Load intelligent monitoring system adaptive to energy storage type charging transformer

Abstract

The invention belongs to the technical field of power equipment monitoring and control, and particularly relates to an intelligent load monitoring system adaptive to an energy storage type charging transformer, which comprises a high-precision load monitoring module, a load analysis engine module, an intelligent control strategy module, a safety protection execution module and a remote monitoring platform, according to the invention, the load analysis engine module identifies the load change trend, predicts the future load demand and captures the load abnormity, and the intelligent control strategy module carries out the parameter adjustment of the energy storage type charging transformer and the execution control of the protection operation based on the output information of the load analysis engine module. The safety operation of the energy storage type charging transformer is ensured, the management and control hidden danger degree of the energy storage type charging transformer is analyzed through a load management and control influence analysis module, the subsequent operation supervision of the energy storage type charging transformer is enhanced when a management and control high influence signal is generated, and reasonable optimization and improvement measures are taken. And the operation safety of the energy storage type charging transformer is further ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring and controlling electric power equipment, and in particular to a load intelligent monitoring system adapted for an energy storage charging transformer. Background Art

[0002] The energy storage charging transformer is a special transformer device that combines the basic principle of the transformer with the energy storage technology to realize the storage and release functions of electric energy. When the grid load is light or there is excess electric energy, the energy storage charging transformer will convert the electric energy into magnetic energy and store it. When the grid load increases or electric energy is needed, it will convert the stored magnetic energy into electric energy output to meet the load demand. With the rapid development of smart grids and renewable energy, energy storage charging transformers play an increasingly important role in power systems. At present, when performing load monitoring on energy storage charging transformers, it is often difficult to automatically and reasonably control the energy storage charging transformer while realizing load monitoring, and it is impossible to comprehensively analyze and accurately evaluate the degree of hidden dangers in the management and control of energy storage charging transformers, which is not conducive to ensuring the operation safety of energy storage charging transformers. In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent load monitoring system suitable for an energy storage charging transformer, which solves the problem that the prior art is difficult to automatically and reasonably control the energy storage charging transformer while realizing load monitoring, and is unable to comprehensively analyze and accurately evaluate the degree of management and control risks of the energy storage charging transformer, which is not conducive to ensuring the safety of the operation of the energy storage charging transformer.

[0004] To achieve the above object, the present invention provides the following technical solutions: The load intelligent monitoring system adapted to the energy storage charging transformer includes a high-precision load monitoring module, a load analysis engine module, an intelligent control strategy module, a safety protection execution module and a remote monitoring platform; the high-precision load monitoring module is deployed at the input and output ends of the energy storage charging transformer, and uses a high-precision sensor to monitor the relevant load parameters of the energy storage charging transformer in real time, and sends the collected load information to the load analysis engine module; the load analysis engine module processes and analyzes the received load information in real time based on big data analysis and machine learning algorithms, identifies load change trends, predicts future load demands, and captures load anomalies, and sends the load analysis results to the intelligent control strategy module and the remote monitoring platform; Based on the output information of the load analysis engine module, the intelligent control strategy module automatically generates the corresponding control strategy and adjusts the parameters of the energy storage charging transformer accordingly, and sends the corresponding control information to the remote monitoring platform; the safety protection execution module has safety control functions including overload protection and short-circuit protection. When the energy storage charging transformer operates under abnormal load conditions, the intelligent control strategy module enables the safety protection execution module to perform corresponding protection operations, and the safety protection execution module sends the execution information to the remote monitoring platform.

[0005] Furthermore, the remote monitoring platform is communicated with the load control impact analysis module. The load control impact analysis module is used to set a detection period, analyze the degree of control risks of the energy storage charging transformer during the detection period, generate a high control impact signal or a low control impact signal through analysis, and send the high control impact signal or the low control impact signal to the remote monitoring platform. When the remote monitoring platform receives the high control impact signal, it will issue a corresponding warning.

[0006] Furthermore, the specific analysis process of the load control impact analysis module includes: Obtain the regulation evaluation symbol HX-1 or HX-2 and the protection evaluation symbol ZX-1 or ZX-2, perform intersection analysis on the regulation evaluation symbol and the protection evaluation symbol, and if HX-2∩ZX-2 is obtained, a low-impact control signal is generated, and in other cases a high-impact control signal is generated.

[0007] Furthermore, the load control impact analysis module is communicatively connected to the self-adjustment detection and evaluation module and the protection detection and evaluation module. The self-adjustment detection and evaluation module evaluates and analyzes the automatic adjustment and control performance of the energy storage charging transformer during the detection period, assigns an adjustment evaluation symbol HX-1 or HX-2 through analysis, and sends the adjustment evaluation symbol HX-1 or HX-2 to the load control impact analysis module; The protection detection and evaluation module evaluates and analyzes the protection execution performance of the security protection execution module during the detection period, assigns a protection evaluation symbol ZX-1 or ZX-2 through analysis, and sends the protection evaluation symbol ZX-1 or ZX-2 to the load control impact analysis module.

[0008] Furthermore, the specific analysis process of the self-adjusting detection and evaluation module is as follows: When the intelligent control strategy module adjusts the parameters of the energy storage charging transformer, if the adjustment of the corresponding parameters cannot be completed within the corresponding specified time, the adjustment process of the corresponding parameters is marked as an inefficient process, the number of inefficient processes corresponding to the corresponding parameters in the detection period is obtained and marked as the parameter adjustment inefficient value, and the parameter adjustment inefficient value is numerically compared with the corresponding preset parameter adjustment inefficient threshold value. If the parameter adjustment inefficient value exceeds the preset parameter adjustment inefficient threshold value, the corresponding parameter is marked as a characteristic parameter; if the characteristic parameter exists in the detection period, the adjustment evaluation symbol HX-1 is assigned; If there are no characteristic parameters during the detection period, a set of preset weight values ​​is set in advance for each type of parameter, and the parameter adjustment inefficiency value of the corresponding parameter is ratio-calculated with the corresponding preset parameter adjustment inefficiency threshold to obtain the parameter adjustment decision value, and the parameter adjustment decision value is multiplied by the corresponding preset weight value to obtain the parameter adjustment analysis value, and the parameter adjustment analysis values ​​of all parameters are summed to obtain the self-adjustment evaluation value, and the self-adjustment evaluation value is numerically compared with the preset self-adjustment evaluation threshold. If the self-adjustment evaluation value exceeds the preset self-adjustment evaluation threshold, the adjustment evaluation symbol HX-1 is assigned; if the self-adjustment evaluation value does not exceed the preset self-adjustment evaluation threshold, the adjustment evaluation symbol HX-2 is assigned.

[0009] Furthermore, the specific analysis process of the protection detection and evaluation module is as follows: The protection elements involved in the security protection execution module are obtained, and the corresponding protection elements are marked as i, where i is a natural number greater than 1; the protection element i is judged to be a non-safe element through element-by-evaluation analysis. If a non-safe element exists, a protection evaluation symbol ZX-1 is assigned; if no non-safe element exists, a protection evaluation symbol ZX-2 is assigned.

[0010] Furthermore, the specific analysis process of component-by-component analysis is as follows: Collect all execution durations of protection element i during the detection period, calculate the average of all execution durations to obtain the response execution coefficient, and compare the execution duration with the corresponding preset execution duration threshold. If the execution duration exceeds the corresponding preset execution duration threshold, the corresponding execution duration is marked as non-standard duration; The number of non-compliance durations corresponding to the protection element i in the historical stage is obtained and marked as the compliance abnormal detection coefficient, and the response execution coefficient and the compliance abnormal detection coefficient are numerically compared with the corresponding preset response execution coefficient threshold and the preset compliance abnormal detection coefficient threshold, respectively. If the response execution coefficient or the compliance abnormal detection coefficient exceeds the corresponding preset threshold, the protection element i is marked as a non-safe element; If the response execution coefficient and the standard-reaching anomaly detection coefficient do not exceed the corresponding preset thresholds, the production date of the protection element i is collected, and the interval between the current date and the production date is marked as the production value, and the total time that the protection element i is in the life damage state in the historical stage is obtained and marked as the life loss value, and the number of startups of the protection element i in the historical stage is obtained and marked as the startup value; the initial evaluation value of the element is obtained by numerically calculating the production value, the life loss value and the startup value, and the initial evaluation value of the element is numerically compared with the corresponding preset initial evaluation threshold of the element. If the initial evaluation value of the element exceeds the corresponding preset initial evaluation threshold of the element, the protection element i is marked as a non-safe element; If the initial evaluation value of the component does not exceed the corresponding preset initial evaluation threshold of the component, the position of the protection component i is collected, and the offset distance between the position and its initial position is marked as the component displacement value, and the contour scan image of the protection element i is collected, and the contour scan image is overlapped and compared with the corresponding standard contour image, thereby obtaining the overlap anomaly value of the protection element i; the component re-evaluation value is obtained by numerically calculating the component displacement value and the overlap anomaly value, and the component re-evaluation value is numerically compared with the preset component re-evaluation threshold. If the component re-evaluation value exceeds the preset component re-evaluation threshold, the protection element i is marked as a non-safe component.

[0011] Furthermore, the analysis and judgment process of the life damage status is as follows: The environmental information of the environment in which the protection element i is located is obtained, and all environmental data that need to be monitored are compared one by one with the corresponding preset environmental data requirements. If there is environmental data that does not meet the corresponding preset environmental data requirements, it is determined that the protection element i is in a life damage state.

[0012] Furthermore, the load control impact analysis module is communicated with the personnel management comprehensive evaluation module, and the load control impact analysis module sends a low control impact signal to the personnel management comprehensive evaluation module. When the personnel management comprehensive evaluation module receives the low control impact signal, it analyzes the work performance of the person in charge of the energy storage charging transformer, and generates a personnel management abnormality signal or a personnel management qualified signal through analysis, and sends the personnel management abnormality signal or the personnel management qualified signal to the remote monitoring platform. When the remote monitoring platform receives the personnel management abnormality signal, it issues a corresponding warning.

[0013] Furthermore, the specific analysis process of the personnel management comprehensive evaluation module is as follows: The time when the responsible personnel inspect and maintain the energy storage charging transformer is collected and marked as the inspection time, the time difference between two adjacent inspection time groups is calculated to obtain the inspection interval time, the average of all inspection interval time lengths in the detection period is calculated to obtain the inspection interval detection value, and the number of inspection interval time lengths exceeding the preset inspection interval time length threshold in the detection period is marked as the inspection interval abnormal value; When there is no responsible personnel in the display area of ​​the remote monitoring platform, it is judged to be in a weak management state, and the total time when the remote monitoring platform is in the weak management state during the detection period is marked as the weak management detection value, and the human management comprehensive evaluation value is obtained by numerically calculating the isolation detection value, the isolation abnormality value and the weak management detection value, and the human management comprehensive evaluation value is numerically compared with the preset human management comprehensive evaluation threshold. If the human management comprehensive evaluation value exceeds the preset human management comprehensive evaluation threshold, a personnel management abnormality signal is generated; if the human management comprehensive evaluation value does not exceed the preset human management comprehensive evaluation threshold, a personnel management qualified signal is generated.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, a high-precision load monitoring module and a load analysis engine module are used to perform load monitoring, identify load change trends, predict future load demands, and capture load anomalies. The intelligent control strategy module performs parameter adjustment and protection operation execution control of the energy storage charging transformer based on the output information of the load analysis engine module to ensure the safe operation of the energy storage charging transformer. The load control impact analysis module is used to analyze the control risk level of the energy storage charging transformer. When a high-impact control signal is generated, the subsequent operation supervision of the energy storage charging transformer is strengthened and reasonable optimization and improvement measures are made to further ensure the operation safety of the energy storage charging transformer. 2. In the present invention, the load control impact analysis module sends a low control impact signal to the personnel management comprehensive evaluation module. When the personnel management comprehensive evaluation module receives the low control impact signal, it analyzes the work performance of the person in charge of the energy storage charging transformer, and strengthens the supervision and management of the relevant responsible personnel when generating a personnel management abnormality signal, thereby further reducing the operating risk of the energy storage charging transformer, and has a high level of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: Figure 1As shown, the load intelligent monitoring system adapted to the energy storage charging transformer proposed in the present invention includes a high-precision load monitoring module, a load analysis engine module, an intelligent control strategy module, a safety protection execution module, a load control impact analysis module and a remote monitoring platform; wherein the remote monitoring platform provides an intuitive and easy-to-use user interface to display the real-time monitoring data, historical trends, alarm information and execution effect of the intelligent control strategy of the energy storage charging transformer and its load; The high-precision load monitoring module is deployed at the input and output ends of the energy storage charging transformer. It uses high-precision sensors to monitor the relevant load parameters (including voltage, current, power factor, etc.) of the energy storage charging transformer in real time. The module also has data collection and preprocessing functions to ensure the accuracy and timeliness of the data, and sends the collected load information to the load analysis engine module; The load analysis engine module processes and analyzes the received load information in real time based on big data analysis and machine learning algorithms, identifies load change trends, predicts future load demand, and captures load anomalies, and sends the load analysis results to the intelligent control strategy module and the remote monitoring platform; the intelligent control strategy module automatically generates corresponding control strategies based on the output information of the load analysis engine module and adjusts the parameters of the energy storage charging transformer accordingly (such as adjusting the output voltage, current and other parameters of the energy storage charging transformer), optimizes energy storage efficiency, reduces energy loss, and sends corresponding control information to the remote monitoring platform; The safety protection execution module involves several protection elements and has safety control functions such as overload protection and short-circuit protection. When the energy storage charging transformer operates under abnormal load conditions, the intelligent control strategy module enables the safety protection execution module to perform corresponding protection operations to ensure the safe operation of the transformer, and the safety protection execution module sends the execution information to the remote monitoring platform.

[0018] The load control impact analysis module is used to set the detection period, preferably, the detection period is thirty days; the control risk level of the energy storage charging transformer during the detection period is analyzed, and a high control impact signal or a low control impact signal is generated through analysis, and the high control impact signal or the low control impact signal is sent to the remote monitoring platform. When the remote monitoring platform receives the high control impact signal, it issues a corresponding warning to remind the responsible personnel to adjust the supervision plan in time, strengthen the subsequent operation supervision of the energy storage charging transformer, and make reasonable optimization and improvement measures to ensure the operation safety of the energy storage charging transformer; the specific analysis process of the load control impact analysis module is as follows: Obtain the regulation assessment symbol HX-1 or HX-2 and the protection assessment symbol ZX-1 or ZX-2, perform intersection analysis on the regulation assessment symbol and the protection assessment symbol, if HX-2∩ZX-2 is obtained, it indicates that the control risk for the energy storage charging transformer is low, then a low control impact signal is generated, and the other cases (HX-1∩ZX-2, HX-2∩ZX-1 or HX-1∩ZX-1) indicate that the control risk for the energy storage charging transformer is high, then a high control impact signal is generated.

[0019] It should be noted that the load control impact analysis module is communicatively connected to the self-adjustment detection and evaluation module and the protection detection and evaluation module. The self-adjustment detection and evaluation module evaluates and analyzes the automatic adjustment and control performance of the energy storage charging transformer during the detection period, and assigns the adjustment evaluation symbol HX-1 or HX-2 through analysis, and sends the adjustment evaluation symbol HX-1 or HX-2 to the load control impact analysis module, which can not only accurately feedback the automatic control performance of the energy storage charging transformer during the detection period, but also provide information support for the analysis process of the load control impact analysis module to ensure the accuracy of its analysis results; the specific analysis process of the self-adjustment detection and evaluation module is as follows: When the intelligent control strategy module adjusts the parameters of the energy storage charging transformer (such as the adjustment of parameters such as voltage and power), if the adjustment of the corresponding parameters cannot be completed within the corresponding specified time, the adjustment process of the corresponding parameters will be marked as an inefficient process, and the number of inefficient processes corresponding to the corresponding parameters in the detection period will be obtained and marked as the parameter adjustment inefficient value, and the parameter adjustment inefficient value will be compared with the corresponding preset parameter adjustment inefficient threshold. If the parameter adjustment inefficient value exceeds the preset parameter adjustment inefficient threshold, it indicates that the adjustment performance of the corresponding parameters in the detection period is poor, and the corresponding parameters are marked as characteristic parameters; if the characteristic parameters exist in the detection period, it indicates that the control risk in the detection period is relatively large, and the adjustment evaluation symbol HX-1 is assigned; If there is no characteristic parameter during the detection period, a set of preset weight values ​​is set in advance for each type of parameter, wherein the values ​​of the preset weight values ​​are all positive numbers, and the more important the adjustment of the corresponding type of parameter is, the larger the value of the preset weight value matching it is; The parameter adjustment inefficiency value of the corresponding parameter is calculated by ratio with the corresponding preset parameter adjustment inefficiency threshold to obtain the parameter adjustment decision value, the parameter adjustment decision value is multiplied by the corresponding preset weight value to obtain the parameter adjustment analysis value, and the parameter adjustment analysis values ​​of all parameters are summed up to obtain the self-adjustment inspection value; The self-adjustment evaluation value is numerically compared with the preset self-adjustment evaluation threshold. If the self-adjustment evaluation value exceeds the preset self-adjustment evaluation threshold, indicating that the control risk during the detection period is relatively large, the adjustment evaluation symbol HX-1 is assigned; if the self-adjustment evaluation value does not exceed the preset self-adjustment evaluation threshold, indicating that the control risk during the detection period is relatively small, the adjustment evaluation symbol HX-2 is assigned.

[0020] Furthermore, the protection detection and evaluation module evaluates and analyzes the protection execution performance of the security protection execution module during the detection period, assigns a protection evaluation symbol ZX-1 or ZX-2 through analysis, and sends the protection evaluation symbol ZX-1 or ZX-2 to the load control impact analysis module, which can not only accurately feedback the execution risk of the security protection action during the detection period, but also provide information support for the analysis process of the load control impact analysis module, further ensuring the accuracy of its analysis results; the specific analysis process of the protection detection and evaluation module is as follows: The protection elements (including circuit breakers, thermal relays, etc.) involved in the safety protection execution module are obtained, and the corresponding protection element is marked as i, where i is a natural number greater than 1; all execution durations of the protection element i during the detection period (i.e., the interval from the protection element i receiving the control instruction to the completion of the corresponding protection action) are collected, and the response execution coefficient is obtained by calculating the average of all execution durations, and the execution duration is numerically compared with the corresponding preset execution duration threshold. If the execution duration exceeds the corresponding preset execution duration threshold, it indicates that the execution of the corresponding protection action is slow, and the corresponding execution duration is marked as non-standard duration; The number of non-compliance time durations corresponding to the protection element i in the historical stage is obtained and marked as the compliance anomaly detection coefficient, and the response execution coefficient and the compliance anomaly detection coefficient are numerically compared with the corresponding preset response execution coefficient threshold and the preset compliance anomaly detection coefficient threshold, respectively. If the response execution coefficient or the compliance anomaly detection coefficient exceeds the corresponding preset threshold, it indicates that the execution performance of the protection element i in the detection period is poor, and the protection element i is marked as a non-safe element; If the response execution coefficient and the standard-reaching abnormal detection coefficient do not exceed the corresponding preset thresholds, indicating that the protection element i has a good execution performance during the detection period, the production date of the protection element i is collected, the interval between the current date and the production date is marked as the production value, and the environmental information of the environment where the protection element i is located is obtained, and all environmental data that need to be monitored (including temperature, humidity, etc.) are compared one by one with the corresponding preset environmental data requirements; If there is environmental data that does not meet the corresponding preset environmental data requirements, it is determined that the protection element i is in a life damage state, the total length of time that the protection element i is in the life damage state in the historical stage is obtained and marked as the life loss value, and the number of times the protection element i is started in the historical stage is obtained and marked as the start value; The production value QSi, the life loss value PMi and the start-up value ZFi are numerically calculated by the formula YXi=(rw×QSi+tu×PMi) / 2+es×ZFi to obtain the component initial evaluation value YXi, wherein rw, tu, es are preset proportional coefficients with values ​​greater than zero, es>tu>rw; and the larger the value of the component initial evaluation value YXi, the worse the overall life condition of the protection component i is, and the greater the potential safety hazard; The component initial evaluation value YXi is numerically compared with the corresponding preset component initial evaluation threshold value. If the component initial evaluation value YXi exceeds the corresponding preset component initial evaluation threshold value, it indicates that the life condition of the protection component i is generally poor and the potential safety hazard is relatively large, and the protection component i is marked as a non-safety component; If the component initial evaluation value YXi does not exceed the corresponding preset component initial evaluation threshold value, indicating that the life condition of the protection component i is generally good, the position of the protection component i is collected, and the offset distance between the position and its initial position is marked as the component displacement value, and the contour scanning diagram of the protection component i is collected, and the contour scanning diagram is overlapped and compared with the corresponding standard contour diagram, thereby obtaining the overlap abnormal value of the protection component i, wherein the larger the value of the overlap abnormal value, the greater the profile change of the protection component i; The component displacement value GPi and the coincidence abnormal value SYi are numerically calculated by the formula HXi=up×GPi+eq×SYi to obtain the component re-evaluation value HXi, wherein up and eq are preset proportional coefficients with values ​​greater than zero, and the larger the value of the component re-evaluation value HXi is, the greater the displacement deformation risk of the protection component i is; the component re-evaluation value HXi is numerically compared with the preset component re-evaluation threshold value, if the component re-evaluation value HXi exceeds the preset component re-evaluation threshold value, it indicates that the displacement deformation risk of the protection component i is relatively large, and the protection component i is marked as a non-safety component; If there are non-safety elements, it indicates that the security protection execution module can hardly guarantee the subsequent timeliness and protection effect, and the protection evaluation symbol ZX-1 is assigned; if there are no non-safety elements, it indicates that the security protection execution module can guarantee the subsequent timeliness and protection effect, and the protection evaluation symbol ZX-2 is assigned.

[0021] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the load control impact analysis module is connected to the personnel management comprehensive evaluation module in communication, and the load control impact analysis module sends a control low impact signal to the personnel management comprehensive evaluation module. When the personnel management comprehensive evaluation module receives the control low impact signal, it analyzes the work performance of the person in charge of the energy storage charging transformer, and generates a personnel management abnormality signal or a personnel management qualified signal through analysis; And send personnel management abnormal signals or personnel management qualified signals to the remote monitoring platform. When the remote monitoring platform receives the personnel management abnormal signals, it will issue a corresponding warning, and promptly strengthen the supervision and management of the relevant responsible personnel in the follow-up, further reduce the operation risk of the energy storage charging transformer, and have a high level of intelligence; the specific analysis process of the personnel management comprehensive evaluation module is as follows: The time when the responsible personnel inspect and maintain the energy storage charging transformer is collected and marked as the inspection time, the time difference between two adjacent inspection time groups is calculated to obtain the inspection interval time, the average of all inspection interval time lengths in the detection period is calculated to obtain the inspection interval detection value, and the number of inspection interval time lengths exceeding the preset inspection interval time length threshold in the detection period is marked as the inspection interval abnormal value; When there is no responsible person in the display area of ​​the remote monitoring platform, it is judged to be in a weak management state, and the total time of the remote monitoring platform in the weak management state during the detection period is marked as the weak management detection value; The human management comprehensive evaluation value TL is obtained by numerically calculating the isolation detection value HN, the isolation abnormality value SF and the weak tube detection value GW through the formula TL=te1×HN+te2×SF+te3×GW; wherein te1, te2 and te3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the human management comprehensive evaluation value TL, the worse the work performance of the person in charge of the energy storage charging transformer during the detection period; The human management comprehensive evaluation value TL is numerically compared with the preset human management comprehensive evaluation threshold. If the human management comprehensive evaluation value TL exceeds the preset human management comprehensive evaluation threshold, it indicates that the work performance of the person in charge of the energy storage charging transformer during the detection period is poor, and a personnel management abnormality signal is generated; if the human management comprehensive evaluation value TL does not exceed the preset human management comprehensive evaluation threshold, it indicates that the work performance of the person in charge of the energy storage charging transformer during the detection period is good, and a personnel management qualified signal is generated.

[0022] The working principle of the present invention is as follows: when in use, the relevant load parameters of the energy storage charging transformer are monitored in real time through the high-precision load monitoring module, the load analysis engine module processes and analyzes the received load information in real time, identifies the load change trend, predicts future load demand and captures load anomalies, and the intelligent control strategy module automatically generates the corresponding control strategy based on the output information of the load analysis engine module and adjusts the parameters of the energy storage charging transformer accordingly. When the energy storage charging transformer is operating under abnormal load conditions, the intelligent control strategy module enables the safety protection execution module to perform corresponding protection operations to ensure the safe operation of the energy storage charging transformer, and analyzes the degree of control risks of the energy storage charging transformer during the detection period through the load control impact analysis module. When a high-impact control signal is generated, the supervision plan is adjusted in time, the subsequent operation supervision of the energy storage charging transformer is strengthened, and reasonable optimization and improvement measures are made to ensure the operation safety of the energy storage charging transformer.

[0023] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A load intelligent monitoring system adapted for energy storage charging transformers, characterized in that: It includes a high-precision load monitoring module, a load analysis engine module, an intelligent control strategy module, a safety protection execution module and a remote monitoring platform; the high-precision load monitoring module uses a high-precision sensor to monitor the relevant load parameters of the energy storage charging transformer in real time, and the load analysis engine module processes and analyzes the received load information in real time, identifies load change trends, predicts future load demand and captures load anomalies; Based on the output information of the load analysis engine module, the intelligent control strategy module automatically generates the corresponding control strategy and adjusts the parameters of the energy storage charging transformer accordingly, and sends the corresponding control information to the remote monitoring platform; and when the energy storage charging transformer operates under abnormal load conditions, the intelligent control strategy module enables the safety protection execution module to perform corresponding protection operations, and the safety protection execution module sends the execution information to the remote monitoring platform.

2. The load intelligent monitoring system adapted for the energy storage charging transformer according to claim 1 is characterized in that: The remote monitoring platform is communicated with the load control impact analysis module. The load control impact analysis module is used to set a detection period, analyze the degree of control risks of the energy storage charging transformer during the detection period, and generate a high control impact signal or a low control impact signal through analysis. When the remote monitoring platform receives the high control impact signal, it will issue a corresponding warning.

3. The load intelligent monitoring system adapted for the energy storage charging transformer according to claim 2 is characterized in that: The specific analysis process of the load control impact analysis module includes: Obtain the regulation evaluation symbol HX-1 or HX-2 and the protection evaluation symbol ZX-1 or ZX-2, perform intersection analysis on the regulation evaluation symbol and the protection evaluation symbol, and if HX-2∩ZX-2 is obtained, a low-impact control signal is generated, and in other cases a high-impact control signal is generated.

4. The load intelligent monitoring system adapted for the energy storage charging transformer according to claim 3 is characterized in that: The load control impact analysis module is communicatively connected to the self-adjustment detection and evaluation module and the protection detection and evaluation module. The self-adjustment detection and evaluation module evaluates and analyzes the automatic adjustment and control performance of the energy storage charging transformer during the detection period, assigns an adjustment evaluation symbol HX-1 or HX-2 through analysis, and sends the adjustment evaluation symbol HX-1 or HX-2 to the load control impact analysis module; The protection detection and evaluation module evaluates and analyzes the protection execution performance of the security protection execution module during the detection period, assigns a protection evaluation symbol ZX-1 or ZX-2 through analysis, and sends the protection evaluation symbol ZX-1 or ZX-2 to the load control impact analysis module.

5. The load intelligent monitoring system adapted for the energy storage charging transformer according to claim 4 is characterized in that: The specific analysis process of the self-adjustment detection and evaluation module is as follows: if the parameter adjustment inefficiency value exceeds the preset parameter adjustment inefficiency threshold, the corresponding parameter is marked as a characteristic parameter; if the characteristic parameter exists during the detection period, the adjustment evaluation symbol HX-1 is assigned; if the characteristic parameter does not exist during the detection period, the parameter adjustment analysis values ​​of all parameters are summed up to obtain the self-adjustment detection and evaluation value. If the self-adjustment detection and evaluation value exceeds the preset self-adjustment detection and evaluation threshold, the adjustment evaluation symbol HX-1 is assigned; otherwise, the adjustment evaluation symbol HX-2 is assigned.

6. The load intelligent monitoring system adapted for energy storage charging transformer according to claim 4 is characterized in that: The specific analysis process of the protection detection and evaluation module is as follows: obtaining the protection elements involved in the security protection execution module, marking the corresponding protection elements as i, where i is a natural number greater than 1; Component by component analysis is used to determine whether the protection component i is a non-safety component. If a non-safety component exists, the protection evaluation symbol ZX-1 is assigned; If there are no non-safety elements, the protection assessment symbol ZX-2 is assigned.

7. The load intelligent monitoring system adapted for the energy storage charging transformer according to claim 6 is characterized in that: The specific analysis process of component-by-component analysis is as follows: Collect all execution durations of protection element i during the detection period, calculate the average of all execution durations to obtain the response execution coefficient, obtain the number of non-compliance durations corresponding to protection element i in the historical stage and mark it as the compliance abnormal detection coefficient. If the response execution coefficient or the compliance abnormal detection coefficient exceeds the corresponding preset threshold, the protection element i is marked as a non-safe element; If the response execution coefficient and the standard-reaching abnormal detection coefficient do not exceed the corresponding preset thresholds, the component initial evaluation value is obtained by numerically calculating the production value, life loss value and startup value. If the component initial evaluation value exceeds the corresponding preset component initial evaluation threshold, the protection component i is marked as a non-safe component; If the component initial evaluation value does not exceed the corresponding preset component initial evaluation threshold, the component re-evaluation value is obtained by numerically calculating the component displacement value and the coincidence abnormal value. If the component re-evaluation value exceeds the preset component re-evaluation threshold, the protection component i is marked as a non-safe component.

8. The load intelligent monitoring system adapted for the energy storage charging transformer according to claim 7 is characterized in that: The analysis and judgment process of life damage status is as follows: Environmental information of the environment in which the protection element i is located is obtained. If there is environmental data that does not meet the corresponding preset environmental data requirements, it is determined that the protection element i is in a life damage state.

9. The load intelligent monitoring system adapted for energy storage charging transformer according to claim 2, characterized in that: The load control impact analysis module is communicated with the personnel management comprehensive evaluation module. When the personnel management comprehensive evaluation module receives a low control impact signal, it analyzes the work performance of the person in charge of the energy storage charging transformer, and generates a personnel management abnormality signal or a personnel management qualified signal through analysis. When the remote monitoring platform receives the personnel management abnormality signal, it issues a corresponding warning.

10. The load intelligent monitoring system adapted for the energy storage charging transformer according to claim 9, characterized in that: The specific analysis process of the personnel management comprehensive evaluation module is as follows: The personnel management comprehensive evaluation value is obtained by numerically calculating the isolation detection value, isolation abnormality value and weak control detection value. If the personnel management comprehensive evaluation value exceeds the preset personnel management comprehensive evaluation threshold, a personnel management abnormality signal is generated; if the personnel management comprehensive evaluation value does not exceed the preset personnel management comprehensive evaluation threshold, a personnel management qualified signal is generated.

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