CN117937522A - Power energy-saving control method of power control cabinet, control cabinet and storage medium - Google Patents
Power energy-saving control method of power control cabinet, control cabinet and storage medium Download PDFInfo
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Abstract
The application discloses an electric power energy-saving control method of an electric power control cabinet, the control cabinet and a storage medium, and relates to the technical field of energy conservation, wherein the method comprises the following steps: acquiring the working state of each device connected with the power control cabinet; acquiring the equipment grade of each equipment under the condition that the working states of the plurality of equipment are idle; among a plurality of devices with idle working states, determining a target device with low device level, and acquiring a power fluctuation signal of each target device; determining the power consumption of each target device according to the power fluctuation signal of each target device; determining reliability data of each target device according to the power consumption of each target device; the method comprises the steps of adopting a pre-constructed energy-saving model, inputting reliability data of each target device into the energy-saving model, and obtaining a first energy-saving control strategy of a plurality of target devices with low device grades; and adjusting the device parameters of each target device according to the first energy saving control strategy, wherein the device parameters comprise power.
Description
Technical Field
The application relates to the technical field of energy conservation, in particular to an electric power energy-saving control method of an electric power control cabinet, the control cabinet and a storage medium.
Background
A power control cabinet is a control and protection device for an electric power system, and is generally used for controlling and distributing electric power, monitoring electric power consumption, protecting electric devices, and the like. It is typically composed of switching devices, protection devices, instrumentation devices, control devices, etc., for controlling, protecting and monitoring the various devices and circuits in the power system.
For the existing power control cabinet, the controlled equipment (such as an elevator) can still keep an operating state under the condition that actual work is not carried out or useful work is generated, and the equipment consumes energy without generating corresponding benefits, or has the conditions that the energy consumption of the equipment is higher in the working process, but the generated useful work is less, and the energy utilization efficiency is lower. Resulting in high energy waste. Therefore, a scheme is needed to reduce the power consumption of the equipment controlled by the power control cabinet and save energy.
Disclosure of Invention
The embodiment of the application aims to provide an electric power energy-saving control method of an electric power control cabinet, the control cabinet and a storage medium, which are used for solving the problem of overhigh electric power consumption in the prior art.
To achieve the above object, a first aspect of the present application provides an electric power energy saving control method of an electric power control cabinet, including:
Acquiring the working state of each device connected with the power control cabinet;
acquiring the equipment grade of each equipment under the condition that the working states of a plurality of the equipment are idle;
among a plurality of idle devices in working state, determining a target device with low device level, and acquiring a power fluctuation signal of each target device;
Determining the power consumption of each target device according to the power fluctuation signal of each target device;
Determining reliability data of each target device according to the power consumption of each target device;
The method comprises the steps of adopting a pre-constructed energy-saving model, and inputting reliability data of each target device into the energy-saving model to obtain a first energy-saving control strategy of a plurality of target devices with low device grades;
And adjusting the device parameters of each target device according to the first energy saving control strategy, wherein the device parameters comprise power.
In an embodiment of the present application, the method further includes:
In a preset first time period, executing a circulation step until the working state of any one of m target devices is not changed after the working state of n target devices in the m target devices is changed from idle speed to normal;
Acquiring total power consumption of m-n residual target devices, and controlling the m-n residual target devices to perform power alternation by adopting a power alternation energy-saving control strategy under the condition that the total power consumption is larger than a preset power consumption threshold, wherein m and n are positive integers, and m is larger than n;
predicting an energy consumption value of each preset time in a future preset time period;
And generating an energy adjustment curve according to the energy consumption value of each preset time in the future preset time period, wherein the energy adjustment curve is used for representing the energy adjustment value of equipment which needs to be adjusted at each preset time in the future preset time period compared with the preset power consumption threshold.
In the embodiment of the present application, the obtaining the total power consumption of m-n remaining target devices, and controlling the m-n remaining target devices to perform power alternation by using a power alternation energy-saving control strategy under the condition that the total power consumption is greater than a preset power consumption threshold value includes:
Acquiring an interaction factor between any two target devices in m-n residual target devices under the condition that the total power consumption is larger than a preset power consumption threshold;
Determining the total power consumption according to the mutual influence factor and the acquired power fluctuation signal of each of m-n remaining target devices;
Acquiring an electric energy adjustment value under the condition that the total electric energy consumption is larger than a preset electric energy consumption threshold value;
Generating a random power reduction strategy, and acquiring a power reduction value of each target device in m-n pieces of residual target devices;
For each random power reduction strategy, determining a current interaction factor between any two target devices in m-n residual target devices by adopting a simulation model;
Adopting a power alternating energy-saving control formula, and calculating the electric energy reduction value of the current random power reduction strategy according to the power reduction value of each target device and the current influence factor;
Taking a random power reduction strategy corresponding to the power reduction value with the largest difference value of the power adjustment values as a power alternating energy-saving control strategy;
And controlling m-n residual target devices to perform power alternation by adopting a power alternation energy-saving control strategy.
In the embodiment of the application, the power reduction value comprises an effective power reduction value and a loss power, and the power alternation energy-saving control formula comprises:
；
Wherein REV is the current power reduction value of the random power reduction strategy, P q is the effective power reduction value of the q-th device, S q is the loss power of the q-th device, t is the operating time of the q-th device, E q+1 is the power reduction value of the q+1th device, E q-1 is the power reduction value of the q-1 th device, and k is the current influencing factor, wherein E q+1=(Pq+1+Sq+1)×t,Eq-1=(Pq-1+Sq-1) ×t.
In the embodiment of the present application, in the preset first period of time, a circulation step is performed until, after the working states of n target devices in the m target devices are changed from idle speed to normal, there is no change in the working state of any one of the m target devices, including:
in a preset first time period, acquiring the working state of each of m target devices in real time, and counting the number of target devices with the working state converted from idle speed to normal;
The step of circularly executing the steps of acquiring the working state of each target device in real time and counting the number of target devices with the working state converted from idle speed to normal under the condition that the working state is not the last moment of the first time period;
And ending the circulation step when the last moment of the first time period and the number of target devices for which the working state is changed from the idle speed to be normal is equal to n.
In the embodiment of the application, the construction method of the energy-saving model comprises the following steps:
Collecting working data sets of equipment connected with the power control cabinet and reliability data of each equipment under different working data sets, wherein the working data sets comprise working states, electronic fluctuation signals and electric energy consumption;
preprocessing the working state, the electronic fluctuation signal and the electric energy consumption of the equipment to obtain preprocessing data, wherein the preprocessing data comprises a training data set and a verification data set;
extracting features related to energy conservation in the training dataset, the features including device power variation, power consumption trend and power fluctuation frequency;
Performing associated modeling on the characteristics related to energy conservation and reliability data of the equipment to obtain an energy conservation model;
And verifying the energy-saving model by adopting a verification data set to obtain a verified energy-saving model.
In the embodiment of the application, the reliability data comprises equipment failure rate and equipment average failure time;
the step of inputting the reliability data of each target device into the energy-saving model by adopting a pre-constructed energy-saving model to obtain a first energy-saving control strategy of a plurality of target devices with low device grades, comprising the following steps:
inputting the reliability data of each target device into the energy-saving model, and outputting electric energy consumption standard values of a plurality of target devices with low device grades;
For the target equipment with low equipment grade, determining a power reduction value according to the corresponding power consumption standard value and the corresponding power consumption;
And controlling the power of the target device with low device level according to the power reduction value of the target device with low device level.
In an embodiment of the present application, the determining, according to the power fluctuation signal of each target device, power consumption of each target device includes:
Performing Fourier transform on the power fluctuation signal, and converting a time domain signal of the power fluctuation signal into a frequency domain signal to obtain a spectrogram;
responding to the selection operation of a first time point, and integrating the energy spectrum density value in a preset frequency range to obtain a power value of the first time point;
and taking the product of the power value and the time length of the power fluctuation signal as the power consumption of each target device.
A second aspect of the present application provides an electric power control cabinet, comprising:
A memory configured to store instructions; and
And the processor is configured to call the instruction from the memory and realize the method for controlling the power energy conservation of the power control cabinet when executing the instruction.
A third aspect of the present application provides a machine-readable storage medium having stored thereon instructions for causing a machine to perform the method of power conservation control of a power control cabinet described above.
According to the technical scheme, in a plurality of devices with idle working states, the device grade of each device is obtained, the target device with low device grade is determined, namely the device with low efficiency is obtained, the power fluctuation signal of the target device is obtained, the power consumption of the target device is determined according to the power fluctuation signal of the target device, the reliability data of the target device is determined according to the power consumption of the target device, a pre-built energy-saving model is adopted, the reliability data of the target device is input into the model, and the first energy-saving control strategy of the target device with low device grade is obtained. The method is beneficial to improving the energy efficiency of the equipment according to the first energy saving control strategy, namely, the first energy saving control strategy is formulated for the equipment with lower energy efficiency by analyzing the working state and the energy consumption data of the equipment, so that the method is beneficial to reducing the consumption of electric power and energy and saving energy.
Additional features and advantages of embodiments of the application will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the embodiments of the application. In the drawings:
Fig. 1 schematically shows a flow chart of a power saving control method of a power control cabinet according to an embodiment of the application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it should be understood that the detailed description described herein is merely for illustrating and explaining the embodiments of the present application, and is not intended to limit the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that, if directional indications (such as up, down, left, right, front, and rear … …) are included in the embodiments of the present application, the directional indications are merely used to explain the relative positional relationship, movement conditions, etc. between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are correspondingly changed.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present application.
Fig. 1 schematically shows a flow chart of a power saving control method of a power control cabinet according to an embodiment of the application. As shown in fig. 1, an embodiment of the present application provides a power saving control method of a power control cabinet, which may include the following steps.
S101, acquiring the working state of each device connected with the power control cabinet.
The operating states of the equipment connected with the power control cabinet include idling and normal. The device operating state being idle means that the device is in an idle or low load state, i.e. the device is running but not actually doing work or producing useful output. Devices at idle may consume some electrical energy. A device operating state being normal indicates that the device is operating in a predetermined mode of operation.
S102, acquiring the equipment grade of each equipment under the condition that the working states of the plurality of equipment are idle.
The device class of the device is used to characterize the energy saving level of the device, wherein a lower device class indicates a lower energy saving level of the device, and energy saving is more desirable. In the case where the operating states of the plurality of devices are all idling, the device that needs energy saving can be determined by acquiring the device class of each device.
S103, among a plurality of devices with idle working states, determining a target device with low device class, and acquiring a power fluctuation signal of each target device.
The target device with low device class can be obtained through the device parameters of each device, and the device parameters can include the device power of the device. The higher the device power, the lower the device class of the device; the lower the device power, the higher its device class. In addition, the device class of each device may be obtained through a preset database, which is not limited herein. The preset database may store each device and its corresponding device class.
The power fluctuation signal refers to fluctuation or variation of the power signal due to load variation, power supply variation, or other factors in the power system. Which typically exists as a variation of voltage or current. The power fluctuation signal may include transient voltage or current spikes, frequency variations, harmonics, etc. The power fluctuation signal may be detected and measured by a power monitoring device, a sensor, or a measuring instrument.
S104, determining the electric energy consumption of each target device according to the electric power fluctuation signal of each target device.
Since the power fluctuation signal can reflect the conditions of load change, power fluctuation and the like of the equipment, and the electric energy consumption is closely related to the load and the power of the equipment, the electric energy consumption condition of the equipment in different time periods can be obtained through analysis and processing of the power fluctuation signal. For example, when the device is in an idle state, the power fluctuation signal may appear as a lower power fluctuation or steady signal; while when the device is in normal operation, the power fluctuation signal may exhibit high power fluctuations or frequent changes.
S105, determining reliability data of each target device according to the power consumption of each target device.
The reliability data may include a device failure rate and a device mean time to failure. For each of the above-mentioned target devices' power consumption, a definition of the fault criteria may be determined. Wherein the defined criterion for the fault may be that the fault is determined when the electrical energy consumption is below or exceeds a first preset threshold. And counting the times of faults of each target device in a period of time according to the definition standard. And determining a total operation time of each target device by a time length of the power consumption data. The failure rate of each target device may be determined according to a formula of failure rate=number of failures/total run time.
Assuming a target device, the power consumption data records the power consumption of the device over the year. First, according to the definition criteria of faults, the number of faults occurring in the device in the year is counted, assuming 10 times. Then, the total running time was calculated, assuming 8760 hours (total hours of one year). Dividing the number of failures by 10 by the total run time 8760 gives a failure rate of 0.00114, i.e. the failure rate of the device is 0.114%.
According to the definition standard, the number of faults of each target device in a period of time can be counted. In addition, the total run time of each target device may be calculated from the power consumption data. The total run time may be determined by the length of time of the power consumption data. Dividing the total running time by the number of faults to obtain the average fault-free time of each target device. The mean time to failure can be calculated by the following formula: mean time to failure = total run time/number of failures.
Assuming a target device, the power consumption data records the power consumption of the device over the year. First, according to the definition criteria of faults, the number of faults occurring in the device in the year is counted, assuming 10 times. Then, the total running time was calculated, assuming 8760 hours (total hours of one year). Dividing the total run time 8760 by the number of failures 10 gives an average no failure time of 876 hours, i.e. the average no failure time of the device is 876 hours.
S106, inputting the reliability data of each target device into the energy-saving model by adopting the pre-constructed energy-saving model, and obtaining a first energy-saving control strategy of a plurality of target devices with low device grades.
Low device levels mean that these devices are of a low level in terms of both reliability and energy consumption, and therefore energy-saving control strategies need to be adopted to reduce energy consumption and optimize energy utilization. The energy-saving model constructed in advance can predict the energy consumption condition of each target device in different working states. The reliability data is input into the energy-saving model, and the energy consumption and the reliability index of the equipment can be comprehensively considered, so that a proper first energy-saving control strategy is obtained, energy saving is realized, and the energy efficiency performance of the equipment is improved.
And S107, adjusting the device parameters of each target device according to the first energy saving control strategy, wherein the device parameters comprise power.
The device parameters, including power, of each target device may be adjusted according to a first energy conservation control strategy. The energy consumption of the equipment can be reduced by adjusting the equipment parameters, and the energy utilization efficiency is improved. By adjusting the parameters of the equipment, the energy-saving target can be realized and the energy utilization efficiency can be improved on the premise of not influencing the normal operation of the equipment. Thus, the energy consumption cost can be reduced, and the consumption of energy resources can be reduced.
For example, an apparatus may have an original power parameter of 1000 watts, and may have its power parameter adjusted to 800 watts according to a power saving control strategy. By reducing the power of the device, the energy consumption can be reduced.
In the embodiment of the application, in a plurality of devices with idle working states, the device grade of each device is obtained, the target device with low device grade is determined, namely the device with low efficiency is obtained, the power fluctuation signal of the target device is obtained, the power consumption of the target device is determined according to the power fluctuation signal of the target device, the reliability data of the target device is determined according to the power consumption of the target device, a pre-built energy-saving model is adopted, the reliability data of the target device is input into the model, and the first energy-saving control strategy of the target device with low device grade is obtained. The method is beneficial to improving the energy efficiency of the equipment according to the first energy saving control strategy, namely, the first energy saving control strategy is formulated for the equipment with lower energy efficiency by analyzing the working state and the energy consumption data of the equipment, so that the method is beneficial to reducing the consumption of electric power and energy and saving energy.
In one implementation manner of this embodiment, the method further includes the following steps:
s201, in a preset first time period, executing a circulation step until the working state of any one of m target devices is not changed after the working state of n target devices in the m target devices is changed from idle speed to normal.
In the first period, it may occur that a part of the target device transitions from the idle state to the normal operation state. In the above scenario, the process that the target device changes from the idle state to the normal working state is a dynamic change is required to meet the condition that after the working states of n target devices in the m target devices change from the idle state to the normal state, no condition that the working states of any one of the m target devices change exists, so that the stability of the target device change in the first period of time can be determined.
S202, acquiring total power consumption of m-n pieces of residual target equipment, and controlling the m-n pieces of residual target equipment to perform power alternation by adopting a power alternation energy-saving control strategy under the condition that the total power consumption is larger than a preset power consumption threshold, wherein m and n are positive integers, and m is larger than n.
The power alternating energy-saving control strategy is to realize the reduction of energy consumption by adjusting the power of the equipment on the premise of not influencing the normal operation of the equipment.
Assume that there are a total of 7 target devices (m=7), wherein the number of devices for which power-alternating power-saving control is required is 3 (n=3). According to the scheme, the total power consumption of the remaining m-n devices is firstly obtained, and whether the total power consumption is larger than a preset power consumption threshold value is judged.
Assume that the total power consumption of 7 devices is 1000 units (e.g., kilowatt-hours). And the preset power consumption threshold is 800 units.
In this case, the total power consumption is greater than a preset power consumption threshold. According to the power alternation energy-saving control strategy, 3 remaining target devices need to be controlled to carry out power alternation.
For example, the operating states of the 3 remaining target devices may be alternately adjusted, so that the 3 remaining target devices operate with different powers in different time periods, so as to reduce the total power consumption.
For example, device a may be operated at a first power for a first period of time, device B may be operated at a second power for a second period of time, and device C may be operated at a third power for a third period of time. This sequence of operations is then repeated in a cycle such that the power of the devices is alternately operated. By the power alternation mode, the total electric energy consumption can be reduced, and the purpose of energy conservation is achieved.
S203, predicting the energy consumption value of each preset time in the future preset time period.
The prediction of the energy consumption value at each preset time in the future preset time period can be performed by adopting a prediction mode based on machine learning, a prediction mode based on a model and a trend analysis mode based on historical data. And will not be described in detail herein. Taking a prediction mode based on machine learning as an example, the energy consumption value at each preset moment in a future time period can be predicted by analyzing historical energy consumption data and observing the trend and the periodical change of the energy consumption value.
S204, generating an energy adjustment curve according to the energy consumption value of each preset time in the future preset time period, wherein the energy adjustment curve is used for representing the energy adjustment value of equipment to be adjusted at each preset time in the future preset time period compared with the preset power consumption threshold.
The future preset time period may be divided into a series of preset moments (e.g. per minute, per hour) for each of which the energy consumption value is calculated. And comparing the energy consumption value at each preset time with a preset electric energy consumption threshold. If the energy consumption value is greater than the threshold value, energy adjustment is required. And for equipment needing to be adjusted, calculating an energy adjustment value needing to be adjusted according to the difference between the energy consumption value and the threshold value. This energy adjustment value may be positive (increasing energy consumption) or negative (decreasing energy consumption), depending on the difference between the energy consumption value and the threshold value. And drawing the energy adjustment value of each preset moment on a time axis to form an energy adjustment curve. The energy adjustment curve may be used to characterize the energy adjustment value of the device that needs to be adjusted at each preset moment in the future preset time period.
The energy adjustment curve is a graph, the horizontal axis represents time, and the vertical axis represents energy adjustment values. Each point on the curve represents a preset time, and its corresponding energy adjustment value represents the energy adjustment amount of the device that needs to be adjusted at that time. The shape and trend of the curve may reflect the need and trend of energy adjustment within a future time period. By analyzing the energy adjustment curve, a corresponding energy adjustment strategy can be formulated to achieve the energy saving objective and optimize energy utilization.
The method and the device can realize reasonable scheduling of equipment, optimization and saving of energy consumption, and provide guidance and decision support for future energy adjustment, so that the effects of energy conservation and energy utilization optimization are achieved.
In one implementation manner of the embodiment, the method includes obtaining total power consumption of m-n remaining target devices, and controlling the m-n remaining target devices to perform power alternation by adopting a power alternation energy-saving control strategy under the condition that the total power consumption is greater than a preset power consumption threshold, including the following steps:
s301, under the condition that the total power consumption is larger than a preset power consumption threshold, obtaining the mutual influence factors between any two target devices in m-n residual target devices.
The interaction factor may reflect a coupling relationship between devices and a degree of interaction. Specifically, the step of obtaining the mutual influence factor between any two target devices in the m-n remaining target devices includes:
S1, collecting data related to target devices, wherein the data comprise interaction, communication, energy consumption and the like among the devices.
Data related to the target device may be obtained by a sensor, monitoring system, or the like.
S2, carrying out correlation analysis on the data related to the target equipment so as to determine the degree of correlation between different equipment.
The correlation analysis method may include a correlation coefficient, covariance, and the like.
And S3, representing the association degree between the devices as a network structure, wherein the devices are nodes, and the relationship between the devices is an edge.
The interaction factors between devices can be identified by network analysis methods such as graph theory, complex network analysis, and the like.
For example, the relationship between devices may be represented by a network structure. The following devices are assumed: A. b, C, D, E, F. A network of devices may be constructed in which each device acts as a node and the relationships between the devices act as edges.
The network may be constructed according to the communication frequency between the devices. If device a communicates with device B frequently, an edge may be added between device a and device B indicating that there is a strong interaction between them. Likewise, if there is also frequent communication between device B and device C, an edge may be added between device B and device C.
The interaction factors between devices can be identified by network analysis methods such as graph theory and complex network analysis. For example, the degree of nodes in a device network, i.e., the number of edges each node connects to other nodes, may be calculated. The higher the degree of the node, the greater the interaction between the device and other devices.
Assume that a device network is constructed as follows:
The device A is connected with the device B;
The device B is connected with the device C;
The device C is connected with the device D;
device D is connected to device E;
Device E is connected to device F.
Through graph theory analysis, the following conclusions can be drawn:
the degree of node of device a is 1, indicating that it is connected only to device B.
The degree of node of device B is 2, indicating that it is connected to devices a and C.
The degree of node of device C is 2, indicating that it is connected to devices B and D.
The degree of node of device D is 2, indicating that it is connected to devices C and E.
The degree of node of device E is 2, indicating that it is connected to devices D and F.
The degree of node of device F is 1, indicating that it is connected only to device E.
Through the analysis of the node degrees, the interaction factor between the devices can be obtained. In this example, the highest degree of nodes for device B and device C indicates a greater interaction with other devices.
S302, determining the total power consumption according to the mutual influence factors and the acquired power fluctuation signals of each of m-n residual target devices.
The power consumption of each target device may be first determined from the power fluctuation signal and the total power consumption determined from the power consumption of each target device and the interaction factor with the neighboring devices.
And S303, acquiring an electric energy adjustment value under the condition that the total electric energy consumption is larger than a preset electric energy consumption threshold.
When the total power consumption exceeds a preset threshold, it means that the consumption of the energy source exceeds an expected range, which may result in waste of the energy source and increase in cost. In order to effectively control the total power consumption, corresponding adjustment measures need to be taken.
By calculating the difference between the current total power consumption and the preset threshold, the amount of energy that needs to be reduced can be determined. This difference may be used as an adjustment target to control the total power consumption within a preset range.
S304, generating a random power reduction strategy, and acquiring a power reduction value of each of m-n remaining target devices.
Generating a random power reduction strategy and acquiring a power reduction value of each of m-n remaining target devices refers to determining a power value required to be reduced by each device through the randomly generated strategy for the remaining m-n target devices when the total power consumption is greater than a preset power consumption threshold.
Specifically, generating a random power reduction strategy refers to randomly selecting some devices among the remaining m-n target devices for power reduction according to a certain rule or algorithm. This strategy may be to randomly select a certain proportion of the devices for power reduction, or to randomly select a certain number of the devices for power reduction.
S305, for each random power reduction strategy, determining the current interaction factor between any two target devices in m-n residual target devices by adopting a simulation model.
The current interaction factor between each two target devices is different. The simulation model is a model that simulates the behavior and performance of a real system through computer simulation and simulation experiments. May be a discrete event simulation model, a continuous time simulation model, a proxy model, etc. The description is not limited thereto.
Wherein the discrete event simulation model is an event-driven based model that simulates the behavior of the system by simulating the occurrence and processing of events. In this model, events such as communication between devices, data exchange, energy consumption, etc., and interactions between devices can be simulated.
The continuous-time simulation model is a model based on time continuity, which simulates the behavior of a system by simulating its state changes. In such a model, mathematical models between devices, including energy consumption models, communication models, etc., can be built, and interactions between devices are analyzed by simulating the progress of time and changes in state.
The proxy model is a model based on proxy behavior that simulates the behavior of a system by simulating the behavior of a device and a decision process. In such a model, a proxy model may be built for each device, modeling interactions and interactions between devices.
S306, calculating the electric energy reduction value of the current random power reduction strategy according to the power reduction value of each target device and the current influence factor by adopting a power alternating energy saving control formula.
The power reduction value of the current random power reduction strategy may be calculated using a power-alternating power-saving control formula based on the power reduction value of each target device and the current impact factor.
The power alternation energy saving control formula can be expressed as:
Power reduction value = power reduction value of target device current impact factor.
Wherein the power reduction value of the target device is the amount of power that each device determined according to the random power reduction strategy needs to reduce. The current impact factor is the degree of interaction between devices determined from a simulation model or other method.
According to the formula, for each target device, multiplying the power reduction value of each target device by the current influence factor to obtain the power reduction value of the current random power reduction strategy.
S307, taking a random power reduction strategy corresponding to the power reduction value with the largest difference value of the power adjustment values as a power alternating energy saving control strategy.
After calculating the power reduction value of each target device, finding out the power reduction value with the largest difference from the power adjustment value, and then taking the corresponding random power reduction strategy as the final power alternation energy-saving control strategy.
Specifically, the power reduction value having the largest difference can be found from the difference between the power reduction value and the power adjustment value of each target device calculated previously. The power reduction value with the greatest difference represents the device or devices that need to be powered down to bring the total power consumption close to or down to a preset threshold.
And S308, controlling m-n residual target devices to perform power alternation by adopting a power alternation energy-saving control strategy.
According to the method, the total power consumption of m-n pieces of residual target equipment is obtained, and the total power consumption is controlled under the condition that the total power consumption is larger than the preset power consumption threshold, so that the total power consumption can be effectively controlled to be close to or reach the preset threshold, and under the condition that the total power consumption is larger than the preset power consumption threshold, the power adjustment value is obtained and used for determining the power to be reduced. And determining a final power alternating energy-saving control strategy according to a random power reduction strategy corresponding to the power reduction value with the largest difference value of the power adjustment values, and realizing power alternating energy-saving control on m-n residual target devices, thereby achieving the effects of reducing total power consumption, optimizing energy utilization and realizing energy-saving targets.
In one implementation of this embodiment, the power reduction value includes an effective power reduction value and a loss power, and the power alternation energy saving control formula includes the following steps:
；；
Wherein REV is the power reduction value of the current random power reduction strategy, P q is the effective power reduction value of the q-th device, S q is the loss power of the q-th device, t is the working time of the q-th device, E q+1 is the power reduction value of the q+1th device, E q-1 is the power reduction value of the q-1 th device, and k is the current influencing factor, wherein E q+1=(Pq+1+Sq+1)×t,Eq-1=(Pq-1+Sq-1) x t.
Power reduction value = Σ [ (power reduction value x operating time) + (current influencing factor x power reduction value of neighboring device) ].
In one implementation manner of the embodiment, in a preset first period of time, a loop step is performed until, after the working states of n target devices in the m target devices are changed from idle speed to normal, there is no change in the working state of any one of the m target devices, including the following steps:
s501, acquiring the working state of each of m target devices in real time in a preset first time period, and counting the number of target devices with the working state converted from idle speed to normal.
S502, under the condition that the working state of each target device is not the last moment of the first time period, circularly executing the steps of acquiring the working state of each target device in real time and counting the number of target devices of which the working state is converted from the idle speed to be normal.
S503, ending the circulation step under the condition that the number of target devices for which the working state is changed from the idle speed to be normal is equal to n at the last moment of the first time period.
And continuously monitoring the working state of the target equipment in a preset first time period, and counting the number of equipment for which the working state is changed from the idle speed to the normal state. These steps are performed by looping until the condition is satisfied: the operating states of n of the m target devices are changed from idle to normal, and after that, the operating state of none of the target devices is changed. This can be used to ensure that a specified number of devices transition from idle to normal operating conditions and remain stable for a certain period of time.
The present embodiment can be used to monitor and control the change of the operating state of the target device, particularly the device that transitions from idle to normal operating state. By setting the conditions and the cycling step, the control of the number and state changes of the target devices can be realized, and the cycling is ended when the specified conditions are met.
In one implementation manner of the embodiment, the method for constructing the energy-saving model includes the following steps:
s601, collecting working data sets of devices connected with the power control cabinet and reliability data of each device under different working data sets, wherein the working data sets comprise working states, electronic fluctuation signals and electric energy consumption.
S602, preprocessing the working state, the electronic fluctuation signal and the electric energy consumption of the equipment to obtain preprocessing data, wherein the preprocessing data comprises a training data set and a verification data set.
The preprocessing may be data cleaning, denoising, normalization, etc. to ensure accuracy and consistency of the data. The training data set may be 80% and the validation data set may be 20%. Typically, the duty cycle of the training data set is greater than the duty cycle of the validation data set.
And S603, extracting features related to energy conservation in the training data set, wherein the features comprise equipment power change, power consumption trend and power fluctuation frequency.
By processing the power data of the device, the change characteristics of the power of the device can be extracted. For example, statistical characteristics such as average value, maximum value, minimum value, standard deviation, etc. of the device power, and dynamic characteristics such as change rate, fluctuation amplitude, etc. of the device power.
By processing the power consumption data of the device, trend characteristics of power consumption can be extracted. The statistical characteristics of average value, maximum value, minimum value, standard deviation and the like of the electric energy consumption, and the dynamic characteristics of the increase rate of the electric energy consumption, trend line slope and the like can be calculated.
By processing the power fluctuation signal of the device, the frequency characteristic of the power fluctuation can be extracted. The spectrum analysis method can be adopted to calculate the spectrum distribution of the power fluctuation signal of the equipment and extract the characteristics of frequency components, such as main frequency, harmonic wave and the like.
S604, performing associated modeling on the characteristics related to energy conservation and reliability data of the equipment to obtain an energy conservation model.
The association modeling may include: according to the selected characteristics and the reliability data, a modeling method is determined, wherein the modeling method can be regression analysis, decision trees, neural networks and the like, and a correlation model is established.
The model may be trained using methods of machine learning or statistical modeling.
And S605, verifying the energy-saving model by adopting a verification data set to obtain a verified energy-saving model.
The method and the device can effectively collect and process the working data of the equipment, extract the characteristics related to energy conservation, establish a correlation model and verify the model through a verification data set.
In one implementation of this embodiment, the reliability data includes a device failure rate and a device average failure time;
the method comprises the steps of adopting a pre-constructed energy-saving model, inputting reliability data of each target device into the energy-saving model, and obtaining a first energy-saving control strategy of a plurality of target devices with low device grades, wherein the method comprises the following steps:
s701, inputting reliability data of each target device into an energy-saving model, and outputting electric energy consumption standard values of a plurality of target devices with low device grades.
S702, for each target device with low device level, determining a power reduction value according to the corresponding power consumption standard value and the corresponding power consumption.
Power reduction value= |electric energy consumption-electric energy consumption standard value|.
S703 controlling the power of the target device with each device class according to the power reduction value of the target device with each device class being low.
Controlling the power of each device according to the power reduction value of the target device with low device class refers to correspondingly adjusting or reducing the power of the devices according to the power reduction value determined in the previous step, so as to achieve the purpose of energy saving.
In the present embodiment, the reliability data is input into the energy-saving model, the electric energy consumption standard value of the device is predicted, the power reduction value is determined according to the standard value and the actual consumption, and the power of the device is finally controlled. The effect of these steps is to reduce the power of the device according to the model prediction and the actual data, thereby achieving the purpose of energy saving.
In one implementation of the present embodiment, determining the power consumption of each target device according to the power fluctuation signal of each target device includes the steps of:
s801, carrying out Fourier transform on the power fluctuation signal, and converting a time domain signal of the power fluctuation signal into a frequency domain signal to obtain a spectrogram.
The fourier transform can transform the signal from the time domain to the frequency domain, and the energy distribution of the signal at different frequencies can be known by analyzing the spectrogram.
S802, responding to the selection operation of the first time point, and integrating the energy spectrum density value in a preset frequency range to obtain the power value of the first time point.
By integrating the energy spectral density values in the spectrogram, a power value in a preset frequency range can be obtained. This power value reflects the energy distribution of the signal in this frequency range.
S803, the product of the power value and the time length of the power fluctuation signal is taken as the power consumption of each target device.
The power value at the first time point is multiplied by the time length of the power fluctuation signal, and the power consumption of each target device in the time period can be obtained.
Assuming power fluctuation signal data of one target device, the sampling frequency is 1000Hz, and the duration is 10 seconds. First, the signal is preprocessed, such as noise removal and filtering. Then, fourier transform is performed on the preprocessed signal.
It is assumed that a spectrogram is obtained after fourier transformation, which contains energy spectral densities at different frequency points. A frequency range, e.g. 0-50Hz, may be selected to calculate the power value of the target device at each point in time.
For each time point, the values of the energy spectral density in the range of 0-50Hz may be integrated to obtain the power value for that time point. This power value represents the power consumption of the target device at that point in time.
In this embodiment, the power fluctuation signal is converted into a frequency domain signal by fourier transformation, the power value is obtained by integral calculation, and the power value is multiplied by the time length to obtain the power consumption of each target device.
The embodiment of the application also provides an electric control cabinet, which can comprise:
A memory configured to store instructions; and
And the processor is configured to call the instruction from the memory and can realize the method for controlling the power energy conservation of the power control cabinet when executing the instruction.
The embodiment of the application also provides a machine-readable storage medium, wherein the machine-readable storage medium is stored with instructions for enabling a machine to execute the power energy-saving control method of the power control cabinet.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. The power energy-saving control method of the power control cabinet is characterized by comprising the following steps of:
Acquiring the working state of each device connected with the power control cabinet;
acquiring the equipment grade of each equipment under the condition that the working states of a plurality of the equipment are idle;
among a plurality of idle devices in working state, determining a target device with low device level, and acquiring a power fluctuation signal of each target device;
Determining the power consumption of each target device according to the power fluctuation signal of each target device;
Determining reliability data of each target device according to the power consumption of each target device;
The method comprises the steps of adopting a pre-constructed energy-saving model, and inputting reliability data of each target device into the energy-saving model to obtain a first energy-saving control strategy of a plurality of target devices with low device grades;
And adjusting the device parameters of each target device according to the first energy saving control strategy, wherein the device parameters comprise power.
2. The method according to claim 1, wherein the method further comprises:
In a preset first time period, executing a circulation step until the working state of any one of m target devices is not changed after the working state of n target devices in the m target devices is changed from idle speed to normal;
Acquiring total power consumption of m-n residual target devices, and controlling the m-n residual target devices to perform power alternation by adopting a power alternation energy-saving control strategy under the condition that the total power consumption is larger than a preset power consumption threshold, wherein m and n are positive integers, and m is larger than n;
predicting an energy consumption value of each preset time in a future preset time period;
And generating an energy adjustment curve according to the energy consumption value of each preset time in the future preset time period, wherein the energy adjustment curve is used for representing the energy adjustment value of equipment which needs to be adjusted at each preset time in the future preset time period compared with the preset power consumption threshold.
3. The method according to claim 2, wherein the obtaining the total power consumption of the m-n remaining target devices, and controlling the m-n remaining target devices to perform power alternation using a power alternation energy saving control strategy if the total power consumption is greater than a preset power consumption threshold, comprises:
Acquiring an interaction factor between any two target devices in m-n residual target devices under the condition that the total power consumption is larger than a preset power consumption threshold;
Determining the total power consumption according to the mutual influence factor and the acquired power fluctuation signal of each of m-n remaining target devices;
Acquiring an electric energy adjustment value under the condition that the total electric energy consumption is larger than a preset electric energy consumption threshold value;
Generating a random power reduction strategy, and acquiring a power reduction value of each target device in m-n pieces of residual target devices;
For each random power reduction strategy, determining a current interaction factor between any two target devices in m-n residual target devices by adopting a simulation model;
Adopting a power alternating energy-saving control formula, and calculating the electric energy reduction value of the current random power reduction strategy according to the power reduction value of each target device and the current influence factor;
Taking a random power reduction strategy corresponding to the power reduction value with the largest difference value of the power adjustment values as a power alternating energy-saving control strategy;
And controlling m-n residual target devices to perform power alternation by adopting a power alternation energy-saving control strategy.
4. The method of claim 3, wherein the power reduction value comprises an effective power reduction value and a lost power, and wherein the power alternation power saving control formula comprises:
；
Wherein REV is the current power reduction value of the random power reduction strategy, P q is the effective power reduction value of the q-th device, S q is the loss power of the q-th device, t is the operating time of the q-th device, E q+1 is the power reduction value of the q+1th device, E q-1 is the power reduction value of the q-1 th device, and k is the current influencing factor, wherein E q+1=(Pq+1+Sq+1)×t,Eq-1=(Pq-1+Sq-1) ×t.
5. The method according to claim 2, wherein the step of performing a cycling step in the preset first period of time until no change occurs in the operating state of any one of the m target devices after the operating state of the n target devices among the m target devices is changed from the idle speed to the normal state, includes:
in a preset first time period, acquiring the working state of each of m target devices in real time, and counting the number of target devices with the working state converted from idle speed to normal;
The step of circularly executing the steps of acquiring the working state of each target device in real time and counting the number of target devices with the working state converted from idle speed to normal under the condition that the working state is not the last moment of the first time period;
And ending the circulation step when the last moment of the first time period and the number of target devices for which the working state is changed from the idle speed to be normal is equal to n.
6. The method according to claim 1, wherein the energy saving model construction method comprises:
Collecting working data sets of equipment connected with the power control cabinet and reliability data of each equipment under different working data sets, wherein the working data sets comprise working states, electronic fluctuation signals and electric energy consumption;
preprocessing the working state, the electronic fluctuation signal and the electric energy consumption of the equipment to obtain preprocessing data, wherein the preprocessing data comprises a training data set and a verification data set;
extracting features related to energy conservation in the training dataset, the features including device power variation, power consumption trend and power fluctuation frequency;
Performing associated modeling on the characteristics related to energy conservation and reliability data of the equipment to obtain an energy conservation model;
And verifying the energy-saving model by adopting a verification data set to obtain a verified energy-saving model.
7. The method of claim 6, wherein the reliability data comprises a device failure rate and a device average failure time;
the step of inputting the reliability data of each target device into the energy-saving model by adopting a pre-constructed energy-saving model to obtain a first energy-saving control strategy of a plurality of target devices with low device grades, comprising the following steps:
inputting the reliability data of each target device into the energy-saving model, and outputting electric energy consumption standard values of a plurality of target devices with low device grades;
For the target equipment with low equipment grade, determining a power reduction value according to the corresponding power consumption standard value and the corresponding power consumption;
And controlling the power of the target device with low device level according to the power reduction value of the target device with low device level.
8. The method of claim 7, wherein said determining the power consumption of each of said target devices based on said power fluctuation signal of each of said target devices comprises:
Performing Fourier transform on the power fluctuation signal, and converting a time domain signal of the power fluctuation signal into a frequency domain signal to obtain a spectrogram;
responding to the selection operation of a first time point, and integrating the energy spectrum density value in a preset frequency range to obtain a power value of the first time point;
and taking the product of the power value and the time length of the power fluctuation signal as the power consumption of each target device.
9. An electrical control cabinet, comprising:
A memory configured to store instructions; and
A processor configured to invoke the instructions from the memory and to enable, when executing the instructions, a method of power saving control of a power control cabinet according to any one of claims 1 to 8.
10. A machine-readable storage medium having stored thereon instructions for causing a machine to perform a method of power conservation control of a power control cabinet according to any one of claims 1 to 8.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014057614A1 (en) * | 2012-10-12 | 2014-04-17 | パナソニック株式会社 | Energy management device, management device, remote management device |
| JP2015122831A (en) * | 2013-12-20 | 2015-07-02 | 三菱電機株式会社 | Power management device and power management system |
| CN112202168A (en) * | 2020-09-28 | 2021-01-08 | 国网江苏省电力有限公司苏州供电分公司 | Multi-element power grid advanced control power supply method and system based on multi-objective coordination optimization |
| CN112577161A (en) * | 2019-09-30 | 2021-03-30 | 北京国双科技有限公司 | Air conditioner energy consumption model training method and air conditioner system control method |
| CN114498641A (en) * | 2022-04-18 | 2022-05-13 | 国网智能电网研究院有限公司 | Distributed flexible resource aggregation control device and control method |
| CN114659236A (en) * | 2022-04-29 | 2022-06-24 | 深圳市英维克信息技术有限公司 | Energy-saving control method, device, equipment and storage medium |
| CN115150923A (en) * | 2021-03-31 | 2022-10-04 | 中国移动通信集团河南有限公司 | Energy-saving control method and device for base station |
| CN115352384A (en) * | 2022-09-30 | 2022-11-18 | 阿波罗智能技术(北京)有限公司 | Energy-saving control method, device and equipment for vehicle and automatic driving vehicle |
| CN115437876A (en) * | 2022-08-10 | 2022-12-06 | 中国电信股份有限公司 | Data center management method and device, electronic equipment and storage medium |
| CN115515208A (en) * | 2021-06-07 | 2022-12-23 | 中兴通讯股份有限公司 | Base station energy saving method, base station energy saving system, base station and storage medium |
| CN116128688A (en) * | 2022-10-28 | 2023-05-16 | 武汉大学 | Marketing evaluation method oriented to energy efficiency industry |
| CN117528735A (en) * | 2023-11-24 | 2024-02-06 | 中国电信股份有限公司技术创新中心 | Energy-saving control method, device and system of network side equipment and related equipment |
| CN117650628A (en) * | 2023-12-01 | 2024-03-05 | 龙坤(无锡)智慧科技有限公司 | Energy efficiency management system based on self-adaptive preconditioning scene |
-
2024
- 2024-03-25 CN CN202410339730.8A patent/CN117937522B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014057614A1 (en) * | 2012-10-12 | 2014-04-17 | パナソニック株式会社 | Energy management device, management device, remote management device |
| JP2015122831A (en) * | 2013-12-20 | 2015-07-02 | 三菱電機株式会社 | Power management device and power management system |
| CN112577161A (en) * | 2019-09-30 | 2021-03-30 | 北京国双科技有限公司 | Air conditioner energy consumption model training method and air conditioner system control method |
| CN112202168A (en) * | 2020-09-28 | 2021-01-08 | 国网江苏省电力有限公司苏州供电分公司 | Multi-element power grid advanced control power supply method and system based on multi-objective coordination optimization |
| CN115150923A (en) * | 2021-03-31 | 2022-10-04 | 中国移动通信集团河南有限公司 | Energy-saving control method and device for base station |
| CN115515208A (en) * | 2021-06-07 | 2022-12-23 | 中兴通讯股份有限公司 | Base station energy saving method, base station energy saving system, base station and storage medium |
| CN114498641A (en) * | 2022-04-18 | 2022-05-13 | 国网智能电网研究院有限公司 | Distributed flexible resource aggregation control device and control method |
| CN114659236A (en) * | 2022-04-29 | 2022-06-24 | 深圳市英维克信息技术有限公司 | Energy-saving control method, device, equipment and storage medium |
| CN115437876A (en) * | 2022-08-10 | 2022-12-06 | 中国电信股份有限公司 | Data center management method and device, electronic equipment and storage medium |
| CN115352384A (en) * | 2022-09-30 | 2022-11-18 | 阿波罗智能技术(北京)有限公司 | Energy-saving control method, device and equipment for vehicle and automatic driving vehicle |
| CN116128688A (en) * | 2022-10-28 | 2023-05-16 | 武汉大学 | Marketing evaluation method oriented to energy efficiency industry |
| CN117528735A (en) * | 2023-11-24 | 2024-02-06 | 中国电信股份有限公司技术创新中心 | Energy-saving control method, device and system of network side equipment and related equipment |
| CN117650628A (en) * | 2023-12-01 | 2024-03-05 | 龙坤(无锡)智慧科技有限公司 | Energy efficiency management system based on self-adaptive preconditioning scene |
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| Publication number | Publication date |
|---|---|
| CN117937522B (en) | 2024-06-04 |
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