Device, system, and method for oil-containing electric power devices or transmission devices

Abstract

The invention relates to a device for an oil-containing electric power device or transmission device, comprising the following: a sensor which is designed to transmit one or a plurality of electromagnetic signals with variable frequencies ranging from 1 Hz to 3000 GHz, in particular from 300 MHz to 300 GHz, into the oil in the oil-containing electric power device or transmission device at the same time and receive reflected and / or propagated electromagnetic signals, and a processing device which is designed 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 signals in the time domain and / or frequency domain are classified with respect to a physical property of the oil, the second signals in the time domain and / or frequency domain are classified with respect to a chemical property of the oil, and the processing device is additionally designed to determine the quality of the oil in the oil-containing electric power device or transmission device on the basis of the comparisons.

Description

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

[0002] TECHNICAL FIELD

[0003] The present disclosure relates to an apparatus, a system, and a method for oil-containing electrical power devices or transmission devices. In particular, the present disclosure relates to an apparatus and a system for an oil-containing electrical power device or transmission device with 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 with 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 called electrical oil-immersed transformers, are power transformers commonly used in power distribution networks. Such oil-immersed transformers are available in various designs.

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

[0007] Another type of oil-immersed transformer includes an oil conservator located above the transformer tank and connected to the tank via a flow channel. The oil conservator can be used to compensate for changes in the oil's volume. The oil conservator serves to accommodate the oil volume that arises due to thermal expansion of the oil during temperature fluctuations in the transformer caused by load changes or changes in the ambient temperature. An air-filled, compressible diaphragm can be arranged inside the oil conservator. Depending on the expansion state of the oil in the transformer, the diaphragm is compressed, with the interior of the transformer tank, the oil conservator, and the flow channel forming a closed system. A magnetic oil level indicator (MOG) can be used to monitor the oil level in the oil conservator.DE202008017356U1 describes an oil-immersed transformer with an oil-filled transformer tank in which the transformer core with primary and secondary windings is located. For insulation, the primary and secondary windings are wrapped with cellulose paper. The oil serves as an electrical insulation medium and as a cooling medium for dissipating heat loss generated during transformer operation. The oil expands and shrinks in volume depending on the operating oil temperature.

[0008] As the oil-immersed transformer ages, the oil becomes contaminated by moisture and fibrous materials in the winding insulation. Dissolved gases produced by chemical reactions in the oil can also contaminate the oil. To ensure safe operation and avoid interruptions or power outages, the oil must be checked regularly and replaced if necessary.

[0009] Oil monitoring is typically performed in one of the following ways: An oil sample is manually taken from the transformer and sent to a laboratory for analysis. The laboratory then tests the oil's insulation resistance and dielectric breakdown voltage. Furan analysis can also be performed in the laboratory. Alternatively, the oil can be analyzed at regular intervals using a measuring device. Gas chromatography can be used for this purpose, but this has the disadvantage of being time-consuming and costly and must be performed by a specialist. Photoacoustic spectroscopy can also be performed.

[0010] 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 personnel expenditures for the installation and configuration of the measuring devices. It is often also necessary for the transformer to be shut down and shut down during the installation of the measuring devices. Furthermore, known measuring techniques can be affected by electromagnetic pulses. For example, measuring sensors 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, for example, operating near the leg and yoke areas of the transformer, are exposed to high temperatures (oil temperature rise due to high-voltage loads). This increased temperature can cause the measuring devices to malfunction, leading to measurement errors or inaccuracies, or even measuring device failure. Measuring devices that collect oil samples from an outlet valve at the bottom of the oil-immersed transformer main tank may also be mixed with contaminated particles. Fibers and moisture from the insulation materials can combine with the oil and cause residues to settle at the bottom of the transformer. Oil samples taken in this area are often contaminated with residues, which can lead to inaccurate oil quality analysis.

[0011] BRIEF SUMMARY

[0012] The object of the present disclosure is 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 achieve this object, a device, in particular a measuring device, for an oil-containing electrical power device or transmission device is proposed, comprising the following: a sensor configured to transmit one or more electromagnetic signals with variable frequencies in the range from 1 Hz to 3000 GHz, in particular with variable frequencies in the range from 300 MHz to 300 GHz, into the oil in the oil-containing electrical power device or transmission device at the same time and to receive reflected and / or propagated electromagnetic signals, and a processing device 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 signals in the time domain and / or frequency domain are classified with respect to a physical property of oil,the second signals are classified in the time domain and / or frequency domain with respect to a chemical property of oil, and the processing device is further configured to determine an oil quality of the oil in the oil-containing electrical power device or transmission device based on the comparisons.

[0014] The oil-filled electrical power device or transmission device may 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 following disclosure and exemplary embodiments refer to oil-filled transformers. However, it should be noted that any subsequent disclosure of an oil-filled transformer also refers to an oil-filled electrical power device or transmission device and can be replaced accordingly with an oil-filled electrical power device or an oil-filled electrical transmission device.

[0015] The oil transformer can be any type of oil transformer, in particular an oil transformer with an oil conservator or an oil transformer without an oil conservator. The sensor can, for example, comprise 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 be quality characteristics defined in the IEC 60422 standard. 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.For example, if the sensor is designed for ultra-wideband impedance spectroscopy, the oil can be exposed to a pulsed alternating current signal. Furthermore, a combined frequency-domain / time-domain technique can be used to characterize the oil. To improve detection accuracy, the sensor can generate a baseband signal generated by combining several upconverted Gaussian signals. Additional components, such as amplifiers and ADC (analog-to-digital) or DAC (digital-to-analog) semiconductor electronics, can be incorporated into the sensor.

[0016] The sensor is configured to generate electromagnetic signals with a frequency of 1 Hz to 3000 GHz and send them to an antenna, which transmits the electromagnetic signals into the oil. Electromagnetic signals with a frequency of 1 Hz to 3000 GHz are then received by the sensor via the antenna. The antenna can be designed as a unit with a transmitting and receiving antenna (in this case, referred to as a sensor module), so that signals transmitted 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 propagating signals are received by the sensor at a location other than the antenna. The signals can, in particular, be pulsed signals in the picosecond range.The interactions of electromagnetic waves with frequencies from 1 Hz to 3000 GHz have the advantage that they generally do not pose serious health risks to humans and yet still provide good measurement results.

[0017] The transmitted electromagnetic signal is deformed 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 deformed 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). The FFT is then examined in detail and compared.

[0018] The frequency of the signals can be adjusted and / or selected depending on the type and level of detail of the required evaluation information. For a more detailed assessment of oil quality, a suitable frequency (either in the lower or upper frequency range) can be selected.

[0019] Even better measurement results can be achieved if the sensor is configured to generate, transmit, and receive electromagnetic signals at 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 from 0.1 GHz to 6 GHz enables insightful (very detailed) studies of the behavior of oil when interacting with electromagnetic waves. The sensor can also be configured to measure temperature, vibration, and / or gas generation in the oil.

[0020] The processing device may be a computing device, such as a laptop or tablet computer. The processing device may be configured to apply a fast Fourier transform to the received signals. The processing device may be communicatively connected to the sensor via a data cable, the Internet, a wireless network, or a cellular network. The processing device and the sensor may also be integrated into a single unit. The antenna, the processing device, and the sensor may also be integrated into a single unit. Furthermore, the processing device may be a cloud server. For the comparisons performed in the processing device, amplitude values ​​(e.g., in units of dB) over time (e.g., in units of ms) may be compared with predetermined and classified amplitude values ​​(e.g., in units of dB) over time (e.g., in units of ms).Furthermore, a power spectrum calculated using a fast Fourier transform can be used, i.e. amplitude values ​​(e.g., in dB) versus frequency (e.g., in GHz) are compared with predetermined and classified amplitude values ​​(e.g., in dB) versus frequency (e.g., in GHz). The comparisons can be made 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 be made between signals in the time domain or signals in the frequency domain.

[0021] Thus, time domain and / or frequency domain signals classified with respect to a physical property of oil and a chemical property of oil are used to determine an oil quality of the oil in the oil transformer, thereby enabling a simple and precise determination of the oil quality in the oil transformer.

[0022] The physical property can be one of viscosity, flash point, interfacial tension, color, and density. The classification of oil based on physical properties relates to the oil's quality. The chemical property can be one of acidity, chemical composition (especially changes in chemical composition caused by dissolved gases), moisture, and the influence of paper polymerization. The classification of oil based on chemical properties relates to the oil's quality. These physical and chemical properties can enable an even more precise determination of the oil quality in the oil-filled transformer.

[0023] For a further improved determination of the oil quality in the oil transformer, the processing device can be 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 signals in the time domain and / or frequency domain are classified with respect to a physical property of oil that differs from the classified physical property of oil of the first signals in the time domain and / or frequency domain, and the fourth signals in the time domain and / or frequency domain are classified with respect to a chemical property of oil that differs from the classified chemical property of oil of the second signals in the time domain and / or frequency domain.As with the first signals, the third signals can be physical properties such as viscosity, flash point, interfacial tension, color, and density, and the classification of oil based on physical properties relates to oil quality. Similarly, the fourth signals, as with the second signals, can be chemical properties such as acidity, chemical composition, especially changes in chemical composition caused by dissolved gases, humidity, and the influence of paper polymerization, and the classification of oil based on chemical properties relates to oil quality.

[0024] The processing device can further be configured to determine an impedance (Z), a conductance (G), an admittance (Y), a susceptance (B), 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 and second signals in the time domain and / or frequency domain. For a more precise determination of the oil quality in the oil transformer, the impedance, conductance, admittance, and / or susceptance in the time domain and / or frequency domain can additionally be compared with the third and fourth signals in the time domain and / or frequency domain.Thus, 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 not magnetic).

[0025] The transmitted signal (the transmitted signal can be either one signal or several signals with different frequencies 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.

[0026] The wider the measurement frequency range, the more detailed the differences in the specific frequency range are. The proposed sensor technology is therefore capable of measuring the electrical parameters of oil over a wide frequency range, for example, in an ultra-wideband range from 0.1 to 6 GHz, providing very detailed information about the behavior of oil when interacting with electromagnetic waves.

[0027] The device may include a machine learning module configured to determine values ​​relating to the physical and chemical properties of the oil based on the impedance, conductance, admittance, susceptance, and / or combinations thereof. The values ​​may then be interpreted and correlated with the performance and / or ratings of the oil-immersed transformer.

[0028] The device may also include a machine learning module configured to optimize the processing of machine learning models and the comparisons at the processing device based on the classifications of the first and second signals, wherein the machine learning models use the first and second signals as training data. The machine learning models may also be configured to optimize the comparisons at the processing device based on the classifications of the third and fourth signals, wherein the machine learning module uses the third and fourth signals as training data.

[0029] Furthermore, machine learning models may be provided which are configured to be processed on a machine learning module and to determine values ​​relating to the physical property and the chemical property of the oil based on the impedance, the conductance, the admittance, the susceptance and / or the combinations thereof.

[0030] Further machine learning models can be developed using data collected and stored in a cloud platform. These machine learning models find patterns and structures in the data from one or more sensors. The trained machine learning models are sent to the processing device to enable calculation / prediction / classification of the data (real-time data) from the sensor. Thus, the machine learning models can include a variety of machine learning models. The machine learning module consists of the necessary storage and computing hardware, e.g., CPU, GPU, NPU, to enable predictions from machine learning models, as well as the training and development of machine learning models.

[0031] The machine learning models can also learn based on regressions. All machine learning models can be trained in the device as part of the machine learning module and used for inference, or they can be trained on another computer, such as a laptop or a cloud computer, and used for inference. The machine learning module can be located in 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 via 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, the antenna communicates with the cloud platform via an additional communication device. The machine learning module can be provided locally in the device.The data can also be sent from an aggregation device to the cloud tier via a highly secure gateway. This data in the cloud tier can be used to power applications based on the Microsoft Azure platform or the Amazon Cloud Platform.

[0032] The machine learning models receive the signals detected by the sensor as inputs. The machine learning models have been 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 can also be an artificial metric created to represent the overall condition of the transformer or the expected performance under specific circumstances.

[0033] Mathematical functions with different weights and parameters are used to map inputs to outputs. These weights and parameters are determined using sensor signal data recorded from transformer oil of known condition, e.g., from the laboratory. Finding the weights and parameters is called training and can be done using a gradient descent 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 making predictions, converting inputs into 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.

[0034] Once sufficient data is stored in a cloud database, unsupervised machine learning models can be developed. Because the data is recorded at relatively rapid intervals, the amount of data originating from the sensor and stored in the cloud is larger than that of the laboratory data / known oil condition data. Data stored in the cloud from a sensor is unlabeled, meaning the true oil quality and 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 in the data can be detected that provide insight into the relative changes in the oil or oil transformer conditions.

[0035] 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 performance prediction and capacity detection based on oil quality, active load management based on transformer capacity, and transformer recommendations.

[0036] The data used for training is stored in a database in the cloud, and when the training process is executed on a cloud device, the learned weights and parameters are sent to the inference device (in the cloud, IoT, or edge layer).

[0037] The sensor can be configured to transmit and receive the electromagnetic signals at or in various openings or valves of the oil-containing electrical power device or transmission device, in particular the oil-immersed transformer. For example, subsequent measurements can be taken at various valve and / or drain openings of the oil-immersed transformer located at the top, bottom, or side of the oil-immersed transformer. Measurements can also be taken at a valve between the radiator and the main tank of the oil-immersed transformer. For an opening located at the bottom of the oil-immersed transformer, no pump for oil extraction is necessary. Other means for providing the oil to the sensor can be provided.

[0038] The sensor may also comprise a plurality of sensors at or in different openings of the oil-containing electrical power device or transmission device, wherein the plurality of sensors are configured to transmit one or more electromagnetic signals with variable frequencies in the range of 1 Hz to 3000 GHz, in particular with variable frequencies in the range of 300 MHz to 300 GHz, into the oil at the same time and to receive reflected electromagnetic signals, and to process the received signals.

[0039] The device may further comprise: an aggregation device having 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. Accordingly, the communication device may be arranged in the sensor or in the processing device.When the aggregation device and the communication device are arranged in the processing device, the communication device sends the aggregated signals to a central unit. The central unit can be, for example, a cloud platform.

[0040] The communication device may also be configured to communicate with SCADA (Supervisory Control And Data Acquisition) systems, either via intelligent electronic devices (LEDs) or directly with remote telemetry units (RTUs) using the standard IEC 61850 client-server protocol (e.g. XMPP open-source protocol based on IEC 61850 or IEC 61850 MMS protocol).

[0041] The processing device may also be configured to process the received signals at the same time.

[0042] The device may further comprise a temperature stabilizer configured to keep the oil temperature constant during the transmission and reception of the electromagnetic signals. This can further improve the determination of the oil quality of the oil-immersed transformer.

[0043] The device may further comprise an automated and controllable calibration device configured to remove irregularities in the received electromagnetic signals. This can further improve the determination of the oil quality of the oil-immersed transformer.

[0044] The device may further comprise 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 comprise a complementary split-ring resonator, planar resonance-based sensor electrodes, or electrodes / probes designed to optimize parasitic effects and double-layer effects of the oil. The antenna is electrically connected to the sensor. To avoid measurement inaccuracies, the antenna may be arranged or attached directly to the oil conservator. The sensor may be arranged remotely from the oil conservator, for example, more than 1 m away. Thus, the antenna and the sensor may be connected via cable or via wireless interfaces (for example, via 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 designed as Vivaldi antennas. Vivaldi antennas can be used advantageously due to their wide bandwidth, low cross-polarization, and constant group delay. The antenna(s) can also be designed as ultra-wideband (UWB) antenna(s), spiral antennas, or other antenna types. The antenna and sensor can also be designed as a single unit or module, for example, in a housing. The sensor can further include a communication interface for communicating with a cloud computing device. The sensor can also be retrofitted with a mechanical design compatible with the valves of the oil-immersed transformer.

[0045] For attaching the antenna, a cell, and means for conveying oil from the oil-immersed transformer into the cell, a fastening device can be provided, which enables a detachable attachment of the antenna, the cell, and the means for conveying oil from the oil-immersed transformer into the cell to or in the oil-immersed transformer. Other fastening methods, such as screws or a magnetic attachment, are also conceivable.

[0046] The more comprehensive the sample data, the better the analysis. Therefore, the device can be mounted at various locations within an oil transformer (upper valve, lower valve, drain valve, radiator valve, orifices on the oil conservator) to obtain different measurement results.

[0047] Placing the sensors at different locations (top, bottom, at the drain, at the radiator valve) is beneficial for the overall analysis. For example, comparing the lower oil temperature with the upper oil temperature provides a good method for verifying whether the insulating fluid is circulating properly. Inadequate circulation leads to accelerated deterioration of the transformer insulation system.

[0048] The processing device may be further configured to make lifetime predictions for components of the oil-filled electrical power device or transmission device based on the determined oil quality, determine anomalies of the oil-filled electrical power device or transmission device, make transformer performance predictions and capacity detections of the oil-filled electrical power device or transmission device, perform active load management based on a transformer capacity of the oil-filled electrical power device or transmission device, and / or make transformer recommendations of the oil-filled electrical power device or transmission device from a plurality of oil-filled transformers. Furthermore, recommendations for oil-filled transformers may be made based on historical environmental, geographical, and transformer performance data related to oil quality.The device may further comprise a cloud platform configured to make lifetime predictions for components of the oil-containing electrical power device or transmission device based on the determined oil quality, to determine anomalies of the oil-containing electrical power device or transmission device, to make predictions for transformer performance and capacity detections of the oil-containing electrical power device or transmission device, to perform active load management based on a transformer capacity of the oil-containing electrical power device or transmission device, and / or to make transformer recommendations of the oil-containing electrical power device or transmission device from a plurality of oil-filled transformers.Furthermore, recommendations for oil-immersed transformers can be made based on historical environmental, geographical and transformer performance data related to oil quality.

[0049] The object stated at the outset is further achieved 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.

[0050] The object posed initially is further achieved 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, and transmitting, by the plurality of sensors, the electromagnetic signals into the oil in the oil-containing electrical power device or transmission device. The aggregated signals can then be sent to a central unit, such as a cloud platform, for data analysis.

[0051] Taking oil samples from different locations of an oil transformer (e.g., upper valve, lower valve, valves near radiators, vents, or valves on the oil conservator) improves the overall results of the oil quality assessment procedure.

[0052] Over time, the chemical and physical properties of the oil deteriorate, and there are relationships between the physical and chemical properties of the oil and the performance of the oil-immersed transformer. For example, the interfacial tension (IFT) (physical property) is inversely related to the operating time of the oil-immersed transformer, while the acidity (chemical property) is directly related to the operating time of the oil-immersed transformer. The moisture content in the oil (chemical property) has a very strong inverse relationship with the breakdown voltage of transformer oil.

[0053] The derivation of the chemical and physical properties of the oil in real time using the results of electrochemical impedance spectroscopy and their linking with the performance of the transformer helps in the qualitative and quantitative monitoring and maintenance of the oil in real time.

[0054] The aspects and variants described above can be combined without this being explicitly described. Each of the described embodiment variants is therefore to be considered optional to each embodiment variant or combinations thereof. The present disclosure is therefore not limited to the individual embodiments and variants in the described order or to a specific combination of the aspects and embodiment variants.

[0055] BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Further advantages, details and features of the methods, devices and systems described here will become apparent from the following description of embodiments and the figures.

[0057] Fig. 1 shows a schematic representation of a first embodiment of an oil transformer with an oil conservator;

[0058] Fig. 2 shows a schematic representation of the oil expansion tank of Fig. 1 with a device for determining the oil quality;

[0059] Fig. 3 shows a schematic representation of a second embodiment of an oil transformer with devices for determining the oil quality; and

[0060] Fig. 4 shows a schematic representation of a third embodiment of a system for a plurality of oil transformers.

[0061] DETAILED DESCRIPTION

[0062] Fig. 1 shows a schematic representation of an embodiment of a

[0063] Oil-immersed transformer with an oil conservator. The oil transformer 10 comprises a transformer tank 12 and an oil conservator 20. A flow channel 30 connects the transformer tank 12 to a first opening 22 in the oil conservator 20. The first opening 22 is arranged at a lower end of the oil conservator 20. The transformer tank 12 comprises a corresponding opening. In the transformer tank 12, the transformer 15 is mounted on support blocks 60 in an oil bath 40. The oil conservator 20 is shown in Fig. 1 as an example above the transformer tank 12 in a cylindrical shape. Other shapes (e.g., cuboid, cube, or prism) and arrangements (at the same height as the transformer tank 12, further above, etc.) of the oil conservator 20 are conceivable.

[0064] Oil 40 is located in the flow channel 30 and the housing 26 of the oil conservator 20. The oil conservator 20 serves to absorb oil due to thermal expansion of the oil during temperature fluctuations in the transformer 15, caused by load changes or changes in the ambient temperature. As indicated by the surface 42 of the oil in the oil conservator 20, the oil level in the oil conservator 20 changes accordingly. The oil in the oil conservator 20 compresses a membrane 28 depending on the oil level in the oil conservator 20, which is decompressed again when the oil level 42 drops. A pressure relief valve 50 for discharging excess gas is provided in a second opening 24 at an upper end of the oil conservator 20. Furthermore, the oil transformer comprises an oil level measuring device 25 for measuring the oil level 42 in the oil expansion tank 20.For the sake of clarity, other components of the oil 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 conservator 20 with the membrane 28, an oil conservator of the Atmoseal type or another type may also be provided.

[0065] Figure 2 shows a schematic representation of an embodiment of an oil expansion tank with a device for determining oil quality. The oil expansion tank 20 is the oil expansion tank 20 shown in Figure 1, with the same reference numerals in Figures 1 and 2 referring to the same elements.

[0066] 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 an oil quality of the oil transformer 10 and a processing device 90. The means 57, 58 for conveying oil 40 from the oil expansion vessel 20 into the cell 55 comprise a hose 57 arranged at least partially 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 tank 20 into the cell 55. The hose 57 extends through the opening 24, with the cell 55 being arranged outside the oil expansion tank 20 at the opening 24.The opening may also be a vent opening on the oil expansion tank 20.

[0067] In Fig. 2, a minimum oil level 42 is shown in the oil expansion tank 20, wherein the hose 57 is designed such that the first end of the hose 57 always extends below the minimum oil level 42.

[0068] The pump 58 pumps oil 40 from the oil expansion tank 20 into the cell 55. It is also conceivable for the pump 58 to pump the oil 40 through the cell 55, i.e., the oil 40 is returned 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 arranged near the antenna 85.

[0069] 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.

[0070] The antenna 85 is mounted on an outer wall of the cell 55 and is electrically connected to the sensor 70.

[0071] Opposite the underside 21 of the oil expansion tank 20, at an upper end of the oil expansion tank 20, is the opening 24, which is closed by a pressure relief valve 50. The antenna 85, which is electrically connected to the sensor 70, is arranged above the opening 24.

[0072] 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 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 storage units, random access memory (RAM) units, and Trusted Platform Modules (TPMs) that meet necessary security and cryptography requirements. TPM modules also help secure 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 transmission and reception of signals by the baseband transmitter 71 and the baseband receiver 72. The sensor 70 is configured to transmit and receive pulsed signals with a frequency of 1 Hz to 3000 GHz via the antenna 85. Preferably, the system operates in the ultra-wideband range, so that the sensor 70 can analyze the signal in the broad 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, which can be used to determine the quality of the oil in the oil expansion tank 20. The antenna 85 is a complementary split-ring resonator or a planar resonance-based sensor electrode. Other types of electrodes / probes designed to optimize parasitic effects and double layer effects with the oil can also be used.Furthermore, the sensor 70 comprises a communication device 79 for communication with the processing device 90. The communication device 79 can comprise wireless (for example, LTE-M or NB-IoT cellular connection SoC modules, WiFi, BLE or NFC interfaces) and / or wired interfaces (for example, I2C, SPI, UART, ADC, PWM, HDMI, VGA, ETHERNET interface).

[0073] The signals generated in the baseband transmitter 71 of sensor 70 can be generated 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 varying the configurations of the flip-flops and shift registers.

[0074] The processing device 90 is a laptop computer or any other type of computer configured to process and visualize the measured values ​​measured by the sensor 70. The processing device 90 may also include microcontroller units, graphical processing units (GPU), and / or central processing units (CPU) or neural processing units (NPU) to perform the necessary calculations for executing machine learning with the machine learning module 95. In particular, the processing device 90 is configured to calculate values ​​relating to the color, water content, and / or acidity of the oil based on the measured values ​​measured by the sensor 70. The processing device 90 may also calculate other values ​​mentioned above with regard to the physical property and the chemical property. For example, a fast Fourier transform may be applied to the measured data.The oil transformer further comprises a temperature, gas, and / or vibration sensor 65. The temperature, gas, and / or vibration sensor 65 is arranged in the oil expansion tank 20. The temperature, gas, and / or vibration sensor 65 can also be provided outside or at the connection interface of the opening 24. The temperature, gas, and / or vibration sensor 65 is configured to send measurement data to the sensor 70 and / or to the processing device 90. For this purpose, the temperature, gas, and / or vibration sensor 65 can comprise a communication interface that enables communication with the sensor 70 and / or the processing device 90. The communication interface can be arranged 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 configured to detect abnormalities in the gas generated by the oil transformer. The sensor can also include additional sensor and electronic components that monitor regular operation and the condition of the oil transformer.

[0075] 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 first and second signals in the time domain and / or frequency domain. The first signals in the time domain and / or frequency domain are classified with respect to a physical property of oil, and the second signals in the time domain and / or frequency domain are classified with respect to a chemical property of oil. 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.The chemical property is one of acidity, chemical composition, particularly changes in chemical composition caused by the involvement of dissolved gases, humidity, and the influence of paper polymerization. The classification of oil based on its chemical property relates to oil quality. Based on the comparisons, the processing device 90 determines the oil quality of the oil 40 in the oil-immersed transformer 10.

[0076] The comparison between the signals and the received signal can be performed using a machine learning model, where the model has been trained on the given signals to find weights and parameters. The machine learning model then predicts the chemical or 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). The described machine learning method also solves the inverse problem by determining the physical and chemical factors that influence the overall condition of the oil transformer.

[0077] The comparisons can be performed by separate machine learning models, for example, one for the first signals 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 through a single computational process.

[0078] For example, the first signals are classified in the time domain and / or frequency domain according to the color of the oil. This allows for the fact that as the color of the oil darkens from pale yellow to yellow, to light yellow, to amber, to brown, to dark brown, and to black, the oil quality progressively deteriorates. For example, the second signals are classified in the time domain and / or frequency domain according to the moisture content of the oil.

[0079] The processing device 90 is involved in the preprocessing of the data, i.e., 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 moisture content of the oil), and in excluding unwanted data packets from the original signal.

[0080] For the comparisons carried out in the processing device, amplitude values ​​(e.g. in units of dB) over time (e.g. in units of ms) can be compared with predetermined and classified amplitude values ​​(e.g. in units of dB) over time (e.g. in units of ms). Furthermore, a power spectrum calculated using a fast Fourier transform can be used, i.e. amplitude values ​​(e.g. in units of dB) over frequency (e.g. in units of GHz) can be compared with predetermined and classified amplitude values ​​(e.g. in units of dB) over frequency (e.g. in units of GHz). The comparisons can be made 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.

[0081] Optionally, the processing device 90 can be configured to compare the received time-domain and / or frequency-domain signals with third and fourth time-domain and / or frequency-domain signals. The third time-domain and / or frequency-domain signals are classified with respect to a physical property of oil that differs from the classified physical property of oil of the first time-domain and / or frequency-domain signals, and the fourth time-domain and / or frequency-domain signals are classified with respect to a chemical property of oil that differs from the classified chemical property of oil of the second time-domain and / or frequency-domain signals.As with the first signals, the third signals can be physical properties such as viscosity, flash point, interfacial tension, color, and density, and the classification of oil based on physical properties relates to oil quality. Similarly, the fourth signals, as with the second signals, can be chemical properties such as acidity, chemical composition, especially changes in chemical composition caused by dissolved gases, humidity, and the influence of paper polymerization, and the classification of oil based on chemical properties relates to oil quality.

[0082] Here, too, the comparison between the third and fourth signals and the received signal can be performed using a machine learning model, which has been trained on the predetermined signals to find optimal weights and model parameters. The machine learning model will then predict the chemical and / or physical property of the oil and return either a value or a probability distribution.

[0083] For example, the third signals in the time domain and / or frequency domain are classified with respect to a viscosity of oil, and the fourth signals in the time domain and / or frequency domain are classified with respect to an acidity of oil.

[0084] The processing device 90 is further configured to determine an impedance, a conductance, an admittance, and / or a susceptance in the time domain and / or frequency domain based on the transmitted and received signals, and to compare the impedance, conductance, admittance, and / or susceptance in the time domain and / or frequency domain with the first and second signals in the time domain and / or frequency domain. In addition, the processing device 90 can compare the impedance, conductance, admittance, and / or susceptance in the time domain and / or frequency domain with the third and fourth signals in the time domain and / or frequency domain. The aforementioned values ​​can also be combined and compared with each other. The processing device 90 is further capable of analyzing historical data recorded by the device and / or other installed similar devices.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. Training of the machine learning module takes place in the processing device 90 or another laptop / computer connected to the cloud.

[0085] The patterns and structures found in the data are used for qualitative and quantitative monitoring of the oil-immersed transformer 10 and can be further used in applications such as oil-immersed transformer lifetime prediction, transformer performance prediction and capacity detection based on oil quality, active load management based on transformer capacity, and transformer recommendations. The calculations of the patterns and structures take place in the processing device 90 or in the cloud platform 200 (see Fig. 4).

[0086] As can be seen from Fig. 2, the processing device 90 further comprises a machine learning module 95 configured to optimize the comparisons at the processing device 90 based on the classifications of the first, second, third, and / or fourth signals. The first, second, third, and / or fourth signals may, in particular, be predetermined signals.

[0087] The comparison between the given signals and the received signal can be performed using a machine learning model, where the model has been trained on the given signals to find weights and parameters. The machine learning model then predicts the chemical or 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).

[0088] The device shown in Fig. 2 further comprises a temperature stabilizer 75 which is designed to keep the temperature of the extracted oil of the oil transformer 10 constant.

[0089] An automatic calibration device 77 is provided in the sensor, which is designed to remove irregularities, noise and / or attenuations in the received electromagnetic signals. The automatic

[0090] Calibration device 77 may also be provided in the processing device 90.

[0091] Fig. 3 shows a schematic representation of a second embodiment of an oil transformer with a device for determining oil quality. The oil transformer can be the oil transformer 10 shown in Figs. 1 and 2 or another oil transformer (such as a hermetically sealed or freely air-permeable oil transformer used in distribution and transmission networks). The same reference numerals refer to the same elements, so a repeated explanation is omitted.

[0092] The oil-immersed transformer comprises a plurality of openings. A sensor 70 is located in each of the openings. Each sensor 70 sends 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 sends the aggregated signals to the processing device 90 or a cloud platform (not shown in Fig. 3). The transmission can occur simultaneously, in particular in real time.

[0093] According to an alternative embodiment, only one sensor 70 is used, and one sensor 70 is placed sequentially into 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.

[0094] In summary, the embodiment of 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 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, directly or via the edge / fog layer. The communication device 79 is configured to communicate as described with regard to Fig. 2. The aggregation device 80 communicates wirelessly with a hypersecure gateway that uses an edge or fog layer (in Fig.3, but see the edge or fog layer with the hypersecure gateway 100 in Fig. 4). With the data collected in the aggregation device 80, sensor fusion algorithms can be applied to investigate real-time behavior of the oil transformer.

[0095] Fig. 4 shows a schematic representation of a third embodiment of a system for a plurality of oil transformers, wherein four oil transformers are shown as examples. The oil transformers can each be the oil transformer 10 shown in Figs. 1 or 3 or another oil transformer, and the sensor 70 can each be the sensor 70 shown in Figs. 2 or 3 or another sensor. The aggregation device 80 can be the aggregation device shown in Fig. 3. The same reference numerals refer to the same elements, so a repeated explanation is omitted.

[0096] The oil-immersed transformers 10 are part of an electrical power grid (not shown in Fig. 4). The sensors 70 are configured to transmit 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. The received data is then processed there.

[0097] 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 executes various applications, such as making lifetime predictions for components of the oil-containing electrical power device or transmission device (10) based on the determined oil quality, determining anomalies of the oil-containing electrical power device or transmission device (10), making predictions for transformer performance and capacity detections of the oil-containing electrical power device or transmission device (10), performing active load management based on a transformer capacity of the oil-containing electrical power device or transmission device (10), and / or making transformer recommendations of the oil-containing electrical power device or transmission device (10) from a plurality of transformers.

[0098] The highly secure gateway 100 is intended for secure communication between the cloud layer and the plurality of sensors 70 or the plurality of aggregation devices 80 in the IoT layer. Therefore, a multi-layer architecture is considered, including an IoT, an edge / fog, and a cloud layer, to describe a decentralized data processing structure located between the cloud and the devices that generate data. 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 are located in the IoT layer, the hypersecure gateway 100 is located in the edge / fog layer, and the measurement data processing takes place in the cloud layer 200.

[0099] The sensors 70 function in the IoT layer as the IoT awareness layer in a smart grid. The Edge / Fog layer enables secure communication between the IoT layer and the cloud layer. For this purpose, the Edge / Fog layer is configured to perform authorization, double certificate authentication, and data preprocessing to detect anomalies. Furthermore, high availability and resilience of a grid / transformer monitoring process are ensured. Furthermore, it is possible to implement double virtualization, as the virtualization technique enables this layer to migrate from one connected environment to another, preventing cascading of faulty data through the system. This is achieved by migrating functions and data from dedicated hardware that is at risk to other hardware.The cloud layer is used to provide applications for monitoring, historical data analysis, artificial intelligence-based applications, and visualizations.

[0100] 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.

[0101] An artificial intelligence (AI) module, such as the machine learning module 95, may also be present in each of the sensors 70, which may be used to optimize the data stored in the aggregation device 80. AI and machine learning capabilities may also be provided to other units.

[0102] 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 service life predictions for components of the oil-containing electrical power device or transmission device (10) based on the determined oil quality, to determine anomalies of the oil-containing electrical power device or transmission device (10), to make predictions about transformer performance and capacity detections of the oil-containing electrical power device or transmission device (10), to carry out active load management based on a transformer capacity of the oil-containing electrical power device or transmission device (10) and / or to make transformer recommendations of the oil-containing electrical power device or transmission device (10) from a plurality of transformers.

[0103] An embodiment of a method for determining the oil quality of an oil transformer using the devices for determining the oil quality shown in Figs. 2 to 4 comprises the method steps: attaching a plurality of sensors 70 of the device to or in various openings and / or valves of the oil transformer 10 and transmitting the electromagnetic signals into the oil in the oil transformers 10 by the plurality of sensors 70.

[0104] Thus, a device, a system, and a method for oil-immersed transformers are provided, with the aid of which the oil quality in an oil-immersed transformer can be determined in a simple and precise manner. As described above, the embodiments of Figures 1 to 4 can also be extended to oil-containing electrical power devices or transmission devices.

[0105] In the examples presented, various features and functions of the present disclosure have been described separately and in specific combinations. However, it is understood that many of these features and functions can be freely combined with one another, unless explicitly excluded.

[0106] Both oil-immersed transformers with and without oil conservator can be used. The comparisons of the received electromagnetic signals with the signals indicating the oil properties can be performed in one of the sensors 70, in the processing device 90, in a central unit, or in a cloud server.

Claims

CLAIMS 1. A device for an oil-containing electrical power device or transmission device (10), comprising a sensor (70) configured to transmit one or more electromagnetic signals with variable frequencies in the range from 1 Hz to 3000 GHz, in particular with variable frequencies in the range from 300 MHz to 300 GHz, into the oil (40) in the oil-containing electrical power device or transmission device (10) at the same time and to receive 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 signals in the time domain and / or frequency domain are classified with respect to a physical property of oil,the second signals are classified in the time domain and / or frequency domain with respect to a chemical property of oil, and the processing device (90) is further configured to determine an oil quality of the oil (40) in the oil-containing electrical power device or transmission device (10) based on the comparisons.

2. Device according to claim 1, wherein the physical property is one of viscosity, flash point, interfacial tension, color and density, the classification with regard to the physical property of oil concerns an oil quality, the chemical property is one of acidity, chemical composition, in particular changes in chemical composition caused by involvement of dissolved gases, humidity and influence of paper polymerization and the classification with regard to the chemical property of oil concerns an oil quality.

3. Device according to one of the preceding claims, wherein the processing device (90) is arranged 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 signals in the time domain and / or frequency domain are classified with respect to a physical property of oil which differs from the classified physical property of oil of the first signals in the time domain and / or frequency domain and the fourth signals in the time domain and / or frequency domain are classified with respect to a chemical property of oil that differs from the classified chemical property of oil of the second signals in the time domain and / or frequency domain.

4. Device according to one of the preceding claims, wherein the processing device (90) is configured to determine an impedance, a conductance, an admittance, a 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, the conductance, the admittance, the susceptance and / or the combinations 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 of claim 4, further comprising Machine learning models configured to be processed on a machine learning module (95) and to determine values ​​relating to the physical property and the chemical property of the oil based on the impedance, the conductance, the admittance, the susceptance and / or the combinations thereof.

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

7. Device according to one of the preceding claims, wherein the sensor (70) is arranged to transmit and receive the electromagnetic signals at or in different openings of the oil-containing electrical power device or transmission device (10).

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

9. The apparatus of 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. Device according to one of the preceding claims, wherein the processing device (90) is arranged to process the received signals at the same time.

11. Device according to one of the preceding claims, further comprising a temperature stabilizer (75) which is arranged to keep the temperature of the oil constant during the transmission and reception of the electromagnetic signals.

12. Device according to one of the preceding claims, further comprising an automated and controllable calibration device (77) which is arranged to To remove irregularities in the received electromagnetic signals.

13. Device according to one of the preceding claims, further comprising an antenna (85) which is arranged to receive the electromagnetic signals of the Sensor (70) into the oil and receive the reflected and / or propagated electromagnetic signals, wherein the antenna (85) comprises a complementary split-ring resonator, planar resonance-based sensor electrodes, or electrodes / probes designed to optimize parasitic effects and double-layer effects of the oil.

14. Device according to one of the preceding claims, further comprising a cloud platform (200) which is configured to make lifetime predictions for components of the oil-containing electrical power device or transmission device (10) based on the determined oil quality, to determine anomalies of the oil-containing electrical power device or transmission device (10), to make predictions for transformer performance and capacity detections of the oil-containing electrical power device or transmission device (10), to carry out 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 of the oil-filled electrical power device or transmission device (10) from a plurality of oil transformers.

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

16. A method for determining an oil quality of an oil-containing electrical power device or transmission device with a device according to one of claims 1 to 14, 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 and Transmitting, by the plurality of sensors (70), the electromagnetic signals into the oil in the oil-containing electrical power device or transmission device.

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