Apparatus, system and method for oil-containing power or power transmission equipment

Abstract

Described is an apparatus for an oil-containing power or power transmission device, comprising: a sensor configured to simultaneously transmit into oil within the oil-containing power or power transmission device one or more electromagnetic signals having a frequency variable in the range of 1 Hz to 3000 GHz, in particular a frequency variable in the range of 300 MHz to 300 GHz, and to receive reflected and / or propagated electromagnetic signals; and a processing device configured to compare the received signal in the time domain and / or the frequency domain with first and second signals in the time domain and / or the frequency domain, the first signal in the time domain and / or the frequency domain being classified according to physical properties of the oil, and the second signal in the time domain and / or the frequency domain being classified according to chemical properties of the oil; the processing device is further configured to determine the oil quality of the oil in the oil-containing power equipment or the power transmission equipment based on the comparison result.

Description

Technical Field

[0001] The present invention relates to a device, a system and a method for oil-containing power equipment or transmission equipment. In particular, the present invention relates to a device and a system for oil-containing power equipment or transmission equipment, by means of which the oil quality in the oil-containing power equipment or transmission equipment can be determined. The present invention also relates to a method for multiple oil-containing power equipment or transmission equipment, by means of which the oil quality in the oil-containing power equipment or transmission equipment can be determined. Background Art

[0002] Oil-immersed distribution transformers, also known as electrical oil-immersed transformers, are power transformers mainly used in energy distribution networks. Such oil-immersed transformers exist in various configurations.

[0003] For example, a sealed transformer is an airtight sealed oil-immersed transformer without an expansion tank or an air cushion, which can prevent the oil from contacting the atmosphere, thereby avoiding the accelerated aging of the oil.

[0004] Another type of oil-immersed transformer has an oil expansion tank arranged above the transformer tank and connected to the transformer tank through a flow channel. Through the oil expansion tank, the change in oil volume can be compensated. The oil expansion tank is used to accommodate the oil volume generated by the thermal expansion of the oil when the temperature fluctuates due to load changes or environmental temperature changes in the transformer. Inside the oil expansion tank, there can be a compressible membrane filled with air. According to the degree of oil expansion in the transformer, the membrane is compressed, and a closed system is formed inside the transformer tank, the oil expansion tank and the flow channel. A magnetic oil gauge (MOG) can be used to monitor the oil level in the oil expansion tank.

[0005] Document DE202008017356U1 describes an oil-immersed transformer having a transformer tank filled with oil, in which a transformer core with primary and secondary windings is arranged. For insulation, the primary and secondary windings can be wrapped with cellulose paper. The oil is used as an electrical insulating medium and as a cooling medium for dissipating the heat losses generated during the operation of the transformer. According to the operating oil temperature, the oil volume will expand and contract.

[0006] As the oil-immersed transformer ages, the oil will be contaminated by fiber materials and moisture in the insulating materials of the windings. In addition, the dissolved gases generated by chemical reactions in the oil will also contaminate the oil. To ensure safe operation and avoid operation interruptions or power outages, the oil must be regularly inspected and, if necessary, replaced.

[0007] The monitoring of oil is usually carried out in one of the following ways: Manually collect an oil sample from the transformer and send the oil sample to a laboratory for analysis. Then, the insulation resistance and dielectric breakdown voltage of the oil are tested in the laboratory. Furan analysis can also be carried out in the laboratory. Alternatively, it is known to analyze the oil regularly in a measuring device. In this way, gas chromatography analysis can be carried out, but its disadvantage is that it is time-consuming and costly, and requires experts to operate. In addition, photoacoustic spectroscopy analysis can be carried out.

[0008] Known techniques for monitoring the oil in an oil-immersed transformer also have the following disadvantages: Some techniques cannot be retrofitted and require relatively high manpower expenditure for the installation and configuration of the measuring device. Generally, during the installation of the measuring device, the transformer also needs to be non-operational and shut down. In addition, known measuring techniques are affected by electromagnetic pulses. For example, a measuring sensor directly attached to the surface of the main tank of the transformer may be affected by partial discharges. Such partial discharges generate electromagnetic pulses in the ultra-high frequency range (300 MHz to 3 GHz), which may cause measurement errors or inaccurate measurements in the measuring device. The ambient and / or surface temperature of the transformer also causes problems. For example, electronic measuring devices operating near the edges and yoke regions of the transformer are exposed to high temperatures (the oil temperature increase caused by high voltage loads). The elevated temperature can cause the measuring device to malfunction, resulting in measurement errors or inaccuracies, and even causing the measuring device to fail. Measuring devices that collect oil samples from the drain valve at the bottom of the main tank of the oil-immersed transformer may also mix in contamination particles. Moisture and fibers from the insulating material combine with the oil, causing residues to deposit at the bottom of the transformer. Oil samples collected in this area are often contaminated by the residues, which may lead to inaccurate oil quality analysis. Summary of the Invention

[0009] The present disclosure is based on the object of providing a device, system and method for an oil-containing power equipment or transmission equipment, by means of which the oil quality in the oil-containing power equipment or transmission equipment can be determined simply and accurately.

[0010] To achieve this object, there is proposed a device, in particular a measuring device, for an oil-containing power equipment or a power transmission equipment, which comprises the following components: a sensor configured to simultaneously transmit one or more electromagnetic signals having a variable frequency in the range of 1 Hz to 3000 GHz, in particular a variable frequency in the range of 300 MHz to 300 GHz, into the oil in the oil-containing power equipment or the power transmission equipment and receive the reflected and / or propagated electromagnetic signals; and a processing device configured to compare the received signals in the time domain and / or the frequency domain with first and second signals in the time domain and / or the frequency domain, wherein the first signal in the time domain and / or the frequency domain is classified according to the physical properties of the oil, and the second signal in the time domain and / or the frequency domain is classified according to the chemical properties of the oil; and the processing device is further configured to determine the quality of the oil in the oil-containing power equipment or the power transmission equipment based on the comparison result.

[0011] The oil-containing power equipment or the power transmission equipment can be any type of oil-containing power equipment or power transmission equipment that uses oil for insulation, cooling or normal operation. A preferred embodiment of the oil-containing power equipment or the power transmission equipment is an oil-immersed transformer. The following disclosure and exemplary embodiments refer to the oil-immersed transformer. However, it should be noted that whenever the oil-immersed transformer is disclosed below, it also means the oil-containing power equipment or the power transmission equipment, which can be correspondingly replaced by the oil-containing power equipment or the oil-containing power transmission equipment.

[0012] The oil-immersed transformer can be any type of oil-immersed transformer, in particular an oil-immersed transformer with an oil expansion tank or an oil-immersed transformer without an oil expansion tank. The sensor can include, for example, a baseband transmitter, a baseband receiver and a digital backend. The electromagnetic signals generated by the baseband transmitter and received by the baseband receiver can be used to determine the quality of the oil in the oil-immersed transformer with the help of the digital backend. The quality of the oil can be one of the quality characteristics defined in IEC 60422. Therefore, the sensor can be a non-contact near-field sensor for dielectric spectroscopy. The sensor can also be configured to perform broadband dielectric spectroscopy (BDS) or electrochemical impedance spectroscopy. If the sensor is configured, for example, for ultra-wideband impedance spectroscopy, the oil may be subjected to a pulsed AC signal. In addition, combined frequency domain / time domain techniques can be used to characterize the oil. To improve the detection accuracy, the sensor can generate a baseband signal generated by combining several up-converted Gaussian signals. Other components, such as amplifiers and ADC (analog-to-digital) or DAC (digital-to-analog) semiconductor electronic devices, can be provided in the sensor.

[0013] The sensor is configured to generate electromagnetic signals with frequencies ranging from 1 Hz to 3000 GHz and transmit them to an antenna, which sends the electromagnetic signals into the oil. Then, the sensor receives electromagnetic signals with frequencies ranging from 1 Hz to 3000 GHz through the antenna. The antenna can be designed as a single unit with both a transmitting and a receiving antenna (referred to as a sensor module in this case) so that the signals sent into the oil are reflected / propagated and received again by the antenna. In particular, it can be a transceiver antenna. The sensor can also be separate from the antenna. In this arrangement, the antenna transmits electromagnetic signals through the oil, and the propagated signals are received by the sensor at a location different from the antenna. These signals can be especially pulse signals in the picosecond range. The interaction of electromagnetic waves with a frequency range of 1 Hz to 3000 GHz has the advantage that they generally do not pose serious health risks to humans and still provide good measurement results.

[0014] After propagating through the oil, the transmitted electromagnetic signals are distorted, which is why the received electromagnetic signals have a different phase angle and a different frequency compared to the original transmitted signals. By convolving the received distorted signals with an ideal signal (the original signal), a pulse response of a certain shape is obtained. Then, the pulse response is sent to an analog-to-digital converter (ADC) to perform a fast Fourier transform (FFT). Then, the FFT is examined and compared in detail.

[0015] The frequency of the signals can be adjusted and / or selected according to the type and level of detail of the information to be evaluated. For a more detailed evaluation of the oil quality, a suitable frequency (within a lower or higher frequency range) can be selected. Even better measurement results can be obtained if the sensor is configured to generate, transmit, and receive electromagnetic signals with frequencies ranging from 300 MHz to 300 GHz. In particular, the sensor can be configured to operate in the ultra-wideband (UWB) range. The UWB range from 0.1 GHz to 6 GHz allows for a (very detailed) study of the behavior of the oil when interacting with electromagnetic waves. The sensor can also be configured to measure the temperature, vibration, and / or gas generation of the oil.

[0016] The processing device can be a computing device, such as a laptop or a tablet. The processing device can be configured to apply a fast Fourier transform to the received signals. The processing device can communicate with the sensor through a data cable, the Internet, a wireless network, or a cellular network. The processing device and the sensor can also be integrated into a single unit. The antenna, the processing device, and the sensor can also be integrated into a single unit. In addition, the processing device can be a cloud server.

[0017] For a comparison made in a processing device, an amplitude value that varies over time (e.g., in milliseconds ms) (e.g., in dB) can be compared with a predefined and classified amplitude value that varies over time (e.g., in ms). Additionally, a power spectrum calculated by fast Fourier transform can be used, i.e., an amplitude value that varies with frequency (e.g., in GHz) (e.g., in dB) can be compared with a predefined and classified amplitude value that varies with frequency (e.g., in GHz) (e.g., in dB). Any type of output in correlation (digital signal processing) or different scales (e.g., logarithmic scale) and / or different graphical representations (e.g., Nyquist plot, Bode plot, etc.) can be used for comparison to compare the changes in phase angle and amplitude. In these comparisons, the time-domain signal or the frequency-domain signal can be compared in each case.

[0018] Therefore, signals in the time domain and / or frequency domain classified according to the physical properties and chemical properties of the oil are used to determine the quality of the oil in the oil-immersed transformer, enabling the quality of the oil in the oil-immersed transformer to be determined simply and accurately.

[0019] The physical property can be one of viscosity, flash point, interfacial tension, color, and density, and the classification according to the physical properties of the oil relates to the oil quality. The chemical property can be one of acid value, chemical composition, especially the chemical composition change caused by the influence of paper polymerization, moisture, and the participation of dissolved gases, and the classification according to the chemical properties of the oil relates to the oil quality. These physical and chemical properties can more precisely determine the quality of the oil in the oil-immersed transformer.

[0020] To further improve the determination of the oil quality in the oil-immersed transformer, the processing device can be configured to compare the received signal in the time domain and / or frequency domain with third and fourth signals in the time domain and / or frequency domain, where the third signal in the time domain and / or frequency domain is classified according to the physical properties of the oil that are different from the classified physical properties of the oil of the first signal in the time domain and / or frequency domain, and the fourth signal in the time domain and / or frequency domain is classified according to the chemical properties of the oil that are different from the classified chemical properties of the oil of the second signal in the time domain and / or frequency domain. Similar to the first signal, the physical property in the third signal can be one of viscosity, flash point, interfacial tension, color, and density, and the classification according to the physical properties of the oil relates to the oil quality. Correspondingly, similar to the second signal, the chemical property in the fourth signal can be one of acid value, chemical composition, especially the chemical composition change caused by the influence of paper polymerization, moisture, and the participation of dissolved gases, and the classification according to the chemical properties of the oil relates to the oil quality.

[0021] The processing device can also be configured to determine impedance (Z), conductance (G), admittance (Y), susceptance (B), and / or a combination thereof in the time domain and / or frequency domain based on the transmitted and received signals, and compare the impedance, conductance, admittance, susceptance, and / or a combination thereof in the time domain and / or frequency domain with the first and second signals in the frequency domain and / or time domain. To more precisely determine the quality of the oil in the oil-immersed transformer, the impedance, conductance, admittance, and / or susceptance in the time domain and / or frequency domain can also be compared with the third and fourth signals in the time domain and / or frequency domain. In this way, the impedance value of the oil is determined, where the impedance (Z) is a combination of resistivity, permittivity, and permeability (since the oil is not magnetic, it can be considered as 1).

[0022] The transmitted signal (the transmitted signal can be a single signal or multiple signals with different frequencies transmitted simultaneously) can be any valid multi-frequency signal (sine, cosine, etc.) for determining the electrical parameters Z, G, Y, and / or B of the oil.

[0023] The larger the measurement frequency range, the more detailed the differences in a specific frequency range become. Therefore, the proposed sensor technology can measure the electrical parameters of the oil in a wide frequency range, such as in the ultra-wideband range from 0.1 to 6 GHz, thereby providing very detailed information about the behavior of the oil when interacting with electromagnetic waves.

[0024] The device can include a machine learning module configured to determine values related to the physical and chemical properties of the oil based on impedance, conductance, admittance, susceptance, and / or a combination thereof. These values can then be interpreted and associated with the performance and / or evaluation of the oil-immersed transformer.

[0025] The device can also include a machine learning module configured to optimize the processing of the machine learning model and the comparison in the processing device based on the classification of the first and second signals, where the machine learning model uses the first and second signals as training data. The machine learning model can also be configured to optimize the comparison in the processing device based on the classification of the third and fourth signals, where the machine learning module uses the third and fourth signals as training data.

[0026] In addition, some machine learning models can be provided, which are configured to be processed on the machine learning module and determine values related to the physical and chemical properties of the oil based on impedance, conductance, admittance, susceptance, and / or a combination thereof.

[0027] Using the collected data stored on a cloud platform, additional machine learning models can be developed. These machine learning models find patterns and structures in data from one or more sensors. The trained machine learning models are sent to a processing device so that calculations / predictions / classifications can be made on data from sensors (real-time data). Thus, the machine learning models can include a variety of machine learning models. The machine learning module consists of the necessary memory and computing hardware such as a CPU, GPU, NPU so as to make predictions from the machine learning models and to enable the training and development of the machine learning models.

[0028] Machine learning models can also learn based on regression. All machine learning models can be trained and used for inference as part of the machine learning module in the device, or they can be trained and used for inference on another computer such as a laptop or a cloud computer. The machine learning module can be located in the device or on the cloud platform. The machine learning module can be hosted or developed on, for example, the Microsoft Azure platform. In this case, the processing device can communicate with the machine learning module via the Internet. Both the processing device and the machine learning module can also be hosted on a cloud platform such as the Microsoft Azure platform. In this case, the antenna communicates with the cloud platform via an additional communication device. The machine learning module can be provided locally in the device. Data can also be sent from the aggregation device to the cloud via a highly secure gateway. Applications can run using this data in the cloud, which is based on the Microsoft Azure platform or the Amazon Cloud platform.

[0029] The machine learning models take the signals recorded by the sensors as input. The machine learning models have been trained such that they map this input to physical or chemical properties that are not directly measured by the sensors. The output of the machine learning models is either a predicted value or a probability distribution of a series of values. The output can also be an artificial metric that is created to represent the overall condition of the transformer or the expected performance in some cases.

[0030] Mathematical functions with different weights and parameters are used to relate the input to the output. These weights and parameters are determined based on sensor signal data recorded from transformer oil in a known state (e.g., from a laboratory). Finding the weights and parameters is called training and can be done using a gradient descent algorithm. The training process of the machine learning models can be carried out on a device in the edge / fog layer or in the cloud. Inference or creating predictions, i.e., converting the input to the output without changing the parameters or weights, can also be carried out on a cloud computer / system, in the edge / fog layer, or on a device in the Internet of Things layer.

[0031] Once sufficient data is stored in the cloud database, unsupervised machine learning models can be developed. Since data is recorded relatively quickly on a regular basis, the amount of sensor data stored in the cloud is larger than the amount of laboratory data / known oil condition data. The sensor data stored in the cloud is unlabeled, which means that the actual oil quality and condition of the oil-immersed transformer are unknown. By processing the data stored in the cloud from a single sensor or multiple sensors installed on one or more oil-immersed transformers, patterns or structures in the data can be identified, providing information about the relative changes in the condition of the oil or the oil-immersed transformer.

[0032] The identified patterns and structures in the data can be used for qualitative and quantitative monitoring of oil-immersed transformers and can be further used in applications such as oil-immersed transformer life prediction, oil-quality-based capacity detection and transformer performance prediction, transformer capacity-based active load management, and transformer recommendations.

[0033] The data used for training is stored in a database on the cloud. If the training process is executed on a cloud device, the learned weights and parameters are sent to an inference device (in the cloud, Internet of Things, or edge layer).

[0034] The sensor can be configured to emit electromagnetic signals at or in various openings or valves of an oil-containing power equipment or transmission equipment (especially an oil-immersed transformer). Subsequent measurements can be performed, for example, at various valves and / or drain ports of the oil-immersed transformer located at the top, bottom, or side of the oil-immersed transformer. Measurements can also be performed at the valve between the main tank and the radiator of the oil-immersed transformer. For openings located at the bottom of the oil-immersed transformer, no pump is required to pump the oil. Other ways to make the oil available to the sensor can be provided.

[0035] The sensor can also include a plurality of sensors at or in various openings of the oil-containing power equipment or transmission equipment, so that the plurality of sensors are configured to simultaneously emit one or more electromagnetic signals with variable frequencies in the range of 1 Hz to 3000 GHz, especially in the range of 300 MHz to 300 GHz, into the oil and receive the reflected electromagnetic signals, and process the received signals.

[0036] The device can also include: an aggregation device having a communication device, configured to receive the processed signals from the plurality of sensors. The aggregation device is configured to aggregate the processed signals from the plurality of sensors, and the communication device is configured to transmit the aggregated signals. The aggregation device can also be configured to aggregate the signals received from the plurality of sensors and include a communication device configured to transmit the aggregated signals. The aggregation device can be arranged in the sensor or the processing device. Accordingly, the communication device can be arranged in the sensor or the processing device. If the aggregation device and the communication device are arranged in the processing device, the communication device transmits the aggregated signals to the central unit. The central unit can be, for example, a cloud platform.

[0037] The communication device may also be configured to communicate with a SCADA (Supervisory Control And Data Acquisition) system directly by using a standard IEC 61850 client-server protocol (such as the IEC 61850-based XMPP open-source protocol or the IEC 61850 MMS protocol) to utilize a remote telemetry unit (RTU) or through an intelligent electronic device (IED).

[0038] The processing device may also be configured to process these received signals simultaneously.

[0039] The device may also include a temperature stabilizer configured to keep the temperature of the oil constant while transmitting and receiving electromagnetic signals. This can further improve the determination of the oil quality of the oil-immersed transformer.

[0040] The device may also include an automatically controllable calibration device configured to eliminate irregularities in the received electromagnetic signals. This can also further improve the determination of the oil quality of the oil-immersed transformer.

[0041] The device may also include an antenna configured to transmit electromagnetic signals generated by the sensor into the oil and receive the reflected and / or propagated electromagnetic signals, where the antenna includes complementary split-ring resonators, planar-resonator-based sensor electrodes, or electrodes / probes designed to optimize the parasitic and bilayer effects of the oil. The antenna is electrically connected to the sensor. To avoid inaccurate measurements, the antenna may be directly arranged or mounted on the oil expansion tank. The sensor may be located away from the oil expansion tank, for example, more than 1 meter away. Thus, the antenna and the sensor may be connected by a cable or through a wireless interface (such as through a wireless local area network WLAN or Bluetooth). The antenna may be a transmitting antenna and a receiving antenna, an integrated transmitting and receiving antenna, or two integrated transmitting and receiving antennas. In particular, the antenna may be designed as a Vivaldi antenna. The Vivaldi antenna can be advantageously used due to its large bandwidth, low cross-polarization, and constant group delay. The antenna may also be designed as an ultra-wideband (UWB) antenna, a helical antenna, or other types of antennas. The antenna and the sensor may also be designed as a single unit or module, for example, in one housing. In addition, the sensor may include a communication interface for communicating with a cloud computer. The sensor may also be retrofitted according to a mechanical design compatible with the oil-immersed transformer valve.

[0042] For attaching an antenna, an oil cell, and a device for transporting oil from an oil-immersed transformer to the oil cell, a fastening device can be provided that is capable of detachably attaching the antenna, the oil cell, and the device for transporting oil from the oil-immersed transformer to the oil cell to or within the oil-immersed transformer. Other forms of attachment, such as screw or magnetic attachment, are also conceivable. The wider the sample data, the better the analysis becomes. Thus, the device can be attached to different locations on the oil-immersed transformer (top valve, bottom valve, drain valve, radiator valve, opening in the oil expansion tank) to obtain various measurement results. Placing sensors at different locations (top, bottom, at the drain, at the radiator valve) is beneficial for overall analysis. For example, comparing lower and higher oil temperatures is a good way to check whether the insulating fluid is circulating properly. Lack of circulation can lead to accelerated deterioration of the transformer insulation system.

[0043] The processing device can also be configured to perform life prediction on components of an oil-containing power equipment or a power transmission equipment based on the determined oil quality, detect abnormalities in the oil-containing power equipment or the power transmission equipment, predict the transformer performance and capacity detection of the oil-containing power equipment or the power transmission equipment, perform active load management based on the transformer capacity of the oil-containing power equipment or the power transmission equipment, and / or provide transformer recommendations for the oil-containing power equipment or the power transmission equipment from multiple oil-immersed transformers. In addition, oil-immersed transformer recommendations can be proposed based on historical environmental, geographical, and transformer performance data related to the oil quality.

[0044] The device can also include a cloud platform configured to perform life prediction on components of an oil-containing power equipment or a power transmission equipment based on the determined oil quality, detect abnormalities in the oil-containing power equipment or the power transmission equipment, predict the transformer performance and capacity detection of the oil-containing power equipment or the power transmission equipment, perform active load management based on the transformer capacity of the oil-containing power equipment or the power transmission equipment, and / or provide transformer recommendations for the oil-containing power equipment or the power transmission equipment from multiple oil-immersed transformers. In addition, oil-immersed transformer recommendations can be proposed based on historical environmental, geographical, and transformer performance data related to the oil quality.

[0045] The object is also achieved by a system that includes one or more oil-containing power equipment or power transmission equipment, and the aforementioned device for each oil-containing power equipment or power transmission equipment.

[0046] The object is also achieved by a method for determining the oil quality of an oil-containing power equipment or a power transmission equipment using the aforementioned device, the method including the steps of: arranging a plurality of sensors of the device at different openings and / or valves or within the oil-containing power equipment or the power transmission equipment, and transmitting electromagnetic signals into the oil within the oil-containing power equipment or the power transmission equipment through the plurality of sensors. Then, the aggregated signals can be sent to a central unit, such as a cloud platform, for data analysis.

[0047] Collecting oil samples at different locations of an oil - immersed transformer (e.g., top valve, bottom valve, valve near radiator, vent or valve on oil expansion tank) improves the overall results of the oil quality assessment method.

[0048] Over time, the chemical and physical properties of oil deteriorate, and there is a correlation between the physical and chemical properties of oil and the performance of an oil - immersed transformer. For example, the interfacial tension IFT (physical property) is negatively correlated with the operating time of an oil - immersed transformer, while the acid value (chemical property) is directly correlated with the operating time of an oil - immersed transformer, and the moisture content in the oil (chemical property) has a very strong negative correlation with the breakdown voltage of transformer oil.

[0049] Deriving the chemical and physical properties of oil in real - time from the results of electrochemical impedance spectroscopy and relating them to the performance of the transformer helps in the qualitative and quantitative real - time monitoring and maintenance of the oil.

[0050] The above aspects and variations can be combined without explicit description. Thus, each of the described embodiments is optional for each embodiment or its combination. Therefore, the present disclosure is not limited to the individual embodiments and variations in the described order or to the specific combinations of the described aspects and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Further advantages, details, and features of the methods, apparatuses, and systems described herein come from the following description of the exemplary embodiments and from the drawings.

[0052] Figure 1 Schematic diagram showing a first exemplary embodiment of an oil - immersed transformer with an oil expansion tank;

[0053] Figure 2 Schematic diagram showing an oil expansion tank with means for determining the quality of oil Figure 1 ;

[0054] Figure 3 Schematic diagram showing a second exemplary embodiment of an oil - immersed transformer with means for determining the quality of oil; and

[0055] Figure 4 Schematic diagram showing a third exemplary embodiment of a system for multiple oil - immersed transformers. DETAILED DESCRIPTION

[0056] Figure 1Schematic diagram showing an exemplary embodiment of an oil-immersed transformer with an oil expansion tank. The oil-immersed transformer 10 includes a transformer oil tank 12 and an oil expansion tank 20. A flow channel 30 connects the transformer oil tank 12 to a first opening 22 in the oil expansion tank 20. The first opening 22 is arranged at the lower end of the oil expansion tank 20. The transformer oil tank 12 includes a corresponding opening. In the transformer oil tank 12, the transformer 15 is mounted on a support block 60 in an oil bath 40. Figure 1 The oil expansion tank 20 in is shown, for example, in a cylindrical form above the transformer oil tank 12. Other shapes (such as a cuboid, cube, or prism) and arrangements (at the same height as the transformer oil tank 12, higher, etc.) of the oil expansion tank 20 are also conceivable.

[0057] The oil 40 is contained in the housing 26 of the oil expansion tank 20 and the flow channel 30. The oil expansion tank 20 is used to contain oil because when the temperature fluctuates in the transformer 15 due to load changes or ambient temperature changes, the oil will thermally expand. As indicated by the surface 42 of the oil in the oil expansion tank 20, the oil level in the oil expansion tank 20 changes accordingly. The oil 40 in the oil expansion tank 20 compresses the membrane 28 according to the oil level 42 in the oil expansion tank 20, and when the oil level 42 drops, the membrane 28 is decompressed again. A pressure relief valve 50 for releasing excess gas is assembled in a second opening 24 at the upper end of the oil expansion tank 20. The oil-immersed transformer also includes an oil level measuring device 25, which is used to measure the oil level 42 in the oil expansion tank 20. For clarity, other components of the oil-immersed transformer, such as the transformer core, coils, and Buchholz protection relay, are not shown in the Figure 1 schematic diagram. As an alternative to the embodiment of the oil expansion tank 20 with the membrane 28, an Atmoseal type or other types of oil expansion tanks can also be provided.

[0058] Figure 2 Schematic diagram showing an exemplary embodiment of an oil expansion tank with a device for determining the quality of oil. The oil expansion tank 20 is Figure 1 the oil expansion tank 20 shown, where Figure 1 and Figure 2 the same reference numerals in represent the same elements. Figure 2 The arrangement shown includes an oil chamber 55 for receiving the oil 40 from the oil expansion tank 20, devices 57, 58 for transporting the oil 40 from the oil expansion tank 20 to the oil chamber 55, an antenna 85 for applying an electromagnetic signal to the oil 40 in the oil chamber 55, a sensor 70 electrically connected to the antenna 85 for measuring the quality of the oil in the oil-immersed transformer 10, and a processing device 90.

[0059] The device 57, 58 for conveying oil 40 from the oil expansion tank 20 to the oil chamber 55 includes a hose 57 and a pump 58. The hose 57 is at least partially arranged within the oil expansion tank 20. The first end of the hose extends into the oil 40 within the oil expansion tank 20, and the second end is connected to the oil chamber 55. The pump 58 is used to pump the oil 40 from the oil expansion tank 20 into the oil chamber 55. The hose 57 extends through an opening 24, where the oil chamber 55 is located outside the oil expansion tank 20 at the opening 24. This opening can also be a vent on the oil expansion tank 20.

[0060] In Figure 2 it, the lowest oil level 42 in the oil expansion tank 20 is shown. The hose 57 is designed such that the first end of the hose 57 always extends below the lowest oil level 42. The pump 58 pumps the oil 40 from the oil expansion tank 20 into the oil chamber 55. It is also conceivable that the pump 58 pumps the oil 40 through the oil chamber 55, that is, the oil 40 returns to the oil expansion tank 20. The pump 58 is designed as an electric pump and is electrically connected to the antenna 85. The pump 58 is located near the antenna 85.

[0061] According to an alternative embodiment (not shown), the oil chamber 55 is located within the oil expansion tank 20, particularly above the maximum oil filling level within the oil expansion tank 20.

[0062] The antenna 85 is mounted on the outer wall of the oil chamber 55 and is electrically connected to the sensor 70.

[0063] Opposite to the bottom side 21 of the oil expansion tank 20, there is an opening 24 at the upper end of the oil expansion tank 20, which is sealed by a pressure relief valve 50. Above the opening 24, the antenna 85 is arranged, and the antenna is electrically connected to the sensor 70.

[0064] The sensor 70 includes a baseband transmitter 71, a baseband receiver 72, and a digital backend 73, and is electrically connected to the processing device 90 via a cable. The baseband transmitter 71 generates a pulsed excitation signal for the antenna 85, and the reflected / propagated signal is forwarded to the baseband receiver 72. The digital backend 73 also includes a semiconductor memory unit, a random access memory (RAM) unit, and a trusted platform module (TPM) that meets the necessary security and encryption requirements. The TPM module also helps to secure the hardware through integrated encryption keys and provides user authentication and authorization mechanisms. In addition, the TPM module is used to sign the raw sensor data to make the data authentic and input it into blockchain technology. The digital backend 73 controls the baseband transmitter 71 and the baseband receiver 72 for signal transmission and reception. In this process, the sensor 70 is configured to transmit and receive pulsed signals with frequencies ranging from 1 Hz to 3000 GHz via the antenna 85. Preferably, the system operates in the ultra-wideband range, such that the sensor 70 analyzes signals in a wide frequency range from 0.1 GHz to 6.0 GHz, and the antenna 85 transmits and receives signals in the range from 300 MHz to 300 GHz. The sensor 70 transmits and receives measurement signals via the antenna 85, and the oil quality in the oil expansion tank 20 can be determined using these signals. The antenna 85 is a complementary split-ring resonator or a sensor electrode based on planar resonance. Other types of electrodes / probes can also be used, and these electrodes / probes are designed to optimize the parasitic effects and bilayer effects of the oil. In addition, the sensor 70 includes a communication device 79 for communicating with the processing device 90. The communication device 79 can include a wireless interface (such as an LTE-M or NB-IoT cellular connection SoC module, WiFi, BLE, or NFC interface) and / or a wired interface (such as an I2C, SPI, UART, ADC, PWM, HDMI, VGA, ETHERNET interface).

[0065] The signal generated in the baseband transmitter 71 of the sensor 70 can be produced using cost-effective semiconductor flip-flops and shift registers. Depending on the required level of detail and resolution of the oil analysis, the frequency of the input signal can be changed by varying the configuration of the flip-flops and shift registers.

[0066] The processing device 90 is a laptop computer or any other type of computer, which is configured to process and visualize the measurement values acquired by the sensor 70. The processing device 90 can also include a microcontroller unit, a graphics processing unit (GPU), and / or a central processing unit (CPU) or a neural processing unit (NPU) to perform the calculations required to run machine learning with the machine learning module 95. In particular, the processing device 90 is configured to calculate values related to the color, water content, and / or acid value of the oil based on the measurement values acquired by the sensor 70. The processing device 90 can also calculate the above-mentioned other values regarding physical and chemical properties. For example, a fast Fourier transform can be applied to the measurement data.

[0067] The oil-immersed transformer further includes temperature, gas and / or vibration sensors 65. The temperature, gas and / or vibration sensors 65 are located in the oil expansion tank 20. The temperature, gas and / or vibration sensors 65 can also be arranged at or outside the connection interface of the opening 24. The temperature, gas and / or vibration sensors 65 are configured to send measurement data to the sensors 70 and / or the processing device 90. For this purpose, the temperature, gas and / or vibration sensors 65 can include a communication interface capable of communicating with the sensors 70 and / or the processing device 90. The communication interface can be arranged at the opening 24, for example, to provide a wired connection to the sensors 70. If the temperature, gas and / or vibration sensors 65 are designed as gas sensors, they can be configured to detect anomalies in the gas generated by the oil-immersed transformer. The sensors can also include additional sensors and electronics for monitoring the normal operation and condition of the oil-immersed transformer.

[0068] To accurately determine the quality of the oil in the oil-immersed transformer 10, the processing device 90 is configured to compare the electromagnetic signals received by the sensors in the time domain and / or frequency domain with the first and second signals in the time domain and / or frequency domain. The first signal in the time domain and / or frequency domain is classified according to the physical properties of the oil, and the second signal in the time domain and / or frequency domain is classified according to the chemical properties of the oil. The physical properties are one of viscosity, flash point, interfacial tension, color and density, and the classification according to the physical properties of the oil relates to the quality of the oil. The chemical properties are one of acid value, chemical composition, especially the chemical composition changes caused by paper polymerization influence, moisture and dissolved gas participation, and the classification according to the chemical properties of the oil relates to the quality of the oil. Based on the comparison results, the processing device 90 determines the quality of the oil 40 in the oil-immersed transformer 10.

[0069] A machine learning model can be used to compare the signals and the received signals. The model has been trained based on predefined signals to find weights and parameters. Then, the machine learning model predicts the chemical or physical properties of the oil and provides a value or a probability distribution. The machine learning process can be performed in the machine learning module 95, the processing device 90 or the cloud platform 200 (see Figure 4 ). The described machine learning method also solves the inverse problem by determining the physical and chemical factors that affect the overall condition of the oil-immersed transformer.

[0070] The comparison can be made through some separate machine learning models. For example, one for the first signal to predict color and one for the second signal to predict moisture content. Alternatively, the machine learning model can generate predictions for one or more chemical and physical properties in a single computational process.

[0071] For example, the first signal in the time domain and / or the frequency domain is classified according to the color of the oil. Thus, it can be considered that if the color of the oil changes from light yellow to dark yellow, light yellow, amber, brown, dark brown, and black, the quality of the oil deteriorates in turn. For example, the second signal in the time domain and / or the frequency domain is classified according to the moisture content in the oil.

[0072] The processing device 90 participates in the preprocessing of data, that is, the process of converting / deriving electromagnetic signals into physical values (for example, converting the Fourier transform output of the received electromagnetic signal into the moisture content of the oil), and excluding unnecessary data packets from the original signal.

[0073] For the comparison performed in the processing device, the amplitude value that changes over time (for example, in milliseconds ms) (for example, in dB) can be compared with the predefined and classified amplitude value that changes over time (for example, in ms) (for example, in dB). In addition, the power spectrum calculated by the fast Fourier transform can be used, that is, the amplitude value that changes over frequency (for example, in GHz) (for example, in dB) can be compared with the predefined and classified amplitude value that changes over frequency (for example, in GHz) (for example, in dB). Any type of output in correlation (digital signal processing) or different scales (such as logarithmic scale) and / or different graphical representations (such as Nyquist diagram, Bode plot, etc.) can be used for comparison to compare the changes in phase angle and amplitude.

[0074] Optionally, the processing device 90 can be configured to compare the received signal in the time domain and / or the frequency domain with the third and fourth signals in the time domain and / or the frequency domain. The third signal in the time domain and / or the frequency domain is classified according to the physical property of the oil that is different from the classified physical property of the oil in the first signal in the time domain and / or the frequency domain, and the fourth signal in the time domain and / or the frequency domain is classified according to the chemical property of the oil that is different from the classified chemical property of the oil in the second signal in the time domain and / or the frequency domain. Similar to the first signal, the physical property in the third signal can be one of viscosity, flash point, interfacial tension, color, and density, and the classification according to the physical property of the oil involves the quality of the oil. Accordingly, similar to the second signal, the chemical property in the fourth signal can be one of acid value, chemical composition, especially the chemical composition change caused by the influence of paper polymerization, moisture content, and dissolved gas participation, and the classification according to the chemical property of the oil involves the quality of the oil.

[0075] For example, the third signal in the time domain and / or the frequency domain is classified according to the viscosity of the oil, while the fourth signal in the time domain and / or the frequency domain is classified according to the acid value of the oil.

[0076] The processing device 90 is configured to determine impedance, conductance, admittance, and / or susceptance in the time domain and / or the frequency domain based on the transmitted and received signals, and compare the impedance, conductance, admittance, and / or susceptance in the time domain and / or the frequency domain with the first and second signals in the frequency domain and / or the time domain. Additionally, the processing device 90 can also compare the impedance, conductance, admittance, and / or susceptance in the time domain and / or the frequency domain with the third and fourth signals in the time domain and / or the frequency domain. The above values can also be combined arbitrarily and compared with each other.

[0077] In addition, the processing device 90 is capable of analyzing historical data recorded by the device and / or other similar installed devices. The recorded data stored in the cloud can be used to find patterns and structures in the data in the form of an unsupervised machine learning module. The training of the machine learning module is carried out in the processing device 90 or in another laptop / computer connected to the cloud.

[0078] The patterns and structures found in the data are used for the qualitative and quantitative monitoring of the oil-immersed transformer 10, and can be further used in applications such as oil-immersed transformer life prediction, oil-quality-based capacity detection and transformer performance prediction, transformer-capacity-based active load management, and transformer recommendations. The calculation of the patterns and structures is carried out in the processing device 90 or in the cloud platform 200 (see Figure 4 ).

[0079] As Figure 2 shown, the processing device 90 further includes a machine learning module 95, which is configured to optimize the comparison in the processing device 90 based on the classification of the first, second, third, and / or fourth signals. The first, second, third, and / or fourth signals can in particular be predefined signals.

[0080] The comparison between the predefined signal and the received signal can be implemented through a machine learning model that has been trained based on the predefined signal to find weights and parameters. Then, the machine learning model predicts the chemical or physical properties of the oil, providing a value or a probability distribution. The machine learning process can be carried out in the machine learning module 95, the processing device 90, or the cloud platform 200 (see Figure 4 ).

[0081] Figure 2 The device shown also includes a temperature stabilizer 75, which is configured to keep the temperature of the extracted oil of the oil-immersed transformer 10 constant.

[0082] An automatic calibration device 77 is provided in the sensor, and the automatic calibration device is configured to eliminate irregularities, noise, and / or attenuation in the received electromagnetic signals. The automatic calibration device 77 can also be provided in the processing device 90.

[0083] Figure 3Schematic diagram showing a second exemplary embodiment of an oil-immersed transformer with means for determining the quality of the oil. The oil-immersed transformer can be Figure 1 and Figure 2 the oil-immersed transformer 10 shown, or it can be another oil-immersed transformer (such as a hermetically sealed or any breathable oil-immersed transformer used in distribution and transmission networks). The same reference numerals denote the same elements, and thus no repeated description is provided.

[0084] The oil-immersed transformer includes a plurality of openings. In each opening, there is a sensor 70. Each sensor 70 sends the received electromagnetic signal to an aggregation device 80, and the aggregation device 80 aggregates these signals. The aggregation device 80 includes a communication device 79. The communication device 79 sends the aggregated signal to a processing device 90 or a cloud platform ( Figure 3 not shown). The transmission can be carried out simultaneously, especially in real time.

[0085] According to an alternative embodiment, only one sensor 70 is used, and the sensor 70 is successively brought to each opening of the oil-immersed transformer to measure the quality of the oil in the oil-immersed transformer. The received electromagnetic signal can be temporarily stored in a storage device in the sensor 70 and then aggregated by the aggregation device 80.

[0086] In summary, Figure 3 the exemplary embodiment can be described as follows: The sensor 70 of the oil-immersed transformer 10 is connected to the aggregation device 80 through a wireless or wired communication link. The data / signals received by the sensor 70 can be processed with the aid of a machine learning module (for example, see the machine learning module 95 in Figure 2 ) and then sent to the aggregation device 80. The sensor 70 can also communicate directly or through an edge / fog layer with a cloud platform ( Figure 3 not shown, but see the edge or fog layer with a highly secure gateway 100 in Figure 4 ) via the communication device 79. The communication device 79 is configured to communicate as described for the communication device 79 in Figure 2 . The aggregation device 80 communicates wirelessly with an ultra-secure gateway using an edge or fog layer ( Figure 3 not shown, see the edge or fog layer with an ultra-secure gateway 100 in Figure 4 ). Using the data collected in the aggregation device 80, a sensor fusion algorithm can be applied to study the real-time behavior of the oil-immersed transformer.

[0087] Figure 4 Schematic diagram showing a third exemplary embodiment of a system for a plurality of oil-immersed transformers, where four oil-immersed transformers are shown by way of example. Each oil-immersed transformer can be Figure 1 or Figure 3The oil-immersed transformer 10 shown or another oil-immersed transformer, each sensor 70 can be Figure 2 or Figure 3 the sensor 70 shown in Figure 3 or another sensor. The aggregation device 80 can be the aggregation device 80 shown in

[0088] The oil-immersed transformer 10 is part of a power supply network ( Figure 4 not shown). The sensors 70 are configured to send the received or aggregated received data to the cloud platform 200 in the cloud layer by using the edge or fog layer of the ultra-secure gateway 100. Then the received data is processed there.

[0089] A processing device 90 ( Figure 4 not shown in the figure) can be provided in each sensor 70 and each aggregation device 80. The aggregation device 80 is part of the Internet of Things (IoT) layer and communicates wirelessly with the edge / fog layer. The cloud layer runs various applications, such as predicting the life of components of the oil-containing power equipment or transmission equipment 10 based on the determined oil quality, detecting anomalies in the oil-containing power equipment or transmission equipment 10, predicting the transformer performance and capacity detection of the oil-containing power equipment or transmission equipment 10, performing active load management based on the transformer capacity of the oil-containing power equipment or transmission equipment 10, and / or providing transformer recommendations for the oil-containing power equipment or transmission equipment 10 from multiple transformers. The ultra-secure gateway 100 is provided for secure communication between the cloud layer and multiple sensors 70 or multiple aggregation devices 80 in the IoT layer. Therefore, a multi-layer architecture is considered, which includes the IoT layer, the edge / fog layer, and the cloud layer, to describe a decentralized data processing structure located between the cloud and the devices generating data. This flexible architecture allows users to place resources (including applications and the data they generate) in logical locations to improve performance. To this end, the sensors 70 are located in the IoT layer, the ultra-secure gateway 100 is located in the edge / fog layer, and the measurement data processing is performed in the cloud layer 200.

[0090] The sensors 70 function as the IoT perception layer in the smart grid in the IoT layer. The edge / fog layer enables secure communication between the IoT layer and the cloud layer. To this end, the edge / fog layer is configured to perform authorization, dual-certificate authentication, and data preprocessing for anomaly detection. In addition, the high availability and fail-safety of the network / transformer monitoring method are ensured. In addition, dual virtualization is possible because this layer is supported by virtualization technology to migrate from a connected environment to another environment and prevent faulty data from propagating through system cascades. This is achieved by migrating functions and data from risky dedicated hardware to other hardware. The cloud layer is used to provide applications for monitoring, historical data analysis, artificial intelligence-based applications, and visualization.

[0091] To establish secure network communication, protocols based on Transmission Control Protocol (TCP) and / or User Datagram Protocol (UDP) can be used for data transmission from the physical Internet of Things layer to the edge layer, and any communication protocol based on TCP and / or Internet Protocol (IP) can be used from the edge layer to the cloud layer.

[0092] Modules for artificial intelligence (AI), such as machine learning module 95, can also be present in each sensor 70, which can be used to optimize the data stored in the aggregation device 80. In addition, AI and machine learning capabilities can be provided to other entities.

[0093] According to Figure 2 and Figure 3 In a further development of the processing device 90 as shown, each processing device 90 can be configured to perform life prediction on components of the oil-containing power equipment or transmission equipment 10 based on the determined oil quality, detect abnormalities of the oil-containing power equipment or transmission equipment 10, predict the transformer performance and capacity detection of the oil-containing power equipment or transmission equipment 10, perform active load management based on the transformer capacity of the oil-containing power equipment or transmission equipment 10, and / or provide transformer recommendations for the oil-containing power equipment or transmission equipment 10 from multiple transformers.

[0094] Using Figures 2 to 4 An exemplary embodiment of a method for determining the oil quality of an oil-immersed transformer using the device for determining oil quality as shown includes the following method steps: arranging a plurality of sensors 70 of the device at or in different openings and / or valves of the oil-immersed transformer 10, and transmitting electromagnetic signals into the oil in the oil-immersed transformer 10 through the plurality of sensors 70.

[0095] Therefore, a device, a system, and a method for an oil-immersed transformer are provided. Using the device, system, and method, the oil quality in the oil-immersed transformer can be determined in a simple and accurate manner. As described above, Figures 1 to 4 The exemplary embodiments of

[0096] In the examples given, the different features and functions of the present disclosure have been described separately from each other and in certain combinations. However, it can be understood that many of these features and functions can be freely combined with each other without explicit exclusion.

[0097] Therefore, either an oil-immersed transformer with an oil expansion tank or an oil-immersed transformer without an oil expansion tank can be used. The comparison of the received electromagnetic signal with the signal indicating the oil properties can be performed in one of the sensors 70, in the processing device 90, in the central unit, or in the cloud server.

Claims

1. A device for an oil-containing power equipment or transmission equipment (10), comprising: a sensor (70) configured to simultaneously transmit one or more electromagnetic signals having a variable frequency in the range of 1 Hz to 3000 GHz, particularly a variable frequency in the range of 300 MHz to 300 GHz, into the oil (40) within the oil-containing power equipment or transmission equipment (10) and receive the reflected and / or propagated electromagnetic signals, and a processing device (90) configured to compare the received signals in the time domain and / or frequency domain with first and second signals in the time domain and / or frequency domain, wherein the first signal in the time domain and / or frequency domain is classified according to the physical properties of the oil, the second signal in the time domain and / or frequency domain is classified according to the chemical properties of the oil, and the processing device (90) is further configured to determine the oil quality of the oil (40) in the oil-containing power equipment or transmission equipment (10) based on the comparison result.

2. The device according to claim 1, wherein the physical property is one of viscosity, flash point, interfacial tension, color, and density, the classification according to the physical properties of the oil relates to the oil quality, the chemical property is one of acid value, chemical composition, particularly chemical composition changes caused by paper polymerization effects, moisture, and dissolved gas participation, and the classification according to the chemical properties of the oil relates to the oil quality.

3. The device according to any one of the preceding claims, wherein the processing device (90) is configured to compare the received signals in the time domain and / or frequency domain with third and fourth signals in the time domain and / or frequency domain, wherein the third signal in the time domain and / or frequency domain is classified according to the physical properties of the oil that are different from the physical properties of the oil classified by the first signal in the time domain and / or frequency domain, and the fourth signal in the time domain and / or frequency domain is classified according to the chemical properties of the oil that are different from the chemical properties of the oil classified by the second signal in the time domain and / or frequency domain.

4. The device according to any one of the preceding claims, wherein the processing device (90) is configured to determine the impedance, conductance, admittance, susceptance, and / or a combination thereof in the time domain and / or frequency domain based on the transmitted and received signals and compare the impedance, conductance, admittance, susceptance, and / or a combination thereof in the time domain and / or frequency domain with the first and second signals in the time domain and / or frequency domain.

5. The device according to claim 4, further comprising: a machine learning model configured to be processed on a machine learning module (95) and determine values related to the physical and chemical properties of the oil based on the impedance, conductance, admittance, susceptance, and / or a combination thereof.

6. The device according to any one of claims 1 to 4, further comprising a machine learning module (95) configured to optimize the comparison in the processing device (90) based on the classification of the first and second signals, and the machine learning module (95) uses the first and second signals as training data.

7. The device according to any one of the preceding claims, wherein the sensor (70) is configured to transmit and receive electromagnetic signals at different openings or within the oil-containing power equipment or transmission equipment (10).

8. The device according to any one of the preceding claims, further comprising A plurality of sensors (70) at different openings or in an oil-containing power equipment or transmission equipment (10), wherein the plurality of sensors are configured to simultaneously transmit one or more electromagnetic signals with a variable frequency in the range of 1 Hz to 3000 GHz, particularly in the range of 300 MHz to 300 GHz, into the oil and receive the reflected and / or propagated electromagnetic signals, and process the received signals.

9. The apparatus according to claim 8, further comprising: An aggregation device (80) having a communication device (79), configured to receive the processed signals from the plurality of sensors (70), wherein the aggregation device (80) is configured to aggregate the processed signals from the plurality of sensors (70), and the communication device (79) is configured to transmit the aggregated signals.

10. The apparatus according to any one of the preceding claims, wherein the processing device (90) is configured to simultaneously process the received signals.

11. The apparatus according to any one of the preceding claims, further comprising A temperature stabilizer (75), configured to keep the temperature of the oil constant while transmitting and receiving electromagnetic signals.

12. The apparatus according to any one of the preceding claims, further comprising An automatically controllable calibration device (77), configured to eliminate the irregularities in the received electromagnetic signals.

13. The apparatus according to any one of the preceding claims, further comprising: An antenna (85), configured to transmit the electromagnetic signals of the sensor (70) into the oil and receive the reflected and / or propagated electromagnetic signals, wherein the antenna (85) includes complementary split-ring resonators, sensor electrodes based on planar resonance, or electrodes / probes designed to optimize the parasitic effects and bilayer effects of the oil.

14. The apparatus according to any one of the preceding claims, further comprising a cloud platform (200), which is configured to perform life prediction on the components of the oil-containing power equipment or transmission equipment (10) based on the determined oil quality, detect abnormalities of the oil-containing power equipment or transmission equipment (10), predict the transformer performance and capacity detection of the oil-containing power equipment or transmission equipment (10), perform active load management based on the transformer capacity of the oil-containing power equipment or transmission equipment (10), and / or provide transformer recommendations for the oil-containing power equipment or transmission equipment (10) from multiple oil-immersed transformers.

15. A system, comprising: One or more oil-containing power equipment or transmission equipment (10), and The apparatus according to any one of the preceding claims for each oil-containing power equipment or transmission equipment (10).

16. A method for determining the oil quality of an oil-containing power equipment or transmission equipment by using the apparatus according to any one of claims 1 to 14, comprising Arranging the plurality of sensors (70) of the apparatus at different openings and / or valves or in an oil-containing power equipment or transmission equipment, and Transmitting electromagnetic signals into the oil in the oil-containing power equipment or transmission equipment through the plurality of sensors (70).

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