A power load control method and system based on big data

Abstract

The present invention discloses a power load control method and system based on big data, which relates to the technical field of power load control, and comprises a monitoring and analysis module, wherein the monitoring and analysis module comprises a data acquisition unit and an analysis unit, wherein the data acquisition unit is used to collect the operation data of the power system in real time; the analysis unit is used to process and analyze the data collected by the data acquisition unit to identify the power mode; the control optimization module comprises a control unit and an optimization unit, wherein the control unit is used to control the power conversion process according to the data of the acquisition unit; by formulating a control power solution based on big data, a higher power conversion efficiency can be achieved, the power consumption under different load conditions can be optimized, and the goal of energy saving and environmental protection can be achieved through the use of new green energy, and a more accurate transient response and a lower ripple voltage can be provided to meet the requirements of high-performance digital loads.

Description

Technical Field

[0001] The present invention relates to the technical field of power load control, and more specifically, to a power load control method and system based on big data. Background Art

[0002] The identified power modules include power usage patterns, power aging modules, and symptom patterns before failures. Real-time data analysis can help to quickly respond to power load changes and optimize overall operations.

[0003] It should also be noted that power load control systems are mainly used in power consumption equipment, power management of new energy vehicles, smart grids and other fields. By integrating advanced monitoring, early warning and control technologies, they can optimize power use, improve energy efficiency and ensure stable operation of the system;

[0004] However, in actual use, traditional power management has a slow response speed, the frequency compensation of the control loop is complex, and the closed-loop gain varies with the input voltage, which may affect the stability of the output voltage. In the power supply system of electrical equipment, the UPS two-stage conversion power supply system has a large energy efficiency loss during normal operation because the energy needs to be converted in two stages before output. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a power load control method and system based on big data.

[0006] A power load control system based on big data, comprising a monitoring and analysis module, wherein the monitoring and analysis module comprises a data acquisition unit and an analysis unit, wherein the data acquisition unit is used to collect operation data of the power system in real time;

[0007] The analysis unit is used to process and analyze the data collected by the data acquisition unit to identify the power mode;

[0008] A control optimization module, the control optimization module comprising a control unit and an optimization unit, the control unit being used to control a power conversion process according to data from the acquisition unit;

[0009] The optimization unit is used to adjust the control strategy of the control unit according to the recognition result of the analysis unit;

[0010] The prediction and warning module is used to predict the time of occurrence of potential failures according to the identification results of the analysis unit and obtain maintenance requirements in advance.

[0011] Preferably, the specific working mode of the analysis unit is as follows, specifically:

[0012] According to the formula , calculate and obtain the current real-time average power of the power supply ,in is the power at the current time t, T is the period of the signal, and dt represents the time interval;

[0013] A real-time average power threshold and fluctuation threshold are set in advance, and the difference between the real-time average power P of the power supply and the preset power is obtained and marked as , if the real-time average power P of the power supply is greater than the average power threshold, and the difference If it is greater than the fluctuation threshold, the surface power supply is in high load mode;

[0014] If the real-time average power P of the power supply is less than the average power threshold, and the difference If the value is less than the fluctuation threshold, the surface power supply is in low load mode.

[0015] Preferably, the specific working method of the analysis unit also includes:

[0016] Get the current remaining capacity SOC of the power supply and the capacity of the power supply in the initial state ;

[0017] According to the formula , calculate and obtain the health index SOH of the power supply;

[0018] A threshold of a power health index is set in advance. If the health index SOH of a power supply is lower than the threshold of the power health index, the power supply is marked as aged. Otherwise, the power supply is marked as normal.

[0019] Preferably, the control unit works as follows:

[0020] The deviation value K is obtained by subtracting the actual output from the expected output of the power supply;

[0021] According to the formula , calculate and obtain the control amount U of the power supply at the current time, where is the proportionality coefficient, is the integration coefficient, is the integration time, is the integration coefficient, is the deviation value at the previous moment, It's time The cumulative sum of represents the integral of the deviation over time;

[0022] The control quantity U of the power supply at the current time is applied to the control of the power load, and then the system outputs a new process variable and enters the next control cycle again.

[0023] Preferably, the specific working mode of the optimization unit is as follows:

[0024] According to the formula , calculate and obtain the first adjustment value ,in It is a preset proportional coefficient used to quantify the increase ratio of the control amount when the load current exceeds the rated current. is the actual load current of the current power supply, Preset power supply load current;

[0025] According to the formula , get the second adjustment value ;

[0026] The second adjustment value It is used as the final adjustment value to control the power load.

[0027] Preferably, the specific working mode of the prediction and warning module is as follows:

[0028] The symptom pattern recognition value of the power supply is obtained according to the analysis unit ;

[0029] According to the formula , calculate and obtain the fault symptom index F of the power supply, where , and is the preset weight coefficient;

[0030] According to the formula , calculate and obtain the time T when the power supply needs to be maintained next time, where V is the average failure time of the power supply in historical data.

[0031] Preferably, the specific process of the analysis unit obtaining the symptom pattern recognition value of the power supply is as follows:

[0032] According to the formula , calculate and obtain the symptom pattern recognition value of the power supply ;

[0033] in is the probability of the jth state in the power signal, and M is the number of types of fault symptoms.

[0034] Preferably, it also includes an integrated management system, which is used to manage integrated renewable energy and optimize the energy use structure, specifically:

[0035] Get the predicted solar power generation and wind power generation ;

[0036] Get the predicted energy demand value ;

[0037] According to solar power generation Plus wind power generation Subtract the energy demand Get the difference between predicted power generation and predicted demand ;

[0038] If the difference If it is greater than zero, it means that there is sufficient green energy, otherwise, it means that power energy supplement is needed;

[0039] If the green energy is sufficient, the required battery capacity C is obtained to store the excess green energy.

[0040] Preferably, the specific method of obtaining the battery capacity C is as follows:

[0041] According to the formula , get the battery capacity C, where is the average power consumed by the electrical equipment per unit time, The length of time the battery needs to provide energy to keep the load running, is the total voltage of the power supply, is the rate at which the power source discharges.

[0042] On the other hand, the present invention also proposes a power load control method based on big data, comprising the following steps:

[0043] Step 1: Collect the operating data of the power system, process and analyze the data to identify the power mode;

[0044] Step 2: Control the power conversion process and adjust the control strategy according to the identification results;

[0045] Step 3: Predict the time of potential failure and obtain maintenance requirements in advance.

[0046] Beneficial effects: By formulating control power supply solutions based on big data, higher power conversion efficiency can be achieved, power consumption under different load conditions can be optimized, and the goal of energy saving and environmental protection can be achieved through the use of new green energy. It can also provide more precise transient response and lower ripple voltage to meet the requirements of high-performance digital loads. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a system flow chart of the present invention;

[0048] Figure 2 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0049] like Figure 1 As shown, a power load control system based on big data includes a monitoring and analysis module, the monitoring and analysis module includes a data acquisition unit and an analysis unit, and the data acquisition unit is used to collect the operation data of the power system in real time; it should be noted that, in this embodiment, the operation data includes parameters such as voltage, current, frequency, and temperature;

[0050] The analysis unit is used to process and analyze the data collected by the data acquisition unit to identify the power mode; it should be noted that the identified power modules specifically include power usage mode, power aging module and symptom mode before failure. Through real-time data analysis, it can be beneficial to quickly respond to power load changes and optimize overall operation.

[0051] It should also be noted that power load control systems are mainly used in power consumption equipment, power management of new energy vehicles, smart grids and other fields. By integrating advanced monitoring, early warning and control technologies, they can optimize power use, improve energy efficiency and ensure stable operation of the system;

[0052] However, in actual use, traditional power management has a slow response speed, the frequency compensation of the control loop is complex, and the closed-loop gain changes with the input voltage, which may affect the stability of the output voltage. In the power supply system of electrical equipment, the UPS two-stage conversion power supply system has a large energy efficiency loss during normal operation because the energy needs to be converted in two stages for output;

[0053] This technical solution can achieve higher power conversion efficiency, optimize power consumption under different load conditions, and achieve energy conservation and environmental protection through the use of new green energy through a brake control power solution based on big data. It can also provide more accurate transient response and lower ripple voltage to meet the requirements of high-performance digital loads.

[0054] A control optimization module, the control optimization module includes a control unit and an optimization unit, the control unit is used to control the power conversion process according to the data of the acquisition unit; it should be noted that the power conversion process can be accurately controlled;

[0055] The optimization unit is used to adjust the control strategy of the control unit according to the identification result of the analysis unit; it should be noted that the power management strategy can be dynamically adjusted based on the output of the data analysis module to achieve optimal power conversion efficiency and load distribution;

[0056] The prediction and warning module is used to predict the time of potential failure according to the identification results of the analysis unit, and obtain maintenance requirements in advance. It should be noted that the hysteresis of fault detection and passive maintenance strategies in traditional systems can be solved, and maintenance costs can be reduced through predictive maintenance, equipment life can be extended, and system reliability can be improved.

[0057] As an optional embodiment: the specific working mode of the analysis unit is as follows, specifically:

[0058] According to the formula , calculate and obtain the current real-time average power of the power supply ,in is the power at the current time t, T is the period of the signal, and dt represents the time interval; it should be noted that, in this embodiment, It is a function of power changing with time. T is the total time length considered, that is, the signal period. The sum of the powers in the entire time period is calculated. Then, the average power is obtained by dividing this sum by the time period T. dt represents an infinitesimal time interval.

[0059] A real-time average power threshold and fluctuation threshold are set in advance, and the difference between the real-time average power P of the power supply and the preset power is obtained and marked as , if the real-time average power P of the power supply is greater than the average power threshold, and the difference If it is greater than the fluctuation threshold, the surface power supply is in high load mode;

[0060] If the real-time average power P of the power supply is less than the average power threshold, and the difference If it is less than the fluctuation threshold, the surface power supply is in low load mode;

[0061] The rest of the cases are marked as normal mode.

[0062] As an optional embodiment: the specific working method of the analysis unit also includes:

[0063] Get the current remaining capacity SOC of the power supply and the capacity of the power supply in the initial state ;

[0064] According to the formula , calculate and obtain the health index SOH of the power supply;

[0065] A threshold of the power health index is set in advance. If the health index SOH of the power supply is lower than the threshold of the power health index, the power supply is marked as aged, otherwise, the power supply is marked as normal. It should be noted that the degree of power supply aging can be evaluated by monitoring the changes of these parameters over time. In this way, the aging mode of the power supply can be evaluated in real time, which is conducive to the subsequent load control adjustment based on the characteristics of the power supply.

[0066] As an optional embodiment: the control unit works as follows:

[0067] The deviation value K is obtained by subtracting the actual output from the expected output of the power supply;

[0068] According to the formula , calculate and obtain the control amount U of the power supply at the current time, where is the proportionality coefficient, is the integration coefficient, is the integration time, is the integration coefficient, is the deviation value at the previous moment, It's time The cumulative sum of represents the integral of the deviation over time; it should be noted that Also known as the integration constant, it is a parameter that reflects the "speed" of the integral action. The longer the integration time, the slower the growth of the integral term, and vice versa, the faster the integral term grows.

[0069] Integration coefficient The value of depends on the system's need to eliminate steady-state errors. If the system needs to eliminate errors faster, a larger value can be selected. If the system has higher requirements for stability, you may need to choose a smaller , in this embodiment The value of can be 1.023;

[0070] Integration time The value of affects the contribution speed of the integral term to the control quantity. This means that the integral term will increase the control amount more slowly, which helps reduce the overshoot and oscillation of the system.

[0071] Smaller This means that the integral term increases the control amount faster, which helps to speed up the elimination of steady-state errors, but may increase the overshoot and oscillation of the system. The best value of is obtained through system debugging, which can be 1.293 in this implementation;

[0072] The control quantity U of the power supply at the current time is applied to the control of the power load, and then the system outputs a new process variable and enters the next control cycle again.

[0073] As an optional embodiment: the specific working mode of the optimization unit is as follows: It should be noted that when the power supply is working in a high-load mode, a larger control amount U is required to meet the load demand. Similarly, when the power supply is in a low-load mode, the control amount U needs to be reduced, and the aging degree of the power supply will also change the demand for the control amount U. This technical solution is obtained by integrating various modes of the power supply;

[0074] According to the formula , calculate and obtain the first adjustment value ,in It is a preset proportional coefficient used to quantify the increase ratio of the control amount when the load current exceeds the rated current. is the actual load current of the current power supply, The preset power supply load current; it should be noted that by calculating the difference between the load current and the rated current, the part exceeding or below the rated current can be obtained, and this part is multiplied by the proportionality coefficient , added to the reference control quantity U, can ensure that the new adjustment value can provide sufficient output to meet the increased or decreased load requirements while taking into account the power supply load;

[0075] It should also be noted that the purpose of this formula is to ensure that the power supply can increase the output by increasing the control amount when the load demand increases, thereby maintaining the power supply performance and stability. It can be adjusted according to the characteristics and design requirements of the power supply to achieve the best load adaptability. A larger value of indicates that the power supply is more sensitive to load changes and the control amount increases more; if The smaller the value of, the more conservative the response of the power supply to the load change, and the less the control amount increases. In this embodiment, The value of can be 0.968;

[0076] According to the formula , get the second adjustment value ; It should be noted that under normal circumstances, the control amount is proportional to the SOH, that is, the higher the battery aging, the greater the required control amount;

[0077] The second adjustment value As the final adjustment value, it is applied to the control of the power load. It should be noted that the control requirements of the power supply under high load and aging battery conditions are taken into consideration to ensure that the power supply system can adapt to different working conditions and remain stable.

[0078] As an optional embodiment: the specific working mode of the prediction and warning module is as follows:

[0079] The symptom pattern recognition value of the power supply is obtained according to the analysis unit ;

[0080] According to the formula , calculate and obtain the fault symptom index F of the power supply, where , and is a preset weight coefficient; it should be noted that, in this embodiment, , and The values ​​of can be 2.304, 5.043 and 2.653;

[0081] According to the formula , calculate and obtain the time T when the power supply needs to be maintained next, where V is the average failure time of the power supply in historical data. It should be noted that if the value of F is high (meaning that the signs of failure are significant), the predicted time will be shorter, which means that the time of maintenance demand will be closer. On the contrary, if the value of F is low, it indicates that the system is in good condition, the predicted time will be longer, and the maintenance demand can be postponed accordingly. By predicting potential failures, maintenance can be arranged in advance to reduce unexpected downtime. Maintenance resources can be reasonably arranged according to the predicted maintenance needs, improve efficiency, reduce unnecessary maintenance and repair costs, and extend the service life of equipment.

[0082] As an optional embodiment: the specific process of the analysis unit obtaining the symptom pattern recognition value of the power supply is as follows:

[0083] According to the formula , calculate and obtain the symptom pattern recognition value of the power supply ;

[0084] in is the probability of the jth state in the power signal, and M is the number of types of fault signs. It should be noted that in this embodiment, j is an integer from 0 to M. Fault signs can be identified by monitoring abnormal behaviors of the power system, such as voltage sag, current surge, or frequency deviation, indicating that the higher the uncertainty of the signal, the more likely it is that a fault will occur.

[0085] As an optional embodiment: it also includes an integrated management system, which is used to manage integrated renewable energy and optimize the energy use structure. Specifically: It should be noted that in actual use, the traditional power management has a slow response speed, the frequency compensation of the control loop is complex, and the closed-loop gain varies with the input voltage, which may affect the stability of the output voltage. In the power supply system of electrical equipment, the UPS two-stage conversion power supply system has a large energy efficiency loss during normal operation because the energy needs to be converted and output at two stages. This technical solution can effectively realize the efficient use of green energy, reduce dependence on traditional energy, achieve energy conservation and environmental protection, reduce operating costs, and improve the greenness and sustainability of the system. At the same time, through real-time control and adjustment, the system can cope with fluctuations in energy supply and load demand to ensure stable operation.

[0086] Get the predicted solar power generation and wind power generation ;

[0087] Get the predicted energy demand value ;

[0088] According to solar power generation Plus wind power generation Subtract the energy demand Get the difference between predicted power generation and predicted demand ;

[0089] If the difference If it is greater than zero, it means that there is sufficient green energy, otherwise, it means that power energy supplement is needed;

[0090] If there is sufficient green energy, the required battery capacity C is obtained to store excess green energy. It should be noted that in the event of an energy supply interruption or emergency, the pre-calculated battery capacity can ensure the continued operation of critical loads until the alternative energy supply is restored; it can reduce the high energy consumption of electrical equipment and the energy efficiency loss of the UPS two-stage conversion power supply system.

[0091] As an optional embodiment: the specific method of obtaining the battery capacity C is as follows:

[0092] According to the formula , get the battery capacity C, where is the average power consumed by the electrical equipment per unit time, The length of time the battery needs to provide energy to keep the load running, is the total voltage of the power supply, The rate at which the power source is discharged. It should be noted that it helps determine the battery capacity required to meet specific backup time requirements and ensure that the battery can provide sufficient energy. By accurately calculating the required battery capacity, a cost-benefit analysis can be performed to avoid cost increases caused by over-configuration. Understanding battery capacity requirements helps develop battery replacement and maintenance plans. In green energy systems, reasonable configuration of battery energy storage can optimize energy use and improve system efficiency.

[0093] On the other hand, a power load control method based on big data is also proposed, which includes the following steps:

[0094] Step 1: Collect the operating data of the power system, process and analyze the data to identify the power mode;

[0095] Step 2: Control the power conversion process and adjust the control strategy according to the identification results;

[0096] Step 3: Predict the time of potential failure and obtain maintenance requirements in advance

[0097] How it works

[0098] The data acquisition unit collects the operating data of the power supply system in real time, such as parameters such as voltage, current, frequency and temperature. The analysis unit processes and analyzes these data to identify the usage mode, aging status and signs before failure of the power supply. The control unit precisely controls the power conversion process based on the collected data.

[0099] The control strategy is dynamically adjusted according to the analysis results to achieve the optimal power conversion efficiency and load distribution. The prediction and warning module predicts the occurrence time of potential faults based on the analysis results and obtains maintenance requirements in advance, thereby reducing maintenance costs, extending equipment life, and improving system reliability. By calculating the real-time average power and comparing it with the preset threshold, the high load, low load or normal mode of the power supply can be identified. At the same time, the aging degree of the power supply is evaluated by calculating the health index (SOH) of the power supply.

[0100] The control unit calculates the control quantity U according to the deviation between the expected output and the actual output of the power supply, combined with parameters such as the proportional coefficient and the integral coefficient, so as to adjust the power supply load; the optimization unit calculates the adjustment value according to the difference between the load current and the rated current and the health index of the power supply to adapt to different working conditions and maintain stability; the prediction and warning module calculates the fault sign indicators of the power supply to predict the time of the next maintenance requirement so as to arrange maintenance in advance; the integrated management system is used to manage renewable energy, optimize the energy use structure, predict solar and wind power generation, and predict energy consumption needs, so as to achieve efficient use of green energy.

[0101] The above are only preferred implementations of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical staff in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of this template.

Claims

1. A power load control system based on big data, characterized in that: It includes a monitoring and analysis module, which includes a data acquisition unit and an analysis unit, and the data acquisition unit is used to collect the operation data of the power supply system in real time; The analysis unit is used to process and analyze the data collected by the data acquisition unit to identify the power mode; A control optimization module, the control optimization module comprising a control unit and an optimization unit, the control unit being used to control a power conversion process according to data from the acquisition unit; The optimization unit is used to adjust the control strategy of the control unit according to the recognition result of the analysis unit; The prediction and warning module is used to predict the time of occurrence of potential failures based on the identification results of the analysis unit and obtain maintenance requirements in advance; The specific working mode of the analysis unit is as follows: According to the formula , calculate and obtain the current real-time average power of the power supply ,in is the power at the current time t, T is the period of the signal, and dt represents the time interval; A real-time average power threshold and fluctuation threshold are set in advance, and the difference between the real-time average power P of the power supply and the preset power is obtained and marked as , if the real-time average power P of the power supply is greater than the average power threshold, and the difference If it is greater than the fluctuation threshold, the surface power supply is in high load mode; If the real-time average power P of the power supply is less than the average power threshold, and the difference If it is less than the fluctuation threshold, the surface power supply is in low load mode; The specific working method of the analysis unit also includes: Get the current remaining capacity SOC of the power supply and the capacity of the power supply in the initial state ; According to the formula , calculate and obtain the health index SOH of the power supply; A threshold of a power health index is set in advance. If the health index SOH of a power supply is lower than the threshold of the power health index, the power supply is marked as aged. Otherwise, the power supply is marked as normal.

2. A power load control system based on big data according to claim 1, characterized in that: The control unit works as follows: The deviation value K is obtained by subtracting the actual output from the expected output of the power supply; According to the formula , calculate and obtain the control amount U of the power supply at the current time, where is the proportionality coefficient, is the integration coefficient, is the integration time, is the integration coefficient, is the deviation value at the previous moment, It's time The cumulative sum of represents the integral of the deviation over time, where is the first adjustment value; The control quantity U of the power supply at the current time is applied to the control of the power load, and then the system outputs a new process variable and enters the next control cycle again.

3. A power load control system based on big data according to claim 2, characterized in that: The specific working mode of the optimization unit is as follows: According to the formula , calculate and obtain the first adjustment value ,in It is a preset proportional coefficient used to quantify the increase ratio of the control amount when the load current exceeds the rated current. is the actual load current of the current power supply, Preset power supply load current; According to the formula , get the second adjustment value ; The second adjustment value It is used as the final adjustment value to control the power load.

4. The power load control system based on big data according to claim 1, characterized in that: The specific working mode of the prediction and early warning module is as follows: The symptom pattern recognition value of the power supply is obtained according to the analysis unit ; According to the formula , calculate and obtain the fault symptom index F of the power supply, where , and is the preset weight coefficient; According to the formula , calculate and obtain the time T when the power supply needs to be maintained next time, where V is the average failure time of the power supply in historical data.

5. The power load control system based on big data according to claim 4 is characterized in that: The specific process of the analysis unit obtaining the symptom pattern recognition value of the power supply is as follows: According to the formula , calculate and obtain the symptom pattern recognition value of the power supply ; in is the probability of the jth state in the power signal, and M is the number of types of fault symptoms.

6. The power load control system based on big data according to claim 1, characterized in that: It also includes an integrated management system, which is used to manage integrated renewable energy and optimize the energy use structure, specifically: Get the predicted solar power generation and wind power generation ; Get the predicted energy demand value ; According to solar power generation Plus wind power generation Subtract the energy demand Get the difference between predicted power generation and predicted demand ; If the difference If it is greater than zero, it means that there is sufficient green energy, otherwise, it means that power energy supplement is needed; If the green energy is sufficient, the required battery capacity C is obtained to store the excess green energy.

7. A power load control system based on big data according to claim 6, characterized in that: The specific method of obtaining the battery capacity C is as follows: According to the formula , get the battery capacity C, where is the average power consumed by the electrical equipment per unit time, The length of time the battery needs to provide energy to keep the load running, is the total voltage of the power supply, is the rate at which the power source discharges.

8. A power load control method based on big data, applicable to the power load control system based on big data described in claim 1, characterized in that: The following steps are involved: Step 1: Collect the operating data of the power system, process and analyze the data to identify the power mode; Step 2: Control the power conversion process and adjust the control strategy according to the identification results; Step 3: Predict the time of potential failure and obtain maintenance requirements in advance.

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