Non-intrusive power load component identification method and system based on event detection and medium

Abstract

The invention discloses a non-intrusive power load component identification method and system based on event detection and a medium. The method comprises the following steps: firstly, obtaining active power and reactive power when a power load operates normally; dividing the power load operation data into data segments with the same duration as sub-windows by using a sliding window; and monitoring load active abrupt change of the sub-windows in sequence, and capturing a power load input operation event. Recording the occurrence moment of the event and the window where the event is located, and dividing the window where the event is located into element windows with the same duration by using a sliding window with a smaller time scale; sequentially calculating the maximum value, the minimum value, the average value and the range of the active and reactive power of the load in the element window, and searching the moment when the event occurs and the power is most stable before and after; calculating an active variable quantity, an impact coefficient and a reactive variable quantity after the load is input; and the input load type is judged according to the impact coefficient and the reactive power variation after the load is input, and load component identification of the motor, the static load and the power electronic interface load is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system simulation analysis, in particular to a power system load model, and particularly relates to a non-intrusive power load component identification method, system and medium based on event detection. Background Art

[0002] Power load is an important component of the power system. The power load model, referred to as the load model, usually refers to the general term for all distribution networks and power-consuming equipment in the power supply area of ​​a 220kV substation. The accuracy of power system simulation analysis is very dependent on the accurate establishment of the load model, and accurately grasping the composition of the power load is crucial for the load model.

[0003] In the load models commonly used in current power system simulation analysis, static loads (including constant impedance, constant current, and constant power components) and motor loads are mainly used to characterize electrical equipment. In recent years, more and more electrical equipment has been connected to the power grid through power electronics technology (switching power supply, variable frequency power supply, etc.), but the existing static load models and motor models cannot accurately describe the dynamic characteristics of the power electronics interface load. Therefore, it is believed that in the new power load model, static load (constant impedance load), power electronics interface load (including the constant current and constant power components in the original static load), and motor load are the three most important components.

[0004] The composition of power load has time-varying characteristics, which is a key factor affecting the accuracy of load model. When establishing a load model, the typical model parameters of various load components can usually remain unchanged, and what needs to change is the power proportion of the load component. In order to adapt to the time-varying nature of load components in load modeling, the power industry is currently building a load modeling system that uses an online statistical synthesis method, but this system can only obtain the power data of the user's port meter and cannot know the load composition inside the user. At present, there are many non-invasive load identification methods, but their purpose is to identify the name of the equipment put into operation. Most of their algorithms rely on artificial neural networks, and high recognition rates are limited to trained electrical equipment. Therefore, it is difficult for existing non-invasive load identification methods to develop low-cost identification equipment, and they cannot meet the urgent need for load component identification in power load modeling. Summary of the invention

[0005] In view of the problems existing in the prior art, according to the first aspect of the present invention, a non-intrusive power load component identification method based on event detection is proposed, which can identify the type of power load put into use, that is, which of the three load components of motor, static load and power electronic interface load is it through a measurement and identification method.

[0006] According to the above-mentioned object of the present invention, the non-intrusive power load component identification method based on event detection comprises the following steps:

[0007] Step 1: Obtain data of the normal operation of the power load, including active power P(t) and reactive power Q(t);

[0008] Step 2: setting a first sliding window of a first fixed length, and dividing the power load operation data into data segments of the same length according to the first sliding window, each data segment being a sub-window;

[0009] Step 3: Use the event detection algorithm to monitor the sudden change of load active power in each sub-window in turn, so as to capture the event of power load being put into operation;

[0010] Step 4, record the time t and the window in which the power load is put into operation;

[0011] Step 5: Setting a second sliding window of a second fixed length, and dividing the window where the power load operation event is located into meta-windows of the same length according to the second sliding window;

[0012] Step 6: Calculate the active power parameters and reactive power parameters of the load in each element window in turn. The active power parameters include: the maximum value of active power P max (i) Minimum value P min (i) Average value P mean (i) and range P stab (i); The reactive power parameters include: the maximum value of reactive power Q max (i) Minimum value Q min (i) Average value Q mean (i) and range Q stab (i);

[0013] Step 7: Based on the active power parameters and reactive power parameters of the load in the meta-window, find the time of the load input event and the time when the power is most stable before and after the input time;

[0014] Step 8, calculating the active power change, impact coefficient and reactive power change after the load is put into operation; and

[0015] Step 9: Determine the load component type based on the impact coefficient and reactive power change after the load is put into operation.

[0016] As a preferred implementation, in step 9, the load component type is determined based on the impact coefficient and reactive power change after the load is put into operation, including:

[0017] If the impact coefficient is greater than 10, the load component type is determined to be a motor;

[0018] If the impact coefficient is greater than 2 and the impact coefficient is less than 10, the input load component type is determined to be a power electronic interface load;

[0019] If the impact coefficient is less than 2, further judgment is made based on the reactive power change, where:

[0020] If the reactive power variation is less than -1, the load component type is a power electronic interface load, otherwise it is determined to be a static load.

[0021] According to a second aspect of the present invention, a computer system is also provided, comprising:

[0022] one or more processors;

[0023] Memory, storing instructions that can be operated;

[0024] When the instructions are executed by one or more processors, the one or more processors mentioned above perform operations, and the operations include the process of executing the aforementioned non-intrusive power load component identification method based on event detection.

[0025] According to a third aspect of the present invention, a computer-readable storage medium is provided for storing one or more programs, wherein the one or more programs include instructions or instruction sets that can be executed by one or more processors.

[0026] When the instructions or instruction sets are executed by one or more processors, the process of the aforementioned non-intrusive power load component identification method based on event detection is performed.

[0027] When different types of power loads are put into operation, they will have different starting characteristics: when motors and power electronic interface loads are put into operation, the active power will have an impact and then quickly return to stability. The impact of the former is more obvious than the latter, and the impact coefficient can reach more than ten times, while the active power impact of static loads when they are put into operation is very small and can be approximately ignored. In addition, the reactive power will be reduced after the power electronic interface load is put into operation, while the effect of static loads on reactive power is very small and can be approximately ignored. To this end, the present invention proposes a non-invasive power load component identification method based on event detection, based on the difference between the impact of active power and the change of reactive power when different types of power loads are put into operation, to identify which of the three load components it belongs to: motors, static loads (constant impedance loads) and power electronic interface loads.

[0028] Compared with the prior art, the non-intrusive power load component identification method based on event detection of the present invention has the following significant advantages:

[0029] (1) Different from conventional non-intrusive load identification methods for identifying specific electrical equipment, the non-intrusive power load component identification method based on event detection of the present invention only identifies the type and power of the input load equipment for the needs of power load modeling, and only needs to obtain the active power and reactive power when the power load is running stably. The requirements for the acquisition equipment are relatively low, and the load component identification process does not involve complex algorithms. It is easy to implement at low cost and has high accuracy.

[0030] (2) The non-intrusive power load composition identification method of the present invention can also determine what load component the equipment that has been shut down belongs to, as well as the power of the equipment that has been shut down. By using the method of the present invention during the period when the user's power consumption is the lowest, through continuous operation and calculation, a more accurate user load composition situation in different time periods can be obtained. By accumulating the load component identification results of different users at the same time, the load component power ratio required for load modeling can be obtained, thereby providing support for establishing an accurate power load model. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 4 is a flow chart of a non-intrusive power load component identification method based on event detection according to an embodiment of the present invention.

[0032] Figure 2 4 is a schematic diagram of an active power curve of an example of a non-intrusive power load component identification method based on event detection according to an embodiment of the present invention.

[0033] Figure 3 4 is a schematic diagram of a reactive power curve of an example of a non-intrusive power load component identification method based on event detection according to an embodiment of the present invention.

[0034] Figure 4 4 is a schematic diagram of an event window and an alarm value of an example of a non-intrusive power load component identification method based on event detection according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the accompanying drawings in the examples of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Various aspects of the present invention are described in the disclosed embodiments with reference to the accompanying drawings, in which many illustrative embodiments are shown. The disclosed embodiments are not necessarily intended to include all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed by the present invention are not limited to any implementation. In addition, some aspects disclosed by the present invention can be used alone or in any appropriate combination with other aspects disclosed by the present invention.

[0037] {Example 1}

[0038] The non-intrusive power load component identification method based on event detection proposed in this embodiment aims to utilize the difference between the impact of active power and the change of reactive power when different types of power loads are put into use, so as to identify whether the put-in load belongs to one of the three load components of motor, static load (constant impedance load) and power electronic interface load. In view of the need of power load modeling, the method of this embodiment only identifies the type and power of the put-in load equipment, and only needs to obtain the active power and reactive power when the power load is running stably. The requirements for the acquisition equipment are relatively low, and the identification process does not involve complex algorithms, which is easy to implement at low cost and has high accuracy.

[0039] like Figure 1 As shown, an example shows a process of a non-intrusive power load component identification method based on event detection, which includes the following steps:

[0040] Step 1: Obtain data of the normal operation of the power load, including active power P(t) and reactive power Q(t);

[0041] Step 2: setting a first sliding window of a first fixed length, and dividing the power load operation data into data segments of the same length according to the first sliding window, each data segment being a sub-window;

[0042] Step 3: Use the event detection algorithm to monitor the sudden change of load active power in each sub-window in turn, so as to capture the event of power load being put into operation;

[0043] Step 4, record the time t and the window in which the power load is put into operation;

[0044] Step 5: Setting a second sliding window of a second fixed length, and dividing the window where the power load operation event is located into meta-windows of the same length according to the second sliding window;

[0045] Step 6: Calculate the active power parameters and reactive power parameters of the load in each element window in turn. The active power parameters include: the maximum value of active power Pmax (i) Minimum value P min (i) Average value P mean (i) and range P stab (i); The reactive power parameters include: the maximum value of reactive power Q max (i) Minimum value Q min (i) Average value Q mean (i) and range Q stab (i);

[0046] Step 7: Based on the active power parameters and reactive power parameters of the load in the meta-window, find the time of the load input event and the time when the power is most stable before and after the input time;

[0047] Step 8, calculating the active power change, impact coefficient and reactive power change after the load is put into operation; and

[0048] Step 9: According to the impact coefficient and reactive power change after the load is put into operation, the load component type is determined, that is, one of the static load, the power electronic interface load and the motor load, wherein the static load is a constant impedance load.

[0049] As an optional example, the second fixed length is smaller than the first fixed length. That is, in step 5, a sliding window with a smaller time scale is used to slide the window where the power load operation event is located, and divide it into meta-windows of the same length.

[0050] As an optional example, in step 3, using a CUSUM event detection algorithm to monitor a sudden change in load active power, thereby capturing an event in which the power load is put into operation, includes:

[0051] For the data within each sub-window range, the difference between each data and the average value is calculated and accumulated in turn. When the difference between the cumulative sum at a certain moment and the minimum cumulative sum exceeds the set threshold, an alarm is triggered, indicating that power load input is detected.

[0052] As an optional example, in step 6, during the calculation of the load active power parameter and reactive power parameter, the range is determined based on the maximum and minimum power values, that is:

[0053] P stab (i) = P max (i)-P min (i);

[0054] Q stab (i) = Q max (i)-Q min (i).

[0055] As an optional example, in step 7, find the active power extreme difference P stab (i) The meta-window corresponding to the maximum value, the time at which the meta-window is located is the load input time p0;

[0056] Find the active power difference P before and after the load input time p0 stab (i) The meta-window corresponding to the minimum value is the time when the active power is most stable, p1 and p2;

[0057] Find the reactive power extreme difference Q before and after the load input time p0 stab (i) The meta-window corresponding to the minimum value is at the time p3 and p4 when the reactive power is most stable.

[0058] As an optional example, in step 8, calculating the active power change, the impact coefficient, and the reactive power change after the load is put into operation includes:

[0059] Active power change △P=P mean (p2)-P mean (p1);

[0060] Impact coefficient IMP=(P max (p0)-P mean (p1)) / △P;

[0061] Reactive power change △Q=Q mean (p4)-Q mean (p3).

[0062] As an optional example, in step 9, determining the load component type according to the impact coefficient and reactive power change after the load is put into operation includes:

[0063] If the impact coefficient is greater than 10, the load component type is determined to be a motor;

[0064] If the impact coefficient is greater than 2 and the impact coefficient is less than 10, the input load component type is determined to be a power electronic interface load;

[0065] If the impact coefficient is less than 2, further judgment is made based on the reactive power change, where:

[0066] If the reactive power variation is less than -1, the load component type is a power electronic interface load, otherwise it is determined to be a static load.

[0067] {Example 2}

[0068] In this embodiment, we combine Figures 2 to 4 As shown, more specifically Figure 1The implementation process of the non-intrusive power load component identification method based on event detection in the above embodiment is further elaborated and illustrated.

[0069] Step 1: Obtain data of the normal operation of the power load, including active power P(t) and reactive power Q(t).

[0070] In this embodiment, the experimental data comes from a test system built in an automatic simulation laboratory, and the data of the power load when it is put into operation is collected by using a fault recorder, with a sampling frequency of 1 kHz.

[0071] In this embodiment, a section is captured, which is 1 minute long and includes the start-up process of the energy-saving lamp. The curve of active power P(t) is as follows: Figure 2 As shown, the curve of reactive power Q(t) is as follows Figure 3 shown.

[0072] Step 2: Set a sliding window of fixed length to divide the power load operation data into data segments of the same length, and each data segment is a sub-window.

[0073] In this embodiment, the window size is set to 10s and is divided into 6 sub-windows.

[0074] Step 3: Use the event detection algorithm (CUSUM) to monitor the sudden change of load active power in each sub-window in turn, so as to capture the event of power load being put into operation.

[0075] In this embodiment, CUSUM is used to calculate the difference between each data and the average value and accumulate them in turn. When the difference between the cumulative sum at a certain moment and the minimum cumulative sum exceeds the set threshold, an alarm is triggered, that is, load input is detected.

[0076] like Figure 4 As shown, the window in which the load input event is detected and the alarm value are exemplarily represented.

[0077] Step 4: Record the time t and the window in which the power load is put into operation.

[0078] Combination Figure 4 As shown, according to the time when the load input event is detected and the window in which it is located, it is recorded that the load input is detected around t=26.895s.

[0079] Step 5: Set a sliding window with a smaller time scale, and divide the window where the power load is put into operation into meta-windows of the same length.

[0080] In this embodiment, the size of the second sliding window is set to 1s and is divided into 10 meta-windows.

[0081] Step 6: Calculate the maximum value P of the load active power in each element window in turn max (i) Minimum value P min (i) Average value P mean (i) and range P stab (i) and the maximum value of reactive power Q max (i) Minimum value Q min (i) Average value Q mean (i) and range Q stab (i).

[0082] Furthermore, the range is equal to the maximum value minus the minimum value, that is, P stab (i) = P max (i)-P min (i), Q stab (i) = Q max (i)-Q min (i).

[0083] P max (i)=[54.977,57.146,57.792,73.590,73.512,73.887,75.245,76.471,77.581,78.845].

[0084] P mean (i)=[53.315,55.083,55.683,58.676,69.703,70.484,73.146,72.647,74.447,76.189].

[0085] P min (i)=[51.699,52.794,53.778,55.929,65.909,67.413,69.292,69.536,71.435,71.992].

[0086] P stab (i)=[3.277,4.352,4.014,17.661,7.602,6.474,5.953,6.935,6.146,6.852].

[0087] Q max (i)=[2.090,2.621,1.759,2.590,-0.381,0.515,-0.999,0.603,0.725,0.128].

[0088] Q mean(i)=[0.015,0.559,0.183,0.596,-4.209,-2.387,-3.912,-3.167,-2.164,-3.412].

[0089] Q min (i)=[-2.201,-1.333,-1.549,-5.097,-6.657,-5.137,-6.719,-6.528,-6.198,-6.041].

[0090] Q stab (i)=[4.291,3.954,3.308,7.687,6.276,5.651,5.720,7.130,6.923,6.169].

[0091] Step 7: Find the time when the load input event occurs and the time before and after when the power is most stable.

[0092] In this embodiment, the moment when the meta-window with the largest active power range difference is the load input time p0, and the meta-window with the smallest active power range difference is found before and after this moment, and the moment when the active power is most stable is p1, p2. Similarly, the moments p3, p4 when the reactive power is most stable before and after the load input are obtained.

[0093] In this example, combined with the above calculation results, it is determined that: p0=4, p1=1, p2=7, p3=3, p4=6.

[0094] Step 8: Calculate the active power change, impact coefficient and reactive power change after the load is put into operation.

[0095] In an embodiment, the calculation process is:

[0096] Active power change △P=P mean (p2)-P mean (p1);

[0097] Impact coefficient IMP=(P max (p0)-P mean (p1)) / △P;

[0098] Reactive power change △Q=Q mean (p4)-Q mean (p3).

[0099] The calculated active power change, impact coefficient and reactive power change are as follows:

[0100] ΔP=19.831, IMP=1.022, ΔQ=-2.570.

[0101] Step 9: Determine the type of load input according to the impact coefficient and reactive power change after the load is input.

[0102] In this embodiment, the basis for determining the input load component type is as follows:

[0103] If the impact coefficient is greater than 10, it is determined that the motor is put into operation. Otherwise, if the impact coefficient is greater than 2, it is determined that the power electronic interface load is put into operation. If the impact coefficient is less than 2, further judgment is required based on the reactive power change. If the reactive power change is less than -1, it is also determined to be a power electronic interface load. Otherwise, it is a static load.

[0104] Combining the calculation results of the above active change, impact coefficient and reactive change, the power electronic interface load is identified according to the above criteria, and its actual operating power is further calculated to be 452.147W, thus realizing the component identification of the power load.

[0105] {Example 3}

[0106] In conjunction with the implementation of the non-intrusive power load component identification method based on event detection in the above embodiment, the present disclosure further provides a computer system, including:

[0107] one or more processors;

[0108] Memory stores instructions that can be operated.

[0109] When the instructions are executed by one or more processors, the one or more processors mentioned above perform operations, and the operations include the process of executing the non-intrusive power load component identification method based on event detection of the aforementioned embodiment.

[0110] {Example 4}

[0111] In conjunction with the implementation of the non-intrusive power load component identification method based on event detection in the above embodiments, according to the present disclosure, a computer-readable storage medium is also proposed for storing one or more programs.

[0112] Among them, the one or more programs include instructions or instruction sets that can be executed by one or more processors, and when the instructions or instruction sets are executed by one or more processors, they execute the process of the non-intrusive power load component identification method based on event detection of the aforementioned embodiment.

[0113] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. A person with ordinary knowledge in the technical field to which the present invention belongs may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the definition of the claims.

Claims

1. A non-intrusive power load component identification method based on event detection, characterized in that: The following steps are involved: Step 1: Obtain data of the normal operation of the power load, including active power P(t) and reactive power Q(t); Step 2: setting a first sliding window of a first fixed length, and dividing the power load operation data into data segments of the same length according to the first sliding window, each data segment being a sub-window; Step 3: Use the event detection algorithm to monitor the sudden change of load active power in each sub-window in turn, so as to capture the event of power load being put into operation; Step 4, record the time t and the window in which the power load is put into operation; Step 5: Setting a second sliding window of a second fixed length, and dividing the window where the power load operation event is located into meta-windows of the same length according to the second sliding window; Step 6: Calculate the active power parameters and reactive power parameters of the load in each element window in turn. The active power parameters include: the maximum value of active power P max (i) Minimum value P min (i) Average value P mean (i) and range P stab (i); The reactive power parameters include: the maximum value of reactive power Q max (i) Minimum value Q min (i) Average value Q mean (i) and range Q stab (i); Step 7: Based on the active power parameters and reactive power parameters of the load in the meta-window, find the time of the load input event and the time when the power is most stable before and after the input time; Step 8, calculating the active power change, impact coefficient and reactive power change after the load is put into operation; and Step 9: Determine the load component type based on the impact coefficient and reactive power change after the load is put into operation.

2. The non-intrusive power load component identification method based on event detection according to claim 1 is characterized in that: The second fixed length is smaller than the first fixed length.

3. The non-intrusive power load component identification method based on event detection according to claim 1 is characterized in that: The load component type includes one of a static load, a power electronic interface load and a motor load, wherein the static load is a constant impedance load.

4. The non-intrusive power load component identification method based on event detection according to claim 1 is characterized in that: In step 3, a CUSUM event detection algorithm is used to monitor the sudden change of load active power, thereby capturing the event of the power load being put into operation, including: For the data within each sub-window range, the difference between each data and the average value is calculated and accumulated in turn. When the difference between the cumulative sum at a certain moment and the minimum cumulative sum exceeds the set threshold, an alarm is triggered, indicating that power load input is detected.

5. The non-intrusive power load component identification method based on event detection according to claim 1 is characterized in that: In step 6, during the calculation of the load active power parameter and reactive power parameter, the range is determined based on the maximum and minimum power values, that is: P stab (i)=P max (i)-P min (i); Q stab (i)=Q max (i)-Q min (i)。 6. The non-intrusive power load component identification method based on event detection according to claim 1 is characterized in that: In step 7, find the active power range difference P stab (i) The meta-window corresponding to the maximum value, the time at which the meta-window is located is the load input time p0; Find the active power difference P before and after the load input time p0 stab (i) The meta-window corresponding to the minimum value is the time when the active power is most stable, p1 and p2; Find the reactive power extreme difference Q before and after the load input time p0 stab (i) The meta-window corresponding to the minimum value is at the time p3 and p4 when the reactive power is most stable.

7. The non-intrusive power load component identification method based on event detection according to claim 1 is characterized in that: In step 8, calculating the active power change, impact coefficient and reactive power change after the load is put into operation includes: Active power change △P=P mean (p2)-P mean (p1); Impact coefficient IMP=(P max (p0)-P mean (p1)) / △P; Reactive power change △Q=Q mean (p4)-Q mean (p3).

8. The non-intrusive power load component identification method based on event detection according to claim 1, characterized in that: In step 9, the load component type is determined based on the impact coefficient and reactive power change after the load is put into operation, including: If the impact coefficient is greater than 10, the load component type is determined to be a motor; If the impact coefficient is greater than 2 and the impact coefficient is less than 10, the input load component type is determined to be a power electronic interface load; If the impact coefficient is less than 2, further judgment is made based on the reactive power change, where: If the reactive power change is less than -1, the load component type is a power electronic interface load, otherwise it is determined to be a static load.

9. A computer system, characterized in that: include: one or more processors; Memory, storing instructions that can be operated; Wherein, when the instruction is executed by one or more processors, the aforementioned one or more processors perform operations, wherein the operations include the process of executing the non-intrusive power load component identification method based on event detection as described in any one of claims 1-8.

10. A computer-readable storage medium for storing one or more programs, characterized in that: The one or more programs include instructions or instruction sets that can be executed by one or more processors; Wherein, when the instructions or instruction sets are executed by one or more processors, the process of the non-intrusive power load component identification method based on event detection as described in any one of claims 1-8 is executed.

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