Transformer substation environment monitoring system and method based on electric power internet of things

Abstract

The invention discloses a substation environment monitoring system and method based on an electric power internet of things, relates to the technical field of environment monitoring, and is used for solving the problem of inaccurate substation environment detection. Comprising a data acquisition module, a data transmission module, a data analysis module, a future prediction module and a data storage module which work cooperatively through signal connection. The data acquisition module acquires environmental data of each area of the transformer substation and performs signal smoothing, analog-to-digital conversion and normalization processing. And the data transmission module adopts an MQTT protocol and a ZigBee protocol to carry out data convergence and long-distance transmission. The data analysis module calculates the overall environment coefficient and the equipment state coefficient of the transformer substation according to the environment data, compares the coefficients with the early warning value, and evaluates the overall operation condition of the transformer substation. The future prediction module performs modeling and prediction on historical data by using an ARIMA model, and early warns possible environment and equipment risks in advance. Through the system, the safety, the stability and the operation efficiency of the transformer substation can be improved.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring technology, and more specifically, to a substation environmental monitoring system and method based on power Internet of Things. Background Art

[0002] Substations are an important part of the power system, responsible for the transformation and distribution of electricity. Their safe and stable operation is crucial to the reliability of power supply. During the operation of substations, the impact of environmental factors on equipment cannot be ignored. Environmental changes such as temperature, electromagnetic radiation, and humidity may cause equipment performance degradation or even cause failure accidents. Therefore, real-time monitoring of the environmental conditions of substations is of great significance to ensure the normal operation of equipment, extend its service life, and improve its operating efficiency.

[0003] Traditional substation environmental monitoring systems rely on manual inspections, and the monitoring data collection and processing are relatively simple, inefficient, and cannot achieve comprehensive and real-time environmental monitoring. At the same time, the early warning mechanism of the existing system is mostly regular inspections, lacking accurate dynamic analysis and prediction capabilities, and is difficult to respond to sudden environmental changes.

[0004] With the rapid development of Internet of Things technology and big data analysis technology, sensor-based environmental monitoring systems are gradually being applied to the field of substations. By collecting and transmitting environmental data in real time, combined with intelligent analysis and prediction models, more efficient and accurate environmental monitoring and early warning can be achieved. However, existing technologies still have certain limitations in data processing, transmission efficiency, and prediction capabilities, especially in multi-region monitoring and multi-factor comprehensive analysis. Therefore, there is an urgent need for a more comprehensive, accurate, and intelligent substation environmental monitoring system.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a substation environment monitoring system and method based on the power Internet of Things to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] In a preferred embodiment, it includes: a data acquisition module, a data transmission module, a data storage module, a data analysis module and a future prediction module, and the modules are connected by signals;

[0009] The data acquisition module is mainly used to collect environmental data from various areas of the substation. It collects data through industrial-grade sensors and processes them through signal smoothing, analog-to-digital conversion and normalization.

[0010] The data transmission module is mainly used for multi-point aggregation of environmental data through lightweight protocols and long-distance data transmission using the ZigBee protocol;

[0011] The data analysis module is mainly used to calculate the environmental score after receiving the environmental data and compare it with the warning value, and to obtain the overall environmental coefficient of the substation by combining the environmental scores of each area;

[0012] The future prediction module is mainly used to use the ARIMA time series model to predict the future environment and equipment status of the substation based on historical data;

[0013] The data storage module is used to store all data in the processing process.

[0014] In a preferred embodiment, the data acquisition module divides the substation into several areas according to the locations of different equipment in the substation, and monitors each area separately;

[0015] The data acquisition module collects the environmental data of the substation by deploying industrial-grade sensors. The sliding average method is used to eliminate the random noise of the signal and smooth the data fluctuations. The specific formula is Among them, St is the smoothing value at the tth moment, N is the window size, and x i is the sampling value at the i-th moment,

[0016] The data acquisition module uses a high-precision analog-to-digital converter to sample the input analog signal at a fixed sampling rate and quantize it into discrete digital values, converting the analog signal into a digital signal.

[0017] In a preferred embodiment, the data transmission module formats all collected environmental data into JSON data format, and uses the MQTT protocol to treat each sensor node as an MQTT client, periodically publishing the environmental data to a specified topic through the MQTT protocol; and aggregates the environmental data through the MQTT proxy;

[0018] After the data is aggregated in the data transmission module, the sensor nodes communicate with the central gateway through the ZigBee protocol. Each sensor node, as a terminal device, sends the collected environmental data to the coordinator through ZigBee, and the coordinator then forwards the environmental data to the data analysis module.

[0019] In a preferred embodiment, the data analysis module obtains the regional environmental score by weighted calculation of the key monitored environmental data, specifically according to the formula: QYDF=Q(1)×HY(1)+Q(2)×HY(2)+…+Q(i)×HY(i), wherein QYDF represents the regional environmental score, i represents the number of environmental factors in the region, Q(i) represents the weight value of the i-th environmental factor, HY(i) represents the specific data value of the i-th environmental factor, and sets the early warning score Yf. The regional environmental score is compared with the early warning score Yf. When the regional environmental score is less than Yf, the regional environment is monitored in a key manner. When the regional environmental score is greater than the early warning score Yf, the environmental condition of the region is stable.

[0020] The data analysis module determines the overall environmental coefficient of the substation based on the data analysis of the environmental scores of each area, according to the formula: ZTHJ=Qb×Qy1+Qk×Qy2+Qp×Qy3, where ZTHJ represents the overall environmental coefficient of the substation, Qy1 represents the environmental score of the main transformer area, Q1 represents the weight value of the environmental score of the main transformer area, Qy2 represents the environmental score of the switchgear area, Q2 represents the weight value of the environmental score of the switchgear area, Qy3 represents the environmental score of the distribution room area, and Q3 represents the weight value of the regional environmental score of the distribution room area.

[0021] In a preferred embodiment, the steps for calculating the overall environmental coefficient of the substation are as follows:

[0022] The data analysis module accesses the data storage module through the RESTful API interface, retrieves the equipment log data, and obtains the equipment status data in each area, including: equipment age and average daily working hours of the equipment;

[0023] The data analysis module determines the equipment status score of each area by weighted sum calculation of the equipment service life and the average daily working hours of the equipment in the area, according to the formula: SBZ = Qs × (SY / SYMAX) + Qt × (t / tmax), where SBZ represents the equipment status score, Qs represents the weight value of the equipment service life, SY represents the equipment service life, SYMAX represents the maximum equipment service life, Qt represents the weight value of the average daily working hours of the equipment, t represents the average daily working hours of the equipment, and tmax represents the maximum daily working hours of the equipment;

[0024] The data analysis module determines the regional equipment status coefficient according to the regional environment score and the regional equipment status score. The specific formula is: QYSB = Qsbz × SBZ + Qqydf × QYDF to calculate the regional status coefficient, where QYSB represents the regional equipment status coefficient, Qsbz represents the weight value of the equipment status score, and Qqydf represents the weight value of the regional environment score.

[0025] The data analysis module sets the early warning threshold value Ys. When the regional equipment status coefficient is greater than Ys, it is necessary to focus on monitoring the equipment in this area. When the regional equipment status coefficient is less than or equal to the early warning threshold value Ys, it indicates that the environmental conditions in this area are stable. When the regional equipment status coefficient is less than or equal to the early warning threshold value Ys, the equipment status is stable, and the equipment status in this area does not need to be considered in determining the overall equipment status coefficient of the substation.

[0026] In a preferred embodiment, the future prediction module extracts the historical environmental data and equipment status data of the last three weeks from the data storage module through the RESTful API, calculates the overall environmental coefficient ZTHJ and the equipment status coefficient QYSB of the substation, and applies time series analysis to make the data series stable through the first-order difference;

[0027] The future prediction module uses the ARIMA model to model the stabilized historical data and calculate the model parameters.

[0028] In a preferred embodiment, it is characterized in that:

[0029] Step 1: Collect environmental data from each area of ​​the substation through industrial-grade sensors, and perform signal smoothing, analog-to-digital conversion, and normalization processing;

[0030] Step 2: Use lightweight protocols to aggregate environmental data at multiple points, and use ZigBee protocols to achieve long-distance data transmission;

[0031] Step 3: Calculate the environmental score of the environmental data and compare it with the warning value to determine whether the environmental status of each area is stable. The overall environmental coefficient of the substation is obtained by combining the environmental scores of each area.

[0032] Step 4: Use the ARIMA time series model to predict the future environment and equipment status of the substation based on historical data.

[0033] The present invention discloses a substation environment monitoring system and method based on the electric power Internet of Things, relates to the field of environmental monitoring technology, and is used to solve the problem of inaccurate substation environment detection; it includes: a data acquisition module, a data transmission module, a data analysis module, a future prediction module and a data storage module, and each module works together through signal connection. The data acquisition module collects environmental data from various areas of the substation, and performs signal smoothing, analog-to-digital conversion and normalization processing. The data transmission module uses the MQTT protocol and the ZigBee protocol for data aggregation and long-distance transmission. The data analysis module calculates the overall environmental coefficient and equipment status coefficient of the substation based on the environmental data, and compares them with the warning value to evaluate the overall operation status of the substation. The future prediction module uses the ARIMA model to model and predict historical data, and warns of possible environmental and equipment risks in advance. Through this system, the safety, stability and operation efficiency of the substation can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the structure of the substation environment monitoring system based on the power Internet of Things of the present invention.

[0035] Figure 2 It is a schematic diagram of the operation of the substation environment monitoring method based on the power Internet of Things of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Example

[0038] The invention discloses a substation environment monitoring system based on electric power Internet of Things, comprising: a data acquisition module, a data transmission module, a data analysis module, a data storage module, a future prediction module, and signal connections between the modules.

[0039] The data storage module is used to store all data in the processing process.

[0040] The data acquisition module is mainly used to collect and process substation environmental data.

[0041] Considering the high efficiency and accuracy of environmental monitoring, the substation is divided into several areas according to the location of different equipment in the substation, and each area is monitored separately. In this embodiment, the substation is divided into the main transformer area, the switchgear area, and the distribution room area. Among them, in the main transformer area, the main transformer is the core equipment of the substation, and a large amount of heat will be generated during operation. Excessive temperature will accelerate the aging of the insulation material, reduce the service life of the transformer, and may even cause a fire; at the same time, the main transformer will generate a strong electromagnetic field when working, which will affect the signal transmission and normal operation of the surrounding equipment. Therefore, the main transformer area is greatly affected by high temperature and electromagnetic radiation, and the temperature and electromagnetic radiation data of this area are mainly detected; in the switchgear area, the insulation performance of the switchgear is highly sensitive to air humidity. High humidity will cause the insulation material to be damp, reduce the breakdown voltage, accelerate the corrosion of the metal part, and affect the mechanical and electrical properties of the equipment; at the same time, the long-term existence of partial discharge will gradually lead to the destruction of the insulation material and cause more serious electrical faults. Therefore, the switchgear area is greatly affected by air humidity and signal quality, and the air humidity and partial discharge signal data of this area are mainly monitored; in the distribution room area, cables and distribution equipment will generate heat during operation. If the ventilation is poor, the heat will accumulate and cause the equipment to overheat. At the same time, if the air contains a lot of dust, moisture or corrosive gases, it will have an adverse effect on the contact points, metal parts and insulation performance of electrical equipment. Therefore, the distribution room area is affected by the concentrated distribution of cables and equipment, and the ventilation status and air quality data of this area are monitored;

[0042] By deploying sensors to collect environmental data of the substation, the signal is smoothed, and a high-precision ADC is used to convert the sensor signal into a digital signal. Through digital signal processing technology, the analog signal is converted into a digital signal, and the obtained environmental data is normalized.

[0043] Specifically, industrial-grade sensors with high precision, strong anti-interference ability and adaptability to harsh environments are used in the complex environment of substations to accurately collect key environmental data in each area to avoid waste of resources and generation of invalid data. Example environmental data is as follows:

[0044] Main transformer area: ambient temperature: 78.3°C, electromagnetic radiation intensity: 480μT,

[0045] Switchgear area: Ambient humidity: 65% RH, Partial discharge signal: 3.2mV,

[0046] Power distribution room area: Air quality: 85μg / m 3 , wind speed: 1.8m / s

[0047] The acquired environmental data is subjected to the Moving Average method to eliminate the random noise of the signal and smooth the data fluctuation. The specific formula is: Among them, St is the smoothing value at the tth moment, N is the window size, and x i is the sampling value at the i-th moment. For example, the temperature sampling values ​​of the main transformer area are 78.3, 77.8, and 78.1. The window size N=3. Substituting the example data into the calculation, the smoothed ambient temperature data is 78.07°C.

[0048] A high-precision analog-to-digital converter (ADC) is used to sample the input analog signal at a fixed sampling rate and quantize it into discrete digital values, converting the analog signal into a digital signal. For example, for the main transformer area ambient temperature data of 78.07, a 24-bit ADC is used with a quantization range of 0°C, 100°C, and the digitized value is 13094711.

[0049] Linear normalization is used to map the environmental data of each region to the interval [0,1], the formula is: In the formula, Normalized_Score is the normalized value. For example, the ambient temperature data of the main transformer area is 78.07°C, and the electromagnetic radiation intensity is 480μT. Substituting the sample data into the normalized formula, the normalized environmental data in the main transformer area is obtained: ambient temperature data: 0.7807, electromagnetic radiation intensity: 0.48.

[0050] It should be noted that the normalization formula given in this embodiment is a general formula and will not be described in detail here.

[0051] The data transmission module is mainly used to aggregate and forward the environmental data obtained from the data acquisition module.

[0052] A lightweight protocol (such as MQTT) is used to aggregate multi-point environmental data, and the aggregated environmental data is communicated over distance using the ZigBee protocol that supports low power consumption and multi-point connections, thereby achieving efficient and reliable transmission of environmental data.

[0053] Specifically, all environmental data collected by the data transmission module is formatted in JSON data format, and the data format should include the following key information:

[0054] Device ID: used to identify the sensor node.

[0055] Area identification: Marks which substation area the data belongs to (such as main transformer area, switchgear area, distribution room area)

[0056] Timestamp: Record the exact time when environmental data is collected to facilitate later data analysis.

[0057] Environmental data: specific environmental monitoring values ​​(such as temperature, electromagnetic radiation, humidity, etc.).

[0058] The data transmission module uses the MQTT protocol to treat each sensor node as an MQTT client, periodically publishes environmental data to the specified topic through the MQTT protocol, and aggregates the environmental data through the MQTT agent.

[0059] After the data is aggregated in the data transmission module, the sensor node communicates with the central gateway (coordinator) through the ZigBee protocol. Each sensor node, as a terminal device, sends the collected environmental data to the coordinator through ZigBee, and the coordinator forwards the environmental data to the data analysis module. For example: in the switchgear area, the humidity is 65%, and the partial discharge signal is 3.2mV. The sensor node sends this data to the gateway through the ZigBee protocol. The gateway aggregates the data and forwards it to the data analysis module

[0060] After acquiring the environmental data forwarded by the data transmission module, the data analysis module analyzes the acquired environmental data.

[0061] Calculate the environmental score of each region based on the environmental data of each region, set the early warning score Yf, and compare the regional environmental score with the early warning score Yf. When the regional environmental score is less than Yf, it is necessary to focus on monitoring the regional environment. When the regional environmental score is greater than the early warning score Yf, it indicates that the regional environmental conditions are stable. The overall environmental coefficient of the substation is determined by combining the environmental scores of each region, and the environmental conditions of the substation are quickly and comprehensively evaluated through the overall environmental coefficient of the substation.

[0062] Specifically, the key monitored environmental data in each region has different degrees of impact on the environment of the corresponding region. In this embodiment, the regional environmental score is obtained by calculating the weight value of each environmental factor in the region and performing weighted calculation on the key monitored environmental data. The specific formula is: QYDF = Q(1) × HY(1) + Q(2) × HY(2) + ... + Q(i) × HY(i), where QYDF represents the regional environmental score, i represents the number of environmental factors in the region, Q(i) represents the weight value of the i-th environmental factor, and HY(i) represents the weight value of the i-th environmental factor. For example, in the main transformer area, the temperature data is calculated to have a weight of 0.6, and the electromagnetic radiation intensity data is 0.4. The sample data is substituted into the calculation to obtain the main transformer area environment score of 0.668. Through similar calculations, the switchgear area environment score is 0.551, and the distribution room area environment score is 0.603. The early warning score is set to 0.6. By comparing the calculated environmental scores of each area with the early warning scores, it is found that the environmental conditions of the main transformer area and the distribution room area are stable, and the switchgear area environment needs to be monitored in particular.

[0063] It should be noted that the monitoring of the overall environment of the substation needs to be determined in combination with the analysis of the environmental scores of each area. After the data analysis module obtains the environmental score data of each area, it determines the overall environmental coefficient of the substation according to the analysis of the environmental score data of each area. The specific formula is: ZTHJ = Qb × Qy1 + Qk × Qy2 + Qp × Qy3, where ZTHJ represents the overall environmental coefficient of the substation, Qy1 represents the environmental score of the main transformer area, Q1 represents the weight value of the environmental score of the main transformer area, Qy2 represents the environmental score of the switchgear area, Q2 represents the weight value of the environmental score of the switchgear area, and Qy3 represents the environmental score of the distribution room area. Q3 represents the weight value of the regional environmental score of the distribution room area; specifically, Q1 is calculated to be 0.4, Q2 is 0.25, and Q3 is 0.35. Substituting the sample data into the formula, the overall environmental coefficient ZTHJ of the substation is calculated to be 0.616.

[0064] It should be noted that environmental factors in the region will also have a certain degree of impact on the equipment status. In this embodiment, by obtaining the equipment status data in each region, including: the service life of the equipment and the average daily working hours of the equipment, the equipment status score in each region is determined by the service life of the equipment in the region and the average daily working hours of the equipment. The regional equipment status coefficient of each region is determined according to the equipment status score in each region combined with the environmental score of each region, and the early warning threshold Ys is set. When the regional equipment status coefficient is greater than Ys, the equipment in the region needs to be monitored intensively. When the regional equipment status coefficient is less than or equal to the early warning threshold Ys, it indicates that the environmental conditions in the region are stable. The overall equipment status coefficient of the substation is determined by combining the equipment status coefficients of each region.

[0065] Specifically, the data analysis module accesses the data storage module through the RESTful API interface to retrieve the device log data. The following sample data is obtained:

[0066] Main transformer area: The equipment service life is 14.5 years; the average daily working time is 14.86 hours.

[0067] Switchgear: The equipment service life is 12.8 years; the average daily working time is 14 hours.

[0068] Power distribution room equipment: The equipment service life is 9.3 years; the average daily working time is 14.57 hours.

[0069] The calculation steps of the overall environmental factor of the substation are as follows:

[0070] Step S1: Determine the equipment status score of each area by weighted sum calculation, combining the equipment service life and the average daily working time of the equipment in the area, specifically according to the formula: SBZ=Qs×(SY / SYMAX)+Qt×(t / tmax), where SBZ represents the equipment status score, Qs represents the weight value of the equipment service life, SY represents the equipment service life, SYMAX represents the maximum equipment service life, Qt represents the weight value of the average daily working time of the equipment, t represents the average daily working time of the equipment, and tmax represents the maximum daily working time of the equipment; for example: Qs is calculated to be 0.4, Qt is 0.6, and the example data is substituted into the calculation to obtain the equipment status score of the main transformer area is 0.542, the equipment status score of the switch area is 0.292, and the equipment status score of the distribution room area is 0.2.

[0071] Step S2: Determine the regional equipment status coefficient according to the analysis of the regional environment score and the regional equipment status. Specifically, calculate the status coefficient of each region according to the formula: QYSB=Qsbz×SBZ+Qqydf×QYDF, where QYSB represents the regional equipment status coefficient, Qsbz represents the weight value of the equipment status score, and Qqydf represents the weight value of the regional environment score. For example, by calculation, Qsbz is 0.6, Qqydf is 0.4, and the example data is substituted into the formula for calculation. The main transformer area equipment status coefficient is 0.592, the switchgear area equipment status coefficient is 0.396, and the distribution room area equipment status coefficient is 0.361.

[0072] Step S3: Set the warning threshold Ys to 0.4. By comparing the equipment status coefficient of each area with the warning threshold Ys, it is found that the main transformer area environment needs to be monitored, and the equipment status of the switchgear area and the distribution room area is stable.

[0073] It should be noted that when the regional equipment status coefficient is less than or equal to the warning threshold value Ys, the equipment status is stable, and the regional equipment status does not need to be considered in determining the overall equipment status coefficient of the substation. The regional equipment status coefficient of the main transformer area is the overall equipment status coefficient of the substation. At the same time, the data analysis module uploads the obtained substation overall environmental coefficient and substation overall equipment status coefficient to the future prediction module.

[0074] The future prediction module is mainly used to use the time series model ARIMA to predict the future environmental conditions of the substation based on the overall environmental coefficient of the substation and the overall equipment status coefficient obtained in the data analysis module, helping managers to take corresponding measures in advance to ensure that the substation environment is always in a safe and stable state, thereby improving the operational safety and efficiency of the substation.

[0075] Specifically, the future prediction module extracts the historical environmental data and equipment status data of the last three weeks from the data storage module through the RESTful API, and calculates the overall environmental coefficient ZTHJ and equipment status coefficient QYSB of the substation to support short-term prediction. The sample data is as follows:

[0076] Historical overall environmental coefficient: 0.613, 0.619, 0.616.

[0077] Historical overall equipment status coefficient: 0.592, 0.596, 0.561.

[0078] The future prediction module uses the sliding average method for smoothing to eliminate random noise in historical data. The smoothed value is calculated with three days as a sliding window. For example, the original historical overall environmental coefficient is 0.613.0.619.0.616. After smoothing, the average value is 0.616. Next, in order to remove the trend changes in the historical overall environmental coefficient, the future prediction module performs first-order difference processing to make the data sequence stable. The difference value is calculated by the formula ΔZt=Zt-Zt-1. For example, the data Z2=0.619, Z1=0.613, and the sample data is brought into the calculation to obtain a difference value of 0.006.

[0079] After data processing, the future prediction module models historical data through the autoregressive integrated moving average (ARIMA) model. First, the autoregressive order p is selected through the partial autocorrelation function (PACF) graph. The PACF graph shows that the lag 1 order is significant, so p = 1 is selected. Then, the unit root test (such as ADF test) is used to determine the difference order d. If the sequence is stable after the first-order difference, d = 1 is taken. Finally, the moving average order q is selected through the autocorrelation function (ACF) graph. If the lag 1 order is significant, q = 1 is taken. The final model is ARIMA (1,1,1).

[0080] In the model training stage, the historical data after differentiation is input into the model, and the model parameters are estimated by the least squares method to obtain the autoregressive coefficient (such as 0.80.80.8) and the moving average coefficient (such as 0.30.30.3). In the prediction stage, according to the formula: ΔZ t =φ·ΔZ t-1 +θ·∈ t-1 Calculate the difference value, where φ and θ are the autoregressive and moving average coefficients, respectively, and e(t-1) is the prediction error at the previous moment. For example, if the previous difference value is -0.003 and the prediction error is 0.001, the new difference value ΔZt=-0.0024 is calculated. Then, the difference value is restored to the original value through the formula Zt=Zt-1+ΔZ, and the predicted environmental coefficient is 0.6136.

[0081] The present invention also relates to a substation environment monitoring method based on the power Internet of Things. It includes:

[0082] Step 1: Collect environmental data from each area of ​​the substation through industrial-grade sensors, and perform signal smoothing, analog-to-digital conversion, and normalization processing.

[0083] Step 2: Use lightweight protocols to aggregate environmental data at multiple points and use the ZigBee protocol to achieve long-distance data transmission.

[0084] Step 3: Calculate the environmental score of the environmental data and compare it with the warning value to determine whether the environmental status of each area is stable. The overall environmental coefficient of the substation is obtained by combining the environmental scores of each area.

[0085] Step 4: Use the ARIMA time series model to predict the future environment and equipment status of the substation based on historical data.

[0086] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0087] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0088] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0089] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0090] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0091] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The substation environment monitoring system based on the power Internet of Things is characterized by: include: Data acquisition module, data transmission module, data storage module, data analysis module and future prediction module, and signal connections between modules; The data acquisition module is mainly used to collect environmental data from various areas of the substation. It collects data through industrial-grade sensors and processes them through signal smoothing, analog-to-digital conversion and normalization. The data transmission module is mainly used for multi-point aggregation of environmental data through lightweight protocols and long-distance data transmission using the ZigBee protocol; The data analysis module is mainly used to calculate the environmental score after receiving the environmental data and compare it with the warning value, and to obtain the overall environmental coefficient of the substation by combining the environmental scores of each area; The future prediction module is mainly used to use the ARIMA time series model to predict the future environment and equipment status of the substation based on historical data; The data storage module is used to store all data in the processing process.

2. The substation environment monitoring system based on the power Internet of Things according to claim 1 is characterized in that: The data acquisition module divides the substation into several areas according to the location of different equipment in the substation, and monitors each area separately; The data acquisition module collects the environmental data of the substation by deploying industrial-grade sensors. The sliding average method is used to eliminate the random noise of the signal and smooth the data fluctuations. The specific formula is Among them, St is the smoothing value at the tth moment, N is the window size, and x i is the sampling value at the i-th moment, The data acquisition module uses a high-precision analog-to-digital converter to sample the input analog signal at a fixed sampling rate and quantize it into discrete digital values, converting the analog signal into a digital signal.

3. The substation environment monitoring system based on the power Internet of Things according to claim 2 is characterized in that: The data transmission module formats all collected environmental data into JSON data format. Through the MQTT protocol, each sensor node is used as an MQTT client to periodically publish the environmental data to the specified topic through the MQTT protocol; and the environmental data is aggregated through the MQTT agent; After the data is aggregated in the data transmission module, the sensor nodes communicate with the central gateway through the ZigBee protocol. Each sensor node, as a terminal device, sends the collected environmental data to the coordinator through ZigBee, and the coordinator then forwards the environmental data to the data analysis module.

4. The substation environment monitoring system based on the power Internet of Things according to claim 3 is characterized in that; The data analysis module obtains the regional environmental score by weighted calculation of the key monitored environmental data, specifically according to the formula: QYDF = Q(1) × HY(1) + Q(2) × HY(2) + ... + Q(i) × HY(i), where QYDF represents the regional environmental score, i represents the number of environmental factors in the region, Q(i) represents the weight value of the i-th environmental factor, HY(i) represents the specific data value of the i-th environmental factor, and sets the early warning score Yf. The regional environmental score is compared with the early warning score Yf. When the regional environmental score is less than Yf, the regional environment is monitored. When the regional environmental score is greater than the early warning score Yf, the regional environmental condition is stable. The data analysis module determines the overall environmental coefficient of the substation based on the data analysis of the environmental scores of each area, according to the formula: ZTHJ=Qb×Qy1+Qk×Qy2+Qp×Qy3, where ZTHJ represents the overall environmental coefficient of the substation, Qy1 represents the environmental score of the main transformer area, Q1 represents the weight value of the environmental score of the main transformer area, Qy2 represents the environmental score of the switchgear area, Q2 represents the weight value of the environmental score of the switchgear area, Qy3 represents the environmental score of the distribution room area, and Q3 represents the weight value of the regional environmental score of the distribution room area.

5. The substation environment monitoring system based on the power Internet of Things according to claim 4 is characterized in that: The calculation steps of the overall environmental factor of the substation are as follows: The data analysis module accesses the data storage module through the RESTful API interface, retrieves the equipment log data, and obtains the equipment status data in each area, including: equipment age and average daily working hours of the equipment; The data analysis module determines the equipment status score of each area by weighted sum calculation of the equipment service life and the average daily working hours of the equipment in the area, according to the formula: SBZ = Qs × (SY / SYMAX) + Qt × (t / tmax), where SBZ represents the equipment status score, Qs represents the weight value of the equipment service life, SY represents the equipment service life, SYMAX represents the maximum equipment service life, Qt represents the weight value of the average daily working hours of the equipment, t represents the average daily working hours of the equipment, and tmax represents the maximum daily working hours of the equipment; The data analysis module determines the regional equipment status coefficient according to the regional environment score and the regional equipment status score. The specific formula is: QYSB = Qsbz × SBZ + Qqydf × QYDF to calculate the regional status coefficient, where QYSB represents the regional equipment status coefficient, Qsbz represents the weight value of the equipment status score, and Qqydf represents the weight value of the regional environment score. The data analysis module sets the early warning threshold value Ys. When the regional equipment status coefficient is greater than Ys, it is necessary to focus on monitoring the equipment in this area. When the regional equipment status coefficient is less than or equal to the early warning threshold value Ys, it indicates that the environmental conditions in this area are stable. When the regional equipment status coefficient is less than or equal to the early warning threshold value Ys, the equipment status is stable, and the equipment status in this area does not need to be considered in determining the overall equipment status coefficient of the substation.

6. The substation environment monitoring system based on the power Internet of Things according to claim 5 is characterized in that: The future prediction module extracts the historical environmental data and equipment status data of the last three weeks from the data storage module through the RESTful API, calculates the overall environmental coefficient ZTHJ and equipment status coefficient QYSB of the substation, and applies time series analysis to make the data series stable through the first-order difference; The future prediction module uses the ARIMA model to model the stabilized historical data and calculate the model parameters.

7. A substation environment monitoring method based on the power Internet of Things, characterized by: Step 1: Collect environmental data from each area of ​​the substation through industrial-grade sensors, and perform signal smoothing, analog-to-digital conversion, and normalization processing; Step 2: Use lightweight protocols to aggregate environmental data at multiple points, and use ZigBee protocols to achieve long-distance data transmission; Step 3: Calculate the environmental score of the environmental data and compare it with the warning value to determine whether the environmental status of each area is stable. The overall environmental coefficient of the substation is obtained by combining the environmental scores of each area. Step 4: Use the ARIMA time series model to predict the future environment and equipment status of the substation based on historical data.

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