A real-time monitoring and control method and system for energy storage power in a substation

Abstract

The present invention relates to the field of monitoring and control technology, and specifically to a real-time monitoring and control method and system for energy storage power in a substation. The method includes: obtaining a characteristic difference characterization value and a noise continuation degree characterization value of the current energy storage power; adjusting a preset measurement noise parameter according to the characteristic difference characterization value and the noise continuation degree characterization value, obtaining the target measurement noise parameter of each substation at the current monitoring moment, filtering the current energy storage power of each substation at the current monitoring moment according to a Kalman filter and the target measurement noise parameter, obtaining the filtered data of each substation at the current monitoring moment, and regulating the power of the energy storage equipment of the corresponding substation according to the filtered data of each substation at the current monitoring moment. And the present invention can reliably regulate the power of the energy storage equipment installed in the substation.

Description

Technical Field

[0001] The present invention relates to the field of monitoring and control technology, and in particular to a real-time monitoring and control method and system for energy storage power in a substation. Background Art

[0002] Substation energy storage refers to the storage and release of electric energy by installing energy storage devices (such as batteries, supercapacitors, etc.) in the distribution network to balance the load of the power grid, ensure the reliability of power supply and improve the stability of power grid operation. This type of energy storage device is usually installed in the power distribution station or substation where the distribution transformer is located to cope with instantaneous load fluctuations and peak load demands in the distribution network and improve the stability and response speed of the power grid; and because real-time monitoring and control of the power of energy storage devices installed in the substation during the operation of the power grid is an important means to balance the load of the power grid, ensure the reliability of power supply and improve the stability of the power grid operation, it is very important to monitor and control the power of energy storage devices installed in the substation in real time.

[0003] In order to accurately and reliably monitor and control the power of the energy storage equipment installed in the substation in the prior art, the Kalman filter is usually used to filter the collected power of the energy storage equipment, and then the power of the energy storage equipment installed in the substation is controlled based on the filtered data; however, in the prior art, when the Kalman filter is used to filter the collected power of the energy storage equipment, the measurement noise parameter of the Kalman filter is generally fixed, and if a higher measurement noise parameter is set during filtering, the filtering result will be too dependent on the predicted value, that is, if a higher measurement noise parameter is set, although the filtering effect is better when encountering stronger noise, it will cause some normal monitoring data to be distorted, and if a lower measurement noise parameter is set, it will cause excessive sensitivity to noise data, that is, if a lower measurement noise parameter is set, Measuring noise parameters will make it impossible to filter out strong noisy data. Therefore, when using a Kalman filter to filter the collected power of the energy storage device, if the measurement noise parameters are fixed, the filtering results will be unreliable or inaccurate, that is, when data filtering is based on fixed measurement noise parameters, the filtering effect will be poor. When the filtering effect is poor, it will lead to unreliable control when the power of the energy storage device installed in the substation is controlled based on the filtered data. Therefore, in order to ensure the reliability of power control of the energy storage device installed in the substation, when using the Kalman filter to filter the collected power of the energy storage device, how to adaptively obtain the measurement noise parameters to ensure the reliability and accuracy of the filtering results has become an urgent problem to be solved. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a method and system for real-time monitoring and control of energy storage power in a substation area. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for real-time monitoring and control of energy storage power in a substation, comprising the following steps:

[0006] At the current monitoring moment, obtain the current energy storage power and historical energy storage power sequence corresponding to each substation, and obtain the collection mark value of each historical energy storage power in the historical energy storage power sequence, where the energy storage power refers to the power of the energy storage device installed in the corresponding substation, and the substations are all located within the control range of the same power grid;

[0007] Obtaining abnormal characterization values ​​of each historical energy storage power according to the neighborhood historical energy storage power of each historical energy storage power in the historical energy storage power sequence, and obtaining abnormal historical energy storage power in the historical energy storage power sequence according to the abnormal characterization values;

[0008] In the historical energy storage power sequence, the nearest neighbor historical energy storage power of the current energy storage power is obtained, and the predicted energy storage power of each substation at the current monitoring time is obtained by using the prediction model; according to the nearest neighbor historical energy storage power of the current energy storage power, the abnormal characterization value of the nearest neighbor historical energy storage power and the predicted energy storage power at the current monitoring time, the characteristic difference characterization value of the current energy storage power is obtained;

[0009] Obtaining a noise continuation degree characterization value of the current energy storage power according to the collection mark values ​​of each historical energy storage power, the abnormal historical energy storage power in the historical energy storage power sequence, and the abnormal characterization value of the historical energy storage power;

[0010] According to the characteristic difference characterization value and the noise continuity degree characterization value, the preset measurement noise parameters are adjusted to obtain the target measurement noise parameters of each substation at the current monitoring time. According to the Kalman filter and the target measurement noise parameters, the current energy storage power of each substation at the current monitoring time is filtered to obtain the filtered data of each substation at the current monitoring time, and the power of the energy storage equipment of the corresponding substation is regulated according to the filtered data of each substation at the current monitoring time.

[0011] In a second aspect, an embodiment of the present invention provides a system for real-time monitoring and control of energy storage power in a substation, including a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the above-mentioned method for real-time monitoring and control of energy storage power in a substation.

[0012] Beneficial effect: The present invention first obtains the current energy storage power and the historical energy storage power sequence corresponding to each substation and the collection mark value of each historical energy storage power in the historical energy storage power sequence; then obtains the abnormal characterization value of each historical energy storage power according to the neighborhood historical energy storage power of each historical energy storage power in the historical energy storage power sequence, and obtains the abnormal historical energy storage power in the historical energy storage power sequence according to the abnormal characterization value; secondly, obtains the nearest neighbor historical energy storage power of the current energy storage power, and uses the prediction model to obtain the predicted energy storage power of each substation at the current monitoring time, and obtains the current energy storage power according to the nearest neighbor historical energy storage power of the current energy storage power, the abnormal characterization value of the nearest neighbor historical energy storage power and the predicted energy storage power at the current monitoring time. The characteristic difference characterization value of the previous energy storage power; then, according to the collection mark value of each historical energy storage power, the abnormal historical energy storage power in the historical energy storage power sequence, and the abnormal characterization value of the historical energy storage power, the noise continuation degree characterization value of the current energy storage power is obtained; finally, according to the characteristic difference characterization value and the noise continuation degree characterization value, the preset measurement noise parameter is adjusted to obtain the target measurement noise parameter of each substation at the current monitoring time, and according to the Kalman filter and the target measurement noise parameter, the current energy storage power of each substation at the current monitoring time is filtered to obtain the filtering data of each substation at the current monitoring time, and the power of the energy storage equipment of the corresponding substation is regulated according to the filtering data of each substation at the current monitoring time. And the present invention can improve the filtering effect according to the adjusted measurement noise parameter, that is, it can improve the reliability and accuracy of the filtering, and can reliably regulate the power of the energy storage equipment installed in the substation based on the more reliable and accurate filtering results. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 The present invention is a flow chart of a method for real-time monitoring and control of energy storage power in a substation. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the embodiments of the present invention.

[0016] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0017] This embodiment provides a method for real-time monitoring and control of energy storage power in a substation area, which is described in detail as follows:

[0018] like Figure 1 As shown, the real-time monitoring and control method of the energy storage power in the substation area includes the following steps:

[0019] Step S001: at the current monitoring moment, obtain the current energy storage power and the historical energy storage power sequence corresponding to each substation, and obtain the collection mark value of each historical energy storage power in the historical energy storage power sequence.

[0020] Currently, in order to accurately and reliably monitor and control the power of the energy storage equipment installed in the substation in real time, a Kalman filter is usually used to filter the collected power of the energy storage equipment, and then the power of the energy storage equipment installed in the substation is controlled based on the filtered data; however, when the Kalman filter is used to filter the collected power of the energy storage equipment, the measurement noise parameter of the Kalman filter is generally fixed, and if a higher measurement noise parameter is set during filtering, the filtering result will be too dependent on the predicted value, that is, if a higher measurement noise parameter is set, although the filtering effect is better when encountering stronger noise, it will cause some normal monitoring data to be distorted, and if a lower measurement noise parameter is set, it will cause excessive sensitivity to noise data, that is, if a lower measurement noise parameter is set, it will be impossible to convert stronger noise into stronger noise. Strong noisy data is filtered out. Therefore, when using the Kalman filter to filter the collected power of the energy storage device, if the measurement noise parameters are fixed, the filtering results may be unreliable or inaccurate, that is, when data filtering is performed based on fixed measurement noise parameters, the filtering effect may be poor. When the filtering effect is poor, when the power of the energy storage device installed in the substation is regulated based on the filtered data, unreliable regulation may occur. In order to ensure the reliability of power regulation of the energy storage device installed in the substation, this embodiment requires adaptive acquisition of the measurement noise parameters of the Kalman filter when using the Kalman filter to filter the collected power of the energy storage device. That is, the measurement noise parameters need to be adaptively adjusted to ensure the reliability and accuracy of the filtering results.

[0021] Through the above analysis, it can be seen that the main purpose of this embodiment is to perform real-time adaptive adjustment on the measurement noise parameters of the Kalman filter, perform data filtering based on the adjusted parameters, and then monitor and control the power of the energy storage equipment in the power grid substation in real time based on the filtered data. The measurement noise parameters are also called measurement noise covariance matrix, and this embodiment only adjusts the measurement noise parameters, and does not adjust other parameters of the Kalman filter; in addition, this embodiment is mainly described by taking the power control method of the energy storage equipment in all substations of any power grid as an example, that is, all substations appearing in this embodiment refer to substations located within the control range of the power grid.

[0022] Since the present embodiment monitors and controls the power of the energy storage equipment in the substation in real time, the present embodiment first uses the acquisition equipment or sensor to obtain the power of the energy storage equipment in each substation at the current moment, and records it as the current energy storage power corresponding to the corresponding substation; and the present embodiment subsequently needs to analyze the characteristic difference characterization value and the noise continuity degree characterization value of the current energy storage power through historical data, and the characteristic difference characterization value and the noise continuity degree characterization value are the basis for the subsequent determination of the adjustment degree. Therefore, the present embodiment also needs to obtain the historical energy storage power sequence corresponding to each substation at the current moment. The specific acquisition process is: first obtain the historical monitoring time period, and at each historical monitoring moment in the historical monitoring time period, The power of the energy storage equipment in each substation is obtained and recorded as the historical energy storage power of the substation. Then, the time series constructed by all the historical energy storage powers of the same substation obtained in the historical monitoring time period is recorded as the historical energy storage power sequence corresponding to the corresponding substation. In addition, in specific applications, the implementer needs to set the historical monitoring time period and the time interval between two adjacent monitoring moments according to actual conditions. For example, in this embodiment, one hour before the current monitoring moment can be used as the historical monitoring time period, that is, the last monitoring moment in the historical monitoring time period is adjacent to the current monitoring moment. In this embodiment, the time interval between two adjacent monitoring moments can be set to 1 second, and the collected power is generally transmitted to the central control system.

[0023] Furthermore, since the noise continuity degree characterization value of the current energy storage power will be reflected later by analyzing the correlation between data located in different historical energy storage power sequences but collected at the same time, this embodiment needs to obtain the collection mark value of the historical energy storage power according to the position of each historical energy storage power in the historical energy storage power sequence corresponding to each substation, that is, the collection mark value of the ath historical energy storage power in each historical energy storage power sequence is a.

[0024] Therefore, this embodiment can obtain the current energy storage power and the historical energy storage power sequence corresponding to each substation and the collection mark value of each historical energy storage power in the historical energy storage power sequence through the above process.

[0025] Step S002: obtaining an abnormal characterization value of each historical energy storage power according to the neighborhood historical energy storage power of each historical energy storage power in the historical energy storage power sequence, and obtaining an abnormal historical energy storage power in the historical energy storage power sequence according to the abnormal characterization value.

[0026] The subsequent embodiment of the present invention is based on the characteristic difference characterization value of the current energy storage power and the noise continuity degree characterization value to adaptively adjust the measurement noise parameter. When the characteristic difference characterization value and the noise continuity degree characterization value are subsequently obtained, it is necessary to refer to the abnormal characterization value of the historical energy storage power and the abnormal historical energy storage power in the historical energy storage power sequence. Therefore, the present embodiment will first obtain the abnormal characterization value of each historical energy storage power according to the neighborhood historical energy storage power of each historical energy storage power in the historical energy storage power sequence. In addition, for ease of understanding, the specific acquisition process of the abnormal characterization value of any historical energy storage power b in any historical energy storage power sequence B will be described as an example, that is, the specific acquisition process of the abnormal characterization value of the historical energy storage power b is:

[0027] First, in the historical energy storage power sequence B, all historical energy storage powers within the preset neighborhood range of the historical energy storage power b are obtained, and the sequence constructed by all historical energy storage powers within the preset neighborhood range of the historical energy storage power b is recorded as the neighborhood sequence of the historical energy storage power b; then, the normalized value of the absolute value of the difference between the historical energy storage power b and the mean of the neighborhood sequence is obtained, and recorded as the first indicator value of the historical energy storage power b. In specific applications, the implementer needs to determine what the historical energy storage power within the preset neighborhood range of the historical energy storage power b specifically refers to according to actual conditions. If the historical energy storage power b is the hth historical energy storage power in the historical energy storage power sequence B, then in this embodiment, all data between the hHth historical energy storage power and the h-1th historical energy storage power in the historical energy storage power sequence B and all data between the h+Hth historical energy storage power and the h+1th historical energy storage power in the historical energy storage power sequence B are recorded as the historical energy storage power within the preset neighborhood range of the historical energy storage power b, and include the hHth historical energy storage power, the h-1th historical energy storage power, the h+ H historical energy storage powers and the h+1th historical energy storage power, and if the number of historical energy storage powers on the left side of the hth historical energy storage power is less than 20, all historical energy storage powers on the left side of the hth historical energy storage power are recorded as historical energy storage powers within a preset neighborhood range of the historical energy storage power b; if the number of historical energy storage powers on the right side of the hth historical energy storage power is less than 20, all historical energy storage powers on the right side of the hth historical energy storage power are recorded as historical energy storage powers within a preset neighborhood range of the historical energy storage power b; in addition, H is a preset second number. In specific applications, the implementer needs to set the value of H according to actual conditions, such as 20 in this embodiment.

[0028] Then, a second indicator value of the historical energy storage power b is obtained, and the process of obtaining the second indicator value of the historical energy storage power b is as follows: if the historical energy storage power b belongs to the first historical energy storage data in the historical energy storage power sequence B, the historical energy storage power on the right side of the historical energy storage power b is obtained, and the normalized value of the absolute value of the difference between the historical energy storage power b and the historical energy storage power on the right side is recorded as the second indicator value of the historical energy storage power b; if the historical energy storage power b belongs to the last historical energy storage data in the historical energy storage power sequence B, the historical energy storage power on the left side of the historical energy storage power b is obtained, and the normalized value of the absolute value of the difference between the historical energy storage power b and the historical energy storage power on the left side is recorded as the second indicator value of the historical energy storage power b; if the historical energy storage power b does not belong to the first historical energy storage data and the last historical energy storage data in the historical energy storage power sequence B, the historical energy storage power b and the historical energy storage power on the right side are normalized. The absolute value of the difference between the historical energy storage power b and the historical energy storage power on the left is recorded as the right difference, the absolute value of the difference between the historical energy storage power b and the historical energy storage power on the left is recorded as the left difference, and the normalized value of the result obtained by adding the right difference and the left difference is recorded as the second index value of the historical energy storage power b; and the right historical energy storage power of the historical energy storage power b refers to the data located on the right side of the historical energy storage power b and adjacent to the historical energy storage power b, and the left historical energy storage power of the historical energy storage power b refers to the data located on the left side of the historical energy storage power b and adjacent to the historical energy storage power b, and the right historical energy storage power and the left historical energy storage power are both located in the historical energy storage power sequence B; finally, the sum of the first index value of the historical energy storage power b and the second index value of the historical energy storage power b is recorded as the abnormal characterization value of the historical energy storage power b; and in this embodiment, the normalized values ​​of the above data are all obtained through the normalization function norm().

[0029] In addition, the specific expression for obtaining the abnormal characterization value of the historical energy storage power b is:

[0030]

[0031] in, is the abnormal characterization value of the historical energy storage power b, norm() is the normalization function, is the value of the historical energy storage power b, F0 is the mean of the neighborhood sequence of the historical energy storage power b, is the second indicator value of the historical energy storage power b, It is the first indicator value of the historical energy storage power b.

[0032] And when and The bigger the The larger the value of The larger the value of is, the greater the probability that the historical energy storage power b is abnormal data. This abnormal data may be caused by grid abnormality, grid load change or environmental interference. On the contrary, when The smaller the value of , the smaller the probability that the historical energy storage power b is abnormal data. This calculation process cannot accurately determine that the data is noise, but can only characterize that the difference between the power and the neighborhood data is large. However, the historical energy storage power with a larger abnormal characterization value is more likely to be noise data than the historical energy storage power with a smaller abnormal characterization value. Therefore, the abnormal characterization value obtained above and the abnormal historical energy storage power obtained subsequently can only be used as reference data for the subsequent process of obtaining the characteristic difference characterization value and the noise continuation degree characterization value.

[0033] Therefore, through the above process, this embodiment can obtain the abnormal characterization value of each historical energy storage power, and after obtaining the abnormal characterization value of the historical energy storage power, the abnormal historical energy storage power in the historical energy storage power sequence is obtained according to each historical abnormal characterization value in the historical energy storage power sequence. The specific acquisition process is:

[0034] For the historical energy storage power b in the historical energy storage power sequence B, determine whether the normalized value of the abnormal characterization value of the historical energy storage power b is not less than the preset judgment threshold. If so, it indicates that the historical energy storage power b is abnormal data, and then the historical energy storage power b can be determined to be an abnormal historical energy storage power; and the normalized value of the abnormal characterization value of the historical energy storage power b refers to the ratio of the abnormal characterization value of the historical energy storage power b to a preset second constant, and the preset second constant is required to be greater than or equal to 2. For example, in this embodiment, the preset second constant can be set to 2.

[0035] In addition, in a specific application, the implementer also needs to set a preset judgment threshold according to the actual situation. For example, in this embodiment, the preset judgment threshold can be set to 0.8.

[0036] Therefore, this embodiment can obtain all abnormal historical energy storage powers in the historical energy storage power sequence according to the above process.

[0037] Step S003, in the historical energy storage power sequence, obtain the nearest historical energy storage power of the current energy storage power, and use the prediction model to obtain the predicted energy storage power of each substation at the current monitoring time; according to the nearest historical energy storage power of the current energy storage power, the abnormal characterization value of the nearest historical energy storage power and the predicted energy storage power at the current monitoring time, obtain the characteristic difference characterization value of the current energy storage power.

[0038] Next, the nearest neighbor historical energy storage power of the current energy storage power corresponding to each substation is obtained. The nearest neighbor historical energy storage power is mainly used to obtain the subsequent characteristic difference representation value. Therefore, the specific acquisition process of the nearest neighbor historical energy storage power of the current energy storage power is: for any substation, the last historical energy storage power in the historical energy storage power sequence corresponding to the substation is used as the nearest neighbor historical energy storage power of the current energy storage power corresponding to the substation.

[0039] After obtaining the nearest historical energy storage power of the current energy storage power, the prediction model is used to obtain the predicted energy storage power of each substation at the current monitoring time. In specific applications, the implementer can select a suitable prediction model to obtain the predicted energy storage power according to the actual situation. For example, the Kalman filter prediction model can be used to obtain the predicted energy storage power of each substation at the current monitoring time. The specific process of using the prediction model to obtain the predicted energy storage power is a well-known technology, so this embodiment will not be described in detail.

[0040] Then, according to the nearest historical energy storage power of the current energy storage power corresponding to each substation, the abnormal characterization value of the nearest historical energy storage power and the predicted energy storage power at the current monitoring time, the characteristic difference characterization value of the current energy storage power corresponding to each substation is obtained. The characteristic difference characterization value can more accurately reflect the possibility that the current energy storage power is noise data. For ease of understanding, this embodiment takes the acquisition process of the characteristic difference characterization value of the current energy storage power corresponding to any substation C as an example to describe, that is, the acquisition process of the characteristic difference characterization value of the current energy storage power corresponding to substation C is:

[0041] First, the current energy storage power corresponding to the substation C is recorded as the power to be analyzed, and the inverse of the result obtained by adding the abnormal characterization value of the nearest historical energy storage power of the power to be analyzed to the preset first constant is recorded as the first inverse value, that is, the first inverse value is , E is the abnormal characterization value of the nearest historical energy storage power of the power to be analyzed, c1 is a preset first constant, and in this embodiment, the value of c1 is 1; then the absolute value of the difference between the power to be analyzed and the nearest historical energy storage power of the power to be analyzed is obtained, and recorded as the first difference, and the product of the first difference and the first reciprocal value is obtained, and recorded as the first product value; then the absolute value of the difference between the power to be analyzed and the predicted energy storage power of the substation C at the current monitoring time is obtained, and recorded as the second difference; finally, the sum of the first product value and the second difference is used as the characteristic difference characterization value of the current energy storage power corresponding to the substation C.

[0042] And on the basis that the abnormal characterization value of the nearest historical energy storage power of the power to be analyzed is smaller, the larger the value of the first difference is, the more likely it is that the current energy storage power corresponding to the substation C is noise data; the larger the value of the second difference is, the more likely it is that the current energy storage power corresponding to the substation C is noise data, and when the abnormal characterization value of the nearest historical energy storage power of the power to be analyzed is smaller, the greater the reference value of the first difference is; it can be seen that when the first inverse value and the first difference are larger and the second difference is larger, the characteristic difference characterization value of the current energy storage power corresponding to the substation C is larger, and the larger the characteristic difference characterization value of the current energy storage power corresponding to the substation C is, the stronger the degree to which the current energy storage power corresponding to the substation C is noise data, that is, the stronger or more obvious the degree to which the current energy storage power is noise. In addition, considering that the energy storage power may be in a stage of rapid rise or fall, the predicted energy storage power of the substation at the current monitoring moment is introduced when calculating the characteristic difference characterization value.

[0043] Therefore, this embodiment can obtain the characteristic difference representation value of the current energy storage power corresponding to each substation through the above process.

[0044] Step S004: obtaining a noise continuation degree characterization value of the current energy storage power according to the collection mark values ​​of the various historical energy storage powers, the abnormal historical energy storage powers in the historical energy storage power sequence, and the abnormal characterization value of the historical energy storage powers.

[0045] Moreover, adjusting the measurement noise parameters only based on the characteristic difference characterization value has limitations. In order to adjust the measurement noise parameters more reliably, the present embodiment obtains the noise continuity degree characterization value of the current energy storage power through further analysis, and then adjusts the measurement noise parameters in combination with the characteristic difference characterization value and the noise continuity degree characterization value. Moreover, the noise continuity degree characterization value is mainly obtained by analyzing the correlation characteristics between multiple substations, and the noise continuity degree characterization value can reflect whether a stronger noise distribution will appear in the future. If the probability of a stronger noise distribution appearing in the future is relatively large, the trust in the predicted value obtained by the Kalman filter based on historical data should be higher, and the trust in the real-time monitoring data should be lower. Therefore, it can be seen that if a stronger noise distribution appears in the future, the measurement noise parameters should be increased, that is, the larger the noise continuity degree characterization value, the higher the value should be adjusted. The higher the measurement noise parameter, the lower the trust in the real-time measurement data, that is, the more the Kalman filter relies on the predicted data, which can reduce the interference of noise on the data filtering process, reduce the filtering error caused by noise, and improve the filtering effect; in addition, when the degree of environmental interference gradually increases, such as the increase in the temperature of the substation and the increase in electromagnetic interference from other equipment in the substation, the noise of the subsequent real-time collected data is more likely to be higher, that is, the increase in the temperature of the substation and the increase in electromagnetic interference from other equipment in the substation will lead to a higher probability of stronger noise distribution in the future, and when affected by internal abnormalities in the power grid, it will cause abnormal power data of multiple substations connected to the power grid. The substation is usually located in a protective device with a certain degree of closure. Environmental interference such as temperature or electromagnetic equipment interference is mostly only for a single substation. Therefore, the correlation between abnormal data in multiple substations caused by environmental interference is poor.

[0046] Based on the above analysis, this embodiment will obtain the noise continuity degree characterization value of the current energy storage power corresponding to each substation according to the collection mark value of each historical energy storage power in the historical energy storage power sequence, the abnormal historical energy storage power in the historical energy storage power sequence, and the abnormal characterization value of each historical energy storage power in the historical energy storage power sequence; and the specific acquisition process of the noise continuity degree characterization value of the current energy storage power corresponding to each substation is:

[0047] First, the associated characterization value of each abnormal historical energy storage power in the historical energy storage power sequence is obtained, and then, based on the associated characterization value of the abnormal historical energy storage power that has been obtained, the noise continuity degree characterization value of the current energy storage power corresponding to each substation is obtained. For ease of understanding, this embodiment takes the acquisition process of the noise continuity degree characterization value of the current energy storage power corresponding to substation C as an example for description, that is, the acquisition process of the noise continuity degree characterization value of the current energy storage power corresponding to substation C is:

[0048] Starting from the last historical energy storage power in the historical energy storage power sequence corresponding to substation C, a preset first number of abnormal historical energy storage powers are continuously obtained, and the obtained preset first number of abnormal historical energy storage powers are sorted in sequence according to the acquisition order, and the sequence obtained by sorting is recorded as the first sequence to be analyzed. In this embodiment, the preset first number is set to 10, then the first sequence to be analyzed is composed of the last 10 abnormal historical energy storage powers in the historical energy storage power sequence, but the data in the first sequence to be analyzed is arranged in reverse order of acquisition time.

[0049] Then, the neighborhood difference of each abnormal historical energy storage power in the first sequence to be analyzed is obtained, and in the first sequence to be analyzed, the abnormal historical energy storage power whose neighborhood difference is greater than the preset difference threshold is obtained, and they are all recorded as characteristic powers; and the neighborhood difference of the i-th abnormal historical energy storage power in the first sequence to be analyzed is the result obtained by subtracting the i-th abnormal historical energy storage power from the i-1-th abnormal historical energy storage power in the first sequence to be analyzed.

[0050] Then, the accumulated values ​​of each characteristic power in the first sequence to be analyzed are obtained, and the accumulated value of the j-th characteristic power in the first sequence to be analyzed is the result obtained by multiplying the neighborhood difference value of the j-th characteristic power, the distance weight value of the j-th characteristic power, and the associated characterization value of the j-th characteristic power, and the distance weight value of the j-th characteristic power is , J is the total amount of energy storage power in the historical energy storage power sequence corresponding to the substation C, is the collected eigenvalue of the jth eigenpower.

[0051] Finally, the result obtained by accumulating the to-be-accumulated values ​​of all characteristic powers in the first sequence to be analyzed is recorded as the noise continuity degree characterization value of the current energy storage power corresponding to the substation C.

[0052] In a specific application, the implementer needs to set the preset difference threshold according to the actual situation. For example, in this embodiment, the preset difference threshold can be set to 0.

[0053] In addition, the specific calculation expression of the noise continuity value of the current energy storage power corresponding to the substation C is:

[0054]

[0055] Among them, Y is the noise continuity value of the current energy storage power corresponding to the station C, N is the number of characteristic powers in the first sequence to be analyzed, is the associated representation value of the jth characteristic power in the first sequence to be analyzed, is the distance weight value of the jth characteristic power, and the closer to the current monitoring time, the larger the distance weight value of the jth characteristic power. is the neighborhood difference of the j-th eigenpower.

[0056] And when , as well as When it is larger, it indicates that the noise continuity value of the current energy storage power corresponding to the substation C is larger, and the larger the noise continuity value of the current energy storage power corresponding to the substation C is, the more it indicates that the current energy storage power corresponding to the substation C is more consistent with the characteristics of the noise generated by the environmental interference, indicating that the noise continuity of the substation C at the current monitoring time is stronger, and the stronger the noise continuity, the greater the probability of a stronger noise distribution in the future. On the contrary, when , as well as The smaller the time, the smaller the noise continuity representation value of the current energy storage power corresponding to area C is, and the smaller the noise continuity representation value of the current energy storage power corresponding to area C is, the less it indicates that the current energy storage power corresponding to area C does not conform to the characteristics of the noise generated by environmental interference, indicating that the noise continuity of area C is weaker at the current monitoring moment, and the weaker the noise continuity, the smaller the probability of stronger noise distribution in the future.

[0057] In this embodiment, the specific process of obtaining the associated characterization value of each abnormal historical energy storage power in the historical energy storage power sequence is as follows: for the f-th abnormal historical energy storage power in any historical energy storage power sequence B: in all historical energy storage power sequences except the historical energy storage power sequence B, all historical energy storage powers with the same collection mark value as the f-th abnormal historical energy storage power are obtained, and the set constructed by all the historical energy storage powers with the same collection mark value as the f-th abnormal historical energy storage power is recorded as the first set of the f-th abnormal historical energy storage power, and then the characteristic difference corresponding to each historical energy storage power in the first set is obtained, and the first set is recorded as the first set of the f-th abnormal historical energy storage power. The cumulative sum of the characteristic difference values ​​corresponding to all historical energy storage powers in the set is recorded as the associated characterization value of the f-th abnormal historical energy storage power, and the characteristic difference value corresponding to the g-th historical energy storage power in the first set is the absolute value of the difference between the abnormal characterization value of the g-th historical energy storage power and the abnormal characterization value of the f-th abnormal historical energy storage power. The larger the associated characterization value of the f-th abnormal historical energy storage power is, the smaller the correlation between the substation corresponding to the f-th abnormal historical energy storage power and other substations at the time of collecting the f-th abnormal historical energy storage power, that is, the f-th abnormal historical energy storage power is more likely to be noise data caused by environmental interference, that is, it is less likely to be caused by power grid abnormality.

[0058] Therefore, this embodiment can obtain the noise continuity degree characterization value of the current energy storage power corresponding to each substation according to the above process.

[0059] Step S005, according to the characteristic difference characterization value and the noise continuity degree characterization value, the preset measurement noise parameters are adjusted to obtain the target measurement noise parameters of each substation at the current monitoring time, and according to the Kalman filter and the target measurement noise parameters, the current energy storage power of each substation at the current monitoring time is filtered to obtain the filtered data of each substation at the current monitoring time, and the power of the energy storage equipment of the corresponding substation is regulated according to the filtered data of each substation at the current monitoring time.

[0060] After obtaining the characteristic difference representation value and the noise continuity degree representation value of the current energy storage power corresponding to each substation, the preset measurement noise parameter is obtained, and the preset measurement noise parameter is a parameter determined based on the traditional measurement noise parameter acquisition method in the Kalman filter. In this embodiment, each substation at the current monitoring moment corresponds to only one current energy storage power. Based on the principle that the measurement noise parameter is generally a constant when the number of observation values ​​is one, the measurement noise parameter in this embodiment is a constant. Therefore, in this embodiment, the preset measurement noise parameter can be set to an empirical value, such as 300.

[0061] Then, according to the characteristic difference characterization value and noise continuity characterization value of the current energy storage power corresponding to each substation, the preset measurement noise parameters are adjusted to obtain the target measurement noise parameters of each substation at the current monitoring time. The basis for the adjustment is that when the characteristic difference characterization value and the noise continuity characterization value are larger, the trust in the predicted value obtained by the Kalman filter based on historical data should be higher, and the trust in the real-time monitoring data should be lower. Therefore, it can be seen that if the characteristic difference characterization value and the noise continuity characterization value are larger, the measurement noise parameter should be increased, and the larger the characteristic difference characterization value and the noise continuity characterization value, the greater the adjustment degree, that is, the higher the measurement noise parameter, the lower the trust in the real-time measurement data, and the more the Kalman filter relies on the predicted data, so as to reduce the interference of noise on the data filtering process and reduce the filtering error caused by noise, that is, to improve the filtering effect; then the specific acquisition process of the target measurement noise parameter of each substation at the current monitoring time is:

[0062] For any substation C: first, the normalized value of the result obtained by multiplying the characteristic difference characterization value of the current energy storage power corresponding to substation C and the noise continuity degree characterization value of the current energy storage power corresponding to substation C is recorded as the target characterization value of the current energy storage power corresponding to substation C. At this time, the normalization function norm() is also used to normalize the data; then the preset adjustment coefficient is obtained, and the preset adjustment coefficient is multiplied by the target characterization value of the current energy storage power corresponding to substation C, and then the result is added with the preset first constant, which is recorded as the adjustment value of substation C at the current monitoring time, and the product of the adjustment value of substation C at the current monitoring time and the preset measurement noise parameter is obtained, and recorded as the target measurement noise parameter of substation C at the current monitoring time; and in specific applications, the implementer needs to set the preset adjustment coefficient according to the actual situation. The preset adjustment coefficient controls the adjustment ratio and can prevent over-adjustment. For example, in this embodiment, it can be set to 0.5, then the target measurement noise parameter of substation C at the current monitoring time is , R is the preset measurement noise parameter, W is the target representation value of the current energy storage power corresponding to the substation C, and when W is larger, it indicates that a higher measurement noise parameter is needed at this time, so that the noise trust in the measurement value is lower, the Kalman filter relies more on model prediction, reduces the sensitivity to the measurement value, and reduces the filtering error caused by noise. On the contrary, when W is smaller, a lower measurement noise parameter is needed.

[0063] After obtaining the target measurement noise parameters of each area at the current monitoring time, the current energy storage power of each area at the current monitoring time is filtered according to the Kalman filter and the target measurement noise parameters of each area at the current monitoring time to obtain the filtered data of each area at the current monitoring time. The specific process of obtaining the filtered data of each area at the current monitoring time is:

[0064] For any substation C, a Kalman filter is constructed whose measurement noise parameter is the target measurement noise parameter of substation C at the current monitoring time, and is recorded as the Kalman filter of substation C at the current monitoring time. Then, the Kalman filter of substation C at the current monitoring time is used to filter the current energy storage power of substation C at the current monitoring time, and the data obtained after filtering is recorded as the filtered data of substation C at the current monitoring time. The process of filtering using the Kalman filter is a well-known technology, so this embodiment will not be described in detail.

[0065] Therefore, this embodiment can obtain the filtering data of each substation at the current monitoring time through the above process, and then adjust the power of the energy storage equipment of the corresponding substation according to the filtering data of each substation at the current monitoring time. In this embodiment, the filtering data of each substation at the current monitoring time will be transmitted to the remote data processing terminal in real time, and the power regulation process of the energy storage equipment of the corresponding substation will be completed according to the received filtering data of each substation at the current monitoring time. The specific process of how to regulate according to the received data is well known, so it will not be described in detail. For example, the power of the energy storage equipment can be adjusted to achieve the purpose of peak shaving and valley filling. In addition, the measurement noise parameters to be regulated in this embodiment will not change, that is, this embodiment has always adjusted the preset measurement noise parameters.

[0066] A real-time monitoring and control system for energy storage power in a substation of this embodiment includes a memory and a processor, and the processor executes a computer program stored in the memory to implement the above-mentioned real-time monitoring and control method for energy storage power in a substation.

[0067] To summarize, this embodiment first obtains the current energy storage power and historical energy storage power sequence corresponding to each substation and the collection mark value of each historical energy storage power in the historical energy storage power sequence; then, according to the neighborhood historical energy storage power of each historical energy storage power in the historical energy storage power sequence, the abnormal characterization value of each historical energy storage power is obtained, and according to the abnormal characterization value, the abnormal historical energy storage power in the historical energy storage power sequence is obtained; secondly, the nearest neighbor historical energy storage power of the current energy storage power is obtained, and the predicted energy storage power of each substation at the current monitoring time is obtained by using the prediction model, and the current energy storage power is obtained according to the nearest neighbor historical energy storage power of the current energy storage power, the abnormal characterization value of the nearest neighbor historical energy storage power and the predicted energy storage power at the current monitoring time. The characteristic difference characterization value of the energy storage power; then, according to the collection mark value of each historical energy storage power, the abnormal historical energy storage power in the historical energy storage power sequence, and the abnormal characterization value of the historical energy storage power, the noise continuity degree characterization value of the current energy storage power is obtained; finally, according to the characteristic difference characterization value and the noise continuity degree characterization value, the preset measurement noise parameter is adjusted to obtain the target measurement noise parameter of each substation at the current monitoring time, and according to the Kalman filter and the target measurement noise parameter, the current energy storage power of each substation at the current monitoring time is filtered to obtain the filtering data of each substation at the current monitoring time, and the power of the energy storage equipment of the corresponding substation is regulated according to the filtering data of each substation at the current monitoring time. And this embodiment can improve the filtering effect according to the adjusted measurement noise parameter, that is, it can improve the reliability and accuracy of the filtering result, and can reliably regulate the power of the energy storage equipment installed in the substation based on the more reliable and accurate filtering result.

[0068] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for real-time monitoring and control of energy storage power in a substation, characterized in that: The method comprises the following steps: At the current monitoring moment, obtain the current energy storage power and historical energy storage power sequence corresponding to each substation, and obtain the collection mark value of each historical energy storage power in the historical energy storage power sequence, where the energy storage power refers to the power of the energy storage device installed in the corresponding substation, and the substations are all located within the control range of the same power grid; Obtaining abnormal characterization values ​​of each historical energy storage power according to the neighborhood historical energy storage power of each historical energy storage power in the historical energy storage power sequence, and obtaining abnormal historical energy storage power in the historical energy storage power sequence according to the abnormal characterization values; In the historical energy storage power sequence, the nearest neighbor historical energy storage power of the current energy storage power is obtained, and the predicted energy storage power of each substation at the current monitoring time is obtained by using the prediction model; according to the nearest neighbor historical energy storage power of the current energy storage power, the abnormal characterization value of the nearest neighbor historical energy storage power of the current energy storage power and the predicted energy storage power at the current monitoring time, the characteristic difference characterization value of the current energy storage power is obtained; Obtaining a noise continuation degree characterization value of the current energy storage power according to the collection mark values ​​of each historical energy storage power, the abnormal historical energy storage power in the historical energy storage power sequence, and the abnormal characterization value of the historical energy storage power; According to the characteristic difference characterization value and the noise continuity degree characterization value, the preset measurement noise parameter is adjusted to obtain the target measurement noise parameter of each substation at the current monitoring time, and according to the Kalman filter and the target measurement noise parameter, the current energy storage power of each substation at the current monitoring time is filtered to obtain the filtering data of each substation at the current monitoring time, and the power of the energy storage equipment of the corresponding substation is regulated according to the filtering data of each substation at the current monitoring time; The method for obtaining the characteristic difference characterization value of the current energy storage power includes: for any substation C, recording the current energy storage power corresponding to the substation C as the power to be analyzed, and recording the inverse of the result obtained by adding the abnormal characterization value of the nearest neighbor historical energy storage power of the power to be analyzed and a preset first constant as the first inverse value; recording the absolute value of the difference between the power to be analyzed and the nearest neighbor historical energy storage power of the power to be analyzed as the first difference, and recording the product of the first difference and the first inverse value as the first product value; recording the absolute value of the difference between the power to be analyzed and the predicted energy storage power of the substation C at the current monitoring time as the second difference, and taking the sum of the first product value and the second difference as the characteristic difference characterization value of the current energy storage power corresponding to the substation C; The method for obtaining the noise continuity level characterization value of the current energy storage power includes: For the f-th abnormal historical energy storage power in any historical energy storage power sequence B: in all historical energy storage power sequences except the historical energy storage power sequence B, obtain all historical energy storage powers with the same collection mark value as the f-th abnormal historical energy storage power, and record the set constructed by all the historical energy storage powers with the same collection mark value as the f-th abnormal historical energy storage power as the first set of the f-th abnormal historical energy storage power, obtain the characteristic difference value corresponding to each historical energy storage power in the first set, and record the cumulative sum of the characteristic difference values ​​corresponding to all historical energy storage powers in the first set as the associated characterization value of the f-th abnormal historical energy storage power, and the characteristic difference value corresponding to the g-th historical energy storage power in the first set is the absolute value of the difference between the abnormal characterization value of the g-th historical energy storage power and the abnormal characterization value of the f-th abnormal historical energy storage power; For any substation C: starting from the last historical energy storage power in the historical energy storage power sequence corresponding to the substation C, continuously obtain a preset first number of abnormal historical energy storage powers, and sort the obtained preset first number of abnormal historical energy storage powers in the order of acquisition to obtain a first sequence to be analyzed; obtain the neighborhood difference of each abnormal historical energy storage power in the first sequence to be analyzed, and in the first sequence to be analyzed, record the abnormal historical energy storage power with a neighborhood difference greater than a preset difference threshold as a characteristic power, and the neighborhood difference of the i-th abnormal historical energy storage power in the first sequence to be analyzed is the result obtained by subtracting the i-th abnormal historical energy storage power from the i-1-th abnormal historical energy storage power in the first sequence to be analyzed; obtain the to-be-accumulated value of each characteristic power in the first sequence to be analyzed, and the to-be-accumulated value of the j-th characteristic power in the first sequence to be analyzed is the result obtained by multiplying the neighborhood difference of the j-th characteristic power, the distance weight value of the j-th characteristic power, and the associated characterization value of the j-th characteristic power, and the distance weight value of the j-th characteristic power is , J is the total amount of energy storage power in the historical energy storage power sequence corresponding to the station C, is the collected characteristic value of the jth characteristic power, c1 is a preset first constant; the result obtained by accumulating the accumulated values ​​of all characteristic powers in the first sequence to be analyzed is recorded as the noise continuation degree characterization value of the current energy storage power corresponding to the station C.

2. A method for real-time monitoring and control of energy storage power in a substation according to claim 1, characterized in that: The collection mark value of the ath historical energy storage power in the historical energy storage power sequence is a.

3. A method for real-time monitoring and control of energy storage power in a substation according to claim 1, characterized in that: The method for obtaining the abnormal characterization value of each historical energy storage power includes: For any historical energy storage power b in the historical energy storage power sequence B: In the historical energy storage power sequence B, all historical energy storage powers within a preset neighborhood range of the historical energy storage power b are obtained, and the sequence constructed by all historical energy storage powers within the preset neighborhood range of the historical energy storage power b is recorded as the neighborhood sequence of the historical energy storage power b; the normalized value of the absolute value of the difference between the historical energy storage power b and the mean of the neighborhood sequence is recorded as the first indicator value; If the historical energy storage power b belongs to the first historical energy storage data in the historical energy storage power sequence B, the historical energy storage power on the right side of the historical energy storage power b is obtained, and the normalized value of the absolute value of the difference between the historical energy storage power b and the historical energy storage power on the right side is recorded as the second indicator value; if the historical energy storage power b belongs to the last historical energy storage data in the historical energy storage power sequence B, the historical energy storage power on the left side of the historical energy storage power b is obtained, and the normalized value of the absolute value of the difference between the historical energy storage power b and the historical energy storage power on the left side is recorded as the second indicator value; if the historical energy storage power b does not belong to the historical energy storage power sequence B, the historical energy storage power on the left side of the historical energy storage power b is obtained, and the normalized value of the absolute value of the difference between the historical energy storage power b and the historical energy storage power on the left side is recorded as the second indicator value. For the first historical energy storage data and the last historical energy storage data in the historical energy storage power sequence B, the absolute value of the difference between the historical energy storage power b and the historical energy storage power on the right is recorded as the right difference, the absolute value of the difference between the historical energy storage power b and the historical energy storage power on the left is recorded as the left difference, and the normalized value of the result obtained by adding the right difference to the left difference is recorded as the second indicator value; the historical energy storage power on the right side of the historical energy storage power b refers to the adjacent data on the right side of the historical energy storage power b, and the historical energy storage power on the left side of the historical energy storage power b refers to the adjacent data on the left side of the historical energy storage power b; The sum of the first index value and the second index value is recorded as the abnormal characterization value of the historical energy storage power b.

4. A method for real-time monitoring and control of energy storage power in a substation according to claim 1, characterized in that: The method for obtaining abnormal historical energy storage power in the historical energy storage power sequence includes: For any historical energy storage power b in any historical energy storage power sequence B, if the normalized value of the abnormal characterization value of the historical energy storage power b is not less than a preset judgment threshold, the historical energy storage power b is determined to be an abnormal historical energy storage power.

5. A method for real-time monitoring and control of energy storage power in a substation according to claim 1, characterized in that: The method for obtaining the nearest neighbor historical energy storage power of the current energy storage power includes: For any substation, the last historical energy storage power in the historical energy storage power sequence corresponding to the substation is used as the nearest neighbor historical energy storage power of the current energy storage power corresponding to the substation.

6. A method for real-time monitoring and control of energy storage power in a substation according to claim 1, characterized in that: The method for obtaining the target measurement noise parameters of each station area at the current monitoring time includes: Obtaining preset measurement noise parameters; For any substation C: the normalized value of the result obtained by multiplying the characteristic difference characterization value of the current energy storage power corresponding to the substation C with the noise continuation degree characterization value of the current energy storage power corresponding to the substation C is recorded as the target characterization value of the current energy storage power corresponding to the substation C; the result obtained by multiplying the preset adjustment coefficient with the target characterization value of the current energy storage power corresponding to the substation C and then adding the result with the preset first constant is recorded as the adjustment value of the substation C at the current monitoring time; the product of the adjustment value of the substation C at the current monitoring time and the preset measurement noise parameter is recorded as the target measurement noise parameter of the substation C at the current monitoring time.

7. A method for real-time monitoring and control of energy storage power in a substation according to claim 1, characterized in that: The method for obtaining the filtering data of each station area at the current monitoring time includes: For any substation C, obtain a Kalman filter whose measurement noise parameter is the target measurement noise parameter of the substation C at the current monitoring moment, and record it as the Kalman filter of the substation C at the current monitoring moment. Use the Kalman filter of the substation C at the current monitoring moment to filter the current energy storage power of the substation C at the current monitoring moment, and record the data obtained after filtering as the filtered data of the substation C at the current monitoring moment.

8. A real-time monitoring and control system for energy storage power in a substation, comprising a memory and a processor, characterized in that: The processor executes the computer program stored in the memory to implement a real-time monitoring and control method for energy storage power in a substation as described in any one of claims 1 to 7.

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