Environment adjusting assembly and application thereof in box-type substation

Abstract

The invention relates to the technical field of environment regulation, and discloses an environment regulation assembly, which comprises a sensor node used for collecting environment parameter data including temperature, humidity, air quality and pressure; the data acquisition unit is connected to the sensor nodes and is used for receiving and processing sensor data and carrying out filtering and data fusion; the wireless transmission unit is connected to the data collector and used for transmitting the processed data to a remote monitoring platform; and the control unit is connected to the adjusting equipment and the remote monitoring platform and is used for adjusting the running state of the adjusting equipment according to an instruction of the remote monitoring platform. Through the high-precision sensor and the data acquisition unit, environmental parameters, including temperature, humidity, air quality and pressure, in the box-type substation can be monitored in real time, the accuracy and reliability of data are ensured, environmental abnormity is found and processed in time, and equipment faults caused by environmental problems are avoided.

Description

Technical Field

[0001] The invention relates to the technical field of environmental regulation, in particular to an environmental regulation component and its application in a box-type transformer substation. Background Art

[0002] In modern power systems, box-type substations have been widely used due to their small footprint, quick installation, and convenient maintenance. Box-type substations are usually installed outdoors and exposed to various complex environmental conditions. Factors such as ambient temperature, humidity, air quality, and pressure will directly affect the operating status and life of their internal equipment. Therefore, effective monitoring and adjustment of the internal environment of box-type substations is an important measure to ensure their safe and reliable operation.

[0003] In the prior art, discrete sensors and adjustment devices are usually used to monitor and adjust the internal environment of box-type substations. These systems usually include temperature sensors, humidity sensors, air quality sensors and pressure sensors, and cooperate with air conditioners, dehumidifiers, air purifiers and ventilation equipment to adjust the environment. At the same time, data collection and transmission mainly rely on wired or single wireless transmission paths, and environmental adjustment strategies are mostly controlled by preset fixed values. In addition, fault warning systems are mostly based on simple threshold judgments, which make it difficult to provide accurate and timely warning information.

[0004] However, there are many problems with the existing technology in practical applications. First, the environmental monitoring system has a delayed response and cannot reflect environmental changes in real time, resulting in the inability to detect and handle environmental anomalies in a timely manner, increasing the risk of equipment failure. Secondly, the adjustment system relies on fixed preset values, lacks flexibility and adaptability, and is difficult to cope with complex and changing environments. The adjustment accuracy and response speed are low, which affects the stability of the environment. Thirdly, the single wireless transmission path is susceptible to interference, and data transmission is unstable, resulting in insufficient continuity and integrity of monitoring data. In addition, the fault warning system relies on simple threshold judgment, with low prediction accuracy, making it difficult to identify potential faults in a timely manner, increasing maintenance costs and equipment failure rates. Finally, the existing adjustment system has low energy utilization efficiency and high operating costs, which does not meet the requirements of modern energy conservation and environmental protection. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides an environmental regulation component and its application in a box-type substation, which solves the problems of delayed environmental monitoring response, lack of flexibility of the regulation system, unstable data transmission, inaccurate fault warning and low energy utilization efficiency in the prior art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an environmental adjustment component and its application in a box-type substation, comprising:

[0007] Sensor nodes, which collect data on environmental parameters, including temperature, humidity, air quality, and pressure;

[0008] A data collector, connected to the sensor node, for receiving and processing sensor data, performing filtering and data fusion;

[0009] A wireless transmission unit, connected to the data collector, for transmitting the processed data to a remote monitoring platform;

[0010] A control unit, connected to the regulating device and the remote monitoring platform, for adjusting the operating state of the regulating device according to instructions from the remote monitoring platform;

[0011] conditioning equipment, including air conditioning systems, dehumidifiers, air purifiers, and ventilation equipment;

[0012] The data collector is configured to process data through a filtering unit, and the filtering algorithm formula is as follows:

[0013] Y(t)=α˙X(t)+(1-α)˙Y(t-1)

[0014] Among them, Y(t) is the filtered data, X(t) is the original data, and α is the filter coefficient.

[0015] Preferably, the sensor node comprises:

[0016] High-precision temperature sensors are arranged at multiple locations inside the box-type substation to detect the ambient temperature;

[0017] Humidity sensors are placed near air-conditioning outlets and electrical equipment to detect humidity levels;

[0018] an air quality sensor configured to detect concentrations of particulate matter and harmful gases in the air;

[0019] A pressure sensor configured to monitor air flow in a ventilation system.

[0020] Preferably, the data collector further comprises:

[0021] A data calibration unit is used to calibrate the filtered data to ensure the accuracy of the data;

[0022] The data normalization unit is used to normalize the calibrated data to unify the data format.

[0023] Preferably, the data collector is configured to perform data fusion, and the data fusion algorithm is as follows:

[0024]

[0025] Among them, F(t) is the fused data, w i is the weight of sensor i, D i (t) is the data collected by sensor i at time t.

[0026] Preferably, the wireless transmission unit comprises:

[0027] A multi-path data transmission module, used for data transmission between multiple transmission paths;

[0028] The self-healing network module is used to monitor the status of the transmission path and automatically switch to the backup path when a path fails.

[0029] Preferably, the control unit comprises:

[0030] An embedded controller configured to receive instructions from a remote monitoring platform and control the operation of the regulating device according to a preset regulating strategy;

[0031] The regulation strategy generation module uses fuzzy logic control or adaptive PID control algorithm to generate regulation strategies based on environmental parameter data.

[0032] Preferably, the adjustment strategy generation module adopts an adaptive PID control algorithm, and the control algorithm formula is as follows:

[0033]

[0034] Among them, u(t) is the control input, e(t) is the error, K p , K i , K d are the proportional, integral and differential coefficients respectively.

[0035] Preferably, the remote monitoring platform includes:

[0036] A cloud platform for receiving, storing, and analyzing sensor data in real time;

[0037] Intelligent analysis system, which performs fault prediction and environmental parameter trend analysis based on big data and machine learning algorithms.

[0038] Preferably, the intelligent analysis system is configured as follows:

[0039] Use time series analysis models to predict trends in environmental parameters;

[0040] When the prediction parameters exceed the preset threshold, an early warning is triggered and the early warning information is sent to the control unit and operation and maintenance personnel.

[0041] Working principle: First, sensor nodes are arranged at various key locations inside the box-type substation, including air outlets of air conditioners, near electrical equipment, and entrances to ventilation systems. The sensor nodes include high-precision temperature sensors, humidity sensors, air quality sensors, and pressure sensors, which are used to collect environmental parameter data in real time. The temperature sensor monitors the temperature at various locations inside the box-type substation, the humidity sensor monitors the air humidity level, the air quality sensor detects the concentration of particulate matter and harmful gases in the air, and the pressure sensor monitors the air flow of the ventilation system. These sensors are connected to the data collector through shielded cables to prevent electromagnetic interference from affecting the accuracy of the data. Then, the data collector is connected to the sensor node to receive and process the sensor data. The data collector includes a filtering unit, a data calibration unit, a data normalization unit, and a data fusion unit. The filtering unit uses a weighted average filtering algorithm to filter the sensor data and eliminate noise interference; then, the data calibration unit performs zero point calibration and sensitivity calibration on the filtered data to ensure the accuracy of the data. The data normalization unit normalizes the calibrated data and unifies the data format. The data fusion unit uses a weighted average algorithm to fuse multi-sensor data to improve the accuracy and robustness of the data.

[0042] Data is transmitted to the remote monitoring platform through a wireless transmission unit. The wireless transmission unit includes a multi-path data transmission module and a self-healing network module to ensure the stability and reliability of data transmission. The multi-path data transmission module uses wireless transmission technologies such as LoRa, Zigbee or Wi-Fi, configures multiple data transmission paths, monitors the signal strength and data transmission rate of each path in real time, and selects the optimal path for data transmission. The self-healing network module monitors the status of the transmission path and automatically switches to the backup path when a path fails to ensure uninterrupted data transmission.

[0043] On the remote monitoring platform, data is received, stored and analyzed in real time. The cloud platform uses cloud service platforms such as Amazon Web Services (AWS) or Microsoft Azure, and is equipped with high-performance servers and databases to receive, store and analyze sensor data in real time, and provide data visualization and remote management functions. The intelligent analysis system is based on big data and machine learning algorithms to perform fault prediction and environmental parameter trend analysis, and uses LSTM models for time series prediction. When the predicted parameters exceed the preset threshold, the intelligent analysis system triggers an early warning and notifies the operation and maintenance personnel through SMS, email or mobile applications to deal with potential problems in a timely manner; the control unit is connected to the regulating equipment and the remote monitoring platform, and adjusts the operating status of the regulating equipment according to the instructions of the remote monitoring platform. The control unit includes an embedded controller and a regulation strategy generation module, which realizes precise control of the environment through intelligent algorithms. The embedded controller receives sensor data and instructions from the remote monitoring platform, and controls the operation of the regulating equipment through preset regulation strategies. The regulation strategy generation module uses fuzzy logic control and adaptive PID control algorithms to generate regulation strategies based on environmental parameter data; the regulating equipment includes air conditioning systems, dehumidifiers, air purifiers and ventilation equipment. The control unit dynamically adjusts the operating status of each regulating device according to the regulation strategy to maintain the stability of the internal environment of the box-type substation. The air conditioning system automatically adjusts the cooling or heating power according to the data of the temperature sensor to maintain the appropriate temperature inside the box-type substation; the dehumidifier automatically starts or stops according to the data of the humidity sensor to adjust the air humidity to prevent equipment failure caused by excessive humidity; the air purifier automatically adjusts the wind speed and filtration intensity according to the data of the air quality sensor to keep the air fresh; the ventilation equipment automatically adjusts the fan speed according to the data of the pressure sensor to ensure the normal operation of the ventilation system and maintain good air flow.

[0044] Through the coordinated work of the above parts, the environmental adjustment system of the present invention realizes efficient monitoring and adjustment of the internal environment of the box-type substation, ensuring the safe operation of the equipment and the stability of the environment.

[0045] The present invention provides an environmental conditioning component and its application in a box-type substation. It has the following beneficial effects:

[0046] 1. The present invention can monitor the environmental parameters inside the box-type substation in real time, including temperature, humidity, air quality and pressure, through high-precision sensors and data collectors, to ensure the accuracy and reliability of data. In the prior art, the environmental monitoring system often reacts slowly and cannot reflect environmental changes in a timely manner. The present invention can detect and handle environmental anomalies in a timely manner through real-time monitoring, thus avoiding equipment failures caused by environmental problems.

[0047] 2. The control unit of the present invention adopts fuzzy logic control and adaptive PID control algorithm, which can dynamically adjust the operating state of the regulating device according to the changes in environmental parameters to ensure the stability of the environment. The regulating system in the prior art usually relies on fixed preset values, lacks flexibility, and is difficult to cope with complex and changing environments. The intelligent regulating system of the present invention improves the regulating accuracy and response speed, optimizes the operation of the equipment, and improves the stability and efficiency of the overall system.

[0048] 3. The wireless transmission unit of the present invention adopts multi-path data transmission and self-healing network design to ensure the stability and reliability of data transmission and reduce system failures and data loss. The wireless transmission system in the prior art is susceptible to interference and data transmission is unstable. The present invention improves the reliability of data transmission and ensures the continuity and integrity of monitoring data through multi-path transmission and self-healing network design.

[0049] 4. The intelligent analysis system of the remote monitoring platform of the present invention can perform fault prediction and environmental parameter trend analysis based on big data and machine learning algorithms, identify potential faults in advance, and issue early warnings in time to improve the safety of the system. The fault warning system in the prior art usually relies on simple threshold judgments and is difficult to accurately predict potential problems. The present invention provides more accurate and timely fault warnings through advanced intelligent analysis technology, reducing equipment failure rate and maintenance costs.

[0050] 5. The present invention optimizes the operation of the regulating equipment through intelligent regulating strategies, reduces energy consumption, improves the energy utilization efficiency of the system, and reduces operating costs. The regulating system in the prior art has low energy utilization efficiency and high operating costs. The present invention achieves energy conservation and emission reduction through optimizing the regulating strategy, meets the requirements of green development, and reduces operating costs and environmental impacts.

[0051] The present invention not only improves the environmental regulation efficiency and reliability of box-type substations, but also solves the problems of monitoring lag, inflexible regulation, unstable data transmission, inaccurate fault warning and low energy utilization efficiency in the prior art, and provides an effective solution for the intelligent management of box-type substations. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0053] 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.

[0054] Example:

[0055] Please see attached Figure 1 The embodiment of the present invention provides an environmental conditioning component and its application in a box-type substation, including:

[0056] Sensor Node

[0057] In this embodiment, the sensor node includes the following configuration:

[0058] Temperature sensor: High-precision NTC (negative temperature coefficient) thermistors are used and arranged in multiple locations inside the box-type substation, such as air outlets of air conditioners, near electrical equipment, and inlet of ventilation systems. Each temperature sensor is connected to the data collector via a shielded cable to prevent electromagnetic interference from affecting data accuracy. These temperature sensors can monitor temperature changes inside the substation in real time and provide basic data for environmental regulation.

[0059] Humidity sensor: Digital humidity sensors are used and placed near air-conditioning outlets and electrical equipment to monitor humidity levels. Humidity sensors integrate temperature compensation functions to ensure data accuracy and reliability. By monitoring humidity levels, it is possible to prevent the impact of excessively high or low humidity on electrical equipment and ensure the normal operation of the equipment.

[0060] Air quality sensor: Laser particle sensors and electrochemical sensors are used to detect the concentration of particulate matter (PM2.5) and harmful gases (such as CO, NO2, etc.) in the air. Sensors are placed at key locations in the ventilation system entrance and inside the substation to ensure comprehensive monitoring of air quality. The data provided by the air quality sensor can help adjust the operation of the air purifier to keep the air fresh.

[0061] Pressure sensor: MEMS (micro-electromechanical system) pressure sensors are used and placed at key locations in the ventilation system to monitor air flow. The pressure sensor is connected to the data collector via a shielded cable to ensure the stability of data transmission. The data from the pressure sensor can reflect the operating status of the ventilation system in real time and ensure good air flow.

[0062] Data Logger:

[0063] In this embodiment, the data collector includes the following functional modules:

[0064] Filter unit: The sensor data is filtered using a weighted average filter algorithm. The filter unit includes an analog filter circuit and a digital filter algorithm. The analog filter circuit uses capacitors and inductors to construct a low-pass filter to preliminarily eliminate high-frequency noise. The digital filter algorithm further smoothes the data through weighted average calculations, as shown in the following formula:

[0065] Y(t)=α˙X(t)+(1-α)˙Y(t-1)

[0066] Among them, Y(t) is the filtered data, X(t) is the original data, and α is the filter coefficient. The function of the filter unit is to reduce the noise interference in the sensor data and improve the accuracy of the data.

[0067] Data calibration unit: includes zero point calibration and sensitivity calibration. Zero point calibration adjusts the zero drift of the sensor by comparing the sensor output with the standard reference value. Sensitivity calibration adjusts the linear relationship between the sensor output and the actual measured value through a multi-point calibration curve. The data calibration unit ensures the accuracy and consistency of the sensor data.

[0068] Data normalization unit: normalize the calibrated data and unify the data format. The normalization formula is:

[0069]

[0070] Among them, Z i (t) is the normalized data, max(D i ) and min(D i ) are the maximum and minimum values ​​of sensor i data respectively. The function of the data normalization unit is to convert the data of different sensors into a unified scale for subsequent processing and analysis.

[0071] Data fusion unit: Use weighted average algorithm to fuse multi-sensor data to improve data accuracy and robustness. The fusion algorithm formula is:

[0072]

[0073] Among them, F(t) is the fused data, w i is the weight of sensor i, Di(t)D_i(t)Di(t) is the data collected by sensor i at time t. The data fusion unit provides more accurate environmental parameters as the input of the control system by integrating the data of multiple sensors.

[0074] Wireless transmission unit:

[0075] In this embodiment, the wireless transmission unit includes the following modules:

[0076] Multi-path data transmission module: Use wireless transmission technologies such as LoRa, Zigbee or Wi-Fi to configure multiple data transmission paths. During the data transmission process, the system monitors the signal strength and data transmission rate of each path in real time, selects the optimal path for data transmission, and ensures the stability of data transmission. The multi-path data transmission module can effectively cope with complex electromagnetic environments and ensure the reliability of data transmission.

[0077] Self-healing network module: monitors the status of the transmission path and automatically switches to the backup path when the path fails. The self-healing network module includes a status monitoring algorithm and a path switching algorithm. The status monitoring algorithm evaluates the path health by collecting indicators such as the signal strength and data packet loss rate of the transmission path in real time. The path switching algorithm automatically switches to the backup path with the best health based on the evaluation results to ensure uninterrupted data transmission. The self-healing network module improves the system's anti-interference ability and stability.

[0078] Control unit:

[0079] In this embodiment, the control unit includes the following components:

[0080] Embedded controller: It uses a high-performance ARM Cortex-M series microcontroller with a multi-channel ADC (analog-to-digital converter) and multiple communication interfaces (such as I2C, SPI, UART, etc.) to receive sensor data and instructions from the remote monitoring platform. The embedded controller controls the operation of the regulating device through the preset regulation strategy. The embedded controller can quickly respond to environmental changes and adjust the operating status of the regulating device.

[0081] Adjustment strategy generation module: Fuzzy logic control and adaptive PID control algorithm are used to generate adjustment strategies according to environmental parameter data. The adaptive PID control algorithm formula is as follows:

[0082]

[0083] Among them, u(t) is the control input, e(t) is the error, K p , K i , K d They are proportional, integral and differential coefficients respectively; the fuzzy logic control algorithm dynamically adjusts the operating parameters of the regulating equipment by establishing a fuzzy rule base to achieve precise control of the environment. The regulation strategy generation module ensures that the regulating equipment can adapt to various environmental changes and maintain the stability of the internal environment of the box-type substation.

[0084] Adjustment equipment:

[0085] In this embodiment, the adjustment device includes:

[0086] Air conditioning system: Adopt variable frequency air conditioning, automatically adjust cooling or heating power according to the data of temperature sensor, and maintain the appropriate temperature inside the box-type substation. The air conditioning system can respond quickly to temperature changes and provide efficient temperature control.

[0087] Dehumidifier: Automatically start or stop based on humidity sensor data to adjust air humidity and prevent equipment failure due to excessive humidity. Dehumidifiers can effectively control humidity levels and protect electrical equipment.

[0088] Air Purifier: Automatically adjusts wind speed and filtration intensity based on data from air quality sensors to keep the air fresh. The air purifier removes particulate matter and harmful gases from the air through a high-efficiency filtration system, providing a healthy working environment.

[0089] Ventilation equipment: Based on the data from the pressure sensor, the fan speed is automatically adjusted to ensure the normal operation of the ventilation system and maintain good air flow. Ventilation equipment provides stable air quality and prevents equipment from overheating by optimizing air flow.

[0090] Remote monitoring platform

[0091] In this embodiment, the remote monitoring platform includes:

[0092] Cloud platform: Use cloud service platforms such as Amazon Web Services (AWS) or Microsoft Azure, configure high-performance servers and databases to receive, store and analyze sensor data in real time. The cloud platform provides users with data visualization and remote management functions through web interfaces or mobile applications. The cloud platform has efficient data processing and storage capabilities and can quickly process and analyze large amounts of data.

[0093] Intelligent analysis system: Based on big data and machine learning algorithms, it performs fault prediction and environmental parameter trend analysis. The intelligent analysis system uses the LSTM (Long Short-Term Memory Network) model for time series prediction. The formula is as follows:

[0094]

[0095] in, is the predicted value for the future k moments, f LSTM is an LSTM model, and X(t) is historical monitoring data. When the prediction parameter exceeds the preset threshold, the intelligent analysis system triggers an early warning and notifies the operation and maintenance personnel through SMS, email or mobile application to deal with potential problems in a timely manner. The intelligent analysis system can identify potential faults in advance and improve the safety of the system.

[0096] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An environmental conditioning component, characterized in that: include: Sensor nodes, which collect data on environmental parameters, including temperature, humidity, air quality, and pressure; A data collector, connected to the sensor node, for receiving and processing sensor data, performing filtering and data fusion; A wireless transmission unit, connected to the data collector, for transmitting the processed data to a remote monitoring platform; conditioning equipment, including air conditioning systems, dehumidifiers, air purifiers, and ventilation equipment; A control unit, connected to the regulating device and the remote monitoring platform, for adjusting the operating state of the regulating device according to instructions from the remote monitoring platform; The data collector is configured to process data through a filtering unit, and the filtering algorithm formula is as follows: Y(t)=α˙X(t)+(1-α)˙Y(t-1) Among them, Y(t) is the filtered data, X(t) is the original data, and α is the filter coefficient.

2. An environmental conditioning assembly according to claim 1, characterized in that: The sensor node comprises: High-precision temperature sensors are arranged at multiple locations inside the box-type substation to detect the ambient temperature; Humidity sensors are placed near air-conditioning outlets and electrical equipment to detect humidity levels; an air quality sensor configured to detect concentrations of particulate matter and harmful gases in the air; A pressure sensor configured to monitor air flow in a ventilation system.

3. An environmental conditioning assembly according to claim 1, characterized in that: The data collector further comprises: A data calibration unit is used to calibrate the filtered data to ensure the accuracy of the data; The data normalization unit is used to normalize the calibrated data to unify the data format.

4. An environmental conditioning assembly according to claim 1, characterized in that: The data collector is configured to perform data fusion, and the data fusion algorithm is as follows: Among them, F(t) is the fused data, w i is the weight of sensor i, D i (t) is the data collected by sensor i at time t.

5. An environmental conditioning assembly according to claim 1, characterized in that: The wireless transmission unit comprises: A multi-path data transmission module, used for data transmission between multiple transmission paths; The self-healing network module is used to monitor the status of the transmission path and automatically switch to the backup path when a path fails.

6. An environmental conditioning assembly according to claim 1, characterized in that: The control unit comprises: An embedded controller configured to receive instructions from a remote monitoring platform and control the operation of the regulating device according to a preset regulating strategy; The regulation strategy generation module uses fuzzy logic control or adaptive PID control algorithm to generate regulation strategies based on environmental parameter data.

7. An environmental conditioning assembly according to claim 1, characterized in that: The adjustment strategy generation module adopts an adaptive PID control algorithm, and the control algorithm formula is as follows: Among them, u(t) is the control input, e(t) is the error, K p , K i , K d are the proportional, integral and differential coefficients respectively.

8. An environmental conditioning assembly according to claim 1, characterized in that: The remote monitoring platform includes: A cloud platform for receiving, storing, and analyzing sensor data in real time; Intelligent analysis system, which performs fault prediction and environmental parameter trend analysis based on big data and machine learning algorithms.

9. An environmental conditioning assembly according to claim 8, characterized in that: The intelligent analysis system is configured as follows: Use time series analysis models to predict trends in environmental parameters; When the prediction parameters exceed the preset threshold, an early warning is triggered and the early warning information is sent to the control unit and operation and maintenance personnel.

10. Application of an environmental conditioning component in a box-type substation, characterized in that: The environmental conditioning component comprises the environmental conditioning component according to any one of claims 1 to 9.

Images