Self-powered wireless temperature sensing device and its energy supply reliability evaluation method

Abstract

This invention discloses a self-powered wireless temperature sensing device and its power supply reliability assessment method. The self-powered wireless temperature sensing device includes a thermoelectric energy harvesting module, an energy management module, and a wireless temperature sensing node. The thermoelectric energy harvesting module converts the waste heat generated by the operation of the high-voltage disconnecting switch into electrical energy. The energy management circuit boosts the output electrical energy and stores the boosted energy using a supercapacitor, thereby achieving self-powering for the high-voltage disconnecting switch wireless temperature sensing device. Simultaneously, a neural network model is trained using the constructed self-powered wireless temperature sensing device to establish a power supply reliability assessment model. This model estimates the remaining charge of the energy storage element in the device under different high-voltage disconnecting switch operating conditions, determines the occurrence time of power failure, and effectively improves the operational reliability of the status monitoring device on the high-voltage disconnecting switch.

Description

Technical Field

[0001] This invention belongs to the field of self-powered sensors, specifically relating to a self-powered wireless temperature sensing device and a method for evaluating its power supply reliability. Background Technology

[0002] High-voltage disconnect switches are among the most widely used switching equipment in substations. They are used to isolate the power grid from equipment under maintenance, and also to switch off-load lines and connect / disconnect small load currents. Due to long-term exposure to the harsh outdoor atmospheric environment, high-voltage disconnect switches frequently fail, becoming a significant issue affecting the safe operation of the power grid. To reduce the failure rate and improve the reliability of power grid operation, it is necessary to monitor the operating status of high-voltage disconnect switches in real time.

[0003] As an energy harvesting device, thermoelectric generators (TEGs) utilize the Seebeck effect of thermoelectric materials to directly convert low-grade heat energy into electrical energy, representing a green and environmentally friendly power supply technology. In recent years, with the emergence of new thermoelectric materials and the optimization of thermoelectric energy conversion devices, their costs have been continuously reduced. Condition monitoring sensors utilizing TEGs for self-powered operation have been applied in wearable devices, the automotive industry, and power plants. Under current excitation, high-voltage disconnect switches inevitably generate heat during long-term operation. Harvesting the thermoelectric energy of high-voltage disconnect switches using TEGs can provide a novel energy replenishment method for sensors used to monitor high-temperature faults in disconnect switches.

[0004] However, the heat generated by the high-voltage disconnect switch is affected by operating conditions such as the switch's operating current, ambient temperature, wind speed, and wind direction. This heat directly impacts the output voltage of the thermoelectric generator, leading to changes in the power output of the energy management module and consequently affecting the reliable operation of the wireless temperature sensing device. Therefore, this invention, based on the designed self-powered wireless temperature sensing device, utilizes a trained neural network model to evaluate the device's power supply reliability, thereby improving the device's operational reliability. Summary of the Invention

[0005] This invention proposes a self-powered wireless temperature sensing device and its power supply reliability assessment method to solve the problem of power supply difficulties for condition monitoring sensors in high-voltage electrical equipment. It utilizes a thermoelectric energy harvesting module and an energy management module to convert the waste heat energy from the operation of the high-voltage disconnecting switch into electrical energy for the wireless temperature sensing device. Simultaneously, the self-powered wireless temperature sensing device is used to train a neural network model, enabling a power supply reliability assessment of the device. This allows for prediction of power failure and effectively improves the power supply reliability of the self-powered wireless temperature sensing device on the high-voltage disconnecting switch.

[0006] This invention is achieved through the following technical solution:

[0007] A self-powered wireless temperature sensing device, suitable for high-voltage disconnect switches, includes a thermoelectric energy harvesting module, an energy management module, and a wireless temperature sensing node.

[0008] The thermoelectric energy harvesting module uses a thermoelectric generator to convert the waste heat generated during the operation of the high-voltage disconnect switch into electrical energy for use by the wireless temperature sensing node.

[0009] The energy management module includes a low-power energy management circuit and an energy storage element. The low-power energy management circuit includes a low-power energy management chip and its peripheral circuits. The low-power energy management circuit boosts the output voltage of the thermoelectric energy harvesting module and maintains maximum power output. The energy storage element uses a supercapacitor to store the electrical energy output by the low-power energy management circuit at maximum power.

[0010] The wireless temperature sensing node includes a terminal temperature sensing node and a coordinator. The terminal temperature sensing node is powered by an energy management module and uses a thermistor arranged on the high-voltage disconnect switch to collect temperature data. The data is transmitted to the coordinator via Zigbee wireless communication. The coordinator uses serial communication to upload the data to a host computer, which monitors the temperature information of the high-voltage disconnect switch in real time.

[0011] A method for assessing the power supply reliability of a self-powered wireless temperature sensing device, comprising the following steps:

[0012] Step 1: Build the self-powered wireless temperature sensing device described above;

[0013] Step 2: Train the neural network model;

[0014] Step 3: Convert the current actual disconnect switch current Ambient temperature Wind speed and wind direction The data is fed into the trained neural network model, and the voltage value of the energy storage element of the self-powered wireless temperature sensor is obtained step by step over time. , where each of the first Voltage value at time All are held by a zero-order hold. Become the voltage value in the next recursive process ;

[0015] Step 4: Construct a set of voltage values ​​obtained in Step 3. Group duration is Time series of voltage changes of energy storage elements , Representative group Total number of groups Based on actual high-voltage disconnector temperature rise data duration And the duration of voltage estimation for energy storage components Decision, total number of groups The calculation formula is: ;

[0016] Step 5: Analyze the obtained voltage change time series of the energy storage element. Statistical analysis was performed on the voltage values ​​in the data to calculate the voltage values. Number of groups with voltage less than the minimum allowable value of energy storage element The minimum allowable value is set as the minimum operating voltage of the energy management circuit. ;

[0017] Step 6: Based on the number of groups With the total number of groups The ratio is used to calculate the probability of insufficient power supply for the self-powered wireless temperature sensor at each time step. This enables the assessment of the power supply reliability of self-powered wireless temperature sensing devices.

[0018] Compared with the prior art, the significant advantages of this invention are:

[0019] (1) This invention utilizes a thermoelectric generator to convert the waste heat generated by the high-voltage disconnect switch into electrical energy, which is boosted by a low-power energy management circuit and a supercapacitor is used as an energy storage element. It can be charged 500,000 times, thereby realizing the self-powered wireless temperature sensing device of the high-voltage disconnect switch. Compared with traditional battery power supply, no external energy source is required, which extends the service life of the wireless sensing device and improves the energy utilization rate.

[0020] (2) The present invention is based on the CC2530 microcontroller to design the hardware of the wireless temperature sensing device, and implements the intermittent working mode of the sensing device through software programming. That is, when the temperature sampling task is in progress, the terminal temperature measurement node works, and the rest of the time is in sleep mode, thereby minimizing the power consumption of the wireless sensing device and improving the service life of the entire device.

[0021] (3) The present invention utilizes a neural network to establish a power supply reliability assessment method for a self-powered wireless temperature sensing device, and estimates the remaining power of the energy storage element under different disconnection switch operating conditions, thereby determining the probability of power failure and effectively improving the reliability of the self-powered wireless temperature sensing device. Attached Figure Description

[0022] Figure 1 This is a block diagram of the self-powered wireless temperature sensing device of the present invention.

[0023] Figure 2 This is a schematic diagram of the low-power energy management circuit of the present invention.

[0024] Figure 3 This is a flowchart of the power supply reliability assessment method for the self-powered wireless temperature sensing device of the present invention.

[0025] Figure 4 This is a flowchart of the neural network model training process of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0027] like Figure 1 As shown, the self-powered wireless temperature sensing device of the present invention is applicable to high-voltage disconnect switches, comprising: a thermoelectric energy harvesting module, which uses a thermoelectric generator to convert the waste heat generated during the operation of the high-voltage disconnect switch into electrical energy that can be used by the wireless temperature sensing node; an energy management module, which includes a low-power energy management circuit and an energy storage element, wherein the low-power energy management circuit can boost the output power of the thermoelectric generator and maintain maximum power output, and the energy storage element uses a supercapacitor to store the electrical energy output by the low-power energy management circuit at maximum power; and a wireless temperature measurement node, which includes a terminal temperature measurement node and a coordinator, wherein the terminal temperature measurement node is powered by the low-power energy management circuit, uses a thermistor arranged on the high-voltage disconnect switch to collect temperature data, transmits the data to the coordinator via Zigbee wireless communication, and the coordinator uploads the data to a host computer via serial communication, and the host computer monitors the temperature information of the high-voltage disconnect switch in real time.

[0028] Specifically, the thermoelectric energy harvesting module includes a thermoelectric generator 1 and a heat dissipation device 3. The hot end of the thermoelectric generator 1 is fixed to the high-voltage disconnector blade 2 with thermally conductive adhesive, and the cold end is attached to the heat dissipation device 3, which is an aluminum profile heat sink.

[0029] Specifically, such as Figure 2 As shown, the low-power energy management circuit includes a BQ25504 chip and its peripheral circuits, which are used to convert the electrical energy output by the thermoelectric energy harvesting module. Since the thermoelectric generator outputs millivolt-level DC power, while the operating voltage of the wireless sensing device is usually 3.3V, the BQ25504 chip is needed to boost the voltage and achieve maximum power output.

[0030] The maximum power output can be achieved by setting the resistance values ​​of the first resistor R1 and the second resistor R2. In terms of thermoelectric power generation, the output power can reach its maximum value when the output voltage is 50% of the open circuit voltage. Therefore, the first resistor R1 and the second resistor R2 are both set to 10MΩ.

[0031] Furthermore, overvoltage protection of the chip can be achieved by setting the resistance values ​​of the third resistor R3 and the fourth resistor R4, undervoltage protection can be achieved by setting the resistance values ​​of the fifth resistor R5 and the sixth resistor R6, and the normal operating voltage of the energy storage element can be set by setting the resistance values ​​of the seventh resistor R7, the eighth resistor R8, and the ninth resistor R9. When the voltage of the energy storage element is within the set normal operating voltage range, the eleventh pin VBAT_OK of the BQ25504 chip will output a high-level signal, and vice versa.

[0032] Furthermore, the first chip U1 uses a load switch TPS22919 with adjustable output discharge function to achieve a 3.3V regulated output. The third pin ON of the first chip U1 is connected to the eleventh pin VBAT_OK of the BQ25504 chip. When the voltage of the energy storage element is lower than the set threshold, the eleventh pin of the BQ25504 chip outputs a low-level signal. At this time, the third pin ON of the first chip U1 receives the low-level signal and disconnects the load, thereby preventing the energy storage element from over-discharging.

[0033] like Figure 3 As shown, the power supply reliability assessment method for a self-powered wireless temperature sensing device according to the present invention includes the following steps:

[0034] Step 1: Build the above-mentioned self-powered wireless temperature sensing device.

[0035] Step 2: Train the neural network model.

[0036] like Figure 4 As shown, the specific steps include:

[0037] Step 2-1: Determine the capacitance value of the energy storage element Initial voltage and the power consumption of the self-powered wireless temperature sensing device ;

[0038] Step 2-2: Change the current of the disconnecting switch respectively Ambient temperature Wind speed and wind direction Using the self-powered wireless temperature sensing device from step 1, a duration of [duration missing] is carried out. The experiment recorded the voltage changes of the energy storage element during the experiment, and the experimental results were used as a sample sequence of voltage changes of the energy storage element. ;

[0039] Steps 2-3: Sample sequence of voltage changes in energy storage elements Divided into two groups, namely the voltage value of the energy storage element at this moment. With the voltage value of the energy storage element at the next moment and utilize the disconnect switch current Ambient temperature Wind speed ,wind direction With the voltage value of the energy storage element The neural network was trained to obtain the isolation switch current. Ambient temperature Wind speed ,wind direction With the voltage value of the energy storage element Voltage value of the energy storage element at the next moment The mapping relationship between them is used to obtain the trained neural network model.

[0040] Step 3: Convert the current actual disconnect switch current Ambient temperature Wind speed and wind direction The data is fed into the trained neural network model, and the voltage value of the energy storage element of the self-powered wireless temperature sensor is obtained step by step over time. , where each of the first Voltage value at time All are held by a zero-order hold. Become the voltage value in the next recursive process , where Z represents the transfer function of the zero-order hold.

[0041] Step 4: Construct a set of voltage values ​​obtained in Step 3. Group duration is Time series of voltage changes of energy storage elements , Representative group Total number of groups Based on actual high-voltage disconnector temperature rise data duration And the duration of voltage estimation for energy storage components Decision, total number of groups The calculation formula is: .

[0042] Step 5: Analyze the obtained voltage change time series of the energy storage element. Statistical analysis was performed on the voltage values ​​in the data to calculate the voltage values. Number of groups with voltage less than the minimum allowable value of energy storage element The minimum allowable value is set as the minimum operating voltage of the energy management circuit. .

[0043] Step 6: Based on the number of groups With the total number of groups The ratio is used to calculate the probability of insufficient power supply for the self-powered wireless temperature sensor at each time step. This enables the assessment of the power supply reliability of self-powered wireless temperature sensing devices.

Claims

1. A method for evaluating the power supply reliability of a self-powered wireless temperature sensing device, characterized in that, The method for evaluating the functional reliability of the self-powered wireless temperature sensing device includes the following steps: Step 1: Set up a self-powered wireless temperature sensing device: The self-powered wireless temperature sensing device is suitable for high-voltage disconnect switches and includes a thermoelectric energy harvesting module, an energy management module, and a wireless temperature sensing node. The thermoelectric energy harvesting module uses a thermoelectric generator to convert the waste heat generated during the operation of the high-voltage disconnect switch into electrical energy for use by the wireless temperature sensing node. The energy management module includes a low-power energy management circuit and an energy storage element. The low-power energy management circuit includes a low-power energy management chip and its peripheral circuits. The low-power energy management circuit boosts the output voltage of the thermoelectric energy harvesting module and maintains maximum power output. The energy storage element uses a supercapacitor to store the electrical energy output by the low-power energy management circuit at maximum power. The wireless temperature sensing node includes a terminal temperature sensing node and a coordinator. The terminal temperature sensing node is powered by an energy management module and uses a thermistor arranged on the high-voltage disconnect switch to collect temperature data. The data is transmitted to the coordinator via Zigbee wireless communication. The coordinator uses serial communication to upload the data to a host computer. The host computer is used to monitor the temperature information of the high-voltage disconnect switch in real time. Step 2: Train the neural network model; Step 3: Convert the current actual disconnect switch current Ambient temperature Wind speed and wind direction The data is fed into the trained neural network model, and the voltage value of the energy storage element of the self-powered wireless temperature sensor is obtained step by step over time. , where each of the first Voltage value at time All are held by a zero-order hold. Become the voltage value in the next recursive process Where Z represents the transfer function of the zero-order hold; Step 4: Construct a set of voltage values ​​obtained in Step 3. Group duration is Time series of voltage changes of energy storage elements , Representative group Total number of groups Based on actual high-voltage disconnector temperature rise data duration And the duration of voltage estimation for energy storage components Decision, total number of groups The calculation formula is: ; Step 5: Analyze the obtained voltage change time series of the energy storage element. Statistical analysis was performed on the voltage values ​​in the data to calculate the voltage values. Number of groups with voltage less than the minimum allowable value of energy storage element The minimum allowable value is set as the minimum operating voltage of the energy management circuit. ; Step 6: Based on the number of groups With the total number of groups The ratio is used to calculate the probability of insufficient power supply for the self-powered wireless temperature sensor at each time step. This enables the assessment of the power supply reliability of self-powered wireless temperature sensing devices.

2. The method for evaluating the power supply reliability of a self-powered wireless temperature sensing device according to claim 1, characterized in that, In step 2, the neural network model is trained, and the specific steps are as follows: Step 2-1: Determine the capacitance value of the energy storage element Initial voltage and the power consumption of the self-powered wireless temperature sensing device ; Step 2-2: Change the current of the disconnecting switch respectively Ambient temperature Wind speed and wind direction Using the self-powered wireless temperature sensing device from step 1, a duration of [duration missing] is carried out. The experiment recorded the voltage changes of the energy storage element during the experiment, and the experimental results were used as a sample sequence of voltage changes of the energy storage element. ; Steps 2-3: Sample sequence of voltage changes in energy storage elements Divided into two groups, namely the voltage value of the energy storage element at this moment. With the voltage value of the energy storage element at the next moment and utilize the disconnect switch current Ambient temperature Wind speed ,wind direction With the voltage value of the energy storage element The neural network was trained to obtain the isolation switch current. Ambient temperature Wind speed ,wind direction With the voltage value of the energy storage element Voltage value of the energy storage element at the next moment The mapping relationship between them is used to obtain the trained neural network model.

3. The method for evaluating the power supply reliability of a self-powered wireless temperature sensing device according to claim 1, characterized in that, The thermoelectric energy harvesting module includes a thermoelectric generator (1) and a heat dissipation device (3). The hot end of the thermoelectric generator (1) is fixed to the high-voltage disconnector blade (2) with thermally conductive adhesive, and the cold end is attached to the heat dissipation device (3), which is an aluminum profile heat sink.

4. The method for evaluating the power supply reliability of a self-powered wireless temperature sensing device according to claim 1, characterized in that, The low-power energy management circuit includes a BQ25504 chip and its peripheral circuitry. The peripheral circuitry includes a first capacitor C1, a second capacitor C2, a third capacitor C3, a fourth capacitor C4, a fifth capacitor C5, a sixth capacitor C6, a seventh capacitor C7, a first resistor R1, a second resistor R2, a third resistor R3, a fourth resistor R4, a fifth resistor R5, a sixth resistor R6, a seventh resistor R7, an eighth resistor R8, a ninth resistor R9, a first inductor L1, and a first chip U1. One end of the first capacitor C1 and one end of the fourth capacitor C4 are respectively connected to the second pin VIN_DC of the BQ25504 chip. The other end of the first capacitor C1 and the fourth capacitor C4 are connected to the second pin VIN_DC of the BQ25504 chip. The other end of capacitor C4 is grounded. One end of the fifth capacitor C5 is connected to pin VREF_SAMP of the BQ25504 chip, and the other end of capacitor C5 is grounded. One end of the second capacitor C2, one end of the sixth capacitor C6, and the first pin of the first chip U1 are connected to pin VSTOR of the BQ25504 chip. The other ends of the second capacitor C2 and the sixth capacitor C6 are connected to pin GND of the first chip U1. Pin ON of the first chip U1 is connected to pin VBAT_OK of the BQ25504 chip. Pin VOUT of the first chip U1 is connected to one end of the seventh capacitor C7. The seventh capacitor C7 is connected to the ground at one end. One end of the third capacitor C3 is connected to the fourteenth pin VBAT of the BQ25504 chip, and the other end of the third capacitor C3 is grounded. One end of the first resistor R1 is connected to the second pin VIN_DC of the energy harvesting chip. The other ends of the first resistor R1 and one end of the second resistor R2 are respectively connected to the third pin VOC_SAMP of the BQ25504 chip. The other end of the second resistor R2 is grounded. One end of the third resistor R3, one end of the fifth resistor R5, and one end of the seventh resistor R7 are respectively connected to the seventh pin VRDIV of the BQ25504 chip. The other end of the third resistor R3 is connected to the fourth pin VBAT of the BQ25504 chip. One end of resistor R4 is connected to the ground. The other end of the fifth resistor R5 is connected to one end of the sixth resistor R6. The other end of the seventh resistor R7 and one end of the eighth resistor R8 are respectively connected to the ninth pin OK_HYST of the BQ25504 chip. The other end of the fourth resistor R4 and the other end of the sixth resistor R6 are grounded. The other end of the eighth resistor R8 and one end of the ninth resistor R9 are respectively connected to the tenth pin OK_PROG of the BQ25504 chip. The other end of the ninth resistor R9 is grounded. One end of the first inductor L1 is connected to the second pin VIN_DC of the BQ25504 chip, and the other end is connected to the sixteenth pin LBST of the BQ25504 chip.

5. The method for evaluating the power supply reliability of a self-powered wireless temperature sensing device according to claim 4, characterized in that: The energy storage element is a supercapacitor, with its positive terminal connected to pin VBAT of the BQ25504 chip and its negative terminal grounded.

6. The method for evaluating the power supply reliability of a self-powered wireless temperature sensing device according to claim 5, characterized in that, When the voltage of the energy storage element is lower than the set threshold, the 11th pin VBAT_OK of the BQ25504 chip will output a low level. After receiving the low level signal, the 3rd pin ON of the first chip U1 will disconnect the switch, cut off the downstream wireless temperature sensing load, and prevent the energy storage element from over-discharging.

7. The method for evaluating the power supply reliability of a self-powered wireless temperature sensing device according to claim 6, characterized in that: The wireless temperature sensing node includes a terminal temperature sensing node and a coordinator. The terminal temperature sensing node uses a CC2530 main control chip, uses a thermistor to collect temperature data from the high-voltage disconnect switch, and communicates with the coordinator through a Zigbee network. The coordinator transmits the temperature data to the host computer via serial communication.

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