a capacitor due to charging and discharging, this fluctuation may sequentially affect subsequent capacitor nodes along the circuit, forming a specific propagation path. The module needs to analyze capacitor operating data to track the propagation sequence, speed, and amplitude changes of energy fluctuations between different nodes, thereby clarifying the characteristics of the wave propagation path along the circuit.
[0075] In addition to the transmission path, it is also necessary to analyze the periodic characteristics and amplitude variation patterns of energy fluctuations. Different types of capacitors respond differently to energy fluctuations in a circuit; some capacitors may produce significant energy fluctuations at specific frequencies, while others may exhibit different fluctuation amplitudes due to differences in parameter levels. The module needs to consider these factors and extract the complete characteristic patterns of energy fluctuations from key parameters to provide a basis for subsequently determining effective adjustment positions.
[0076] Based on the established characteristics, the module begins to determine effective adjustment locations. At this point, analysis needs to be conducted in conjunction with the circuit topology and energy fluctuation transmission paths. In the circuit, certain locations have a more critical impact on energy fluctuations, such as key nodes on the energy fluctuation transmission path or capacitor locations that play a dominant role in the overall circuit energy distribution. Adjusting energy at these locations allows for more effective control of energy fluctuations and optimizes system energy management.
[0077] Taking a transmission path as an example, if energy fluctuations are transmitted from capacitor A to capacitor B and then to capacitor C, then capacitor B may be located in the middle of the transmission path. Adjusting at this point can affect both the preceding and following energy fluctuations simultaneously. The module needs to calculate and analyze to determine which adjustments at which points can produce the greatest suppression or optimization effect on energy fluctuations; these points are the effective adjustment points.
[0078] When determining effective adjustment locations, the capacitor's parameter type and grade must also be considered. Different capacitor types (such as electrolytic capacitors and ceramic capacitors) function differently in a circuit, and capacitors with higher parameter grades may be more sensitive to energy fluctuations. The module needs to evaluate the parameter characteristics of each capacitor location, and in conjunction with energy fluctuation characteristics, select locations that can effectively influence energy fluctuations while also being feasible for adjustment.
[0079] After determining the effective adjustment locations, the module needs to traverse these locations to configure the control nodes. Control nodes are energy regulation nodes, and their configuration process requires comprehensive consideration of the circuit characteristics, energy fluctuation features, and system optimization objectives of the adjustment locations.
[0080] For each effective regulation location, the module needs to determine the type and regulation method of the control node. For example, at one location, a voltage regulation control node may be configured to optimize energy distribution by adjusting the voltage; at another location, a current regulation control node may be needed to suppress energy fluctuations by controlling the current. The regulation cycle of the control node also needs to be set according to the characteristics of energy fluctuations. If the energy fluctuation cycle is short, the regulation cycle of the control node should be shortened accordingly to achieve real-time and effective regulation.
[0081] When configuring control nodes, the collaborative operation between nodes must also be considered. Multiple control nodes in the circuit may operate simultaneously, and the module must ensure that the adjustment parameters of each node are coordinated to avoid adjustment conflicts. For example, the adjustment cycles and adjustment amplitudes of adjacent control nodes must be consistent to guarantee the energy optimization effect of the entire circuit.
[0082] Furthermore, the configuration of control nodes must consider the real-time performance and reliability of the system. Control nodes must be able to respond promptly to changes in energy fluctuations, and their adjustment process must not adversely affect the normal operation of the circuit. The module needs to determine the optimal configuration of the control nodes through simulation and testing to ensure their effective functioning in actual operation.
[0083] Through the above steps, the optimization strategy generation module completes the entire process from traversing key parameters and determining energy fluctuation characteristics, to identifying effective adjustment positions, and then configuring control nodes and setting adjustment cycles. In this process, the module closely integrates circuit topology and energy parameters, and through meticulous analysis and calculation, achieves precise configuration of control nodes, providing clear guidance for subsequent charging and discharging execution and active energy regulation, enabling the system to perform energy optimization management more efficiently.
[0084] Example 3: During operation, the active regulation module first faces the challenge of varying regulation cycles for key parameters within the energy topology. These differences are determined by the parameter characteristics, installation location, and functional role of different capacitors in the circuit. For instance, in the same energy topology, an electrolytic capacitor at the front end may have a shorter voltage regulation cycle due to its primary energy storage function, while a ceramic capacitor at the back end may have a relatively longer current regulation cycle due to its filtering effect on high-frequency signals.
[0085] Based on this differentiation in adjustment cycles, the module needs to first identify each adjustment cycle. During identification, it's necessary to combine the marking information of key parameters in the energy topology, including parameter location and type. By analyzing this information, the capacitor location corresponding to each adjustment cycle, i.e., the pre-adjustment capacitor location, is determined. For example, when an adjustment cycle of 50 milliseconds is identified and the corresponding parameter type is voltage, the pre-adjustment capacitor location for that adjustment cycle can be determined to be a certain electrolytic capacitor node at the front end of the energy topology.
[0086] After determining the location of the pre-adjusted capacitor, the module needs to output active regulating energy in a directional manner based on the energy regulator. The energy regulator is the core component for realizing active energy regulation, and the form and magnitude of its output energy need to be determined according to the specific requirements of the pre-adjusted capacitor location. For example, for a pre-adjusted capacitor location where the voltage parameter needs to be adjusted, the energy regulator needs to output corresponding voltage regulating energy to adjust the voltage state of the capacitor; for a location where the current parameter needs to be adjusted, it outputs current regulating energy.
[0087] Before outputting actively regulated energy, the module needs to determine the initial regulated energy. Determining the initial regulated energy requires considering the current energy state of the pre-regulated capacitor location, the parameter level at that location, and the expected target value for that parameter in the energy topology. For example, if the current voltage at the pre-regulated capacitor location is 20V, and the parameter level requires it to be maintained at 25V, and the expected target value for that location in the energy topology is also 25V, then the initial regulated energy needs to be calculated and determined based on this 5V difference and parameters such as the capacitor's capacitance.
[0088] After determining the initial regulation energy, the module needs to correct it to obtain the final active regulation energy. There are two main types of energy correction methods. The first method is to determine multi-cycle amplitudes to correct and mark the initial regulation energy as a type of active regulation energy. Here, multi-cycle amplitudes refer to the amplitude of energy parameter fluctuations at the pre-regulation capacitor position over multiple regulation cycles. By analyzing the amplitude changes over multiple cycles, the fluctuation pattern of the energy parameter can be understood, thereby correcting the initial regulation energy.
[0089] For example, over 10 consecutive regulation cycles, the voltage amplitude at the pre-regulation capacitor position fluctuates between 23V and 27V, with an average amplitude of 25V. The initial regulation energy is calculated based on the difference between single cycles. Therefore, it may be necessary to adjust the initial regulation energy based on the average amplitude over multiple cycles to better reflect actual energy fluctuations. After correction, this type of active regulation energy is marked for subsequent tracking and management.
[0090] The second correction method involves introducing a tracking medium, combined with the initial conditioning energy, as a type II active conditioning energy. The tracking medium can be a virtual or physical medium capable of following energy fluctuations and reflecting their changing trends. By introducing a tracking medium, the energy changes at the pre-conditioning capacitor's location can be monitored in real time, and this information can be fed back into the calculation of the initial conditioning energy.
[0091] For example, the tracking medium can be a sensor that monitors current changes in real time, and its output signal can reflect the current fluctuation trend at the position of the pre-adjusted capacitor. Once the initial adjustment energy is determined, it is dynamically corrected by combining the real-time current change information fed back by the tracking medium, so that it can more accurately adapt to real-time changes in energy fluctuations. The resulting type II active adjustment energy has stronger real-time performance and adaptability.
[0092] During energy correction, the module needs to ensure that the calculation and output processes of Type I and Type II active regulation energy are independent yet collaborative. Type I active regulation energy focuses on correction based on statistical analysis of historical multi-period data, exhibiting a certain degree of stability and regularity; Type II active regulation energy relies on real-time feedback from the tracking medium, demonstrating stronger dynamic adaptability.
[0093] Once the active adjustment energy is determined, the module directs its output to the pre-adjustment capacitor position via the energy regulator for energy adjustment. During the adjustment process, the module needs to monitor the adjustment effect in real time and observe whether the parameter changes at the pre-adjustment capacitor position meet expectations. If the adjustment effect is found to be unsatisfactory, the initial adjustment energy needs to be re-evaluated and corrected again until a satisfactory adjustment effect is achieved.
[0094] Through the above series of steps, the active regulation module achieves active energy regulation based on the regulation period and determines the compensation energy signal. In this process, the module fully considers multiple aspects such as the differentiation of the regulation period, the determination and correction of the initial regulation energy, and the directional output of the energy regulator, ensuring the accuracy and effectiveness of active energy regulation.
[0095] Example 4: In actual operation, the core of the state assessment module lies in analyzing and locating anomalies in the basic energy signal and compensation energy signal through the decision-making unit, thereby determining the operating state characteristics of the system. The decision-making unit contains an active decision-making area and a passive decision-making area. These two areas are relatively independent in function and can process different types of signals respectively.
[0096] Taking a specific circuit scenario as an example, suppose there is a series circuit consisting of multiple capacitors, including an electrolytic capacitor C1, a ceramic capacitor C2, and a film capacitor C3. When the system is in a normal charging and discharging state, the charging and discharging execution module will perform passive energy acquisition based on the control node to obtain a basic energy signal. At this time, the basic energy signal may be represented by data such as voltage changes and current flow across each capacitor. For example, the voltage of the electrolytic capacitor C1 gradually rises from 0V to its rated voltage of 10V during charging, and the current is relatively large at the beginning of charging and then gradually decreases. These data constitute part of the basic energy signal.
[0097] Once the basic energy signal is transmitted to the state assessment module, the module activates the active decision-making area to process it. During signal processing, the active decision-making area checks each parameter of the signal according to preset analysis rules and algorithms. For example, regarding the voltage signal of electrolytic capacitor C1, the active decision-making area checks whether its rise rate is within the normal range and whether there are voltage jumps or abnormal plateaus. Suppose that during a charging process, the voltage of C1 suddenly stops rising at 5V and does not continue to rise. The active decision-making area will detect this anomaly and, through algorithmic analysis, determine that it may be due to electrolyte desiccation or plate damage within C1, thus identifying the anomaly location as C1 and the anomaly type as voltage anomaly.
[0098] Meanwhile, the active regulation module actively regulates the system's energy based on the regulation period, generating a compensation energy signal. Continuing with the circuit example above, assume the regulation period is set to perform active regulation every 100 milliseconds. When an abnormal voltage is detected in C1, the active regulation module outputs active regulation energy to the location of C1 through the energy regulator, attempting to raise its voltage to a normal level. At this time, the compensation energy signal may manifest as an additional injected current or voltage pulse. For example, when C1 is stuck at 5V, the active regulation module outputs a 1A current pulse lasting 20 milliseconds to attempt to push the voltage up further.
[0099] After the compensation energy signal is generated, it is transmitted to the state assessment module, which then activates the passive decision-making area to analyze it. Unlike the active decision-making area, the passive decision-making area focuses more on analyzing the interaction and influence between the compensation energy signal and the base energy signal. For example, after injecting a current pulse, the passive decision-making area monitors the voltage response of C1. If the voltage continues to rise under the pulse and gradually approaches 10V, it indicates that the compensation energy signal has a positive effect, and the system may be returning to normal. If the voltage remains stagnant or even shows a downward trend, the passive decision-making area will further analyze the situation and may determine that the fault in C1 is more serious, and compensation energy alone cannot solve the problem, requiring more in-depth anomaly localization.
[0100] In this process, the relative independence of the active and passive decision-making regions is reflected in their ability to process different signals simultaneously without interference between their respective analysis processes. For example, while the active decision-making region analyzes the voltage anomaly of C1, the passive decision-making region can analyze the compensation energy signals of C2 and C3 to determine their energy regulation effects. Suppose that after compensation energy regulation, the current fluctuation range of C2 expands from the normal 0.5A±0.1A to 0.5A±0.3A. The passive decision-making region will record this change and, combined with the analysis results of the active decision-making region on the basic energy signal of C2, comprehensively determine whether a new anomaly has occurred in C2.
[0101] After combining the analysis results of the basic energy signal and the compensation energy signal, the status assessment module will make a comprehensive judgment on the operating status characteristics of the entire system. Taking the above circuit as an example, if the active decision-making area detects an abnormal voltage of C1 and the passive decision-making area detects an abnormal current fluctuation of C2, the status assessment module will integrate these abnormal information and determine that the current operating status characteristics of the system are that C1 and C2 may be faulty and require further maintenance or replacement.
[0102] In practical applications, the active and passive decision-making regions of a decision-making unit employ different analysis strategies and algorithms based on varying circuit structures and capacitor types. For instance, in a high-frequency filter circuit, the active decision-making region might focus more on impedance changes under high-frequency signals, while the passive decision-making region, when analyzing compensation energy signals, might focus more on the effect of high-frequency pulses on capacitor impedance regulation. Similarly, in an energy storage circuit, the active decision-making region focuses on analyzing changes in leakage current and equivalent series resistance of electrolytic capacitors, while the passive decision-making region focuses on the impact of compensation energy on leakage current and resistance.
[0103] In this way, the state assessment module can fully utilize the independent analytical capabilities of the active and passive decision-making zones to comprehensively and meticulously process the basic energy signals and compensation energy signals, thereby accurately locating anomalies in the system and determining operational status characteristics. This process not only promptly identifies potential problems in the capacitor's charging and discharging process but also provides accurate data for subsequent early warning feedback and operation and maintenance management, ensuring the system operates safely and efficiently.
[0104] Example 5: During operation, the early warning feedback module needs to prioritize the capacitor monitoring records and provide visual feedback to the terminal to achieve effective management of the capacitor's operating status. Taking a capacitor bank in a power electronic device as an example, the capacitor bank includes electrolytic capacitors C1 and C2 and ceramic capacitor C3. The monitoring records generated by each capacitor during operation include information such as parameter location, parameter type, parameter level, anomaly type, and anomaly location.
[0105] Assume that the parameter location of electrolytic capacitor C1 is at the power input terminal of the device, the parameter type is voltage, the parameter level is high, the anomaly type is overvoltage, and the anomaly location is C1 itself; the parameter location of ceramic capacitor C3 is in the filter circuit, the parameter type is current, the parameter level is medium, the anomaly type is excessive current fluctuation, and the anomaly location is the branch where C3 is located. The early warning feedback module first sorts these records based on parameter level as the first priority feature and the scope of impact as the second priority feature. Since C1's parameter level is high, its anomaly may directly affect the stability of the device's power input, affecting the entire device, while C3's parameter level is medium, and its impact is mainly limited to the filter circuit. Therefore, C1's monitoring record will be ranked at the front of the risk sequence, and C3's record will be ranked later.
[0106] When classifying operating status characteristics by their common source, if the system detects overvoltage anomalies in C1 and voltage anomalies in C2 both caused by voltage fluctuations at the power input terminal, then these two abnormal operating status characteristics will be classified into the same category: power input voltage anomalies. However, if the excessive current fluctuations in C3 are caused by capacitor aging, they will be classified as an independent component aging category. This common-source classification allows for the integration of scattered anomaly information, facilitating centralized processing later.
[0107] Based on the classification results, the module labels the risk sequences. For example, abnormal records of C1 and C2 (power input voltage anomalies) are labeled as "power supply risk," and abnormal records of C3 are labeled as "filter branch risk." Then, terminal visualization feedback and capacitor operation and maintenance management are implemented. The terminal interface uses a zoned display method: high-parameter-level anomalies, such as C1's overvoltage anomaly, are displayed in red; medium-parameter-level anomalies, such as C3's excessive current fluctuation, are displayed in yellow; and low-parameter-level anomalies are displayed in green. In the visualization interface, the red area highlights the parameter location and anomaly type of C1, while the yellow area displays the relevant anomaly data for C3, allowing maintenance personnel to intuitively identify the risk level.
[0108] Suppose that a thin-film capacitor C4 also exists in the system. Its parameter location is energy storage circuit, parameter type is capacitance value, parameter level is low, anomaly type is slight capacity decay, and anomaly location is C4. In this case, the monitoring record for C4, due to its low parameter level and small impact range, will be placed at the end of the risk sequence and displayed in the green area of the terminal interface. Maintenance personnel can prioritize handling high-risk anomalies in the red area based on the color-coded risk zones and the displayed risk sequence, then proceed to handle issues in the yellow and green areas in sequence.
[0109] When performing capacitor maintenance, if C1 in the red area has an abnormal overvoltage, it may be necessary to immediately check the voltage regulator in the power input circuit and replace or repair C1; if C3 in the yellow area has excessive current fluctuations, technicians can be arranged to test the filter branch where C3 is located to determine whether C3 needs to be replaced or the circuit parameters need to be adjusted; if C4 in the green area has a slight capacity decay, it can be recorded and its parameter changes can be monitored regularly, without taking emergency measures for the time being.
[0110] For example, when multiple capacitor malfunctions in the system are caused by excessively high ambient temperatures, the early warning feedback module will categorize these malfunctions as "abnormal ambient temperature" and mark this category in the risk sequence. The area containing the relevant malfunction records on the terminal interface will be highlighted in red or yellow, prompting maintenance personnel to check the equipment's heat dissipation system, reduce the ambient temperature, and resolve the malfunctions of multiple capacitors at their source.
[0111] The parameter location information in the capacitor monitoring log helps maintenance personnel quickly pinpoint the specific location of an anomaly in the circuit, such as C1 being located at the power input terminal and C3 being located in the filter circuit. Parameter type information allows maintenance personnel to understand whether the anomaly involves parameters such as voltage, current, or capacitance. Parameter level information clarifies the severity of the anomaly. Anomaly type and location further refine the nature and location of the problem. Through the comprehensive presentation of this information, the terminal visualization feedback provides maintenance personnel with a clear and comprehensive view of the capacitor's operating status.
[0112] In practical applications, the early warning feedback module continuously receives the operating status characteristics output by the status assessment module, constantly updates the capacitor monitoring records, and dynamically adjusts the risk sequence based on parameter levels and impact ranges. For example, if the current fluctuation anomaly of C3 develops from an initial medium level to a high level, its position in the risk sequence will shift accordingly, and the area displaying the anomaly on the terminal interface will change from yellow to red to remind maintenance personnel that the severity of the anomaly has increased and the processing speed needs to be accelerated.
[0113] Through the above process, the early warning feedback module effectively monitors and manages the capacitor's operating status, helping maintenance personnel efficiently identify risks, locate problems, and take corresponding maintenance measures to ensure the stable operation of the capacitor charging and discharging system. Throughout the process, the module strictly sorts and categorizes anomalies according to parameter levels and impact ranges, presenting the results through an intuitive visual interface, providing clear guidance for capacitor operation and maintenance management.
[0114] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0115] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A high-efficiency capacitor charging and discharging energy optimization management system, characterized in that, The system includes: An energy topology construction module is used to acquire capacitor distribution information and construct an energy topology, wherein the energy topology is a circuit topology and includes multiple capacitor types of preset circuits; An energy parameter acquisition module is used to retrieve capacitor operation data for a preset time period, mine key parameters, and mark the energy topology. The key parameters include parameter location, parameter type, parameter level, and parameter frequency, and the key parameters have a front-end and back-end relationship. An optimization strategy generation module is used to traverse the energy topology, combine the key parameters, configure control nodes and set adjustment cycles, wherein the control nodes are energy adjustment nodes. A charge / discharge execution module is used to perform passive energy acquisition and determine the basic energy signal based on the control node; An active adjustment module is used to perform active energy adjustment based on the adjustment period and determine a compensation energy signal; The status assessment module is used to combine the basic energy signal and the compensation energy signal with the decision-making unit to perform signal analysis and anomaly localization, and determine the operating status characteristics. The early warning feedback module is used to generate capacitance monitoring records and provide terminal visualization feedback based on the operating status characteristics.
2. The high-efficiency capacitor charging and discharging energy optimization management system as described in claim 1, characterized in that, The energy parameter acquisition module is used for: The key parameters are traversed, and multiple parameter groups are formed based on their correlations, where the correlations include numerical correlations and temporal correlations. Based on the capacitor location, the multiple parameter groups are associated with each other from the front end to the back end, and the association rules are determined to establish the relationship between the front end and the back end.
3. The high-efficiency capacitor charging and discharging energy optimization management system as described in claim 1, characterized in that, The optimization strategy generation module is used for: By traversing the key parameters, the characteristic patterns of energy fluctuations are determined, including the propagation path of the fluctuations along the circuit. Based on the aforementioned characteristics, the effective adjustment position is determined; Traverse the effective adjustment positions and configure the control node.
4. The high-efficiency capacitor charging and discharging energy optimization management system as described in claim 1, characterized in that, The active adjustment module is used for: The adjustment cycles of the key parameters of the energy topology markers vary; Identify the adjustment cycle and determine the position of the pre-adjustment capacitor; Based on the energy regulator, the active energy is output in a directional manner to regulate the energy at the position of the pre-adjustment capacitor.
5. The high-efficiency capacitor charging and discharging energy optimization management system as described in claim 4, characterized in that, The active adjustment module is also used for: Determine the initial conditioning energy; The initial adjustment energy is corrected and used as the active adjustment energy; The energy correction methods include: Determine the multi-cycle amplitude, correct and label the initial adjustment energy as a type of active adjustment energy; A tracking medium is introduced, which, combined with the initial conditioning energy, serves as a type II active conditioning energy.
6. The high-efficiency capacitor charging and discharging energy optimization management system as described in claim 1, characterized in that, The status assessment module is used for: The decision-making unit includes an active decision-making area and a passive decision-making area; Receive the basic energy signal and activate the active decision-making area to perform signal analysis and anomaly localization; The compensation energy signal is received, and the passive decision-making area is activated to perform signal analysis and anomaly localization. The active decision-making area and the passive decision-making area are relatively independent.
7. The high-efficiency capacitor charging and discharging energy optimization management system as described in claim 1, characterized in that, The early warning feedback module is used for: The capacitor monitoring records are prioritized based on parameter level as the first priority feature and the scope of influence as the second priority feature to determine the risk sequence. The operational status characteristics are categorized based on their common origin to determine the categorization result; Based on the classification results, the risk sequences are marked, and terminal visualization feedback and capacitor operation and maintenance management are performed.
8. The high-efficiency capacitor charging and discharging energy optimization management system as described in claim 1, characterized in that, The energy topology construction module is used for: Obtain capacitor model information, installation location information, and circuit connection information as the capacitor distribution information; Based on the capacitor distribution information, the energy topology is constructed using a node connection method, wherein the node connection method includes the definition of capacitor nodes and the mapping of connection relationships.
9. The high-efficiency capacitor charging and discharging energy optimization management system as described in claim 2, characterized in that, The energy parameter acquisition module is also used for: Perform timing verification on the multiple parameter groups and determine the verification rules; Based on the verification rules, the association rules are modified to form the final association rules.
10. The high-efficiency capacitor charge / discharge energy optimization management system as described in claim 7, characterized in that, The capacitance monitoring record includes information on parameter location, parameter type, parameter level, anomaly type, and anomaly location. The terminal's visual feedback is displayed through interface partitions, where high-parameter-level anomalies are shown in red, medium-parameter-level anomalies in yellow, and low-parameter-level anomalies in green.