Equipment monitoring method and device for complementary power station, equipment and storage medium

Abstract

The invention discloses an equipment monitoring method and device for a complementary power station, equipment and a storage medium, and relates to the technical field of artificial intelligence systems in the production field, the method utilizes the characteristic that resistance heating of the Joule law is positively correlated with the square of current, when the output of electrical equipment is increased, the current is correspondingly increased, and the output of the electrical equipment is increased. When the output of the electrical equipment is increased, the heating value of the electrical equipment is increased, and the whole process shows a positive correlation characteristic, so that when the output of the electrical equipment is increased, the temperature of the equipment also rises, the detection interval is set based on the temperature, and the higher the temperature is, the shorter the detection interval is; the abnormal time of the electrical equipment becomes shorter and shorter based on the higher temperature, the service life of the electrical equipment is effectively protected, information collection is carried out based on visual thermal imaging, and compared with signal integration and manual inspection in the prior art, the monitoring method has the advantages that the robustness is good, and the reliability is high. The device has good universality in the range of three-party power generation equipment of clean energy power generation equipment such as wind power generation equipment, photovoltaic power generation equipment and hydroelectric power generation equipment.

Description

Technical Field

[0001] The present application relates to the technical field of artificial intelligence systems in the production field, and in particular to a method, device, equipment and storage medium for monitoring equipment in a complementary power station. Background Art

[0002] In order to meet the needs of building a new power system, the construction of an integrated clean energy base of photovoltaic, hydropower and storage has a positive practical role. The base uses hydropower (including pumped storage) in the basin as a regulating power source, and couples the output of hydropower and solar power generation to complement each other and send them out.

[0003] In the mainstream new energy power generation of hydro-photovoltaic complementarity, the real-time power of photovoltaics is greatly affected by climate, weather, and day and night, which makes the photovoltaic power generation curve fluctuate violently, with steep rises and falls, and has obvious randomness and volatility; while hydropower generation has strong stability. In order to ensure the balance and stability of the active power of the power grid, the clean energy base needs hydropower units to adjust the active power output according to the load characteristics of the power grid and the real-time power of new energy, smooth out the fluctuations in the new energy load, and ensure the stability of the power grid while meeting the load demand of the power system and the stable operation of the hydropower units.

[0004] At present, the equipment monitoring of complementary power stations usually adopts the method of signal monitoring system combined with manual inspection. The signals are collected separately and then aggregated to the centralized control end for monitoring. Usually, a warning signal is generated when the electrical equipment of the complementary power station is abnormal, and the warning signal is sent to the dispatching monitoring end. When the dispatching personnel monitor the warning signal at the dispatching monitoring end, they will notify the substation personnel to inspect and handle the substation equipment. The substation personnel will eliminate the abnormality of the substation equipment to eliminate the warning signal, and conduct manual inspections regularly during non-abnormal periods to ensure stable operation of the equipment.

[0005] Due to the strong randomness of the photovoltaic end, photovoltaic power generation equipment often experiences load fluctuations, which requires the hydropower end to smooth out the load fluctuations generated by the photovoltaic end and make real-time adjustments. The electrical equipment at the photovoltaic end and the electrical equipment at the hydropower end usually operate under load fluctuations or instability, which can easily lead to false alarms in the signal monitoring system, such as erroneous reporting of non-abnormal situations or non-reporting of abnormal situations, making the signal monitoring system inaccurate. Summary of the invention

[0006] The main purpose of the present application is to provide a method, device, equipment and storage medium for monitoring equipment of a complementary power station, so as to solve the problem of inaccurate signal monitoring system of the complementary power station in the prior art.

[0007] In order to achieve the above objectives, this application provides the following technical solutions:

[0008] A device monitoring method for a complementary power station, the complementary power station comprising a hydropower terminal and a photovoltaic terminal connected to a grid, all electrical devices of the hydropower terminal and the photovoltaic terminal are monitored by at least one external camera terminal, the device monitoring method for the complementary power station comprising:

[0009] Step S1, respectively obtaining the minimum historical output and the maximum historical output of the hydropower terminal and the photovoltaic terminal;

[0010] Step S2, taking the minimum value of the two minimum historical outputs as the minimum value of the output interval, and taking the maximum value of the two maximum historical outputs as the maximum value of the output interval, to construct the output interval;

[0011] Step S3, dividing the output interval into at least two consecutive sub-intervals on average;

[0012] Step S4, defining a time period based on each sub-interval, and the length of all time periods increases from long to short as the value of the output interval increases from small to large;

[0013] Step S5, respectively obtaining the real-time output of the hydropower terminal and the photovoltaic terminal;

[0014] Step S6, classifying all real-time outputs into all sub-intervals through a classification algorithm, and obtaining a time period based on a real-time output;

[0015] Step S7, collecting thermal images of all electrical devices through the external shooting terminal at intervals of the time period;

[0016] Step S8, judging whether the temperature of each electrical device exceeds a preset temperature threshold based on all thermal images;

[0017] Step S9: marking the electrical equipment that exceeds the preset temperature threshold as an abnormally over-temperature equipment.

[0018] As a further improvement of the present application, step S6, classifying all real-time outputs into all sub-intervals through a classification algorithm, and obtaining a time period based on a real-time output, includes:

[0019] Step S61: define a set to be classified A = {a1, a2, ..., a j ,…,a m}, where a j is the jth real-time output in the set A to be classified, and m is the number of all real-time outputs;

[0020] Step S62: define a category set C = {c1, c2, ..., c i ,…,c n}, where ci is the i-th time period in the category set C;

[0021] Step S63, calculating the conditional probability of the signal set A to be classified in each time period according to formula (1):

[0022]

[0023] Among them, P(Ac i ) is the conditional probability of the signal set A to be classified in the i-th time period, P(c i ) is the marginal probability of the i-th time period, P(a j c i ) is the conditional probability of the jth real-time output in the i-th time period;

[0024] Step S64, classifying each real-time output into the time period with the highest conditional probability;

[0025] Step S65, respectively obtain the time period of each real-time output, and obtain a time period based on a real-time output.

[0026] As a further improvement of the present application, in step S7, thermal images of all electrical devices are collected by the external shooting end at intervals of the time period, and then, in step S8, based on all thermal images, it is determined whether the temperature of each electrical device exceeds a preset temperature threshold, and before that, it includes:

[0027] Step S10, packing all pixel color values ​​of a thermal image into a color value data set;

[0028] Step S20, normalizing all color value data sets to obtain normalized data sets;

[0029] Step S30, dividing the normalized data set into a training set and a validation set according to a preset ratio;

[0030] Step S40, defining a neural network model in which an input layer, a hidden layer, and an output layer are sequentially connected;

[0031] Step S50, outputting the training set to the input layer, performing several trainings through the neural network model, and obtaining a root mean square error between the validation set and the current training result based on each training;

[0032] Step S60, obtaining a minimum value of the root mean square error among all training results, and obtaining a training result corresponding to the minimum value as a prediction model;

[0033] Step S70, outputting the verification set to the prediction model to obtain a predicted color value data set;

[0034] Step S80, restoring the predicted color value data set to predicted thermal imaging and substituting it into step S8.

[0035] As a further improvement of the present application, step S20, normalizing all color value data sets to obtain normalized data sets, includes:

[0036] According to formula (2), all color values ​​of the current color value dataset are normalized:

[0037]

[0038] Among them, θ R ' GB is the normalized color value obtained after normalization, θ RGB is the color value of the current color value dataset, is the average value of all color values ​​in the current color value dataset, and δ is the standard deviation of all color values ​​in the current color value dataset.

[0039] As a further improvement of the present application, the neural network model is characterized by formula (3):

[0040]

[0041] Wherein, y is the neural network model; x q is the qth input node of the input layer, each input node corresponds to a normalized data set in the training set, is the weight from the p-th input node of the input layer to the q-th input node of the hidden layer; is the bias of the qth input node connected to the hidden layer; is the bias of the output layer; tansig(x) is the transfer function, and The numbers in the brackets of the symbols are the number of layers. The superscript (1) is the first layer, that is, the input layer, and the superscript (1, 2) is the first to second layer, that is, the input layer to the hidden layer.

[0042] As a further improvement of the present application, the root mean square error is characterized by formula (4):

[0043]

[0044] Wherein, RMSE is the root mean square error, q is the number of the input nodes or the normalized data set, and x p is the color value of the pth normalized data set, This is the training result after the color value training of the p-th normalized data set is completed.

[0045] As a further improvement of the present application, in step S9, the electrical equipment exceeding the preset temperature threshold is marked as an abnormal over-temperature equipment, and then, the following steps are included:

[0046] Step S100, obtaining the location information of the abnormally over-temperature device and the color value of the thermal imaging corresponding to the abnormally over-temperature device;

[0047] Step S200, sending the location information and the color value of the thermal imaging corresponding to the abnormally over-temperature device to an external receiving end.

[0048] In order to achieve the above objectives, this application also provides the following technical solutions:

[0049] A device monitoring device for a complementary power station, the device monitoring device is applied to the device monitoring method as described above, and the device monitoring device comprises:

[0050] A historical output maximum value acquisition module, used to respectively acquire the minimum historical output and the maximum historical output of the hydropower terminal and the photovoltaic terminal;

[0051] An output interval construction module, used to construct the output interval by taking the minimum value of the two minimum historical outputs as the minimum value of the output interval and taking the maximum value of the two maximum historical outputs as the maximum value of the output interval;

[0052] An output sub-interval division module, used to evenly divide the output interval into at least two consecutive sub-intervals;

[0053] A time period definition module, used to define a time period based on each sub-interval, and the length of all time periods increases from long to short as the value of the output interval increases from small to large;

[0054] A real-time output acquisition module, used to respectively acquire the real-time outputs of the hydropower terminal and the photovoltaic terminal;

[0055] A real-time output classification module is used to classify all real-time outputs into all sub-intervals through a classification algorithm, and obtain a time period based on a real-time output;

[0056] A thermal imaging acquisition module, used for collecting thermal images of all electrical devices through the external shooting end at intervals of the time period;

[0057] A thermal imaging judgment module, used to judge whether the temperature of each electrical device exceeds a preset temperature threshold based on all thermal images;

[0058] The abnormal over-temperature device marking module is used to mark the electrical equipment exceeding the preset temperature threshold as an abnormal over-temperature device.

[0059] In order to achieve the above objectives, this application also provides the following technical solutions:

[0060] An electronic device 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 device monitoring method of the complementary power station as described above is implemented.

[0061] To achieve the above objectives, this application also provides the following technical solutions:

[0062] A storage medium stores program instructions, and when the program instructions are executed by a processor, the device monitoring method of the complementary power station as described above is implemented.

[0063] The present application obtains the minimum historical output and maximum historical output of the hydropower end and the photovoltaic end respectively; takes the minimum value of the two minimum historical outputs as the minimum value of the output interval, and takes the maximum value of the two maximum historical outputs as the maximum value of the output interval to construct an output interval; divides the output interval into at least two continuous sub-intervals on average; defines a time period based on each sub-interval, and the length of all time periods increases from long to short as the value of the output interval increases from small to large; obtains the real-time output of the hydropower end and the photovoltaic end respectively; classifies all real-time outputs into all sub-intervals through a classification algorithm, and obtains a time period based on one real-time output; collects thermal images of all electrical equipment through an external shooting end during the interval time period; determines whether the temperature of each electrical equipment exceeds a preset temperature threshold based on all thermal images; and marks electrical equipment that exceeds the preset temperature threshold as abnormally over-temperature equipment. The present application utilizes the property of Joule's law that the resistance heating is positively correlated with the square of the current. When the output of the electrical equipment increases, the current increases accordingly, which will increase the heat generated by the electrical equipment. The entire process shows a positive correlation characteristic. Therefore, when the output of the electrical equipment increases, the temperature of the equipment will also rise accordingly. The present application also sets the detection interval based on the temperature. The higher the temperature, the shorter the detection interval, which ensures that the abnormal time of the electrical equipment becomes shorter and shorter as the temperature increases, effectively protecting the service life of the electrical equipment. The present application collects information based on visual thermal imaging. Compared with the signal integration and manual inspection of the prior art, the monitoring method of the present application has good robustness and good versatility within the scope of wind, solar and hydropower generation equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic diagram of the process steps of an embodiment of the device monitoring method of a complementary power station of the present application;

[0065] Figure 2 This is a functional module diagram of an embodiment of the equipment monitoring device of the complementary power station of the present application;

[0066] Figure 3 This is a schematic diagram of the structure of an embodiment of the electronic device of the present application;

[0067] Figure 4 This is a schematic diagram of the structure of an embodiment of the storage medium of the present application. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0069] The terms "first", "second" and "third" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present application (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. 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 also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0070] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0071] like Figure 1As shown, this embodiment provides an embodiment of a method for monitoring equipment in a complementary power station. In this embodiment, the complementary power station includes a hydropower terminal and a photovoltaic terminal that are connected to the grid. All electrical equipment at the hydropower terminal and the photovoltaic terminal are monitored by at least one external shooting terminal.

[0072] Preferably, this embodiment can also be applied to a comprehensive power station with wind, solar and water complementarity.

[0073] Specifically, the device monitoring method comprises the following steps:

[0074] Step S1, respectively obtaining the minimum historical output and the maximum historical output of the hydropower terminal and the photovoltaic terminal.

[0075] Preferably, output can be understood as power generation. The power generation of a photovoltaic power station is calculated based on its installed capacity and lighting conditions. Generally speaking, the annual power generation of a photovoltaic power station can be calculated by multiplying the installed capacity by the equivalent full-power hours. For example, a one-megawatt photovoltaic power station refers to an installed capacity of 1 megawatt, or 1,000 kilowatts. The operation of a photovoltaic power station also involves reactive voltage control, including the configuration of reactive power sources and the satisfaction of basic voltage control requirements to ensure the effective operation of the power station and the stability of the power system. Specifically in terms of the output range, taking a photovoltaic power station in a certain region as an example, the maximum photovoltaic output in spring (February-May) exceeds that in summer (June-September). For example, the maximum photovoltaic output from February to May 2022 was 34,740 kW, 31,590 kW, 30,990 kW, and 35,270 kW, respectively, while the maximum output from June to September of the same year was 33,630 kW, 32,970 kW, 26,190 kW, and 25,020 kW, respectively. This shows that although there is plenty of sunshine in the summer, due to other factors (such as equipment maintenance, shadowing, etc.), the photovoltaic output capacity in the spring exceeds that in the summer. In addition, the output power curves of domestic distributed photovoltaic power stations show that the photovoltaic output varies in different seasons and months. For example, the typical daily output power curve of an 800kWp rooftop distributed photovoltaic power station in a certain area of ​​Guangdong shows that the proportion of power generation in the power generation period of each month is different, which reflects that the photovoltaic output is affected by seasonal changes.

[0076] Preferably, the output of the hydropower station is relatively stable. The output of non-regulated and daily regulated hydropower stations is usually calculated based on the daily average output of the designed low-water days. Since the site selection of the hydropower station is based on stable water flow, the output range of the hydropower station is usually little affected by the daily flow change. The output of the seasonal regulation hydropower station and the annual regulation hydropower station involves the average output after reservoir regulation. The output of the seasonal regulation hydropower station refers to the average output of the water supply period in the designed dry year after reservoir regulation, while the output of the annual regulation hydropower station is based on the runoff regulation calculation and water energy calculation of a long series of hydrological data. The average output of the water supply period is calculated, and the average output guarantee rate curve of the water supply period is drawn. The average output value corresponding to the design guarantee rate (Pd) of the hydropower station on the curve is the guaranteed output value of the annual regulation hydropower station. The output calculation of the multi-year regulation hydropower station is similar to that of the one-year regulation hydropower station, but the flow and head in the formula refer to the average flow and average head of the multi-year series of designed low water. This type of hydropower station has a wider output range because it can better utilize water resources, especially during the dry season.

[0077] For example, a hydropower station on a certain river is equipped with four mixed-flow turbine generator sets with a single unit capacity of 50,000 kilowatts, with a total installed capacity of 200,000 kilowatts. That is, the output of the power station can reach 200,000 kilowatts when operating at full load. When there is no influence of head and scheduling policy, the output of the hydropower station generally will not fluctuate significantly.

[0078] Step S2, taking the minimum value of the two minimum historical outputs as the minimum value of the output interval, and taking the maximum value of the two maximum historical outputs as the maximum value of the output interval, to construct the output interval.

[0079] Step S3, dividing the output interval into at least two consecutive sub-intervals on average.

[0080] Step S4, defining a time period based on each sub-interval, and the length of all time periods increases from long to short as the value of the output interval increases from small to large.

[0081] For example, the output intervals are [0.1MW, 0.5MW), [0.5MW, 1.0MW), [1.0MW, 1.5MW), [1.5MW, 2.0MW), [2.0MW, 2.5MW], and the time periods can be defined as 2.5h, 2.0h, 1.5h, 1.0h, and 0.5h respectively.

[0082] It should be noted that the above output range examples do not represent the actual situation, and the output range should be set according to the actual statistical data of the power station.

[0083] Step S5, respectively obtaining the real-time output of the hydropower terminal and the photovoltaic terminal.

[0084] Step S6: classify all real-time outputs into all sub-intervals through a classification algorithm, and obtain a time period based on a real-time output.

[0085] Step S7, collecting thermal images of all electrical devices through an external shooting terminal at intervals.

[0086] Preferably, the principle of thermal imaging is based on the fact that all objects emit infrared radiation, and the intensity of this radiation is proportional to the temperature of the object. Thermal imaging technology detects these infrared radiations and converts them into electrical signals, and finally generates a thermal image on the display, thereby showing the temperature distribution of the object. The working process of thermal imaging includes the following steps: first, the thermal imager receives the infrared radiation energy distribution pattern of the measured target through the infrared detector and the optical imaging objective; then, this energy is reflected on the photosensitive element of the infrared detector to obtain an infrared thermal image; finally, these images correspond to the heat distribution field on the surface of the object, and different colors represent different temperatures. Temperatures represented by different colors in thermal imaging: In thermal images, different colors represent different temperatures. Usually, red represents high temperature, blue represents low temperature, and black represents the lowest temperature area.

[0087] Step S8: determining whether the temperature of each electrical device exceeds a preset temperature threshold based on all thermal images.

[0088] Preferably, the commonly used thermal imaging red represents high temperature, and blue represents low temperature, and the temperature can be judged by the color value.

[0089] Preferably, the normal operating temperature range of the electrical equipment of the complementary power station is usually -40°C to 85°C, and the recommended operating temperature range is -20°C to 50°C. When the temperature exceeds 85°C, it can be considered that the temperature of the electrical equipment is abnormal.

[0090] Step S9: marking the electrical equipment that exceeds the preset temperature threshold as an abnormally over-temperature equipment.

[0091] Further, step S6, classifying all real-time outputs into all sub-intervals through a classification algorithm, and obtaining a time period based on a real-time output, specifically includes the following steps:

[0092] Step S61: define a set to be classified A = {a1, a2, ..., a j ,…,a m}, where a j is the jth real-time output in the set A to be classified, and m is the number of all real-time outputs.

[0093] Step S62: define a category set C = {c1, c2, ..., c i ,…,c n}, where c i is the i-th time period in the category set C.

[0094] Step S63, calculate the conditional probability of the signal set A to be classified in each time period according to formula (1):

[0095]

[0096] Among them, P(Ac i ) is the conditional probability of the signal set A to be classified in the i-th time period, P(c i ) is the marginal probability of the i-th time period, P(a j c i ) is the conditional probability of the jth real-time output in the i-th time period.

[0097] Step S64, classify each real-time output into the time period with the highest conditional probability.

[0098] Step S65, respectively obtain the time period of each real-time output, and obtain a time period based on a real-time output.

[0099] Further, in step S7, thermal images of all electrical devices are collected through an external camera at intervals, and then, in step S8, based on all thermal images, it is determined whether the temperature of each electrical device exceeds a preset temperature threshold. Before that, it includes:

[0100] Step S10: Pack all pixel color values ​​of a thermal image into a color value data set.

[0101] Preferably, this embodiment uses RGB color values.

[0102] Step S20, normalizing all color value data sets to obtain normalized data sets.

[0103] Preferably, this embodiment prefers the normalization method of zero-mean normalization (Z-score normalization), which gives the mean and standard deviation of the original data to standardize the data, and the processed data conforms to the standard normal distribution, that is, the mean is 0 and the standard deviation is 1. For the normalization method, this embodiment can also use batch normalization. Compared with simple normalization, in the previous neural network training, only the input layer data is normalized, but not in the middle layer. Although the data set of the input node is normalized, the data distribution of the input data after matrix multiplication is likely to change greatly, and as the number of hidden layer network layers continues to deepen, the change in data distribution will become greater and greater. Therefore, the normalization processing performed by batch normalization in the middle layer of the neural network makes the training effect better.

[0104] Step S30, dividing the normalized data set into a training set and a validation set according to a preset ratio.

[0105] Preferably, the preset ratio is 8:2, so as to divide the normalized data set into a training set and a sample set in a ratio of 8:2.

[0106] Preferably, in actual application, a certain proportion of validation sets are required to verify the accuracy of the model, that is, verification is required after training is completed, and the sample set prediction is started only after the verification is successful. Usually, the image data is divided into training set, validation set, and sample set in a ratio of 70%:15%:15%, that is, 70% of the data is the training set, 15% of the data is the validation set, and 15% of the data is the sample set.

[0107] Step S40, defining a neural network model in which an input layer, a hidden layer, and an output layer are sequentially signal-connected.

[0108] Step S50, output the training set to the input layer, perform several trainings through the neural network model, and obtain the root mean square error between the verification set and the current training result based on each training.

[0109] Step S60, obtaining the minimum value of the root mean square error among all training results, and obtaining the training result corresponding to the minimum value as the prediction model.

[0110] Step S70, outputting the verification set to the prediction model to obtain a predicted color value data set.

[0111] Step S80, restore the predicted color value data set to the predicted thermal image and substitute it into step S8.

[0112] Furthermore, in step S20, all color value data sets are normalized to obtain normalized data sets, including:

[0113] According to formula (2), all color values ​​of the current color value dataset are normalized:

[0114]

[0115] Among them, θ R ' GB is the normalized color value obtained after normalization, θ RGB is the color value of the current color value dataset, is the average value of all color values ​​in the current color value dataset, and δ is the standard deviation of all color values ​​in the current color value dataset.

[0116] Furthermore, the neural network model is represented by formula (3):

[0117]

[0118] Among them, y is the neural network model; x q is the qth input node of the input layer, each input node corresponds to a normalized data set in the training set, is the weight from the pth input node of the input layer to the qth input node of the hidden layer; is the bias of the qth input node connected to the hidden layer; is the bias of the output layer; tansig(x) is the transfer function, and The numbers in the brackets of the symbols in the formula (3) are the number of layers. The superscript (1) is the first layer, that is, the input layer, and the superscript (1, 2) is the first layer to the second layer, that is, the input layer to the hidden layer.

[0119] Preferably, training model training a neural network usually requires providing a large amount of data, namely a data set; the data set is generally divided into three categories, namely the above-mentioned training set, validation set and test set.

[0120] Among them, one epoch is the process of training once with all the samples in the training set. The so-called training once refers to one forward pass and one back pass. When the number of samples in an epoch (i.e., training set) is too large, training once may consume too much time, and it is not necessary to use all the data in the training set for each training. In this case, the entire training set needs to be divided into multiple small blocks, that is, divided into multiple batches for training. An epoch consists of one or more batches, where a batch is a part of the training set. Each training process only uses a part of the data, i.e., a batch. The process of training a batch is an iteration.

[0121] Preferably, the neural network training specifically includes a perceptron, which is composed of two layers of neurons. The input layer receives external input signals and transmits them to the output layer. The output layer is MP neurons, and the step function is y=f(∑w·x-β).

[0122] Preferably, given a training data set, the weight w and the training bias β can be obtained by learning, and β can be understood as a fixed value with an input of -1,0.

[0123] It should be noted that the above formula is not interchangeable with the symbolic meaning of other formulas in the embodiment, and this step function is only used for principle explanation and does not participate in the calculation of other formulas.

[0124] Preferably, in this embodiment, the number of neural network training times can be set to 1000 times.

[0125] Preferably, the learning rate from the 1st to the 500th epoch can be set to 0.01, the learning rate from the 501st to the 750th epoch can be set to 0.001, and the learning rate from the 751st to the 1000th epoch can be set to 0.0001.

[0126] It can be understood that the neural network training of this embodiment mainly includes the following ideas:

[0127] ① Initialize the weights and bias items in the network.

[0128] Initializing parameter values ​​(output unit weights, bias terms and hidden unit weights, bias terms are all model parameters) is to activate forward propagation, obtain the output value of each layer element, and then obtain the value of the loss function.

[0129] ②Activate forward propagation to obtain the output value of each layer and the expected value of the loss function of each layer.

[0130] ③According to the loss function, calculate the error term of the output unit and the error term of the hidden unit.

[0131] Calculate various errors, calculate the gradient of the parameters with respect to the loss function, or calculate partial derivatives according to the chain rule of calculus. For partial derivatives of vectors or matrices in a composite function, the partial derivative of the function inside the composite function is always multiplied on the left; for partial derivatives of scalars in a composite function, the partial derivative of the function inside the composite function can be multiplied on the left or on the right.

[0132] ④Update the weights and bias items in the neural network.

[0133] ⑤ Repeat ② to ④ until the loss function is less than the preset bias or the number of iterations is used up, and the parameters output at this time are the current optimal parameters.

[0134] Furthermore, the root mean square error is expressed by formula (4):

[0135]

[0136] Among them, RMSE is the root mean square error, q is the number of input nodes or normalized data sets, and x p is the color value of the pth normalized data set, This is the training result after the color value training of the p-th normalized data set is completed.

[0137] Further, in step S9, the electrical equipment exceeding the preset temperature threshold is marked as abnormal over-temperature equipment, and then, the following steps are included:

[0138] Step S100, obtaining location information of abnormally over-temperature equipment and color values ​​of thermal images corresponding to the abnormally over-temperature equipment.

[0139] Step S200, sending the location information and the color value of the thermal imaging corresponding to the abnormally over-temperature device to an external receiving end.

[0140] This embodiment obtains the minimum historical output and the maximum historical output of the hydropower end and the photovoltaic end respectively; takes the minimum value of the two minimum historical outputs as the minimum value of the output interval, and takes the maximum value of the two maximum historical outputs as the maximum value of the output interval to construct an output interval; divides the output interval into at least two continuous sub-intervals on average; defines a time period based on each sub-interval, and the length of all time periods increases from long to short as the value of the output interval increases from small to large; obtains the real-time output of the hydropower end and the photovoltaic end respectively; classifies all real-time outputs into all sub-intervals through a classification algorithm, and obtains a time period based on one real-time output; collects thermal images of all electrical equipment through an external shooting end during the interval time period; determines whether the temperature of each electrical equipment exceeds a preset temperature threshold based on all thermal images; and marks the electrical equipment that exceeds the preset temperature threshold as an abnormally over-temperature device. This embodiment utilizes the property of Joule's law that the resistance heating is positively correlated with the square of the current. When the output of the electrical equipment increases, the current increases accordingly, which will increase the heat generated by the electrical equipment. The whole process shows a positive correlation characteristic, so when the output of the electrical equipment increases, the equipment temperature will also rise accordingly. This embodiment also sets the detection interval based on the temperature. The higher the temperature, the shorter the detection interval, which ensures that the abnormal time of the electrical equipment becomes shorter and shorter as the temperature increases, effectively protecting the service life of the electrical equipment. This embodiment collects information based on visual thermal imaging. Compared with the signal integration and manual inspection of the prior art, the monitoring method of this embodiment has good robustness and good versatility within the scope of wind, solar and water power generation equipment.

[0141] like Figure 2 As shown, this embodiment provides an embodiment of a device monitoring apparatus for a complementary power station. In this embodiment, the device monitoring apparatus is applied to the device monitoring method in the above embodiment.

[0142] Specifically, the equipment monitoring device includes a historical output maximum value acquisition module 1, an output interval construction module 2, an output sub-interval division module 3, a time period definition module 4, a real-time output acquisition module 5, a real-time output classification module 6, a thermal imaging acquisition module 7, a thermal imaging judgment module 8, and an abnormal over-temperature equipment marking module 9, which are electrically connected in sequence.

[0143] Among them, the historical output maximum value acquisition module 1 is used to obtain the minimum historical output and the maximum historical output of the hydropower terminal and the photovoltaic terminal respectively; the output interval construction module 2 is used to take the minimum value of the two minimum historical outputs as the minimum value of the output interval and the maximum value of the two maximum historical outputs as the maximum value of the output interval to construct the output interval; the output sub-interval division module 3 is used to divide the output interval into at least two continuous sub-intervals on an even basis; the time period definition module 4 is used to define a time period based on each sub-interval, and the length of all time periods increases from small to large as the value of the output interval increases. to short; the real-time output acquisition module 5 is used to obtain the real-time output of the hydropower end and the photovoltaic end respectively; the real-time output classification module 6 is used to classify all real-time outputs into all sub-intervals through a classification algorithm, and obtain a time period based on a real-time output; the thermal imaging acquisition module 7 is used to collect thermal images of all electrical equipment through an external shooting end at intervals; the thermal imaging judgment module 8 is used to judge whether the temperature of each electrical equipment exceeds the preset temperature threshold based on all thermal images; the abnormal over-temperature equipment marking module 9 is used to mark the electrical equipment that exceeds the preset temperature threshold as abnormal over-temperature equipment.

[0144] Furthermore, the real-time output classification module 6 specifically includes a first real-time output classification submodule, a second real-time output classification submodule, a third real-time output classification submodule, a fourth real-time output classification submodule, and a fifth real-time output classification submodule, which are electrically connected in sequence; the first real-time output classification submodule is electrically connected to the real-time output acquisition module 5, and the fifth real-time output classification submodule is electrically connected to the thermal imaging acquisition module 7.

[0145] The first real-time output classification submodule is used to define a to-be-classified set A={a1, a2,…, a j ,…,a m}, where a j is the jth real-time output in the set A to be classified, and m is the number of all real-time outputs.

[0146] The second real-time output classification submodule is used to define a category set C = {c1, c2, ..., c i ,…,c n}, where c i is the i-th time period in the category set C.

[0147] The third real-time output classification submodule is used to calculate the conditional probability of the signal set A to be classified in each time period according to formula (1):

[0148]

[0149] Among them, P(Ac i) is the conditional probability of the signal set A to be classified in the i-th time period, P(c i ) is the marginal probability of the i-th time period, P(a j c i ) is the conditional probability of the jth real-time output in the i-th time period.

[0150] The fourth real-time output classification submodule is used to classify each real-time output into a time period with the highest conditional probability.

[0151] The fifth real-time output classification submodule is used to respectively obtain the time period of each real-time output, and obtain a time period based on a real-time output.

[0152] Furthermore, the thermal imaging acquisition module 7 and the thermal imaging judgment module 8 also include a pixel color value packaging module, a color value data set normalization processing module, a normalized data set partitioning module, a neural network model definition module, a neural network model training module, a prediction model acquisition module, a prediction color value data set acquisition module, and a prediction thermal imaging restoration and substitution module, which are electrically connected in sequence; the pixel color value packaging module is electrically connected to the thermal imaging acquisition module 7, and the prediction thermal imaging restoration and substitution module is electrically connected to the thermal imaging judgment module 8.

[0153] Among them, the pixel color value packaging module is used to package all pixel color values ​​of a thermal image into a color value data set; the color value data set normalization processing module is used to normalize all color value data sets to obtain a normalized data set; the normalized data set division module is used to divide the normalized data set into a training set and a verification set according to a preset ratio; the neural network model definition module is used to define a neural network model in which the input layer, the hidden layer, and the output layer are sequentially connected by signals; the neural network model training module is used to output the training set to the input layer, perform several trainings through the neural network model, and obtain the root mean square error between the verification set and the current training result based on each training; the prediction model acquisition module is used to obtain the minimum value of the root mean square error in all training results, and obtain the training result corresponding to the minimum value as the prediction model; the predicted color value data set acquisition module is used to output the verification set to the prediction model to obtain a predicted color value data set; the predicted thermal imaging restoration and substitution module is used to restore the predicted color value data set to the predicted thermal imaging and substitute it into the thermal imaging judgment module 8.

[0154] Furthermore, the color value data set normalization processing module is specifically used to perform standard normalization on all color values ​​of the current color value data set according to formula (2):

[0155]

[0156] Among them, θ R 'GB is the normalized color value obtained after normalization, θ RGB is the color value of the current color value dataset, is the average value of all color values ​​in the current color value dataset, and δ is the standard deviation of all color values ​​in the current color value dataset.

[0157] Furthermore, the neural network model definition module is equipped with a neural network model represented by formula (3):

[0158]

[0159] Among them, y is the neural network model; x q is the qth input node of the input layer, each input node corresponds to a normalized data set in the training set, is the weight from the pth input node of the input layer to the qth input node of the hidden layer; is the bias of the qth input node connected to the hidden layer; is the bias of the output layer; tansig(x) is the transfer function, and The numbers in the brackets of the symbols are the number of layers. The superscript (1) is the first layer, that is, the input layer, and the superscript (1, 2) is the first to second layer, that is, the input layer to the hidden layer.

[0160] Furthermore, the prediction model acquisition module is equipped with the root mean square error represented by formula (4):

[0161]

[0162] Among them, RMSE is the root mean square error, q is the number of input nodes or normalized data sets, and x p is the color value of the pth normalized data set, This is the training result after the color value training of the p-th normalized data set is completed.

[0163] Furthermore, the equipment monitoring device also includes an abnormal over-temperature equipment information acquisition module and a normal over-temperature equipment information sending module which are electrically connected in sequence; the abnormal over-temperature equipment information acquisition module is electrically connected to the abnormal over-temperature equipment marking module 9.

[0164] Among them, the abnormal over-temperature device information acquisition module is used to obtain the location information of the abnormal over-temperature device and the color value of the thermal imaging corresponding to the abnormal over-temperature device; the abnormal over-temperature device information sending module is used to send the location information and the color value of the thermal imaging corresponding to the abnormal over-temperature device to the external receiving end.

[0165] It should be noted that this embodiment is a functional module embodiment based on the above method embodiment. The preferred, expanded, limited and exemplary parts of this embodiment can refer to the above embodiment and will not be described in detail in this embodiment.

[0166] This embodiment obtains the minimum historical output and the maximum historical output of the hydropower end and the photovoltaic end respectively; takes the minimum value of the two minimum historical outputs as the minimum value of the output interval, and takes the maximum value of the two maximum historical outputs as the maximum value of the output interval to construct an output interval; divides the output interval into at least two continuous sub-intervals on average; defines a time period based on each sub-interval, and the length of all time periods increases from long to short as the value of the output interval increases from small to large; obtains the real-time output of the hydropower end and the photovoltaic end respectively; classifies all real-time outputs into all sub-intervals through a classification algorithm, and obtains a time period based on one real-time output; collects thermal images of all electrical equipment through an external shooting end during the interval time period; determines whether the temperature of each electrical equipment exceeds a preset temperature threshold based on all thermal images; and marks the electrical equipment that exceeds the preset temperature threshold as an abnormally over-temperature device. This embodiment utilizes the property of Joule's law that the resistance heating is positively correlated with the square of the current. When the output of the electrical equipment increases, the current increases accordingly, which will increase the heat generated by the electrical equipment. The whole process shows a positive correlation characteristic, so when the output of the electrical equipment increases, the equipment temperature will also rise accordingly. This embodiment also sets the detection interval based on the temperature. The higher the temperature, the shorter the detection interval, which ensures that the abnormal time of the electrical equipment becomes shorter and shorter as the temperature increases, effectively protecting the service life of the electrical equipment. This embodiment collects information based on visual thermal imaging. Compared with the signal integration and manual inspection of the prior art, the monitoring method of this embodiment has good robustness and good versatility within the scope of wind, solar and water power generation equipment.

[0167] Figure 3 An embodiment of the electronic device of the present application is shown, see Figure 3 The electronic device 10 includes a processor 101 and a memory 102 coupled to the processor 101 .

[0168] The memory 102 stores program instructions for implementing the device monitoring method of the complementary power plant of any of the above embodiments.

[0169] The processor 101 is used to execute program instructions stored in the memory 102 to perform equipment monitoring of the complementary power station.

[0170] The processor 101 may also be referred to as a CPU (Central Processing Unit). The processor 101 may be an integrated circuit chip having a signal processing capability. The processor 101 may 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 may be a microprocessor or the processor may also be any conventional processor, etc.

[0171] Further, Figure 4 This is a schematic diagram of the structure of a storage medium according to an embodiment of the present application. Figure 4 The storage medium 11 of the embodiment of the present application stores program instructions 111 that can implement all the above methods, wherein the program instructions 111 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0172] In the several embodiments provided in 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 only schematic. For example, the division of units is only a logical function division. There may 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 mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0173] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0174] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for monitoring equipment in a complementary power station, wherein the complementary power station comprises a hydropower terminal and a photovoltaic terminal connected to the grid, and all electrical equipment in the hydropower terminal and the photovoltaic terminal are monitored by at least one external camera terminal, characterized in that: The equipment monitoring method of the complementary power station comprises: Step S1, respectively obtaining the minimum historical output and the maximum historical output of the hydropower terminal and the photovoltaic terminal; Step S2, taking the minimum value of the two minimum historical outputs as the minimum value of the output interval, and taking the maximum value of the two maximum historical outputs as the maximum value of the output interval, to construct the output interval; Step S3, dividing the output interval into at least two consecutive sub-intervals on average; Step S4, defining a time period based on each sub-interval, and the length of all time periods increases from long to short as the value of the output interval increases from small to large; Step S5, respectively obtaining the real-time output of the hydropower terminal and the photovoltaic terminal; Step S6, classifying all real-time outputs into all sub-intervals through a classification algorithm, and obtaining a time period based on a real-time output; Step S7, collecting thermal images of all electrical devices through the external shooting terminal at intervals of the time period; Step S8, judging whether the temperature of each electrical device exceeds a preset temperature threshold based on all thermal images; Step S9: marking the electrical equipment that exceeds the preset temperature threshold as an abnormally over-temperature equipment.

2. The device monitoring method according to claim 1, characterized in that: Step S6, classifying all real-time outputs into all sub-intervals through a classification algorithm, and obtaining a time period based on a real-time output, including: Step S61: define a set to be classified A = {a1, a2, ..., a j ,…,a m }, where a j is the jth real-time output in the set A to be classified, and m is the number of all real-time outputs; Step S62: define a category set C = {c1, c2, ..., c i ,…,c n }, where c i is the i-th time period in the category set C; Step S63, calculating the conditional probability of the signal set A to be classified in each time period according to formula (1): Among them, P(Ac i ) is the conditional probability of the signal set A to be classified in the i-th time period, P(c i ) is the marginal probability of the i-th time period, P(a j c i ) is the conditional probability of the jth real-time output in the i-th time period; Step S64, classifying each real-time output into the time period with the highest conditional probability; Step S65, respectively obtain the time period of each real-time output, and obtain a time period based on a real-time output.

3. The device monitoring method according to claim 1, characterized in that: Step S7, collecting thermal images of all electrical devices through the external shooting end at intervals of the time period, and then, step S8, judging whether the temperature of each electrical device exceeds a preset temperature threshold based on all thermal images, before that, including: Step S10, packing all pixel color values ​​of a thermal image into a color value data set; Step S20, normalizing all color value data sets to obtain normalized data sets; Step S30, dividing the normalized data set into a training set and a validation set according to a preset ratio; Step S40, defining a neural network model in which an input layer, a hidden layer, and an output layer are sequentially connected; Step S50, outputting the training set to the input layer, performing several trainings through the neural network model, and obtaining a root mean square error between the validation set and the current training result based on each training; Step S60, obtaining a minimum value of the root mean square error among all training results, and obtaining a training result corresponding to the minimum value as a prediction model; Step S70, outputting the verification set to the prediction model to obtain a predicted color value data set; Step S80, restoring the predicted color value data set to predicted thermal imaging and substituting it into step S8.

4. The device monitoring method according to claim 3, characterized in that: Step S20, normalizing all color value data sets to obtain normalized data sets, including: According to formula (2), all color values ​​of the current color value dataset are normalized: Among them, θ R ' GB is the normalized color value obtained after normalization, θ RGB is the color value of the current color value dataset, is the average value of all color values ​​in the current color value dataset, and δ is the standard deviation of all color values ​​in the current color value dataset.

5. The device monitoring method according to claim 3, characterized in that: The neural network model is represented by formula (3): Wherein, y is the neural network model; x q is the qth input node of the input layer, each input node corresponds to a normalized data set in the training set, is the weight from the p-th input node of the input layer to the q-th input node of the hidden layer; is the bias of the qth input node connected to the hidden layer; is the bias of the output layer; tansig(x) is the transfer function, and The numbers in the brackets of the symbols in the formula (3) are the number of layers. The superscript (1) is the first layer, that is, the input layer, and the superscript (1, 2) is the first layer to the second layer, that is, the input layer to the hidden layer.

6. The device monitoring method according to claim 3, characterized in that: The root mean square error is represented by formula (4): Wherein, RMSE is the root mean square error, q is the number of the input nodes or the normalized data set, and x p is the color value of the pth normalized data set, This is the training result after the color value training of the p-th normalized data set is completed.

7. The device monitoring method according to claim 1, characterized in that: Step S9, marking the electrical equipment exceeding the preset temperature threshold as abnormal over-temperature equipment, and then comprising: Step S100, obtaining the location information of the abnormally over-temperature device and the color value of the thermal imaging corresponding to the abnormally over-temperature device; Step S200, sending the location information and the color value of the thermal imaging corresponding to the abnormally over-temperature device to an external receiving end.

8. An equipment monitoring device for a complementary power station, the equipment monitoring device being applied to the equipment monitoring method according to any one of claims 1 to 7, characterized in that: The equipment monitoring device comprises: A historical output maximum value acquisition module, used to respectively acquire the minimum historical output and the maximum historical output of the hydropower terminal and the photovoltaic terminal; An output interval construction module, used to construct the output interval by taking the minimum value of the two minimum historical outputs as the minimum value of the output interval and taking the maximum value of the two maximum historical outputs as the maximum value of the output interval; An output sub-interval division module, used to evenly divide the output interval into at least two consecutive sub-intervals; A time period definition module, used for defining a time period based on each sub-interval, and the length of all time periods increases from long to short as the value of the output interval increases from small to large; A real-time output acquisition module, used to respectively acquire the real-time outputs of the hydropower terminal and the photovoltaic terminal; A real-time output classification module is used to classify all real-time outputs into all sub-intervals through a classification algorithm, and obtain a time period based on a real-time output; A thermal imaging acquisition module, used for collecting thermal images of all electrical devices through the external shooting end at intervals of the time period; A thermal imaging judgment module, used to judge whether the temperature of each electrical device exceeds a preset temperature threshold based on all thermal images; The abnormal over-temperature device marking module is used to mark the electrical equipment exceeding the preset temperature threshold as an abnormal over-temperature device.

9. 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 device monitoring method as described in any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium stores program instructions, and when the program instructions are executed by the processor, the device monitoring method according to any one of claims 1 to 7 can be implemented.

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