An Automatic Control Method, Device, Equipment and Storage Medium for a High-Voltage Circuit Breaker

Abstract

The present application discloses an automatic control method, device, equipment and storage medium for a high-voltage circuit breaker, relating to the technical field of control or regulation systems. The method intuitively reflects the local sunlight intensity through the image brightness, divides and classifies the brightness of the power grid and the environment where the high-voltage circuit breaker is located into intervals, defines the magnitude of the weight value to reflect the strength of the light intensity, and controls the number of contacts connected according to the weight value under any sunlight intensity, ensuring the stability of photovoltaic power generation. At the same time, it avoids the fluctuations or unnecessary losses brought to the power grid by redundant control or insufficient control of the high-voltage circuit breaker by manual or devices. Finally, the opening and closing of the contacts are accurately controlled through the PID algorithm to further prevent the contacts from being out of control.

Description

Technical Field

[0001] The present application relates to the technical field of control or regulation systems, and particularly to an automatic control method, device, equipment and storage medium for a high-voltage circuit breaker. Background Art

[0002] With the increasing safety requirements for electricity consumption in aspects such as domestic electricity, industrial electricity, and agricultural electricity, it is necessary to develop the overhead power supply network at the city and district levels and long-distance high-voltage transmission lines in an overall manner. This puts forward higher requirements for the power distribution of power system lines, the operation stability of high-voltage switches, the safety of control systems, the efficient utilization of resources such as electric energy and communication, and the in-depth development of smart grid technology. Therefore, the intelligent level of high-voltage electrical equipment has attracted increasing attention.

[0003] The high-voltage circuit breaker needs to improve the stability and operation intelligence level of the high-voltage switch in the power station. As an important device in the power station power system, the high-voltage circuit breaker has the functions of opening and closing. Among them, the control function means that the high-voltage switch in the power station can switch part of the load through opening and closing operations, and the protection function is to reduce the influence range of power grid fluctuations, power outage faults, etc. in the power station grid, and to disconnect the faulty part from the power grid through opening.

[0004] The high-voltage circuit breaker in the power station should not only switch the load current under normal working conditions of the power station, but also be able to cut off the faulty line in a timely manner in the event of faults such as overvoltage and overcurrent in the power station power system, so as to achieve the protection of the power system and electrical equipment. The opening and closing operations of the high-voltage switch are completed by different types of operating mechanisms such as electromagnetic, hydraulic, spring, and motor, and the response ability and control performance of the operating mechanism will affect the opening and closing ability of the high-voltage switch.

[0005] At present, the opening and closing of the high-voltage circuit breaker in the power station are mainly controlled manually by operators at the terminal monitoring base station, or through a fuse device or a fuse system. For example, after emergencies such as the above-mentioned power grid fluctuations and power outage faults occur, the circuit breaker is tripped through the fuse device or fuse system, and then reclosed after the sudden event is eliminated. Moreover, there is no clear opening and closing operation for daily operation, so that the power grid and the high-voltage circuit breaker are always in a continuous working state during daily operation. For photovoltaic power generation with random power generation characteristics, the continuous working state is obviously redundant, which will not only cause fatigue and faults in the continuously working related components, resulting in shortened service life or direct damage of the related components. Summary of the Invention

[0006] The main objective of this application is to provide an automatic control method, device, equipment, and storage medium for a high-voltage circuit breaker, so as to solve the problem in the prior art that the redundant control of the high-voltage circuit breaker easily causes fatigue and failures of various related components in continuous operation, resulting in a shortened service life or direct damage of each related component.

[0007] To achieve the above objective, this application provides the following technical solutions:

[0008] An automatic control method for a high-voltage circuit breaker, where the high-voltage circuit breaker is used to control the opening and closing of several power grid lines, and the automatic control method includes:

[0009] Step S1, obtaining several image data of the outdoor environment where the high-voltage circuit breaker is located based on the natural time period;

[0010] Step S2, respectively obtaining the average image brightness of each image data through a vision algorithm;

[0011] Step S3, defining at least two consecutive brightness intervals in ascending order according to the numerical size of the visible light brightness;

[0012] Step S4, defining a weight coefficient based on each brightness interval, and the magnitudes of all weight coefficients increase from small to large as the numerical values of all visible light brightness increase from low to high, and all weight coefficients are within the interval [0, 1];

[0013] Step S5, respectively classifying all average image brightnesses into all brightness intervals through a classification algorithm, and obtaining a weight coefficient based on each average image brightness;

[0014] Step S6, defining the weight coefficient as the closing ratio, and obtaining the number of contacts of the high-voltage circuit breaker;

[0015] Step S7, obtaining the product of the closing ratio and the number of contacts, and defining the product as the actual closing number;

[0016] Step S8, sending the actual closing number to the high-voltage circuit breaker;

[0017] Step S9, controlling the contacts corresponding to the actual closing number to close through an adaptive control algorithm.

[0018] As a further improvement of this application, step S2, respectively obtaining the average image brightness of each image data through a vision algorithm, includes:

[0019] Step S21, importing the cv2 software package through the import statement in python;

[0020] Step S22, read each image data through the cv2.imread function of the cv2 package and store it in the img variable;

[0021] Step S23, convert all the image data in the img variable to grayscale images through the cv2.cvtColor function of the cv2 package and store them in the gray variable;

[0022] Step S24, obtain the average brightness of each grayscale image in the gray variable through the cv2.mean function of the cv2 package and store it in the average_brightness variable;

[0023] Step S25, define all the average brightness in the average_brightness variable as the average value of all image brightnesses.

[0024] As a further improvement of this application, in step S5, classify all the average values of image brightness into all brightness intervals through a classification algorithm, and obtain a weight coefficient based on each average value of image brightness, including:

[0025] Step S51, define a set of data to be classified according to all the average values of image brightness , where is the set of data to be classified, is the th average value of image brightness in the set of data to be classified, is the number of all the average values of image brightness;

[0026] Step S52, define a set of categories according to the number of all brightness intervals , where is the th brightness interval in the set of categories , is the number of all brightness intervals;

[0027] Step S53, calculate the conditional probability of each average value of image brightness in the set of data to be classified in each brightness interval according to formula (1):

[0028] (1);

[0029] where is the conditional probability of the set of data to be classified in the th brightness interval, is the marginal probability of the th brightness interval, is the th brightness interval under the condition of the The conditional probability of the average image brightness;

[0030] Step S54, classify each average image brightness into the brightness interval with the highest conditional probability respectively;

[0031] Step S55, define the weight coefficient of the current brightness interval as the weight coefficients of all average image brightnesses within the current brightness interval.

[0032] As a further improvement of this application, in step S9, control the closing of the contacts corresponding to the actual closing quantity through an adaptive control algorithm, including:

[0033] Step S91, obtain the contact motion feature vector of the high-voltage circuit breaker;

[0034] Step S92, construct an adaptive tracking control model for contact motion based on the PID algorithm;

[0035] Step S93, calculate the control signals of each contact corresponding to the actual closing quantity respectively based on the contact motion adaptive tracking control model;

[0036] Step S94, output the control signals to the contacts corresponding to the actual closing quantity for closing.

[0037] As a further improvement of this application, step S91, obtain the contact motion feature vector of the high-voltage circuit breaker, including:

[0038] Step S911, perform segmented processing on the motion signal of the current contact, and calculate the energy entropy of all motion signals of the current contact according to formula (2):

[0039] (2);

[0040] Wherein, is the energy entropy of all motion signals of the current contact, is the th segment of the motion signal of the current contact, is the total number of segments of the motion signal of the current contact, is the motion signal characterization of the current contact;

[0041] Step S912, calculate the feature vector of the motion signal of the current contact based on the energy entropy of the current contact according to formula (3):

[0042] (3);

[0043] Wherein, is the feature vector of the current contact, is based on the motion time parameter of the current contact Characterization of the moving distance.

[0044] As a further improvement of the present application, the contact motion adaptive tracking control model is characterized by Equation (4):

[0045] (4);

[0046] Wherein, is the control signal, is the difference between the current contact position and the desired closing position, is the proportional gain of the contact motion adaptive tracking control model, is the integral time constant of the contact motion adaptive tracking control model, is the differential time constant of the contact motion adaptive tracking control model, is the motion time parameter.

[0047] To achieve the above object, the present application also provides the following technical solutions:

[0048] An automatic control device for a high-voltage circuit breaker, the automatic control device is applied to the automatic control method of the high-voltage circuit breaker as described above, and the automatic control device includes:

[0049] An image data acquisition module, configured to acquire a plurality of image data of the outdoor environment where the high-voltage circuit breaker is located based on a natural time period;

[0050] An image brightness average value acquisition module, configured to respectively acquire the image brightness average value of each image data through a vision algorithm;

[0051] A brightness interval definition module, configured to define at least two consecutive brightness intervals in ascending order according to the numerical value of the visible light brightness;

[0052] A weight coefficient definition module, configured to define a weight coefficient based on each brightness interval, and the magnitudes of all weight coefficients increase from small to large as the numerical values of all visible light brightness increase from low to high, and all weight coefficients are within the interval [0, 1];

[0053] An image brightness average value classification module, configured to respectively classify all image brightness average values into all brightness intervals through a classification algorithm, and obtain a weight coefficient based on each image brightness average value;

[0054] A contact closing ratio definition module, configured to define the weight coefficient as the closing ratio and obtain the number of contacts of the high-voltage circuit breaker;

[0055] An actual closing number definition module, configured to obtain the product of the closing ratio and the number of contacts, and define the product as the actual closing number;

[0056] An actual closing quantity sending module, configured to send the actual closing quantity to the high-voltage circuit breaker;

[0057] A high-voltage circuit breaker control module, configured to control the closing of the contacts corresponding to the actual closing quantity through an adaptive control algorithm.

[0058] To achieve the above object, the present application also provides the following technical solutions:

[0059] An electronic device, including a processor and a memory coupled to the processor, where the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the automatic control method of the high-voltage circuit breaker as described above is implemented.

[0060] To achieve the above object, the present application also provides the following technical solutions:

[0061] A storage medium, where program instructions are stored in the storage medium, and when the program instructions are executed by a processor, the automatic control method of the high-voltage circuit breaker as described above can be implemented.

[0062] The present application obtains a plurality of image data of the outdoor environment where the high-voltage circuit breaker is located based on the natural time period; respectively obtains the average image brightness of each image data through a vision algorithm; defines at least two consecutive brightness intervals in ascending order according to the numerical magnitude of the visible light brightness; defines a weight coefficient based on each brightness interval, and the magnitudes of all weight coefficients increase from small to large as the numerical values of all visible light brightness increase from low to high, and all weight coefficients are within the interval [0, 1]; respectively classifies all average image brightnesses into all brightness intervals through a classification algorithm, and obtains a weight coefficient based on each average image brightness; defines the weight coefficient as the closing ratio, and obtains the number of contacts of the high-voltage circuit breaker; obtains the product of the closing ratio and the number of contacts, and defines the product as the actual closing quantity; sends the actual closing quantity to the high-voltage circuit breaker; controls the closing of the contacts corresponding to the actual closing quantity through an adaptive control algorithm. The present application intuitively reflects the local sunlight intensity through the image brightness, divides and classifies the brightness of the power grid and the environment where the high-voltage circuit breaker is located, defines the magnitude of the weight value to reflect the strength of the light intensity, and controls the number of contacts connected according to the weight value under any sunlight intensity, ensuring the stability of photovoltaic power generation. At the same time, it avoids the fluctuations or unnecessary losses brought to the power grid by redundant control or insufficient control of the high-voltage circuit breaker by artificial or devices. Finally, the opening and closing of the contacts are precisely controlled through the pid algorithm, further preventing the contacts from being out of control. Description of the Drawings

[0063] Figure 1 It is a schematic flow chart of the steps of an embodiment of the automatic control method of the high-voltage circuit breaker of the present application;

[0064] Figure 2 Schematic diagram of functional modules of an embodiment of the automatic control device for the high-voltage circuit breaker of the present application;

[0065] Figure 3 Schematic diagram of the structure of an embodiment of the electronic device of the present application;

[0066] Figure 4 Schematic diagram of the structure of an embodiment of the storage medium of the present application. Detailed implementation manners

[0067] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present application.

[0068] The terms "first", "second", and "third" in the present application are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", and "third" may explicitly or implicitly include at least one of such features. In the description of the present application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present application are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes unlisted steps or units, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0069] Referring to "embodiment" herein means that the specific features, structures, or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0070] Such as Figure 1As shown in the figure, this embodiment provides an embodiment of the automatic control method for a high-voltage circuit breaker. In this embodiment, the high-voltage circuit breaker is used to control the opening and closing of several power grid lines.

[0071] Specifically, the high-voltage circuit breaker in this embodiment can be set as an Integrated Gate-Commutated Thyristor (IGCT), or also known as a Gate-Commutated Thyristor (GCT), that is, a gate-commutated thyristor. The IGCT combines the characteristics of a Gate Turn-Off (GTO) thyristor and a Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET). It is a new type of power electronic device that emerged in the late 1990s. The overall function of the IGCT is similar to that of a Gate Turn-Off (GTO) thyristor, and it is a fully controllable power switch, and its conduction and turn-off are both controlled by a control signal (gate). Due to the combination of MOSFET characteristics, its performance is more excellent compared with traditional GTOs. Its capacity is equivalent to that of GTOs, but its switching speed is 10 times faster than that of GTOs, and the large and complex snubber circuit required for GTO applications can be omitted.

[0072] Specifically, the IGCT has the following characteristics:

[0073] ① Low switching losses: The switching frequency can be arbitrarily selected to meet the needs of the final application. Previously, power equipment could only operate within 250Hz under rated current, while the operating frequency of the IGCT can reach 4 times this speed. For example, in a motor drive system, if a faster switching speed is selected, the system efficiency can be improved. On the other hand, if a lower switching speed is selected for the IGCT, the efficiency of the inverter system will be improved, and the losses will be lower.

[0074] ② Simplified auxiliary circuit: The uniqueness of the IGCT lies in that it can operate without a snubber circuit, which is very beneficial for design. An inverter without a snubber circuit has low losses, a compact structure, fewer components used, and better reliability. The freewheeling diode is integrated into the IGCT structure, simplifying the equipment based on the IGCT.

[0075] ③ Low gate drive power: The GTO uses a traditional anode short-circuit structure to achieve a low on-state voltage drop and low turn-off losses, which leads to an increase in the gate trigger current. The transparent anode emission technology adopted by the IGCT makes the trigger current and the trailing-edge current very small, and the total on-state gate current is only 1 / 10 of that of the GTO, reducing the gate trigger probability.

[0076] ④Short storage time: The integration of reliable IGCT devices and large-scale antiparallel diodes can not only reduce the storage time, but also greatly reduce the absolute value and discreteness of the turn-off time, enabling IGCTs to be safely applied to medium- and high-voltage series connections. If an overcurrent failure occurs, the device burns out and shuts itself off, unlike IGBTs that can pose a danger to adjacent components, enhancing the safety of the overall circuit.

[0077] Specifically, the automatic control method includes the following steps:

[0078] Step S1, obtaining a plurality of image data of the outdoor environment where the high-voltage circuit breaker is located based on the natural time period.

[0079] Preferably, the image data of the outdoor environment where the high-voltage circuit breaker is located can be directly obtained by an external camera, and there is no difficulty in obtaining it. However, the same shooting angle should be adopted as much as possible to ensure the accuracy of subsequent brightness detection.

[0080] Step S2, obtaining the average image brightness of each image data through a vision algorithm.

[0081] Step S3, defining at least two consecutive brightness intervals in ascending order according to the numerical values of the visible light brightness.

[0082] For example, six brightness intervals can be defined in sequence, specifically , , , , , , and the critical values of each interval can be adjusted according to actual needs.

[0083] Step S4, defining a weight coefficient based on each brightness interval, and the magnitudes of all weight coefficients increase from small to large as the numerical values of all visible light brightness increase from low to high, and all weight coefficients are within the interval [0, 1].

[0084] Preferably, according to the above six brightness intervals, the defined weight coefficients are 0, 0.2, 0.4, 0.6, 0.8, and 1.0 in sequence.

[0085] Step S5, classifying all average image brightnesses into all brightness intervals through a classification algorithm, and obtaining a weight coefficient based on each average image brightness.

[0086] Step S6, defining the weight coefficient as the closing ratio and obtaining the number of contacts of the high-voltage circuit breaker.

[0087] Step S7, obtaining the product of the closing ratio and the number of contacts, and defining the product as the actual closing number.

[0088] Step S8, send the actual number of closures to the high-voltage circuit breaker.

[0089] Step S9, control the closure of the contacts corresponding to the actual number of closures through an adaptive control algorithm.

[0090] Further, in step S2, obtain the average image brightness of each image data through a vision algorithm, including:

[0091] Step S21, import the cv2 software package through the import statement in python.

[0092] Step S22, read each image data through the cv2.imread function of the cv2 software package and store it in the img variable.

[0093] Step S23, convert all the image data in the img variable to grayscale images through the cv2.cvtColor function of the cv2 software package and store it in the gray variable.

[0094] Step S24, obtain the average brightness of each grayscale image in the gray variable through the cv2.mean function of the cv2 software package and store it in the average_brightness variable.

[0095] Step S25, define all the average brightness in the average_brightness variable as the average image brightness.

[0096] Preferably, the main implementation code for steps S21 to S26 is as follows:

[0097] import cv2

[0098] import matplotlib.pyplot as plt

[0099] def is_dark(image_path, threshold=100):

[0100] img = cv2.imread(image_path)

[0101] gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

[0102] average_brightness = cv2.mean(gray)[0]

[0103] if average_brightness<threshold:

[0104] return True

[0105] else:

[0106] return False

[0107] Furthermore, in step S5, all the average image brightness values are classified into all the brightness intervals respectively by a classification algorithm, and a weight coefficient is obtained based on each average image brightness value, including:

[0108] Step S51, define the data set to be classified according to all the average image brightness values , where is the data set to be classified, is the -th average image brightness value in the data set to be classified, is the number of all the average image brightness values.

[0109] Step S52, define the category set according to the number of all the brightness intervals , where is the category set is the -th brightness interval in the category set, is the number of all the brightness intervals.

[0110] Step S53, calculate the conditional probability of each average image brightness value in the data set to be classified in each brightness interval according to formula (1):

[0111] (1).

[0112] Where is the conditional probability of the data set to be classified in the -th brightness interval, is the marginal probability of the -th brightness interval, is the conditional probability of the -th brightness interval for the -th average image brightness value.

[0113] Step S54, classify each average image brightness value into the brightness interval with the highest respective conditional probability.

[0114] Step S55, define the weight coefficient of the current brightness interval as the weight coefficients of all the average image brightness values within the current brightness interval.

[0115] Preferably, the classification algorithm in this embodiment preferably uses Naive Bayes classification. Naive Bayes classification assumes that the presence of a specific feature in a class is independent of the presence of any other feature, that is, each feature is independent of each other. Therefore, it has certain constraints on the actual situation. If there is an association between attributes, the classification accuracy will decrease. However, in actual application processes, the classification effect of Naive Bayes is relatively accurate. For a given item to be classified, Naive Bayes calculates the probability of each category appearing under the condition that this item appears. Whichever is the largest is considered that this item to be classified belongs to that category.

[0116] Specifically, the definition of Naive Bayes is as follows:

[0117] ① Let x = {a1, a2, a3, ……, an} be an item to be classified, and each a is a feature of x.

[0118] ② There is a class set c = {y1, y2, y3, ……, ym}.

[0119] ③ Calculate P(y1|x), P(y2|x), ……, P(ym|x).

[0120] ④ If P(yk|x) = max{P(y1|x), P(y2|x), ……, P(ym|x)}, then x ∈ yk.

[0121] Then calculate each conditional probability in step ③ through the following steps:

[0122] Find a set of items to be classified with known classifications, and this set is called the training sample set.

[0123] Statistically obtain the conditional probability estimates of each feature attribute under each category. That is:

[0124] P(a1|y1), P(a2|y1), ……, P(an|y1).

[0125] P(a1|y2), P(a2|y2), ……, P(an|y2).

[0126] ……

[0127] P(a1|ym), P(a2|ym), ……, P(an|ym).

[0128] Assume that each feature attribute is conditionally independent. Then according to Bayes' theorem:

[0129] P(yi|x) = P(x|yi)P(yi) / P(x).

[0130] Since the denominator is a constant for all categories, it is only necessary to maximize the numerator. Also, because each feature attribute is conditionally independent, then:

[0131] P(x|yi)P(yi) = P(a1|yi)P(a2|yi)……P(an|yi)P(yi).

[0132] It should be noted that the above preferred content is also an explanation of the principle, and its symbol meanings are not interoperable with those of other formulas in this embodiment.

[0133] Further, in step S9, controlling the closing of the contacts corresponding to the actual closing quantity through an adaptive control algorithm includes:

[0134] Step S91, obtaining the motion feature vector of the high-voltage circuit breaker contacts.

[0135] Step S92, constructing an adaptive tracking control model for the contact motion based on the PID algorithm.

[0136] Step S93, calculating the control signals for each contact corresponding to the actual closing quantity respectively based on the adaptive tracking control model for the contact motion.

[0137] Step S94, outputting the control signals to the contacts corresponding to the actual closing quantity for closing.

[0138] Preferably, before extracting the motion feature vector of the current contact, it is necessary to analyze the PID tracking control structure of the contact motion.

[0139] For example: In this embodiment, the ZN63 high-voltage circuit breaker is tested. During the test, eye-catching auxiliary markers are pasted at the contact positions of the high-voltage circuit breaker, and the ink dots on the markers are used as the contact motion marks to reduce the influence of similar marks on the control accuracy. In this embodiment, the contact motion conditions are analyzed in the closing and opening states of the circuit breaker.

[0140] The initial coordinates of the contact ink dot mark are defined as (0, 0). After the contact moves, the coordinates of the ink dot mark become (25.71, -8.13), and the coordinates of the last contact motion are (5.73, -11.13). During the above contact motion process, a PID controller is used for tracking control.

[0141] When the high-voltage circuit breaker is in the closing state as obtained above, after the tracking time reaches 22 ms, the contact motion speed starts to decline; before 22 ms, the contact motion speed is in a continuously increasing state. During this process, 22 ms is the adaptation node for tracking control, and 0 - 22 ms is the PID adaptation process. In the opening state, after the tracking time reaches 15 ms, the contact motion speed starts to decline; before 15 ms, the contact motion speed is in a continuously increasing state. During this process, 15 ms is the adaptation node for tracking control, and 0 - 15 ms is the PID adaptation process.

[0142] Further, in step S91, obtain the contact motion feature vector of the high-voltage circuit breaker, including:

[0143] In step S911, segment the motion signal of the current contact, and calculate the energy entropy of all motion signals of the current contact according to Equation (2):

[0144] (2).

[0145] Wherein, is the energy entropy of all motion signals of the current contact, is the th segment of the motion signal of the current contact, is the total number of segments of the motion signal of the current contact, is the motion signal representation of the current contact.

[0146] In step S912, calculate the feature vector of the motion signal of the current contact based on the energy entropy of the current contact according to Equation (3):

[0147] (3).

[0148] Wherein, is the feature vector of the current contact, is the motion path representation of the current contact based on the motion time parameter .

[0149] Further, the contact motion adaptive tracking control model is represented by Equation (4):

[0150] (4).

[0151] Wherein, is the control signal, is the difference between the current contact position and the desired closing position, is the proportional gain of the contact motion adaptive tracking control model, is the integral time constant of the contact motion adaptive tracking control model, is the differential time constant of the contact motion adaptive tracking control model, is the motion time parameter.

[0152] In this embodiment, a plurality of image data of the outdoor environment where the high-voltage circuit breaker is located are obtained based on the natural time cycle; the average image brightness of each image data is obtained respectively through a vision algorithm; at least two consecutive brightness intervals are defined in ascending order according to the numerical value of the visible light brightness; a weight coefficient is defined for each brightness interval, and the magnitudes of all weight coefficients increase from small to large as the numerical values of all visible light brightness increase from low to high, and all weight coefficients are within the interval [0, 1]; all the average image brightness values are classified into all the brightness intervals respectively through a classification algorithm, and a weight coefficient is obtained based on each average image brightness value; the weight coefficient is defined as the closing ratio, and the number of contacts of the high-voltage circuit breaker is obtained; the product of the closing ratio and the number of contacts is obtained, and the product is defined as the actual closing number; the actual closing number is sent to the high-voltage circuit breaker; the contacts corresponding to the actual closing number are controlled to close through an adaptive control algorithm. In this embodiment, the local sunlight intensity is intuitively reflected through the image brightness, the brightness of the power grid and the environment where the high-voltage circuit breaker is located is divided into intervals and classified, the magnitude of the weight value is defined to reflect the strength of the light intensity, and the number of contacts connected is controlled according to the weight value under any sunlight intensity, which ensures the stability of photovoltaic power generation. At the same time, it avoids the fluctuations or unnecessary losses to the power grid caused by redundant control or insufficient control of the high-voltage circuit breaker by manual or devices. Finally, the opening and closing of the contacts are accurately controlled through the PID algorithm, further preventing the contacts from being out of control.

[0153] As Figure 2 shown, this embodiment provides an embodiment of the automatic control device for the high-voltage circuit breaker. In this embodiment, the automatic control device is applied to the automatic control method in the above-mentioned embodiment. The automatic control device includes an image data acquisition module 1, an average image brightness acquisition module 2, a brightness interval definition module 3, a weight coefficient definition module 4, an average image brightness classification module 5, a contact closing ratio definition module 6, an actual closing number definition module 7, an actual closing number sending module 8, and a high-voltage circuit breaker control module 9 that are electrically connected in sequence.

[0154] Among them, the image data acquisition module 1 is used to acquire a plurality of image data of the outdoor environment where the high-voltage circuit breaker is located based on the natural time period; the image brightness average value acquisition module 2 is used to respectively acquire the image brightness average value of each image data through a vision algorithm; the brightness interval definition module 3 is used to sequentially define at least two consecutive brightness intervals from low to high according to the numerical value of the visible light brightness; the weight coefficient definition module 4 is used to define a weight coefficient based on each brightness interval, and the magnitudes of all weight coefficients increase from small to large as the numerical values of all visible light brightness increase from low to high, and all weight coefficients are within the interval [0, 1]; the image brightness average value classification module 5 is used to respectively classify all image brightness average values into all brightness intervals through a classification algorithm, and obtain a weight coefficient based on each image brightness average value; the contact closing ratio definition module 6 is used to define the weight coefficient as the closing ratio and obtain the number of contacts of the high-voltage circuit breaker; the actual closing number definition module 7 obtains the product of the closing ratio and the number of contacts and defines the product as the actual closing number; the actual closing number sending module 8 is used to send the actual closing number to the high-voltage circuit breaker; the high-voltage circuit breaker control module 9 is used to control the closing of the contacts corresponding to the actual closing number through an adaptive control algorithm.

[0155] Further, the image brightness average value acquisition module 2 specifically includes a first image brightness average value acquisition sub-module, a second image brightness average value acquisition sub-module, a third image brightness average value acquisition sub-module, a fourth image brightness average value acquisition sub-module, and a fifth image brightness average value acquisition sub-module that are electrically connected in sequence; the first image brightness average value acquisition sub-module is electrically connected to the image data acquisition module, and the fifth image brightness average value acquisition sub-module is electrically connected to the brightness interval definition module.

[0156] Among them, the first image brightness average value acquisition sub-module is used to import the cv2 software package through the import statement of python; the second image brightness average value acquisition sub-module is used to respectively read each image data through the cv2.imread function of the cv2 software package and store it in the img variable; the third image brightness average value acquisition sub-module is used to convert all the image data in the img variable into grayscale images through the cv2.cvtColor function of the cv2 software package and store them in the gray variable; the fourth image brightness average value acquisition sub-module is used to respectively obtain the average brightness of each grayscale image in the gray variable through the cv2.mean function of the cv2 software package and store it in the average_brightness variable; the fifth image brightness average value acquisition sub-module is used to define all the average brightness in the average_brightness variable as all the image brightness average values.

[0157] Further, the image brightness average value classification module 5 specifically includes a first image brightness average value classification sub-module, a second image brightness average value classification sub-module, a third image brightness average value classification sub-module, a fourth image brightness average value classification sub-module, and a fifth image brightness average value classification sub-module that are electrically connected in sequence; the first image brightness average value classification sub-module is electrically connected to the weight coefficient definition module, and the fifth image brightness average value classification sub-module is electrically connected to the contact closing ratio definition module.

[0158] Among them, the first image brightness average value classification sub-module is used to define the data set to be classified according to all the image brightness average values , where, is the data set to be classified, is the -th image brightness average value in the data set to be classified, is the number of all the image brightness average values.

[0159] The second image brightness average value classification sub-module is used to define the category set according to the number of all the brightness intervals , where, is the category set is the -th brightness interval in the category set, is the number of all the brightness intervals.

[0160] The third image brightness average value classification sub-module is used to calculate the conditional probability of each image brightness average value in the data set to be classified in each brightness interval according to formula (1):

[0161] (1).

[0162] Among them, is the conditional probability of the data set to be classified in the -th brightness interval, is the marginal probability of the -th brightness interval, is the conditional probability of the -th image brightness average value under the -th brightness interval.

[0163] The fourth image brightness average value classification sub-module is used to classify each image brightness average value into the brightness interval with the highest conditional probability of each of them respectively.

[0164] The fifth image brightness average value classification sub-module is used to define the weight coefficient of the current brightness interval as the weight coefficient of all the image brightness average values in the current brightness interval.

[0165] Further, the high-voltage circuit breaker control module 9 specifically includes a first high-voltage circuit breaker control sub-module, a second high-voltage circuit breaker control sub-module, a third high-voltage circuit breaker control sub-module, and a fourth high-voltage circuit breaker control sub-module that are electrically connected in sequence; the first high-voltage circuit breaker control sub-module is electrically connected to the actual closing quantity sending module, and the fourth high-voltage circuit breaker control sub-module is electrically connected to the sunrise and sunset time acquisition module.

[0166] Among them, the first high-voltage circuit breaker control sub-module is used to obtain the motion feature vector of the high-voltage circuit breaker contact; the second high-voltage circuit breaker control sub-module is used to construct an adaptive tracking control model for contact motion based on the PID algorithm; the third high-voltage circuit breaker control sub-module is used to calculate the control signals of the contacts corresponding to each actual closing quantity respectively based on the adaptive tracking control model for contact motion; the fourth high-voltage circuit breaker control sub-module is used to output the control signals to the contacts corresponding to the actual closing quantity for closing.

[0167] Further, the first high-voltage circuit breaker control sub-module specifically includes a first high-voltage circuit breaker control unit and a second high-voltage circuit breaker control unit that are electrically connected in sequence; the first high-voltage circuit breaker control unit is electrically connected to the actual closing quantity sending module, and the second high-voltage circuit breaker control unit is electrically connected to the second high-voltage circuit breaker control sub-module.

[0168] Among them, the first high-voltage circuit breaker control unit is used to segment the motion signals of the current contact, and calculate the energy entropy of all the motion signals of the current contact according to Equation (2):

[0169] (2).

[0170] Among them, is the energy entropy of all the motion signals of the current contact, is the th segment of the motion signals of the current contact, is the total number of segments of the motion signals of the current contact, is the motion signal representation of the current contact.

[0171] The second high-voltage circuit breaker control unit is used to calculate the feature vector of the motion signals of the current contact based on the energy entropy of the current contact according to Equation (3):

[0172] (3).

[0173] Among them, is the feature vector of the current contact, is the motion path representation of the current contact based on the motion time parameter of the current contact.

[0174] Further, the second high-voltage circuit breaker control sub-module is equipped with a contact motion adaptive tracking control model characterized by formula (4):

[0175] (4).

[0176] Wherein, is the control signal, is the difference between the current contact position and the desired closing position, is the proportional gain of the contact motion adaptive tracking control model, is the integral time constant of the contact motion adaptive tracking control model, is the differential time constant of the contact motion adaptive tracking control model, is the motion time parameter.

[0177] It should be noted that this embodiment is a functional module embodiment based on the above method embodiment. For the preferred, extended, limited, and exemplified parts of this embodiment, please refer to the above embodiment, and this embodiment will not be elaborated herein.

[0178] This embodiment obtains several image data of the outdoor environment where the high-voltage circuit breaker is located based on the natural time period; respectively obtains the average image brightness of each image data through a vision algorithm; defines at least two consecutive brightness intervals in ascending order according to the numerical size of the visible light brightness; defines a weight coefficient for each brightness interval, and the magnitudes of all weight coefficients increase from small to large as the numerical values of all visible light brightness increase from low to high, and all weight coefficients are within the interval [0, 1]; classifies all average image brightnesses into all brightness intervals through a classification algorithm, and obtains a weight coefficient based on each average image brightness; defines the weight coefficient as the closing ratio, and obtains the number of contacts of the high-voltage circuit breaker; obtains the product of the closing ratio and the number of contacts, and defines the product as the actual closing number; sends the actual closing number to the high-voltage circuit breaker; controls the closing of the contacts corresponding to the actual closing number through an adaptive control algorithm. This embodiment intuitively reflects the local sunlight intensity through the image brightness, divides and classifies the brightness of the power grid and the environment where the high-voltage circuit breaker is located, reflects the strength of the light intensity by defining the magnitude of the weight value, and controls the number of contacts connected according to the weight value under any sunlight intensity, ensuring the stability of photovoltaic power generation. At the same time, it avoids the fluctuations or unnecessary losses brought to the power grid by redundant control or insufficient control of the high-voltage circuit breaker by manual or devices. Finally, the opening and closing of the contacts are precisely controlled through the PID algorithm to further prevent the contacts from being out of control.

[0179] Figure 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As Figure 3As shown, the electronic device 10 includes a processor 101 and a memory 102 coupled to the processor 101.

[0180] The memory 102 stores program instructions for implementing the automatic control method of the high-voltage circuit breaker in any of the above embodiments.

[0181] The processor 101 is configured to execute the program instructions stored in the memory 102 to perform automatic control of the high-voltage circuit breaker.

[0182] Among them, the processor 101 can also be referred to as a CPU (Central Processing Unit). The processor 101 may be an integrated circuit chip with signal processing capabilities. The processor 101 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0183] Furthermore, Figure 4 is a schematic structural diagram of a storage medium according to an embodiment of the present application. Refer to Figure 4 The storage medium 11 of the embodiment of the present application stores program instructions 111 that can implement all of the above methods. Among them, the program instructions 111 can be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes, or a terminal device such as a computer, a server, a mobile phone, or a tablet.

[0184] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in an electrical, mechanical, or other form.

[0185] In addition, each functional unit in various embodiments of the present application may be integrated into one processing unit, may exist physically as individual units, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software functional units. The above is only the implementation manner of the present application and does not limit the patent scope of the present application. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. An automatic control method for a high-voltage circuit breaker, wherein the high-voltage circuit breaker is used to control the opening and closing of a plurality of power grid lines, characterized in that: The automatic control method comprises: Step S1, acquiring a plurality of image data of the outdoor environment where the high-voltage circuit breaker is located based on a natural time period; Step S2, obtaining the average image brightness of each image data by using a visual algorithm; Step S3, defining at least two continuous brightness intervals in order from low to high according to the value of the visible light brightness; Step S4, defining a weight coefficient based on each brightness interval, the sizes of all weight coefficients increase from small to large as the values ​​of all visible light brightness increase from low to high, and all weight coefficients are within the interval [0, 1]; Step S5, classifying all image brightness averages into all brightness intervals by a classification algorithm, and obtaining a weight coefficient based on the brightness average of each image; Step S6, defining the weight coefficient as a closing ratio, and obtaining the number of contacts of the high-voltage circuit breaker; Step S7, obtaining the product of the closing ratio and the number of contacts, and defining the product as the actual closing number; Step S8, sending the actual closing number to the high-voltage circuit breaker; Step S9, controlling the closing of contacts corresponding to the actual closing number through an adaptive control algorithm; The step S9 comprises: Step S91, obtaining a high-voltage circuit breaker contact motion feature vector; Step S92, constructing a contact motion adaptive tracking control model based on a PID algorithm; Step S93, calculating the control signal of each contact corresponding to the actual number of closed contacts based on the contact motion adaptive tracking control model; Step S94, outputting the control signal to the contacts corresponding to the actual closing number for closing; The step S91 includes: Step S911, the motion signal of the current contact is processed in segments, and the energy entropy of all the motion signals of the current contact is calculated according to formula (2): （2）； in, is the energy entropy of all motion signals of the current contact, The current contact Segment motion signal, is the total number of segments of the current contact motion signal, It is the motion signal representation of the current contact; Step S912, calculating the characteristic vector of the motion signal of the current contact according to the energy entropy of the current contact based on equation (3): （3）； in, is the characteristic vector of the current contact, The motion time parameters of the current contact Representation of movement path.

2. The automatic control method according to claim 1, characterized in that: Step S2, obtaining the average brightness of each image data by a visual algorithm, including: Step S21, importing the cv2 software package through the python import statement; Step S22, reading each image data respectively through the cv2.imread function of the cv2 software package and storing it in the img variable; Step S23, converting all image data in the img variable into a grayscale image through the cv2.cvtColor function of the cv2 software package and storing it in the gray variable; Step S24, obtaining the average brightness of each grayscale image in the gray variable through the cv2.mean function of the cv2 software package and storing it in the average_brightness variable; Step S25, defining all average brightness in the average_brightness variable as the average brightness of all images.

3. The automatic control method according to claim 1, characterized in that: Step S5, classifying all image brightness averages into all brightness intervals by a classification algorithm, and obtaining a weight coefficient based on the brightness average of each image, including: Step S51, defining a data set to be classified according to the average brightness of all images ,in, is the data set to be classified, is the first The average brightness of the image, is the number of average brightness values ​​of all images; Step S52: define a category set according to the number of all brightness intervals ,in, For the category set The brightness range, is the number of all brightness intervals; Step S53, calculating the conditional probability of the average brightness of each image in the data set to be classified in each brightness interval according to formula (1): （1）； in, For the The conditional probability of the data set to be classified within the brightness interval, For the The edge probability of the brightness interval is For the In the brightness range The conditional probability of the average brightness of the images; Step S54, classifying the average brightness of each image into the brightness interval with the highest conditional probability; Step S55: defining the weight coefficient of the current brightness interval as the weight coefficient of the average brightness of all images within the current brightness interval.

4. The automatic control method according to claim 3, characterized in that: The contact motion adaptive tracking control model is represented by equation (4): （4）； in, is the control signal, is the difference between the current contact position and the expected closing position, is the proportional gain of the contact motion adaptive tracking control model, is the integral time constant of the contact motion adaptive tracking control model, is the differential time constant of the contact motion adaptive tracking control model, is the motion time parameter.

5. An automatic control device for a high-voltage circuit breaker, the automatic control device being applied to the automatic control method for a high-voltage circuit breaker according to any one of claims 1 to 4, characterized in that: The automatic control device comprises: An image data acquisition module, used to acquire a plurality of image data of an outdoor environment where the high-voltage circuit breaker is located based on a natural time period; An image brightness average value acquisition module is used to obtain the image brightness average value of each image data through a visual algorithm; A brightness interval definition module, used to define at least two consecutive brightness intervals in order from low to high according to the value of visible light brightness; A weight coefficient definition module is used to define a weight coefficient based on each brightness interval. The size of all weight coefficients increases from small to large as the values ​​of all visible light brightness increase from low to high. All weight coefficients are within the interval [0, 1]. An image brightness average classification module is used to classify all image brightness averages into all brightness intervals through a classification algorithm, and obtain a weight coefficient based on each image brightness average; A contact closing ratio definition module, used to define the weight coefficient as the closing ratio and obtain the number of contacts of the high-voltage circuit breaker; An actual closing quantity definition module obtains the product of the closing ratio and the number of contacts, and defines the product as the actual closing quantity; An actual closing number sending module, used for sending the actual closing number to the high-voltage circuit breaker; The high-voltage circuit breaker control module is used to control the closing of contacts corresponding to the actual closing number through an adaptive control algorithm.

6. An electronic device, characterized in that: It comprises a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the automatic control method of the high-voltage circuit breaker as described in any one of claims 1 to 5 is implemented.

7. A storage medium, characterized in that: The storage medium stores program instructions, and when the program instructions are executed by the processor, the automatic control method of the high-voltage circuit breaker according to any one of claims 1 to 6 can be implemented.

Images