Remote control method for power distribution room switchboard power supply and cut-off

Abstract

The application relates to the technical field of power distribution room stop and send power control, in particular to a remote control method for the power box stop and send power of a power distribution room. The method comprises the following steps: collecting various power data and various environmental influence data of the power distribution room, and obtaining the power operation state at each moment based on the data; smoothing the power operation state at each moment by using different sizes of sliding windows, and then constructing a window size linear model; obtaining the local data difference value at each moment, then combining the window size linear model to obtain the smooth window size at the moment, and then smoothing the power operation state to obtain a smooth power operation state sequence; predicting the power operation state at the next moment based on the smooth power operation state sequence, and obtaining the local change trend at the current moment; and controlling the stop and send power of the power distribution room at the current moment according to the smooth power operation state and the local change trend at the current moment. The application can effectively control the stop and send power of the power distribution room.

Description

Technical Field

[0001] This invention relates to the field of power supply and shutdown control technology in power distribution rooms, and specifically to a remote control method for power supply and shutdown of electrical boxes in power distribution rooms. Background Technology

[0002] As a critical node in power transmission and distribution, the operation of power outages and restorations in distribution rooms is a vital link in ensuring the safe and stable operation of the power system. Traditionally, power outage and restoration operations in distribution rooms primarily rely on manual on-site operation. However, with the continuous expansion and increasing complexity of power systems, as well as the ever-growing demands for reliability and operational efficiency in power supply, the limitations of this approach have become increasingly apparent. For example, in some large industrial parks or core urban areas, distribution rooms are widely distributed and dispersed. To complete a single power outage or restoration task, operators may need to travel long distances between various distribution rooms, not only consuming significant time and manpower costs but also potentially causing operational delays due to traffic and other factors, thus affecting the timeliness of power supply.

[0003] Existing remote control methods lack automated operation functions and mainly rely on monitoring data to notify staff to take action when abnormal situations occur. However, this method suffers from problems such as untimely and inaccurate information transmission and makes it difficult to achieve flexible control and intelligent management of power distribution boxes. Therefore, it is necessary to realize automatic monitoring and fault early warning of power boxes based on real-time collection and analysis of power box operation data, so as to improve the timeliness and accuracy of power outages and restorations. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a remote control method for power outages and restorations of electrical boxes in a power distribution room. The specific technical solution adopted is as follows:

[0005] One embodiment of the present invention provides a remote control method for power outage and restoration of a power distribution box in a power distribution room, the method comprising:

[0006] Collect various power data and environmental impact data from the power distribution room; based on the differences between various power data and their rated value ranges at a given moment, and the differences between various environmental impact data and their reference values, obtain the power operation status at that moment.

[0007] A sliding window is used to smooth the power operation status at each time point. The average difference between the power operation status at each time point and the smoothed power operation status at each time point is recorded as the average data difference of the sliding window. A linear model of window size is constructed using the average data difference corresponding to sliding windows of different sizes and the size of the sliding window.

[0008] The local mutation coefficient at a given moment is obtained based on the change in the power operating state at that moment; the local data difference at that moment is obtained using the power operating state at that moment, the local mutation coefficient, and the power operating states of the two adjacent moments before that moment; the local data difference at that moment is substituted into the linear model of the window size to obtain the smoothed window size at that moment; the smoothed window size at each moment is used to construct a window to smooth the power operating state at each moment to obtain a smoothed power operating state sequence;

[0009] Predict the power operation state at the next moment based on the smoothed power operation state sequence; obtain the local change trend at the current moment based on the smoothed power operation state at the current moment and the power operation state at the next moment; control the power outage and power supply of the power distribution room at the current moment according to the smoothed power operation state at the current moment and the local change trend.

[0010] Preferably, various power data and environmental impact data of the power distribution room are collected, including:

[0011] Various electrical data for the power distribution room include the voltage, current, and power of the electrical boxes; various environmental impact data for the power distribution room include equipment temperature data, mechanical vibration data, and smoke concentration data of the electrical boxes.

[0012] Preferably, the power operation status at a given moment is obtained based on the differences between various power data and their rated value ranges, as well as the differences between various environmental impact data and their reference values. This includes:

[0013] Obtain the absolute value of the difference between a certain type of power data at a given time and the median value of the rated range of that type of power data, denoted as the power difference of that type of power data at that time; encode the difference between that type of power data at that time and the minimum value of the rated range of that type of power data to obtain the first code of that type of power data at that time. When the difference is less than 0, the first code is -1; when it is greater than 0, the first code is 1; when it is equal to 0, the first code is 0; add the hyperparameter to the product of the power difference of that type of power data at that time and the first code, and calculate the first reciprocal of the corresponding power data at that time; calculate the average of the first reciprocals of all types of power data at that time to obtain the power consumption status at that time;

[0014] The absolute value of the difference between a certain type of environmental impact data at a given time and its reference value is added to a first preset value, and the result is inversely calculated to obtain the second reciprocal of that type of environmental impact data at that time. Starting from that type of environmental impact data at that time, a preset number of such environmental impact data points are sequentially taken from previous times to form a data sequence of that type of environmental impact data at that time. The reciprocal of the variance of this data sequence is then recorded as the third reciprocal of that type of environmental impact data at that time. The average of the products of the second and third reciprocals of all types of environmental impact data at that time is used to obtain the environmental state at that time.

[0015] The power operation status at that moment is obtained by multiplying the power consumption status and the environmental status at that moment.

[0016] Preferably, a linear model of the window size is constructed using the average data difference corresponding to sliding windows of different sizes and the size of the sliding window, including:

[0017] By using the average data difference corresponding to sliding windows of different sizes and the size of the sliding window, a linear fitting is performed to obtain the fitted curve, which is denoted as the window size linear model. The horizontal axis of the window size linear model is the size of the sliding window, and the vertical axis is the average data difference corresponding to sliding windows of different sizes.

[0018] Preferably, the local abrupt change coefficient at a given moment is obtained based on the changes in the power operating state at that moment, including:

[0019] Obtain the absolute value of the slope between the power operation state at a given moment and the power operation state at the previous moment, and calculate the reciprocal of the absolute value of the slope with a first preset value, denoted as the fourth reciprocal; sequentially obtain a preset number of power operation states preceding the power operation state at this moment to form a first power operation state sequence; obtain the variance of the first power operation state sequence, denoted as the first variance, and obtain the variance between the first power operation state sequence and the power operation state at this moment, denoted as the second variance; add the absolute value of the difference between the first variance and the second variance to the first preset value and calculate its reciprocal to obtain the fifth reciprocal; multiply the difference between the first preset value and the fourth reciprocal, and the difference between the first preset value and the fifth reciprocal to obtain the local mutation coefficient at this moment.

[0020] Preferably, the local data difference at a given moment is obtained using the power operating state at a given moment, the local mutation coefficient, and the power operating states of two adjacent moments preceding that moment, including:

[0021] The power operating states at two adjacent times before a given time are denoted as the first adjacent power operating state and the second adjacent power operating state. The differences between the power operating state at the given time and the first and second adjacent power operating states are obtained, and the average difference at that time is calculated. The average difference at that time is multiplied by the local mutation coefficient at that time to obtain the local data difference at that time.

[0022] Preferably, the local change trend at the current moment is obtained based on the smoothed power operation state at the current moment and the power operation state at the next moment, including:

[0023] The local trend of change at the current moment is obtained by subtracting the reciprocal of the absolute value of the difference between the current smoothed power operation state and the power operation state at the next moment from the first preset value.

[0024] Preferably, controlling the power outage and restoration of the distribution room at the current moment based on the smoothed power operation status and local change trends includes:

[0025] The minimum value of the smoothed power operation state at each time point in history where no power outage or restoration operation was required is obtained as the power operation state threshold; the maximum value of the local change trend at each time point in history where no power outage or restoration operation was required is obtained as the lower limit threshold of the local change trend; and the minimum value of the local change trend at each time point in history where power outage or restoration operation was required is obtained as the upper limit threshold of the local change trend.

[0026] If the smoothed power operation status at the current moment is less than the power operation status threshold but greater than 0, a power outage operation is required. If the local change trend at the current moment is greater than 0 but less than the lower limit threshold of the local change trend, the operation status of the power distribution room is monitored. If the local change trend at the current moment is greater than or equal to the lower limit threshold of the local change trend but less than the upper limit threshold of the local change trend, an early warning is issued. If the local change trend at the current moment is greater than or equal to the upper limit threshold of the local change trend, a power outage operation is performed in the power distribution room.

[0027] If the smoothed power operation status at the current moment is less than 0, a power supply operation is required. If the local change trend at the current moment is greater than 0 and less than the lower limit threshold of the local change trend, the operation status of the power distribution room is monitored. If the local change trend at the current moment is greater than or equal to the lower limit threshold of the local change trend and less than the upper limit threshold of the local change trend, an early warning is issued. If the local change trend at the current moment is greater than or equal to the upper limit threshold of the local change trend, the power distribution room performs a power supply operation.

[0028] The embodiments of the present invention have at least the following beneficial effects: This application monitors various power data and environmental impact data of the power distribution room in real time, and then analyzes various power data and environmental impact data of the power distribution room at each moment to obtain the power operation status at each moment; further, a sliding window is used to smooth the power operation status at each moment, and the average value of the difference between the power operation status at each moment and the smoothed power operation status at each moment is recorded as the average data difference of the sliding window. Then, a linear model of window size is constructed based on the average data difference corresponding to sliding windows of different sizes and the size of the sliding window. Then, the smoothing window size at each moment is obtained by combining the local data difference at each moment, realizing window adaptation during smoothing and improving the smoothing effect; then, the power operation status at the next moment is predicted based on the smoothed power operation status sequence; the local change trend at the current moment is obtained based on the smoothed power operation status at the current moment and the power operation status at the next moment; the power outage and restoration of the power distribution room at the current moment are controlled according to the smoothed power operation status at the current moment and the local change trend; and the automatic monitoring and fault warning of the electrical box are realized by analyzing the predicted power operation status at the next moment, improving the timeliness and accuracy of power outage and restoration. Attached Figure Description

[0029] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating a remote control method for power supply and shutdown of a power distribution box in a power distribution room, as provided in an embodiment of the present invention. Detailed Implementation

[0031] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a remote control method for power supply and shutdown of a distribution room electrical box according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0032] Unless otherwise defined, 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.

[0033] The following describes in detail, with reference to the accompanying drawings, a specific scheme for a remote control method for power supply and shutdown of a power distribution room box provided by the present invention.

[0034] Example:

[0035] The main application scenario of this invention is to realize the power outage and restoration control of the power distribution room based on the real-time acquisition and analysis function of power data and environmental data of the power distribution room, thereby improving the timeliness and accuracy of power outage and restoration.

[0036] Please see Figure 1 The diagram illustrates a method flowchart for remote control of power supply to and from a distribution room electrical box according to an embodiment of the present invention. The method includes the following steps:

[0037] Step S1: Collect various power data and environmental impact data of the power distribution room; based on the differences between various power data and the rated value range of various power data at a certain moment, and the differences between various environmental impact data and the reference value of various environmental impact data, obtain the power operation status at that moment.

[0038] This application requires the collection of various data from the power distribution room, specifically various power data and environmental impact data. The power data includes voltage, current, and power of the distribution box; the environmental impact data includes equipment temperature data, mechanical vibration data, and smoke concentration data. Voltage, current, and power data are collected using voltage sensors, current sensors, and a power quality analyzer; equipment temperature data, mechanical vibration data, and smoke concentration data are collected using an infrared thermometer, an accelerometer, and an ionization smoke sensor. After data collection, the data is standardized to remove dimensions for easier subsequent analysis.

[0039] Power consumption status data is calculated based on the rated values ​​of various power data under normal conditions, and operating status data is calculated based on the reference values ​​of various environmental impact data. Power outages in the distribution room depend on the following situations: overvoltage / overcurrent, low power, abnormal equipment status, gas leaks, excessively high ambient temperatures, etc.; power restoration is required when: power is restored after fault resolution, or power is restored after environmental conditions improve. Therefore, various power data need to reflect different power outage and restoration states.

[0040] Therefore, the absolute value of the difference between a certain type of power data at a given time and the median value of the rated range of that type of power data is obtained and denoted as the power difference of that type of power data at that time. The difference between the power data at that time and the minimum value of the rated range of that type of power data is encoded to obtain the first code of that type of power data at that time. When the difference is less than 0, the first code is -1; when it is greater than 0, the first code is 1; and when it is equal to 0, the first code is 0. The product of the power difference of that type of power data at that time and the first code is added to the hyperparameter and the first reciprocal of the power data at that time is calculated. The average of the first reciprocals of all types of power data at that time is calculated to obtain the power consumption status at that time.

[0041] The specific computational model for a given state at a given moment is as follows:

[0042] ,

[0043] in, This represents the power consumption status at time t. This represents the i-th type of electricity data at time t. This represents the midpoint of the rated value range for the i-th type of power data. This represents the minimum value within the rated range of the i-th type of power data. This indicates a hyperparameter to prevent the denominator from being zero. Let be the power difference of the i-th type of power data at time t, representing the magnitude of the difference between the i-th type of power data and the rated intermediate data value. The larger the difference, the higher the degree of anomaly in the power data at that time. The first encoding of the i-th type of power data at time t represents the... The first code is obtained by encoding the positive or negative value of the current data. This code is used to determine whether the current data is abnormally high or abnormally low. If the value is negative, it indicates that the current data is abnormally low; otherwise, it is normal or abnormally high. The abnormally high or abnormally low data determines whether to perform a power outage or power restoration operation. N represents the type of power data. The larger the power status value, the better the power status.

[0044] Furthermore, different types of environmental impact data are analyzed. Specifically, the absolute value of the difference between a certain type of environmental impact data at a given time and its reference value is added to a first preset value, and the result is inversely calculated to obtain the second reciprocal of that type of environmental impact data at that time. Starting from the environmental impact data at that time, a preset number of environmental impact data of the same type are sequentially taken before that time to form a data sequence of that type of environmental impact data at that time. The reciprocal of the variance of the data sequence of that type of environmental impact data at that time is recorded as the third reciprocal of that type of environmental impact data at that time. The average of the product of the second and third reciprocals of various environmental impact data at that time is used to obtain the environmental state at that time.

[0045] The specific calculation model for environmental conditions is as follows:

[0046] ,

[0047] in, This represents the environmental state at time t. This represents the numerical value of the j-th environmental impact data at time t. This represents the reference value for the j-th type of environmental impact data. For example, the rated temperature for low-voltage switchgear is 15 degrees Celsius, and the relative humidity is below 75%. For high-voltage switchgear, the rated temperature is 25 degrees Celsius, and the relative humidity is below 50%. Smoke is generally not required, meaning the reference value for smoke concentration is 0. This represents the difference between the j-th type of environmental impact data at time t and the reference value of the j-th type of environmental impact data. The larger the difference, the higher the degree of anomaly in the data at that time. It is the second reciprocal; This represents the variance of the data sequence of the j-th type of environmental impact data at time t compared to the previous preset number of data points. In other words, it is the variance of the data sequence of the j-th type of environmental impact data at time t. The preset number is 5. The third reciprocal indicates the stability of the data affected by the j-th environmental condition. A higher environmental value indicates a better equipment environment; the first preset value is 1.

[0048] After obtaining the power consumption and power status data at each moment, the power operation status at each moment can be obtained. The specific calculation model for the power operation status is as follows:

[0049] ,

[0050] in, This represents the power operation status at time t. This represents the power consumption status at time t. Let represent the environmental state at time t, where The higher the value, the better the power operation status. A negative value indicates that power needs to be supplied. When the value is positive, the closer it is to 0, the more likely a power outage is needed.

[0051] This allows us to obtain the power operation status at each moment.

[0052] Step S2: Use a sliding window to smooth the power operation status at each time point, and obtain the average value of the difference between the power operation status at each time point and the smoothed power operation status at each time point. This average value is recorded as the average data difference of the sliding window. A linear model of window size is constructed using the average data difference corresponding to sliding windows of different sizes and the size of the sliding window.

[0053] The above steps acquire the power operation status at various times. However, power data may be subject to various transient interferences during acquisition, transmission, and processing. These interferences can cause data anomalies such as spikes, abrupt changes, and noise. These interferences primarily originate from electromagnetic interference, such as electromagnetic pulses generated by transformers, circuit breakers, and capacitors during switching or malfunctions; power fluctuations and transient overvoltages, such as voltage drops or overshoots caused by short circuits, ground faults, or load abrupt changes; inrush currents generated when large motors and welding machines start up, causing voltage fluctuations; intermittent output from distributed power sources such as photovoltaics and wind power, which may lead to localized voltage fluctuations; and communication interference. Therefore, the actual power operation status obtained may contain interfering data. Since interference is generally unavoidable in power scenarios, it is necessary to smooth the power operation status to reduce its impact.

[0054] Therefore, it is necessary to calculate the smoothed power operation state when using a fixed sliding window for smoothing, and to adapt the window accordingly. The larger the sliding window, the better the smoothing effect.

[0055] First, different sizes of sliding windows are set, with sizes of 3, 4, 5, 6, 7, 8, 9, and 10 (and other values ​​can be used; this example is for reference only). First, a sliding window of size 3 is used to smooth the power operation state at each time point. The average difference between the power operation state at each time point and the smoothed power operation state at each time point is recorded as the average data difference for the sliding window of size 3. Next, a sliding window of size 4 is used for smoothing, and the average data difference corresponding to the sliding window of size 4 is obtained. Similarly, the average data differences corresponding to sliding windows of other sizes are obtained. Thus, the average data differences corresponding to sliding windows of different sizes can be obtained.

[0056] Furthermore, a linear fit is performed using the average data difference corresponding to sliding windows of different sizes and the size of the sliding window to obtain the fitted curve, denoted as the window size linear model. The x-axis of the window size linear model represents the size of the sliding window, and the y-axis represents the average data difference corresponding to sliding windows of different sizes. Thus, a window size linear model can be constructed based on the relationship between the window size and the average data difference.

[0057] Step S3: Obtain the local mutation coefficient at a given moment based on the changes in the power operating state at that moment; obtain the local data difference at that moment using the power operating state at a given moment, the local mutation coefficient, and the power operating states of the two adjacent moments before that moment; input the local data difference at a given moment into the linear model of the window size to obtain the smoothed window size at that moment; construct a window using the smoothed window size at each moment to smooth the power operating state at each moment to obtain the smoothed power operating state sequence.

[0058] When smoothing the power operation status at each moment, it is necessary to obtain the changes in the power operation status at each moment. Therefore, the local mutation coefficient at that moment is obtained from the changes in the power operation status at a certain moment.

[0059] Specifically, the absolute value of the slope between the power operation state at a given moment and the power operation state at the previous moment is obtained, and the reciprocal of the absolute value of the slope and a first preset value is calculated and recorded as the fourth reciprocal; a preset number of power operation states preceding the power operation state at this moment are sequentially obtained to form a first power operation state sequence; the variance of the first power operation state sequence is obtained and recorded as the first variance, and the variance of the first power operation state sequence and the power operation state at this moment is obtained and recorded as the second variance; the absolute value of the difference between the first variance and the second variance is added to the first preset value and the reciprocal is calculated to obtain the fifth reciprocal; the difference between the first preset value and the fourth reciprocal, and the difference between the first preset value and the fifth reciprocal are multiplied to obtain the local mutation coefficient at this moment.

[0060] The specific calculation model for the local mutation coefficient is as follows:

[0061] ,

[0062] in, The coefficient representing the local abrupt change in the power operating state at time t. This represents the absolute value of the slope between the power operating state at time t and the power operating state at the previous time. The larger the slope value, the more significant the change in the data. It is the fourth reciprocal; This represents the variance of the first n power operation states at time t, which is also the variance of the first power operation state sequence. n is 5 (preset number). Let represent the variance between the power operation state at time t and the first power operation state sequence. The fifth reciprocal is given, and the first preset value is 1. If the difference in variance is large, it indicates that the data at time t is significantly different from the previous data, and the possibility of a sudden change is high.

[0063] After obtaining the local mutation coefficients of the data, it is necessary to calculate the window size for each power operating state during smoothing. Therefore, the local data difference at a given moment is obtained using the power operating state at a given moment, the local mutation coefficients, and the power operating states of the two adjacent moments preceding that moment.

[0064] Specifically, the power operating states at two adjacent times before a given time are denoted as the first adjacent power operating state and the second adjacent power operating state. The differences between the power operating state at the given time and the first and second adjacent power operating states are obtained, and their average values ​​are calculated to obtain the average difference at that time. The average difference at that time is multiplied by the local mutation coefficient at that time to obtain the local data difference at that time.

[0065] This allows us to obtain the local data difference at each moment. The above obtained the window size linear model. By substituting the local data difference at each moment into the window size linear model, we can obtain the window size corresponding to the smoothing of the power operation state at each moment. It should be noted that the window size obtained after substituting may not be an integer. Therefore, the result obtained after substituting is rounded up. In addition, for any result less than 3, the window size is recorded as 3.

[0066] Therefore, the power operation status at each moment is smoothed by using the window of power operation status data obtained adaptively at each moment to obtain a smoothed power operation status sequence.

[0067] Step S4: Predict the power operation state at the next moment based on the smoothed power operation state sequence; obtain the local change trend at the current moment based on the smoothed power operation state at the current moment and the power operation state at the next moment; control the power outage and power supply of the distribution room at the current moment according to the smoothed power operation state at the current moment and the local change trend.

[0068] The above steps obtain a smoothed power operation state sequence. Then, the ARIMA algorithm is used to predict the power operation state sequence to obtain the power operation state at the next moment.

[0069] Furthermore, the local trend of change at the current moment is obtained based on the smoothed power operation state at the current moment and the power operation state at the next moment. Specifically, the local trend of change at the current moment is obtained by subtracting the reciprocal of the absolute value of the difference between the smoothed power operation state at the current moment and the power operation state at the next moment from a first preset value.

[0070] The specific calculation model for local change trends is as follows:

[0071] ,

[0072] in, This represents the local trend of change in the power operation state at time t (the current time). This represents the smoothed power operation state at time t. This indicates a prediction of the power operation status at the next moment. If the predicted data differs significantly from the current data, it indicates a significant trend in the power operation status data. In this case, the power operation status is changing rapidly and requires immediate action (power outage / restoration).

[0073] Finally, the power supply and shutdown of the distribution room at the current moment needs to be controlled based on the smoothed power operation status and local change trends at the current moment.

[0074] Specifically, the minimum value of the smoothed power operation state at each time point in history when no power outage or restoration operation was required is obtained as the power operation state threshold; the maximum value of the local change trend at each time point in history when no power outage or restoration operation was required is obtained as the lower limit threshold of the local change trend; and the minimum value of the local change trend at each time point in history when power outage or restoration operation was required is obtained as the upper limit threshold of the local change trend.

[0075] If the smoothed power operation status at the current moment is less than the power operation status threshold but greater than 0, a power outage operation is required. If the local change trend at the current moment is greater than 0 but less than the lower limit threshold of the local change trend, the operation status of the power distribution room is monitored. If the local change trend at the current moment is greater than or equal to the lower limit threshold of the local change trend but less than the upper limit threshold of the local change trend, an early warning is issued. If the local change trend at the current moment is greater than or equal to the upper limit threshold of the local change trend, a power outage operation is performed in the power distribution room.

[0076] If the smoothed power operation status at the current moment is less than 0, a power supply operation is required. If the local change trend at the current moment is greater than 0 and less than the lower limit threshold of the local change trend, the operation status of the power distribution room is monitored. If the local change trend at the current moment is greater than or equal to the lower limit threshold of the local change trend and less than the upper limit threshold of the local change trend, an early warning is issued. If the local change trend at the current moment is greater than or equal to the upper limit threshold of the local change trend, the power distribution room performs a power supply operation.

[0077] For example, the lower limit threshold for local change trends is 0.2, the upper limit threshold for local change trends is 0.5, and the threshold for power operation status is... When the smoothed power operating state at the current moment is less than When the value is greater than 0, it indicates that a power outage operation is required. If the local change trend δ at the current moment is greater than 0 and less than 0.2, it indicates that the trend is not obvious and observation and waiting are possible. If the local change trend at the current moment is greater than or equal to 0.2 and less than 0.5, the operator can be notified for a warning. If the local change trend at the current moment is greater than or equal to 0.5, a power outage operation should be performed directly.

[0078] When the smoothed power operation status at the current moment is less than 0, it indicates that power supply operation is required. If the local change trend δ at the current moment is greater than 0 and less than 0.2, it indicates that the trend is not obvious and observation and waiting are possible. If the local change trend at the current moment is greater than or equal to 0.2 and less than 0.5, the operator can be notified for a warning. If the local change trend at the current moment is greater than or equal to 0.5, power supply operation is performed directly.

[0079] This allows for the operation of power outages and restorations in the power distribution room. In summary, this application enables remote control of power outages and restorations in the power distribution room's electrical boxes.

[0080] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0081] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A remote control method for power outage and restoration of a power distribution box in a power distribution room, characterized in that, The method includes: Collect various power data and environmental impact data from the power distribution room; based on the differences between various power data and their rated value ranges at a given moment, and the differences between various environmental impact data and their reference values, obtain the power operation status at that moment. A sliding window is used to smooth the power operation status at each time point. The average difference between the power operation status at each time point and the smoothed power operation status at each time point is recorded as the average data difference of the sliding window. A linear model of window size is constructed using the average data difference corresponding to sliding windows of different sizes and the size of the sliding window. The local mutation coefficient at a given moment is obtained based on the change in the power operating state at that moment; the local data difference at that moment is obtained using the power operating state at that moment, the local mutation coefficient, and the power operating states of the two adjacent moments before that moment; the local data difference at that moment is substituted into the linear model of the window size to obtain the smoothed window size at that moment; the smoothed window size at each moment is used to construct a window to smooth the power operating state at each moment to obtain a smoothed power operating state sequence; Predict the power operation state at the next moment based on the smoothed power operation state sequence; obtain the local change trend at the current moment based on the smoothed power operation state at the current moment and the power operation state at the next moment; control the power outage and power supply of the power distribution room at the current moment according to the smoothed power operation state at the current moment and the local change trend.

2. The remote control method for power outage and restoration of a power distribution box in a power distribution room according to claim 1, characterized in that, The data collected from the power distribution room includes various power data and environmental impact data, including: Various electrical data for the power distribution room include the voltage, current, and power of the electrical boxes; various environmental impact data for the power distribution room include equipment temperature data, mechanical vibration data, and smoke concentration data of the electrical boxes.

3. The remote control method for power outage and restoration of a power distribution room box according to claim 1, characterized in that, The method of obtaining the power operation status at a given moment based on the differences between various power data and their rated value ranges, and the differences between various environmental impact data and their reference values, includes: Obtain the absolute value of the difference between a type of power data at a given time and the median value of the rated range of that type of power data, and denot it as the power difference of that type of power data at that time; encode the difference between that type of power data at that time and the minimum value of the rated range of that type of power data to obtain the first code of that type of power data at that time; add a hyperparameter to the product of the power difference of that type of power data at that time and the first code, and calculate the first reciprocal of the corresponding power data at that time; calculate the mean of the first reciprocals of all types of power data at that time to obtain the power consumption status at that time; The absolute value of the difference between a certain type of environmental impact data at a given time and its reference value is added to a first preset value, and the result is inversely calculated to obtain the second reciprocal of that type of environmental impact data at that time. Starting from that type of environmental impact data at that time, a preset number of such environmental impact data points are sequentially taken from previous times to form a data sequence of that type of environmental impact data at that time. The reciprocal of the variance of this data sequence is then recorded as the third reciprocal of that type of environmental impact data at that time. The average of the products of the second and third reciprocals of all types of environmental impact data at that time is used to obtain the environmental state at that time. The power operation status at that moment is obtained by multiplying the power consumption status and the environmental status at that moment.

4. The remote control method for power outage and restoration of a power distribution room box according to claim 1, characterized in that, The method of constructing a linear model of window size using the average data difference corresponding to sliding windows of different sizes and the size of the sliding window includes: By using the average data difference corresponding to sliding windows of different sizes and the size of the sliding window, a linear fitting is performed to obtain the fitted curve, which is denoted as the window size linear model. The horizontal axis of the window size linear model is the size of the sliding window, and the vertical axis is the average data difference corresponding to sliding windows of different sizes.

5. The remote control method for power outage and restoration of a power distribution box in a power distribution room according to claim 1, characterized in that, The process of obtaining the local mutation coefficient at a given moment based on the changes in the power operating state includes: Obtain the absolute value of the slope between the power operation state at a given moment and the power operation state at the previous moment, and calculate the reciprocal of the absolute value of the slope with a first preset value, denoted as the fourth reciprocal; sequentially obtain a preset number of power operation states preceding the power operation state at this moment to form a first power operation state sequence; obtain the variance of the first power operation state sequence, denoted as the first variance, and obtain the variance between the first power operation state sequence and the power operation state at this moment, denoted as the second variance; add the absolute value of the difference between the first variance and the second variance to the first preset value and calculate its reciprocal to obtain the fifth reciprocal; multiply the difference between the first preset value and the fourth reciprocal, and the difference between the first preset value and the fifth reciprocal to obtain the local mutation coefficient at this moment.

6. The remote control method for power outage and restoration of a power distribution room box according to claim 1, characterized in that, The method of obtaining the local data difference at a given moment using the power operation state at a given moment, the local mutation coefficient, and the power operation states at two adjacent moments before that moment includes: The power operating states at two adjacent times before a given time are denoted as the first adjacent power operating state and the second adjacent power operating state. The differences between the power operating state at the given time and the first and second adjacent power operating states are obtained, and the average difference at that time is calculated. The average difference at that time is multiplied by the local mutation coefficient at that time to obtain the local data difference at that time.

7. The remote control method for power outage and restoration of a power distribution room box according to claim 1, characterized in that, The process of obtaining the local change trend at the current moment based on the smoothed power operation state at the current moment and the power operation state at the next moment includes: The local trend of change at the current moment is obtained by subtracting the reciprocal of the absolute value of the difference between the current smoothed power operation state and the power operation state at the next moment from the first preset value.

8. The remote control method for power outage and restoration of a power distribution box in a power distribution room according to claim 1, characterized in that, The method of controlling the power outage and restoration of the distribution room at the current moment based on the smoothed power operation status and local change trends includes: The minimum value of the smoothed power operation state at each time point in history where no power outage or restoration operation was required is obtained as the power operation state threshold; the maximum value of the local change trend at each time point in history where no power outage or restoration operation was required is obtained as the lower limit threshold of the local change trend; and the minimum value of the local change trend at each time point in history where power outage or restoration operation was required is obtained as the upper limit threshold of the local change trend.

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