Expanded device, system, and method for oil-containing electrical power devices or transmission devices

Abstract

The invention relates to a device for an oil-containing electrical power device or transmission device, the device comprising the following: a sensor which is configured to acquire measured values which correspond to a concentration of a furan compound, in particular 2-furfuraldehyde, 2 FAL, in the oil in the oil-containing electrical power device or transmission device; and a processing device which is configured to compare the acquired measured values with predefined values and to determine an oil quality of the oil in the oil-containing electrical power device or transmission device on the basis of the comparison. The invention further relates to a system and a method for oil-containing electrical power devices or transmission devices.

Description

[0001] Extended device, system and method for oil-containing electrical power devices or transmission devices

[0002] TECHNICAL AREA

[0003] The present disclosure relates to a device, a system, and a method for oil-containing electrical power devices or transmission devices. In particular, the present disclosure relates to a device and a system for an oil-containing electrical power device or transmission device by means of which the oil quality in the oil-containing electrical power device or transmission device can be determined, and to a method for a plurality of oil-containing electrical power devices or transmission devices by means of which the oil quality in the oil-containing electrical power devices or transmission devices can be determined.

[0004] BACKGROUND

[0005] Oil-insulated distribution transformers, also known as electrical oil-filled transformers, are power transformers primarily used in power distribution networks. These oil-filled transformers are available in various designs.

[0006] Hermetic transformers, for example, are airtight sealed oil transformers without an expansion vessel or gas cushion, which prevents contact between the oil and the atmosphere, thus avoiding accelerated aging of the oil.

[0007] Another type of oil-filled transformer incorporates an oil expansion vessel located above the transformer tank and connected to it via a flow channel. This expansion vessel compensates for changes in the oil's volume. It accommodates the volume of oil that expands due to thermal expansion caused by temperature fluctuations within the transformer, whether due to load changes or changes in ambient temperature. Inside the expansion vessel, a compressible, air-filled diaphragm may be present. Depending on the expansion state of the oil within the transformer, the diaphragm compresses, creating a closed system between the transformer tank, the expansion vessel, and the flow channel. A magnetic oil level indicator (MOG) can be used to monitor the oil level in the expansion vessel.German patent DE202008017356U1 describes an oil-filled transformer with an oil-filled transformer tank in which the transformer core with primary and secondary windings is arranged. The primary and secondary windings are wrapped with cellulose paper for insulation. The oil serves as an electrical insulating medium and as a cooling medium to dissipate heat generated during transformer operation. The oil expands and contracts depending on the operating temperature.

[0008] As an oil-filled transformer ages, the oil becomes contaminated by moisture and fibrous materials in the winding insulation. Dissolved gases produced by chemical reactions within the oil can also contaminate it. To ensure safe operation and prevent interruptions or power outages, the oil must be checked regularly and changed as needed.

[0009] Oil monitoring is typically carried out in one of the following ways: An oil sample is manually taken from the transformer and sent to a laboratory for analysis. In the laboratory, the oil's insulation resistance and dielectric breakdown voltage are tested. A furan analysis can also be performed. Alternatively, the oil can be analyzed at regular intervals using a measuring device. High-performance liquid chromatography (HPLC) can be used, but this method has the disadvantage of being time-consuming and expensive and requiring a specialist.

[0010] Known techniques for monitoring the oil in an oil-filled transformer also have the following disadvantages: some techniques cannot be retrofitted and require a relatively high level of personnel for the installation and configuration of the measuring devices. Often, it is also necessary for the transformer to be shut down and not operated during the installation of the measuring devices. Furthermore, known measuring techniques can be affected by electromagnetic pulses. For example, measuring sensors that are attached directly to the surface of the transformer's main tank can be affected by partial discharges. Such partial discharges can generate electromagnetic pulses in the ultra-high frequency range (300 MHz to 3 GHz), which can lead to measurement errors or inaccuracies in the measuring devices. The ambient and / or surface temperature of the transformer can also cause problems.Electronic measuring devices, such as those operated near the leg and yoke area of ​​the transformer, are exposed to high temperatures (temperature increase of the oil due to high-voltage loads). This elevated temperature can cause the measuring devices to malfunction, leading to measurement errors or inaccuracies, and ultimately, device failure. Furthermore, measuring devices that collect oil samples from a drain valve at the bottom of the oil-filled transformer main tank can be contaminated with particles. Fibers and moisture from the insulating materials can combine with the oil, causing residues to settle at the bottom of the transformer. Oil samples taken from this area are often contaminated by these residues, which can lead to an inaccurate analysis of the oil quality.

[0011] BRIEF SUMMARY

[0012] The present disclosure therefore aims to provide a device, a system and a method for oil-containing electrical power devices or transmission devices, with the aid of which the oil quality in an oil-containing electrical power device or transmission device can be determined in a simple and precise manner.

[0013] To solve this problem, a device for an oil-containing electrical power device or transmission device is proposed, comprising: a sensor configured to detect measured values ​​corresponding to a concentration of a furan compound in the oil in the oil-containing electrical power device or transmission device, and a processing device configured to compare the detected measured values ​​with predetermined values ​​and, based on the comparison, to determine the oil quality of the oil in the oil-containing electrical power device or transmission device.

[0014] Analysis of furan compounds, particularly 2-furfuraldehyde (2FAL), provides information about the aging state of oil-filled electrical power or transmission equipment, especially oil-filled transformers, or of the oil and / or insulating paper used in them. Analysis of furan compounds, particularly 2FAL, can also indicate degraded insulating materials in oil, especially transformer oil. An elevated concentration of furan compounds, particularly 2FAL, in oil can indicate thermal stress and degradation of cellulose insulation and / or polymerization.

[0015] Generally, oil-cooled transformers contain two main insulating components: the transformer oil and the paper insulation. During normal operation, the components of a transformer are subjected to stresses from electrical loads or environmental factors. In addition to these stresses, chemical reactions occur and chemical compounds are produced. For example, chemical reactions can take place in the insulating paper, generating furan compounds. These furan compounds then dissolve in the transformer oil. Therefore, measuring furan compounds in the transformer oil can indirectly monitor the condition of the transformer's insulating paper.

[0016] The most important types of chemical reactions in insulating paper are hydrolysis, oxidation, and pyrolysis. Hydrolysis is favored by the presence of moisture, oxidation by the presence of oxygen, and pyrolysis by high operating and internal temperatures of the transformer.

[0017] Therefore, measuring the moisture content, oxygen content, and thermal stress of the transformer oil can be used to predict and monitor the aging of oil, paper, and transformer.

[0018] Several furan compounds are typically formed by chemical reactions in insulating paper. These include: 2-furaldehyde (2FAL), 5-hydroxymethyl-2-furaldehyde (5HMF), 2-furfurol alcohol (2FOL), 5-methyl-2-furaldehyde (5MEF), and 2-acetylfuran (2ACF). Of these compounds, the concentration or detection of 2FAL correlates most strongly with the condition of the insulating paper, including its degree of polymerization.

[0019] The following disclosure and embodiments refer to a 2FAL concentration. However, it should be noted that in any subsequent disclosure, the reference to 2FAL can be replaced by any furan compound.

[0020] The oil-filled electrical power device or transmission device can be any type of oil-filled electrical power device or transmission device that uses oil for insulation, cooling, or normal operation. A preferred embodiment of the oil-filled electrical power device or transmission device is an oil-filled transformer. The oil-filled transformer can be any type of oil-filled transformer, in particular an oil-filled transformer with an oil expansion vessel or an oil-filled transformer without an oil expansion vessel. The following disclosure and embodiments relate to oil-filled transformers.However, it should be noted that in every subsequent disclosure of an oil-filled transformer, an oil-filled electrical power device or transmission device is also meant and can accordingly be replaced by an oil-filled electrical power device or an oil-filled electrical transmission device.

[0021] The sensor can be any type of sensor configured to detect measurements corresponding to the concentration of a furan compound, particularly 2FAL, in the oil within the oil-filled electrical power or transmission device. For example, the sensor uses microwaves transmitted into and received from the oil to detect the measurements. Specifically, the sensor can employ impedance spectroscopy. An impedance spectroscopy sensor can consist of several components: a signal generator that produces an excitation signal, such as a variable-frequency sine wave; electrodes that are in contact with the system under test; and a measuring instrument for measuring the resulting voltage and current.

[0022] The sensor can also include microwave-assisted extraction (MAE) and subsequent analysis using suitable detection systems such as high-performance liquid chromatography (HPLC). Furthermore, the sensor can be configured to use chromatographic methods for acquiring the measured values.

[0023] The processing device can be a computing device, such as a laptop or tablet computer, or a microcontroller, that receives the measured values ​​from the sensor. The processing device can calculate and analyze the impedance. The impedance can be displayed, for example, as a Nyquist plot or a Bode plot.

[0024] The sensor can further be configured to send electromagnetic signals with frequencies in the range of 1 Hz to 3000 GHz, in particular frequencies in the range of 300 MHz to 300 GHz, into the oil in the oil-containing electrical power device or transmission device and to receive reflected and / or propagated electromagnetic signals as the measured values, and the processing device can be configured to compare the received signals in the time domain and / or frequency domain with initial signals in the time domain and / or frequency domain as the predetermined values, wherein the initial signals in the time domain and / or frequency domain are classified with respect to a concentration of a furan compound, in particular 2FAL, in oil and the classification relates to an oil quality, and the processing device is further configured toto determine the oil quality of the oil in the oil-containing electrical power device or transmission device based on the comparisons.

[0025] The sensor can also include a baseband transmitter, a baseband receiver, and a digital backend, whereby the oil quality in the oil transformer can be determined using the electromagnetic signals generated by the baseband transmitter and the electromagnetic signals received by the baseband receiver. The oil quality can refer to quality characteristics defined in the IEC 60422 standard. For example, 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 designed for ultra-wideband impedance spectroscopy, for instance, the oil can be stimulated with a pulsed AC signal. Furthermore, a combined frequency-domain / time-domain technique can be used for oil characterization.To improve detection accuracy, the sensor can generate a baseband signal created by combining several up-converted Gaussian signals. Additional components, such as amplifiers and ADCs (analog-to-digital converters) or DACs (digital-to-analog converters), can be integrated into the sensor.

[0026] The sensor can be configured to generate electromagnetic signals with a frequency of 1 Hz to 3000 GHz and transmit them to an antenna, which then transmits the electromagnetic signals into the oil. These electromagnetic signals, with a frequency of 1 Hz to 3000 GHz, are then received by the sensor via the antenna. The antenna can be a single unit comprising both transmitting and receiving elements (in which case it is referred to as a sensor module), so that signals transmitted into the oil are reflected / propagated and received again by the antenna. In particular, this can be a transceiver antenna. Alternatively, the sensor can be separate from the antenna. In this configuration, the antenna transmits electromagnetic signals through the oil, and the propagating signals are received by the sensor at a location other than the antenna. These signals can be, in particular, pulsed signals in the picosecond range.The interactions of electromagnetic waves with frequencies from 1 Hz to 3000 GHz have the advantage of generally posing no serious health risks to humans while still providing good measurement results. The antenna or probe itself can be designed so that its impedance (Z), conductivity (G), admittance (Y), susceptance, and / or other electrical properties are influenced by the presence of 2FAL. The probe / antenna can be made of a material, metal alloy, composite, or other material to which 2FAL binds, thereby altering the underlying electrical properties of the probe, which can be detected by electromagnetic waves. The mechanism by which the probe functions is not limited to chemical bonding and can utilize any chemical or physical interaction between the probe and 2FAL.

[0027] The antenna can also include lithographic coating layers made of various materials such as palladium, nickel and / or MXene.

[0028] Chemical sensor probes offer a versatile approach to detecting a variety of dissolved gases in transformer oil, including hydrogen and carbon monoxide. These gases exhibit a strong affinity for certain materials, such as palladium, and form chemical bonds or bind to them upon contact. The resulting interactions lead to measurable changes in the electrical properties of palladium-containing materials, alloys, or composites. Integrating these materials into probe designs enables the quantification of hydrogen, carbon monoxide, and other gases and impurities in transformer oil according to, but not limited to, IEC 60422, and their correlation with the transformer's operational health.

[0029] In addition, the antenna and sensor system can determine the moisture content (H2O), oxygen content and heat exposure to predict the aging or failure of paper and oil.

[0030] The transmitted electromagnetic signal is distorted after propagating through the oil, which is why the received electromagnetic signal has a different phase angle and frequency than the originally transmitted signal. By convolving the received distorted signal with the ideal signal (original signal), an impulse response of a specific shape is obtained. The impulse response is then sent to an analog-to-digital converter (ADC) to obtain a Fast Fourier Transform (FFT). Subsequently, the FFT is examined and compared in detail.

[0031] The frequency of the signals can be adjusted and / or selected depending on the type and level of detail required for the evaluation information. For a more detailed assessment of oil quality, a suitable frequency (either in the lower or upper frequency range) can be chosen.

[0032] Even better measurement results can be achieved if the sensor is configured to generate, transmit, and receive electromagnetic signals with a frequency of 300 MHz to 300 GHz. In particular, the sensor can be configured to operate in the ultra-wideband (UWB) range. The UWB range of 0.1 GHz to 6 GHz enables insightful (highly detailed) studies of the behavior of oil when interacting with electromagnetic waves. The sensor can also be configured to measure the temperature, acoustics, vibration, and / or gas production of the oil. Furthermore, a breakdown voltage can be determined using an indirect estimation method.

[0033] The processing device can be configured to apply a fast Fourier transform to the received signals. The processing device can communicate with the sensor via 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. Similarly, the antenna, processing device, and sensor can be integrated into a single unit. Furthermore, the processing device can be a cloud server.

[0034] For comparisons performed in the processing device, amplitude values ​​(e.g., in dB) over time (e.g., in ms) can be compared with predefined and classified amplitude values ​​(e.g., in dB) over time (e.g., in ms). Furthermore, a power spectrum calculated using a fast Fourier transform can be used; that is, amplitude values ​​(e.g., in dB) over frequency (e.g., in GHz) can be compared with predefined and classified amplitude values ​​(e.g., in dB) over frequency (e.g., in GHz). The comparisons can be performed using correlations (digital signal processing) or any type of output at different scales (e.g., logarithmic scale) and / or various graphical representations (e.g., Nyquist plot, Bode plot, etc.) to compare changes in phase angle and amplitude.The comparisons can always involve comparing signals in the time domain or signals in the frequency domain.

[0035] Thus, signals in the time domain and / or frequency domain, classified with respect to the concentration of a furan compound, in particular 2FAL, in oil, are used to determine the oil quality, thereby enabling a simple and precise determination of the oil quality in the device for an oil-containing electrical power device or transmission device.

[0036] The processing device can further utilize a calibration curve to analyze standard solutions of furan compounds, particularly 2FAL, at known concentrations. This enables the quantification of the furan compound, especially 2FAL, in the oil by comparing the measured values ​​with the calibration curve. The data obtained can be evaluated to determine the concentration of the furan compound, particularly 2FAL, in the oil. The concentration can be expressed, for example, in ppm (parts per million).

[0037] Furthermore, the emitted and received electromagnetic signals can have different frequencies.

[0038] For even better measurement data acquisition, the sensor can be configured to send the electromagnetic signals into the oil in the oil-filled electrical power device or transmission device at the same time.

[0039] For a further improved determination of oil quality, the processing device can also be configured to compare the received signals in the time domain and / or frequency domain with second signals in the time domain and / or frequency domain, wherein the second signals in the time domain and / or frequency domain are classified with respect to a chemical property of oil or a physical property of oil, wherein the classification of the chemical property of oil or the physical property of oil differs from the classification of the first signals in the time domain and / or frequency domain, the chemical property being one of acidity, chemical composition, in particular a change in chemical composition caused by the involvement of dissolved gases, moisture and the influence of paper polymerization, and the classification with respect to the chemical property of oil relating to oil quality.and the physical property is one of viscosity, flash point, interfacial tension, color, and density, and the classification with regard to the physical properties of oil relates to oil quality. These physical and chemical properties allow for an even more precise determination of oil quality.

[0040] The processing device can further be configured to determine, based on the transmitted and received signals, an impedance (Z), conductance (G), admittance (Y), susceptance (B), and / or combinations thereof in the time domain and / or frequency domain, and to compare the impedance, conductance, admittance, susceptance, and / or combinations thereof in the time domain and / or frequency domain with the initial signals in the time domain and / or frequency domain. For a more precise determination of the oil quality, the impedance, conductance, admittance, and / or susceptance in the time domain and / or frequency domain can additionally be compared with further signals in the time domain and / or frequency domain. In this way, an impedance value of oil is determined, where the impedance (Z) is a combination of the resistivity, the dielectric constant, and the permeability (which is considered to be 1 since oil is non-magnetic).

[0041] The transmitted signal (the transmitted signal can be either one signal or multiple signals, which in particular are transmitted simultaneously) can be any efficient multi-frequency signal (sine, cosine, etc.) to determine the electrical characteristics Z, G, Y and / or B of the oil.

[0042] The wider the measurement frequency range, the more detailed the differences within the specific frequency range. The proposed sensor technology is therefore capable of measuring the electrical parameters of oil over a broad frequency range, for example, in an ultra-wideband range of 0.1 to 6 GHz, providing highly detailed information about the behavior of oil when interacting with electromagnetic waves.

[0043] The device may further include machine learning models that are configured to be processed on a machine learning module and to determine values ​​relating to the concentration of the furan compound, in particular of 2FAL, in oil based on the impedance, conductance, admittance, susceptance and / or combinations thereof.

[0044] The device can include a machine learning module configured to determine values ​​relating to the concentration of the furan compound, in particular 2FAL, in oil based on the impedance, conductance, admittance, susceptance, and / or combinations thereof. These values ​​can then be interpreted and related to the performance and / or ratings of the oil transformer.

[0045] The device can also include a machine learning module configured to optimize comparisons at the processing device based on the classifications of the initial signals, with the machine learning module using these initial signals as training data. Further machine learning models can be developed using collected data stored on a cloud platform. These machine learning models identify patterns and structures in the data originating from one or more sensors. The trained machine learning models are sent to the processing device to enable computation, prediction, and classification of the data (real-time data) from the sensor. Thus, the machine learning models can encompass a variety of different machine learning models. The machine learning module consists of the necessary storage and computing hardware, such as...CPU, GPU, NPU, to enable predictions from machine learning models as well as the training and development of machine learning models.

[0046] The machine learning models can also learn based on regressions. All machine learning models can be trained and used for inference within the device as part of the machine learning module, 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 reside within the device or on a cloud platform. For example, the machine learning module can be hosted or developed on the Microsoft Azure™ platform. In this case, the processing device can communicate with the machine learning module over the internet. The processing device and the machine learning module can also both be hosted on a cloud platform, such as the Microsoft Azure platform. In this case, an antenna communicates with the cloud platform via an additional communication device. The machine learning module can also be located locally within the device.The data can also be sent from an aggregation device to the cloud layer via a highly secure gateway. This data in the cloud layer can then be used to run applications based on the Microsoft Azure platform or the Amazon Cloud Platform™.

[0047] The machine learning models receive the signals captured by the sensor as input. These models may be trained to map this input to a physical (or chemical) characteristic not directly measured by the sensor. The outputs of the machine learning models are either a predicted value or a probability distribution for a range of values. The output may also be an artificial metric created to represent the overall condition of the oil transformer or its expected performance under specific conditions.

[0048] Mathematical functions with different weights and parameters are used to map inputs to outputs. These weights and parameters are determined from sensor signal data recorded from transformer oil of known condition, e.g., from the laboratory or in the field. Finding the weights and parameters is called training and can be done using a gradient descent algorithm or any other machine learning algorithm. The training process for the machine learning model can be performed in the cloud or on a device in an edge / fog layer. Inference, or the generation of predictions—the conversion of inputs to outputs without changing the parameters or weights—can also be performed on the device in an IoT layer, in the edge / fog layer, or on a cloud computer / system.

[0049] Once sufficient data (from the lab, real-world applications, simulations, or other sources) is stored in a cloud database, unsupervised machine learning models can be developed. Because the data is recorded relatively quickly and at regular intervals, the amount of sensor-generated data stored in the cloud is greater than the amount of lab data or known oil condition data. The sensor data stored in the cloud is unlabeled, meaning that the true oil quality and the condition of the oil transformer are unknown.By processing the data stored in the cloud from a single sensor or multiple sensors installed on one or more oil transformers, patterns or structures can be detected in the data that provide information about the relative changes in the oil or oil transformer conditions and ultimately correlate with the furan compound, especially 2FAL, concentration and the condition of the transformer / device.

[0050] The patterns and structures found in the data can be used for qualitative and quantitative monitoring of the oil transformer and can be further used in applications such as oil transformer lifetime prediction, transformer power prediction and capacity detection based on oil quality, active load management based on transformer capacity and transformer recommendations.

[0051] The data used for training is stored in a database in the cloud, and when the training process is run on a cloud device, the learned weights and parameters are sent to the inference device (in the cloud, IoT or edge layer).

[0052] The sensor can be configured to send and receive electromagnetic signals at or within various openings or valves of the oil-filled electrical power or transmission device, particularly the oil-filled transformer. For example, subsequent measurements can be taken at various valve and / or drain openings of the oil-filled transformer located at the top, bottom, or side. Measurements can also be taken at a valve between the heating element and the main tank of the oil-filled transformer. For an opening located at the bottom of the oil-filled transformer, a pump for oil extraction is not necessary. Other means of supplying oil to the sensor may be provided.

[0053] The sensor can also comprise a plurality of sensors on or in various openings of the oil-containing electrical power device or transmission device, wherein the plurality of sensors is configured to send one or more electromagnetic signals with frequencies in the range of 1 Hz to 3000 GHz, in particular with frequencies in the range of 300 MHz to 300 GHz, into the oil and to receive reflected electromagnetic signals and to process the received signals.

[0054] The device may further comprise the following: an aggregation device with a communication device configured to receive the processed signals from the plurality of sensors, wherein 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 may further comprise a communication device configured to transmit the aggregated signals. The aggregation device may be arranged in the sensor or in the processing device. Similarly, the communication device may be arranged in the sensor or in the processing device.When the aggregation device and the communication device are arranged within the processing device, the communication device sends the aggregated signals to a central unit. This central unit could, for example, be a cloud platform.

[0055] The communication device can also be configured to communicate with Supervisory Control and Data Acquisition (SCADA) systems, either via smart electronic devices (LEDs) or directly with remote telemetry units (RTUs) using the standard IEC 61850 client-server protocol (e.g., the XMPP open-source protocol based on IEC 61850 or the IEC 61850 MMS protocol). Furthermore, the MODBUS protocol can be used.

[0056] The processing device can further be configured to process the received signals simultaneously. The device can also include a temperature stabilizer designed to maintain a constant oil temperature during the transmission and reception of electromagnetic signals. This further improves the determination of the oil quality in the oil transformer.

[0057] The device can further include an automated and controllable calibration device designed to remove irregularities in the received electromagnetic signals. This can further improve the determination of the oil quality of the oil transformer.

[0058] The device may further include an antenna configured to transmit the electromagnetic signals generated by the sensor into the oil and to receive the reflected and / or propagated electromagnetic signals. The antenna may include a complementary gap-ring resonator, planar resonance-based sensor electrodes, or electrodes / probes designed to optimize parasitic and double-layer effects of the oil. The antenna is electrically connected to the sensor. To avoid measurement inaccuracies, the antenna may be located directly on or attached to the oil expansion vessel. The sensor may be located remotely from the oil expansion vessel, for example, more than lm away. The antenna and sensor may be connected via cables or wireless interfaces (e.g., Wi-Fi or Bluetooth).The antenna can 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(s) can be configured as Vivaldi antennas. Vivaldi antennas can be advantageous due to their wide bandwidth, low cross-polarization, and constant group delay. The antenna(s) can also be configured as ultra-wideband (UWB) antenna(s), spiral antennas, or other antenna types. The antenna(s) can also be made of metals such as palladium or nickel, alloys, composite materials, and / or MXene, which may be formed into a nanomaterial structure, particularly by a lithographic process. The antenna(s) can also be differential or stripline probes. The antenna(s) can also utilize a capacitive sensor interface.The antennas can further include lithographic coating layers made of various materials such as palladium, nickel, and / or MXene. The antenna and sensor can also be designed as a single unit or module, for example, within a housing. The sensor can also include a communication interface for communicating with a cloud computer. The sensor can also be retrofitted with a mechanical design compatible with the oil transformer valves.

[0059] A fastening device can be provided for attaching the antenna, the cell, and the means for conveying oil from the oil-filled transformer into the cell. This device allows for the detachable attachment of the antenna, the cell, and the means for conveying oil from the oil-filled transformer into the cell to the oil-filled transformer on or in the transformer. Other fastening methods, such as screws or magnetic attachment, are also conceivable.

[0060] The more comprehensive the sample data, the better the analysis. Therefore, the device can be attached to various locations on an oil transformer (top valve, bottom valve, drain valve, radiator valve, openings on the oil expansion tank) to obtain different measurement results. Placing the sensors at different locations (top, bottom, at the drain, at the radiator valve) is advantageous for the overall analysis.

[0061] The processing device can further be configured to make lifetime predictions for components of the oil-filled electrical power device or transmission device based on the specific oil quality, the specific 2FAL content of the oil and / or a specific lifetime of insulating paper, to determine anomalies of the oil-filled electrical power device or transmission device, to make predictions of transformer performance and capacity detections of the oil-filled electrical power device or transmission device, to perform active load management based on a transformer capacity of the oil-filled electrical power device or transmission device and / or to make transformer recommendations for the oil-filled electrical power device or transmission device from a variety of oil-filled transformers.Furthermore, recommendations for oil-filled transformers can be made based on historical environmental, geographical and transformer performance data regarding oil quality, in addition to 2FAL or furan compound content.

[0062] The device may further include a cloud platform or a control device configured to make lifetime predictions for components of the oil-filled electrical power device or transmission device based on the specified oil quality, to determine anomalies of the oil-filled electrical power device or transmission device, to make predictions of transformer performance and capacity detections of the oil-filled electrical power device or transmission device, to perform active load management based on a transformer capacity of the oil-filled electrical power device or transmission device, and / or to make transformer recommendations for the oil-filled electrical power device or transmission device from a variety of oil-filled transformers.Furthermore, recommendations for oil-filled transformers can be made based on historical environmental, geographical, and transformer performance data regarding oil quality.

[0063] In particular, a control device can control a load of the oil transformer based on the specific quality of the transformer oil.

[0064] The problem set out at the outset is further solved by a system comprising one or a plurality of oil-containing electrical power devices or transmission devices and the device described above for each oil-containing electrical power device or transmission device.

[0065] The problem initially posed is further solved by a method for determining the oil quality of an oil-containing electrical power device or transmission device using a device described above, wherein the method comprises the following steps: attaching a plurality of sensors of the device to or in various openings and / or valves of the oil-containing electrical power device or transmission device; detecting, by each of the plurality of sensors, measured values ​​corresponding to a concentration of a furan compound, in particular 2FAL, in the oil in the oil-containing electrical power device or transmission device; comparing, by the processing device, the detected measured values ​​with predetermined values; and

[0066] Determine, by the processing device, based on comparisons of the oil quality of the oil in the oil-containing electrical power device or transmission device. Aggregated signals can then be sent to a central unit, such as a cloud platform, for data analysis.

[0067] The aspects and variants described above can be combined without this being explicitly stated. Each of the described design variants is therefore optional to any other design variant or combination thereof. This disclosure is thus not limited to the individual designs and variants in the described order or to any specific combination of aspects and design variants. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Further advantages, details and features of the methods, devices and systems described here will become apparent from the following description of exemplary embodiments and the figures.

[0069] Fig. 1 shows a schematic representation of a first embodiment of an oil transformer with an oil expansion vessel;

[0070] Fig. 2 shows a schematic representation of the oil expansion vessel of Fig. 1 with a device for determining the oil quality;

[0071] Fig. 3 shows a schematic representation of a second embodiment of an oil transformer with devices for determining the oil quality; and

[0072] Fig. 4 shows a schematic representation of a third embodiment of a system for a plurality of oil transformers.

[0073] DETAILED DESCRIPTION

[0074] Fig. 1 shows a schematic representation of an embodiment of an oil-filled transformer with an oil expansion vessel. The oil-filled transformer 10 comprises a transformer tank 12 and an oil expansion vessel 20. A flow channel 30 connects the transformer tank 12 to a first opening 22 in the oil expansion vessel 20. The first opening 22 is located at a lower end of the oil expansion vessel 20. The transformer tank 12 includes a corresponding opening. The transformer 15 is mounted on supports 60 in an oil bath 40 within the transformer tank 12. In Fig. 1, the oil expansion vessel 20 is shown in an exemplary cylindrical form above the transformer tank 12. Other shapes (e.g., cuboid, cube, or prism) and arrangements (at the same height as the transformer tank 12, further up, etc.) of the oil expansion vessel 20 are conceivable.

[0075] Oil 40 is located in the flow channel 30 and the housing 26 of the oil expansion vessel 20. The oil expansion vessel 20 serves to accommodate oil expansion due to thermal expansion of the oil during temperature fluctuations in the transformer 15, caused by load changes or changes in ambient temperature. As indicated by the surface area 42 of the oil in the oil expansion vessel 20, the oil level in the oil expansion vessel 20 changes accordingly. Depending on the oil level in the oil expansion vessel 20, the oil compresses a diaphragm 28, which is decompressed again when the oil level 42 falls. A pressure relief valve 50 is provided in a second opening 24 at an upper end of the oil expansion vessel 20 for releasing excess gas. Furthermore, the oil transformer includes an oil level measuring device 25 for measuring the oil level 42 in the oil expansion vessel 20.For the sake of clarity, other components of the oil-filled transformer, such as the transformer core, the coils, and a Buchholz protection relay, are not shown in the schematic representation of Fig. 1. As an alternative to the embodiment of the oil expansion vessel 20 with the diaphragm 28, an oil expansion vessel of the Atmoseal type or another type can also be provided.

[0076] Fig. 2 shows a schematic representation of an embodiment of an oil expansion vessel with a device for determining the oil quality. The oil expansion vessel 20 is the same as the oil expansion vessel 20 shown in Fig. 1, where the same reference numerals in Figs. 1 and 2 refer to the same elements.

[0077] The arrangement shown in Fig. 2 comprises a cell 55 for receiving oil 40 from the oil expansion vessel 20, means 57, 58 for conveying oil 40 from the oil expansion vessel 20 into the cell 55, an antenna 85 for applying an electromagnetic signal to the oil 40 in the cell 55, a sensor 70 electrically connected to the antenna 85 for measuring the oil quality of the oil transformer 10, and a processing device 90.

[0078] The means 57, 58 for conveying oil 40 from the oil expansion vessel 20 to the cell 55 comprise a hose 57, at least partially located in the oil expansion vessel 20, with a first end extending into the oil 40 in the oil expansion vessel 20 and a second end connected to the cell 55, and a pump 58 for pumping the oil 40 from the oil expansion vessel 20 to the cell 55. The hose 57 extends through the opening 24, with the cell 55 located outside the oil expansion vessel 20 at the opening 24. The opening may also be a vent opening on the oil expansion vessel 20.

[0079] Fig. 2 shows a minimum oil level 42 in the oil expansion vessel 20, wherein the hose 57 is designed such that the first end of the hose 57 always extends below the minimum oil level 42.

[0080] The pump 58 pumps oil 40 from the oil expansion vessel 20 into the cell 55. It is also conceivable that the pump 58 pumps the oil 40 through the cell 55, i.e., that the oil 40 is returned to the oil expansion vessel 20. The pump 58 is designed as an electric pump and is electrically connected to the antenna 85. The pump 58 is arranged near the antenna 85.

[0081] According to an alternative embodiment (not shown), the cell 55 is arranged in the oil expansion vessel 20, in particular above a maximum oil level of the oil expansion vessel 20.

[0082] The antenna 85 is attached to an outer wall of cell 55 and is electrically connected to the sensor 70.

[0083] Opposite the underside 21 of the oil expansion vessel 20, an opening 24 is located at the upper end of the oil expansion vessel 20, which is closed by a pressure relief valve 50. Above the opening 24, the antenna 85 is arranged, which is electrically connected to the sensor 70.

[0084] The sensor 70 comprises 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 pulsed excitation signals for the antenna 85, with reflected / propagated signals being forwarded to the baseband receiver 72. The digital backend 73 also includes semiconductor memory units, random access memory (RAM) units, and Trusted Platform Modules (TPMs) that meet necessary security and cryptography requirements. TPM modules also help secure the hardware with integrated cryptographic keys and provide mechanisms for user authentication and authorization. Furthermore, TPM modules are used to sign the raw sensor data to authenticate the data and feed it into blockchain technologies.The digital backend 73 controls the sending and receiving of signals by the baseband transmitter 71 and the baseband receiver 72. The sensor 70 is configured to send and receive pulsed signals with a frequency of 1 Hz to 3000 GHz via the antenna 85.

[0085] Preferably, the system operates in the microwave range, so that the antenna 85 transmits and receives signals in the range of 300 MHz to 300 GHz. The sensor 70 transmits and receives measurement signals via the antenna 85, which can be used to determine a concentration of 2 FAL in the oil 40 in the oil expansion vessel 20. The antenna 85 is a complementary gap-ring resonator or a planar resonance-based sensor electrode. Other types of electrodes / probes designed to optimize parasitic effects and bilayer effects with the oil can also be used. Furthermore, the sensor 70 includes a communication device 79 for communication with the processing device 90. The communication device 79 can include wireless (for example, LTE-M or NB-IoT mobile communication SoC modules, WiFi, BLE or NFC interfaces) and / or wired interfaces (for example, I2C, SPI, UART, ADC, PWM, HDMI, VGA, ETHERNET interface).

[0086] The signals generated in the baseband transmitter 71 of the sensor 70 can be produced using inexpensive semiconductor flip-flops and shift registers. Depending on the level of detail and resolution of the oil analysis, the frequency of the input signal can be varied accordingly by changing the configurations of the flip-flops and shift registers.

[0087] The processing device 90 is a laptop computer or any other type of computer configured to process and visualize the measured values ​​from the sensor 70. The processing device 90 may also include microcontroller units, graphics processing units (GPUs), and / or central processing units (CPUs) or neural processing units (NPUs) to perform the necessary calculations for running the machine learning with the machine learning module 95.

[0088] The sensor 70 is configured to acquire measured values ​​corresponding to a concentration of 2FAL in the oil 40 in the oil transformer 10. Furthermore, the processing device 90 is configured to compare the acquired measured values ​​with predetermined values ​​and, based on this comparison, to determine the oil quality of the oil 40 in the oil transformer 10.

[0089] The oil transformer may further include a temperature, gas, and / or vibration sensor 65. The temperature, gas, and / or vibration sensor 65 is arranged in the oil expansion vessel 20. The temperature, gas, and / or vibration sensor 65 may also be located outside or at the connection interface of the opening 24. The temperature, gas, and / or vibration sensor 65 is configured to transmit measurement data to the sensor 70 and / or the processing device 90. For this purpose, the temperature, gas, and / or vibration sensor 65 may include a communication interface that enables communication with the sensor 70 and / or the processing device 90. The communication interface may be located at the opening 24 and, for example, provide a wired connection to the sensor 70.If the temperature, gas, and / or vibration sensor 65 is configured as a gas sensor, it can be set up to detect abnormalities in the gas produced by the oil transformer. The sensor can also include additional sensor and electronic components that monitor the regular operation and condition of the oil transformer.

[0090] For a precise determination of the oil quality of the oil in the oil transformer 10, the processing device 90 is configured to compare the electromagnetic signals received by the sensor in the time domain and / or frequency domain with initial signals in the time domain and / or frequency domain. These initial signals in the time domain and / or frequency domain are classified according to a concentration of 2FAL in the oil. Based on these comparisons, the processing device 90 determines the oil quality of the oil 40 in the oil transformer 10.

[0091] The comparison between the signals can be performed using a machine learning model, which has been trained on the given signals to determine weights and parameters. The machine learning model then predicts the oil's properties and provides either a value or a probability distribution. The processing of the machine learning model can take place in the machine learning module 95, the processing device 90, or the cloud platform 200 (see Fig. 4). The described machine learning method also solves the inverse problem by determining the physical factors that influence the overall condition of the oil transformer.

[0092] The processing device 90 is involved in the preprocessing of the data, i.e., in the process of translating / deriving the electromagnetic signal into a physical value (e.g., converting the Fourier transform output of the received electromagnetic signal into the 2FAL concentration in the Ω), and in excluding unwanted data packets from the original signal.

[0093] For comparisons performed in the processing device, amplitude values ​​(e.g., in dB) over time (e.g., in ms) can be compared with predefined and classified amplitude values ​​(e.g., in dB) over time (e.g., in ms). Furthermore, a power spectrum calculated using a fast Fourier transform can be used; that is, amplitude values ​​(e.g., in dB) over frequency (e.g., in GHz) can be compared with predefined and classified amplitude values ​​(e.g., in dB) over frequency (e.g., in GHz). The comparisons can be performed using correlations (digital signal processing) or any type of output at different scales (e.g., logarithmic scale) and / or various graphical representations (e.g., Nyquist plot, Bode plot, etc.) to compare changes in phase angle and amplitude.Optionally, the processing device 90 can be configured to compare the received signals in the time domain and / or frequency domain with second signals in the time domain and / or frequency domain.The second signals in the time domain and / or frequency domain are classified with respect to a chemical property of oil or a physical property of oil that differs from the classification of the first signals in the time domain and / or frequency domain. The chemical property is one of acidity, chemical composition, in particular a change in chemical composition caused by the involvement of dissolved gases, moisture, and the influence of paper polymerization. The classification with respect to the chemical property of oil relates to an oil quality. The physical property is one of viscosity, flash point, interfacial tension, color, and density. The classification with respect to the physical property of oil relates to an oil quality.

[0094] The processing device 90 is further configured to determine, based on the transmitted and received signals, an impedance, conductance, admittance, and / or susceptance in the time domain and / or frequency domain, and to compare the impedance, conductance, admittance, and / or susceptance in the time domain and / or frequency domain with the first and / or second signals in the time domain and / or frequency domain. Additionally, the processing device 90 can compare the impedance, conductance, admittance, and / or susceptance in the time domain and / or frequency domain with other signals in the time domain and / or frequency domain. The aforementioned values ​​can also be combined and compared with each other in any way.

[0095] The processing device 90 is also capable of analyzing historical data recorded by the device and / or other similar devices installed. Recorded data stored in the cloud can be used to identify patterns and structures within the data using an unsupervised machine learning module. Training of the machine learning module takes place on the processing device 90 or on another laptop / computer connected to the cloud.

[0096] The patterns and structures found in the data are used for the qualitative and quantitative monitoring of the oil-filled transformer 10 and can be further used in applications such as oil-filled transformer lifetime prediction, transformer power prediction and capacity detection based on oil quality, active load management based on transformer capacity, and transformer recommendations. The pattern and structure calculations take place in the processing device 90 or in the cloud platform 200 (see Fig. 4).

[0097] As can be seen from Fig. 2, the processing device 90 further comprises a machine learning module 95, which is configured to optimize the comparisons at the processing device 90 based on the classifications of the signals. The first and second signals can, in particular, be predefined signals.

[0098] The comparison between the input signals and the received signal can be performed using a machine learning model, which has been trained on the input signals to determine weights and parameters. The machine learning model then predicts the physical property of the oil and provides either a value or a probability distribution. The processing of the machine learning model can take place in the machine learning module 95, in the processing device 90, or in the cloud platform 200 (see Fig. 4).

[0099] The device shown in Fig. 2 further comprises a temperature stabilizer 75, which is designed to keep the temperature of the oil extracted from the oil transformer 10 constant.

[0100] The sensor includes an automatic calibration device 77, which is designed to remove irregularities, noise, and / or attenuation in the received electromagnetic signals. The automatic calibration device 77 can also be included in the processing device 90.

[0101] Furthermore, a control device (not shown in the figures) can be provided which controls a load of the oil transformer 10 based on the specific quality of the transformer oil 40.

[0102] In another embodiment, the antenna 85 can be provided directly in the oil expansion vessel 20 or at an opening of the oil expansion vessel 20, so that the means 57, 58 for conveying oil 40 from the oil expansion vessel 20 can be dispensed with.

[0103] Fig. 3 shows a schematic representation of a second embodiment of a

[0104] Oil transformer with a device for determining oil quality. In the

[0105] The oil-filled transformer can be the oil-filled transformer 10 shown in Figures 1 and 2, or another oil-filled transformer (such as a hermetically sealed or freely air-permeable oil-filled transformer used in distribution and transmission networks). The same reference numerals refer to the same elements, so further explanation is unnecessary.

[0106] The oil transformer comprises a plurality of openings. A sensor 70 is located in each opening. Each sensor 70 transmits the received electromagnetic signals to an aggregation device 80, which aggregates the signals. The aggregation device 80 includes a communication device 79. The communication device 79 transmits the aggregated signals to the processing device 90 or a cloud platform (not shown in Fig. 3). Transmission can occur simultaneously, particularly in real time.

[0107] According to an alternative embodiment, only one sensor 70 is used, and one sensor 70 is successively placed in each of the openings of the oil transformer to measure the oil quality in the oil transformer. The received electromagnetic signals can be temporarily stored in a storage device in the sensor 70 and subsequently aggregated by the aggregation device 80.

[0108] In summary, the embodiment shown in Fig. 3 can be described as follows: The sensors 70 of the oil transformer 10 are connected to the aggregation device 80 via a wireless or wired communication link. The data / signals received by the sensors 70 can be processed using a machine learning module, see for example the machine learning module 95 in Fig. 2, and then sent to the aggregation device 80. The sensors 70 can also communicate directly with a cloud platform (not shown in Fig. 3, but see the cloud layer with the cloud platform 200 in Fig. 4) using the communication device 79, or via the edge / fog layer. The communication device 79 is configured to communicate as described with respect to Fig. 2. The aggregation device 80 communicates wirelessly with a hypersecure gateway that uses an edge or fog layer (in Fig. 2).3 not shown, but see the edge or fog layer with the hypersecure gateway 100 in Fig. 4). The data collected in the aggregation device 80 can be used to apply sensor fusion algorithms to investigate the real-time behavior of the oil transformer.

[0109] Fig. 4 shows a schematic representation of a third embodiment of a system for a plurality of oil-filled transformers, with four oil-filled transformers shown as examples. The oil-filled transformers can be the oil-filled transformer 10 shown in Figs. 1 or 3, or another oil-filled transformer, and the sensor 70 can be the sensor 70 shown in Figs. 2 or 3, or another sensor. The aggregation device 80 can be the one shown in Fig.

[0110] The aggregation device shown in Figure 3 is the same. The same reference numerals refer to the same elements, so further explanation is unnecessary.

[0111] The oil-filled transformers 10 are part of an electrical power supply network (not shown in Fig. 4). The sensors 70 are configured to send the received or aggregated received data, via an edge or fog layer using a hypersecure gateway 100, to a cloud platform 200 in the cloud layer. There, the received data is subsequently processed.

[0112] A processing device 90 (not shown in Fig. 4) can be provided in each sensor 70 as well as in 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 performs various applications, such as making lifetime predictions for components of the oil-filled electrical power device or transmission device 10 based on the specified oil quality, determining anomalies of the oil-filled electrical power device or transmission device 10, making predictions about transformer performance and capacity detection of the oil-filled electrical power device or transmission device 10, performing active load management based on a transformer capacity of the oil-filled electrical power device or transmission device 10, and / or making transformer recommendations for the oil-filled electrical power device or transmission device 10 from a variety of transformers.

[0113] The highly secure Gateway 100 is designed for secure communication between the cloud layer and the multitude of sensors 70 or the multitude of aggregation devices 80 in the IoT layer. Therefore, a multi-layered architecture is considered, comprising an IoT layer, an edge / fog layer, and a cloud layer, to describe a decentralized data processing structure located between the cloud and the data-generating devices. This flexible architecture allows users to place resources, including applications and the data they generate, in logical locations to improve performance. For this purpose, the sensors 70 reside in the IoT layer, the hyper-secure Gateway 100 in the edge / fog layer, and measurement data processing takes place in the cloud layer 200. Within the IoT layer, the sensors 70 function as the IoT awareness layer in a smart grid.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. Furthermore, it ensures high availability and fault tolerance through network / transformer monitoring. Additionally, it allows for dual virtualization, as this layer, through virtualization technology, can migrate from one connected environment to another, preventing the cascading of erroneous data across the system. This is achieved by migrating functions and data from vulnerable dedicated hardware to alternative hardware.The cloud layer is used to provide applications for monitoring, historical data analysis, artificial intelligence-based applications, and visualizations.

[0114] To establish secure network communication, either Transmission Control Protocol (TCP) and / or User Datagram Protocol (UDP)-based protocols can be used for data transport from the physical IoT layer to the edge layer, and any TCP and / or Internet Protocol (IP)-based communication protocol can be used from the edge layer to the cloud layer.

[0115] An artificial intelligence (AI) module, such as the machine learning module 95, can also be present in each of the sensors 70, which can be used to optimize the data stored in the aggregation device 80. Furthermore, AI and machine learning functions can be provided for other units.

[0116] According to further developments of the processing devices 90 shown in Figs. 2 and 3, the respective processing devices 90 can be configured to make lifetime predictions for components of the oil-filled electrical power device or transmission device 10 based on the determined oil quality, to determine anomalies of the oil-filled electrical power device or transmission device (10), to make predictions about transformer performance and capacity detection of the oil-filled electrical power device or transmission device 10, to perform active load management based on a transformer capacity of the oil-filled electrical power device or transmission device 10 and / or to make transformer recommendations for the oil-filled electrical power device or transmission device 10 from a plurality of transformers.An embodiment of a method for determining the oil quality of an oil transformer with the devices for determining the oil quality shown in Figures 2 to 4 comprises the following process steps: attaching a plurality of sensors 70 of the device to or in various openings and / or valves of the oil transformer 10; detecting, by each of the plurality of sensors 70, measured values ​​corresponding to a concentration of 2-furfuraldehyde, 2FAL, in the oil 40 in the oil-containing electrical power device or transmission device 10; comparing, by the processing device 90, the detected measured values ​​with predetermined values; and determining, by the processing device 90, the oil quality of the oil 40 in the oil-containing electrical power device or transmission device 10 based on the comparisons.

[0117] Thus, a device, a system, and a method for oil-filled transformers are provided, with which the oil quality in an oil-filled transformer can be determined simply and precisely. As described above, the embodiments shown in Figures 1 to 4 can also be extended to oil-filled electrical power devices or transmission devices.

[0118] In the examples presented, different features and functions of the present disclosure have been described separately as well as in specific combinations. It is understood, however, that many of these features and functions can be freely combined with one another, unless explicitly excluded.

[0119] This allows the use of both oil-filled transformers with and without oil expansion vessels. The comparison of the received electromagnetic signals with the signals indicating the oil's properties can be performed in one of the sensors 70, in the processing device 90, in a central unit, or in a cloud server.

[0120] Although the embodiments relate to oil-filled transformers, the present invention can be applied to any type of oil-filled electrical power device or oil-filled electrical transmission device.

Claims

REQUIREMENTS 1. Device for an oil-containing electrical power device or transmission device (10), comprising a sensor (70) configured to detect measured values ​​corresponding to a concentration of a furan compound in the oil (40) in the oil-containing electrical power device or transmission device (10) and a processing device (90) configured to compare the detected measured values ​​with predetermined values ​​and to determine, based on the comparison, an oil quality of the oil (40) in the oil-containing electrical power device or transmission device (10).

2. Device according to claim 1, wherein the sensor (70) is configured to send electromagnetic signals with frequencies in the range of 1 Hz to 3000 GHz, in particular with frequencies in the range of 300 MHz to 300 GHz, into the oil (40) in the oil-containing electrical power device or transmission device (10) and to receive reflected and / or propagated electromagnetic signals as the measured values, and the processing device (90) is configured to compare the received signals in the time domain and / or frequency domain with first signals in the time domain and / or frequency domain as the predetermined values, wherein the first signals in the time domain and / or frequency domain are classified with respect to a concentration of the furan compound, in particular 2-furfuraldehyde, 2FAL, in oil and the classification relates to an oil quality, and the processing device (90) is further configured toto determine the oil quality of the oil (40) in the oil-containing electrical power device or transmission device (10) based on the comparisons.

3. Device according to claim 2, wherein the sensor (70) is configured to send the electromagnetic signals into the oil (40) in the oil-containing electrical power device or transmission device (10) at the same time.

4. Device according to one of claims 2 to 3, wherein the processing device (90) is further configured to compare the received signals in the time domain and / or frequency domain with second signals in the time domain and / or frequency domain, wherein the second signals are classified in the time domain and / or frequency domain with respect to a chemical property of oil or a physical property of oil that differs from the classification of the first signals in the time domain and / or frequency domain; the chemical property is one of acidity, chemical composition, in particular a change in chemical composition caused by the involvement of dissolved gases, moisture and the influence of paper polymerization; the classification with respect to the chemical property of oil relates to an oil quality; the physical property is one of viscosity, flash point, interfacial tension, color and density; and the classification with respect to the physical property of oil relates to an oil quality.

5. Device according to one of claims 2 to 4, wherein the processing device (90) is further configured to determine an impedance, conductance, admittance, susceptance and / or combinations thereof in the time domain and / or frequency domain based on the transmitted and received signals and to compare the impedance, conductance, admittance, susceptance and / or combinations thereof in the time domain and / or frequency domain with the first signals in the time domain and / or frequency domain.

6. Device according to claim 5, further comprising Machine learning models designed to be processed on a machine learning module (95) and to determine values ​​relating to the concentration of the furan compound, in particular 2FAL, in oil based on the impedance, conductance, admittance, susceptance and / or combinations thereof.

7. Device according to one of claims 2 to 5, further comprising a machine learning module (95) configured to optimize the comparisons at the processing device (90) based on the classifications of the first signals, wherein the machine learning module (95) uses the first signals as training data.

8. Device according to any one of claims 2 to 7, wherein the sensor (70) is configured to send and receive the electromagnetic signals at or in various openings of the oil-containing electrical power device or transmission device (10).

9. Device according to one of claims 2 to 8, further comprising a plurality of sensors (70) on or in various openings of the oil-containing electrical power device or transmission device (10), wherein the plurality of sensors is configured to send several electromagnetic signals with frequencies in the range of 1 Hz to 3000 GHz, in particular with frequencies in the range of 300 MHz to 300 GHz, and in particular at the same time into the oil, and to receive reflected and / or propagated electromagnetic signals, and to process the received signals.

10. Device according to claim 9, further comprising an aggregation device (80) with 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 send the aggregated signals.

11. Device according to any one of claims 2 to 10, wherein the processing device (90) is configured to process the received signals at the same time.

12. Device according to any one of claims 2 to 11, further comprising a temperature stabilizer (75) configured to keep the temperature of the oil constant during the sending and receiving of the electromagnetic signals.

13. Device according to any one of claims 2 to 12, further comprising an automated and controllable calibration device (77) configured to remove irregularities in the received electromagnetic signals.

14. Device according to any one of claims 2 to 13, further comprising an antenna (85) configured to transmit the electromagnetic signals of the sensor (70) into the oil and to receive the reflected and / or propagated electromagnetic signals, wherein the antenna (85) comprises a complementary gap-ring resonator, planar resonance-based sensor electrodes, or electrodes / probes designed to optimize parasitic effects and double-layer effects of the oil, wherein, optionally, the antenna (85) is made of metals such as palladium or nickel, alloys, composite materials and / or MXenes, which in particular have been formed into a nanomaterial structure by a lithography process, the antenna (85) is a differential or stripline probe, and / or the antenna (85) comprises a capacitive sensor interface.

15. Device according to one of the preceding claims, further comprising a cloud platform (200) or a control device configured to make lifetime predictions for components of the oil-filled electrical power device or transmission device (10) based on the determined oil quality, to determine anomalies of the oil-filled electrical power device or transmission device (10), to make predictions of transformer power and capacity detection of the oil-filled electrical power device or transmission device (10), to perform active load management based on a transformer capacity of the oil-filled electrical power device or transmission device (10) and / or to make transformer recommendations for the oil-filled electrical power device or transmission device (10) from a plurality of oil-filled transformers.

16. System comprising one or a plurality of oil-containing electrical power devices or transmission devices (10) and the device according to any of the preceding claims for each oil-containing electrical power device or transmission device (10).

17. Method for determining the oil quality of an oil-containing electrical power device or transmission device comprising a device according to any one of claims 1 to 15, comprising Attaching a plurality of sensors (70) of the device to or in various openings and / or valves of the oil-containing electrical power device or transmission device, Detect, by each of the plurality of sensors (70), of measured values ​​corresponding to a concentration of a furan compound, in particular 2-furfuraldehyde, 2FAL, in the oil (40) in the oil-containing electrical power device or transmission device (10), The processing device (90) compares the recorded measured values ​​with predetermined values ​​and Determine, by the processing device (90), based on the comparisons of an oil quality of the oil (40) in the oil-containing electrical power device or transmission device (10).

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