Patent Description:
Transformers are important components of electric power systems. Transformers can have various configurations, depending on their intended use. The manufacture of transformers designed for use in electric power generation, transmission, and/or distribution is a very complex task, due to the specific requirements imposed on such transformers. This applies in particular to the manufacture of a transformer winding.

The design of transformer windings is optimized in view of its performance for a given amount of copper used. The design of transformer windings is also optimized to take up a small amount of space while still maintaining high reliability and efficient cooling and insulation. This makes the manufacturing process complex.

Errors during the winding process of forming a transformer winding are fortunately rare, but any failure has the potential of having severe negative effects on power supply, operation safety, and workforce safety.

Similar challenges exist in the winding process of forming windings of other electric power system components such as reactors.

Thus, quality control for a transformer winding or for windings of other electric power system components is an important issue to reduce the risk of a failure which can have catastrophic consequences.

Unfortunately, such a quality control is not easy to perform using conventional techniques. Mistakes are difficult to detect in view of the complexity of the winding process. Many mistakes, such as mistakes relating to the position of crossovers or erroneous deviations from design data, are hidden from view in the transformer winding. The challenges in error detection are particularly pronounced when a winding apparatus provides both a rotary and a translatory degree of freedom, causing the transformer winding to be both rotated around an axis and displaced in a translatory manner during manufacture.

<NPL> discloses a machine vision technique for classification of electrical coil winding defects, which uses a ResNet-<NUM> and Generative Adversarial Network (GAN) technique.

In view of the above, there is a need for methods and systems which mitigate the risk that a winding of an electric power system component that is not formed in accordance with its specification is used in a transformer or a transformer core or another electric power system component. There is in particular a need for methods and systems which allow quality issues to be detected while the winding process of forming the winding is in progress.

According to aspects of the invention, a method and a system as recited in the independent claims are provided. The dependent claims define preferred embodiments.

According to an aspect of the invention, there is provided a method of monitoring a winding process of forming a winding of an electric power system component (such as a transformer winding) on a winding apparatus. The method comprises processing, by a computer vision system, at least one image showing at least part of the winding (e.g., the transformer winding) captured during the winding process, wherein processing the at least one image comprises performing an object classification. The method further comprises detecting, by the computer vision system, a discrepancy between a result of the object classification and design data for the winding (e.g., the transformer winding).

The method of monitoring a winding process allows mistakes to be detected while the winding (e.g., the transformer winding) is still being formed. Thereby, mitigating and/or correcting actions may be taken during manufacture of the transformer winding.

The method of monitoring a winding process utilizes a computer vision system in combination with design data to detect a discrepancy therebetween. The discrepancy is an indicator that an error has occurred or may have occurred. Thus, the computer vision system provides assistance during the winding process of forming a transformer winding.

The method may comprise effecting, by the computer vision system, at least one action responsive to the detected discrepancy.

Thereby, one or several actions may be performed automatically or semi-automatically when there is a discrepancy detected by the computer vision system.

The at least one action may comprise causing, by the computer vision system, a human machine interface (HMI) to output information that depends on the detected discrepancy.

Thereby, the detected discrepancy may be used as trigger for an output action that can alert in operator of the winding apparatus that there has been or might have been a mistake in the winding process.

The information may comprise one, several, or all of: an alarm and/or warning; information indicating a location of the detected discrepancy; information indicating a time at which the detected discrepancy occurred; information on a root cause of the detected discrepancy; instructions for correcting the detected discrepancy.

Thereby, the detected discrepancy may be used as trigger for an output action that can alert in operator of the winding apparatus and that can optionally provide additional information that assists the operator in identifying the root cause of the mistake or that assists the operator in taking suitable or corrective a mitigating actions to correct the mistake in the winding process.

The at least one action may comprise a mitigating action and/or a corrective action.

Thereby, the detected discrepancy may be used as trigger for an action that may be performed automatically or semiautomatically to mitigate and/or correct the mistake that has been identified by the computer vision system.

The action may comprise stopping a rotation and/or translatory displacement of the winding apparatus.

Thereby, visual inspection or corrections to the winding process may be facilitated.

The winding apparatus may comprise a winding support on which the winding (e.g., the transformer winding) is formed. The winding apparatus may comprise one or several motors that drive the winding support. The winding apparatus may comprise a first motor that rotates the winding support and a second motor that causes a translatory displacement of the winding support, preferably along a rotation axis of the winding support.

Thereby, manufacture of the winding (e.g., the transformer winding) is facilitated. Additionally, such a configuration of the winding apparatus lends itself to a monitoring method using a computer vision system. An image acquisition device, such as a camera, can be mounted stationary, as the winding apparatus displaces the transformer winding as it is being formed so as to keep a top thereof at approximately a same height.

The method further comprises determining at least one image acquisition time of the at least one image during the winding process.

Thereby, the computer vision system can determine at which image acquisition times during the winding process the at least one image provides particularly good information for facilitating detection of mistakes. For illustration, the one or several image acquisition times may depend on how shields overlaid on conductors and/or fillers of the winding (e.g., the transformer winding) are positioned relative to the image acquisition device.

The method may further comprise performing an image acquisition device control and/or an image selection based on the determined at least one image acquisition time.

Thereby, image acquisition may be controlled in such a manner that the at least one image provides particularly good information for facilitating detection of mistakes. Alternatively or additionally, images that are particularly suitable for detection of mistakes that occurred during the winding process (e.g., because shields overlaid on conductors or fillers are positioned suitably relative to the image acquisition device) can be selected from a stream of images provided by the image acquisition device.

The at least one image acquisition time is determined based on at least one position of the winding apparatus. The at least one image acquisition time is determined based on a rotatory position and a translatory position of the winding apparatus. The design data are used in combination with the positions of the winding apparatus to determine the at least one image acquisition time. For illustration, the design data may be used to determine at which rotary and/or translatory positions of the winding apparatus shields, conductors, fillers, and/or crossovers and/or other winding components or structural features are suitably positioned in a field of view of the image acquisition device.

The at least one image acquisition time may be determined based on a stream of images acquired by the at least one image acquisition device. The computer vision system may use image processing techniques, such as edge detection techniques, Fourier-based techniques, and/or contrast-enhancing techniques, to identify the images in the stream that are most likely to provide information on whether mistake was made in the winding process.

The computer vision system may use a first machine learning (ML) model for determining the at least one image acquisition time. The first ML model may have a first input layer that receives pixels of images of the stream. The first ML model may have a first output layer that outputs a value indicating whether or not further analysis is to be performed on any of the images. Alternatively or additionally, the first input layer may receive winding apparatus positions, such as a rotatory and/or translatory position of a winding support on which the transformer winding is being formed.

Processing the at least one image may comprise identifying a region of interest (ROI) in which the object classification is to be performed.

Thereby, object classification can be focused on a region in which a discrepancy, if any, is likely to be visible.

Boundaries of the ROI may comprise edges of shields of the transformer winding.

Thereby, object classification can be focused on a region between adjacent shields that are interposed between successive discs of a transformer winding.

Alternatively or additionally, boundaries of the ROI may comprise inner and outer circumferential edges of a layer formed by conductors and fillers.

Thereby, object classification can be focused on a region in which winding errors are prone to being encountered.

Identifying the ROI may comprise performing an edge detection.

Thereby, the ROI may be identified efficiently.

Identifying the ROI may comprise applying at least one ROI classifier to the at least one image. Several ROI classifiers may be applied to the at least one image. Each of the one or more ROI classifiers may indicate, for each pixel, whether the respective pixel is classified as being within the ROI or outside the ROI.

The one or several ROI classifiers may comprise at least one second ML model. The at least one second ML model may have a second input layer that receives pixels of the at least one image. The at least one second ML model may have a second output layer that outputs, for every pixel, a value indicating whether or not the respective pixel is in the ROI.

The at least one second ML model may comprise a deep learning ML model.

Thereby, the object classification task can be performed reliably.

The at least one a second ML model may comprise a U-Net ML model.

Thereby, the ROI identification task can be performed reliably.

The object classification may comprise performing at least one third ML model. The at least one third ML model may have a third input layer that receives pixels located within the ROI. The at least one third ML model may have a third output layer that outputs object classifiers indicating to which one of several object classes the respective pixel in the ROI is assigned. The third output layer may indicate a probability that an object belongs to a given object class (such as filler, conductor, shield, and/or crossover).

The third ML model may comprise a random forest (RF) model, without being limited thereto.

Thereby, the object classification task can be performed in a manner that provides particularly good results during inference.

The object classification may classify at least conductors, fillers, and shields in the at least one image.

Alternatively, or additionally, the object classification may classify crossovers. The object classification may classify inner and outer crossovers.

Alternatively, or additionally, the object classification may classify other components and/or other structural features of the transformer winding.

Thereby, object classes are implemented which lend themselves for a comparison with design data.

Several classifiers may be used, each associated with and trained for recognizing a given object class (such as filler, conductor, shield, and/or crossover).

The object classification may define a sequence of objects that comprises at least conductors and fillers as arranged in a radial direction (e.g. from inside for outside or from outside to inside), as identified in the ROI of the image.

Processing the at least one image may comprise using one or several position measurements of the winding apparatus. The one or several position measurements may comprise a rotatory and/or translatory position measurement. The one or several position measurements may be received from sensors. Alternatively, the one or several position measurements may be received from a controller of the winding apparatus.

The one or several position measurements may be used for determining which images are to be analyzed further, e.g., by performing ROI determination and object classification. Alternatively or additionally, the one or several position measurements may be used in assisting the ROI determination.

Processing the at least one image may comprise executing at least one ML model having an input layer that receives at least part of the at least one image and/or winding apparatus positions, wherein the at least one ML model has an output layer, wherein the output layer provides at least one of: at least one image acquisition time defining the at least one image that is to be processed; an ROI in which the object classification is to be performed; and/or object classifiers indicating to which one of several predetermined object classes objects respectively are assigned.

The method may further comprise determining, based on the design data, a target sequence of objects in a radial direction of the transformer winding.

Thereby, the design data are converted into a format that can be readily compared to the results of the object classification.

The design data may comprise at least one crossover diagram.

The design data may define the winding as a three-dimensional volumetric component. The design data may have various formats, such as one or several crossover diagrams, without being limited thereto.

Determining the target sequence of objects may comprise generating, based on the at least one crossover diagram, a machine readable representation indicating the target sequence. The machine readable representation may comprise an XML representation, without being limited thereto.

Detecting the discrepancy may comprise comparing the target sequence and the result of the object classification.

The target sequence may be determined automatically by the computer vision system, or a processing system communicatively coupled thereto.

The winding (e.g., the transformer winding) may comprise several layers (also referred to as discs in the art). The at least one image may show at least one of the several layers at an upper face of the winding as it is being formed on the winding apparatus. The image of the uppermost layer at the upper face of the winding may be processed in the method to detect the discrepancy.

Processing at least one image and comparing the result of the object classification to the design data may be repeated in an ongoing basis (i.e. continually) during the winding process.

When the method uses one or several ML models (such as one, several, or all of the first, second, and third ML models mentioned above), the ML model may be trained using labeled image data. The labeled image data may comprise synthetically generated images which are generated from real-world images that show an error during the winding process. Generation of the synthetically generated images may comprise one or several image modification operations, such as rotations, translations, mirroring, adding distortions, etc..

The one or several ML models may be trained using supervised training or semi--supervised training.

The method may further comprise training the ML model using labeled image data.

The training may be performed by a computer system distinct and separate from the computer vision system or may be performed by the computer vision system.

Training the ML model may comprise synthetically generating images from real-world images that show an error during the winding process. Generation of the synthetically generated images may comprise one or several image modification operations, such as rotations, translations, mirroring, adding distortions, etc..

The synthetically generated images allow a more balanced training set to be provided, improving the results when the ML model is subsequently employed for inference during field use of the computer vision system.

The ML model may be trained to perform at least one the classification task. The at least one classification task may be or may comprise one or several of: classifying an image is being an image that is to be processed; classifying pixels as belonging to an ROI; performing an object classification.

The ML model may comprise at least one of: a deep learning model, such as a U-Net model; a random forest model; a recurrent neural network (RNN); a convolutional neural network (CNN).

Synthetically generating images may comprise generating the synthetic images using an adversarial ML model.

The transformer winding may be a transformer winding of a power generation and transmission system, such as a high-voltage a medium voltage power transmission system, of a power distribution system, of a railway transformer, or of a reactive or inductive system, or a shunt reactor, such as a variable reactor or other shunt reactor.

There is also disclosed a method of training at least one ML model for use in monitoring a winding process of forming a winding of an electric power system component (such as a transformer winding) on a winding apparatus.

The method may comprise training one or several ML models (such as one, several, or all of the first, second, and third ML models mentioned above), using labeled image data showing windings (such as transformer windings) as they are being formed.

The training method may comprise synthetically generating at least some training images from real-world images that show an error during the winding process. Generating the synthetically generated images may comprise one or several image modification operations, such as rotations, translations, mirroring, adding distortions, etc..

The training method may comprise training the at least one ML model to perform at least one classification task. The at least one classification task may be or may comprise one or several of: classifying an image is being an image that is to be processed; classifying pixels as belonging to an ROI; performing an object classification.

The at least one ML model may comprise at least one of: a deep learning model, such as an Unet model; a random forest model; a recurrent neural network (RNN); a convolutional neural network (CNN).

The training method may be performed by a computer vision system that executes the trained ML model(s) during inference or by a computing system separate from the computer vision system.

The training method may comprise deploying the trained ML model(s) to a computer vision system for monitoring a winding process of forming a transformer winding on a winding apparatus.

According to another aspect of the invention, there is provided a method of manufacturing a winding of an electric power system components (such as a transformer winding). The method comprises controlling, by at least one control device, a winding apparatus on which the winding (e.g., the transformer winding) is formed. The method comprises performing the method of monitoring a winding process according to any one of the embodiments while the transformer winding is being formed.

The method may further comprise correcting at least one mistake made during the winding process responsive to the detected discrepancy.

The method may further comprise discarding the transformer winding responsive to the detected discrepancy.

The method may be performed automatically by a system which comprises a winding apparatus and a computer vision system operative to perform the monitoring method discussed in detail herein. The computer vision system may be operated if he coupled to a controller of the winding apparatus.

According to another aspect of the invention, there is provided a method of manufacturing a transformer core. The method comprises assembling the transformer core comprising the at least one transformer winding manufactured using the method of manufacturing a transformer winding according to an embodiment.

Assembling the transformer core may comprise assembling the at least one transformer winding with a yoke.

The yoke may comprise a laminate comprising several layers. This allows the magnetic flux to be transferred efficiently in the transformer core.

The transformer core may be a transformer core of a power transmission system, such as a high-voltage a medium voltage power transmission system, of a power distribution system, of a railway transformer, or of a reactive or inductive system.

According to another aspect of the invention, there is provided a method of manufacturing a transformer. The method comprises assembling the transformer comprising the at least one transformer winding manufactured using the method of manufacturing a transformer winding according to an embodiment.

Assembling the transformer may comprise assembling the at least one transformer winding with a yoke.

Assembling the transformer may comprise arranging the at least one transformer winding in a transformer tank.

Assembling the transformer may comprise filling an interior of the transformer tank with an insulating fluid, such as an insulation oil.

The transformer may be a transformer of a power transmission system, such as a high-voltage a medium voltage power transmission system, of a power distribution system, or of a railway system, or a reactor, such as a variable reactor or shunt Reactor.

According to another aspect of the invention, there is provided a system configured to monitor a winding process of forming a winding of an electric power system component (such as a transformer winding). The system comprises a computer vision system configured to process at least one image showing at least part of a winding (e.g., a transformer winding) captured during the winding process to perform an object classification, detect a discrepancy between results of the object classification and design data for the winding, and perform an action responsive to the detected discrepancy.

The system for monitoring a winding process allows mistakes to be detected by the winding of the electric power system component is still being formed. This enables mitigating and/or correcting actions to be taken during manufacture of the transformer winding.

The system for monitoring a winding process utilizes a computer vision system in combination with design data to detect a discrepancy therebetween. The discrepancy is an indicator that an error has occurred or may have occurred. Thus, the computer vision system provides assistance during the winding process of forming the winding of the electric power system component (e.g., the transformer winding).

The system may further comprise a human machine interface (HMI). The computer vision system is configured to control the HMI responsive to the detected discrepancy.

The system may be configured such that the information may comprise one, several, or all of: an alarm and/or warning; information indicating a location of the detected discrepancy; information indicating a time at which the detected discrepancy occurred; information on a root cause of the detected discrepancy; instructions for correcting the detected discrepancy.

The system may be configured such that the at least one action may comprise a mitigating action and/or a corrective action. The system may be configured to generate and output a control signal or control command for the winding apparatus or an HMI.

The system may be configured such that the action may comprise stopping a rotation and/or translatory displacement of the winding apparatus.

The system may further comprise the winding apparatus. The winding apparatus may comprise a winding support on which the winding (e.g., the transformer winding) is formed. The winding apparatus may comprise one or several motors that drive the winding support. The winding apparatus may comprise a first motor that rotates the winding support and a second motor that causes a translatory displacement of the winding support, preferably along a rotation axis of the winding support.

The system is configured to determine at least one image acquisition time of the at least one image during the winding process.

The system may be configured to perform an image acquisition device control and/or an image selection based on the determined at least one image acquisition time.

The system is configured such that the at least one image acquisition time is determined based on at least one position of the winding apparatus. The at least one image acquisition time is determined based on a rotatory and a translatory position of the winding apparatus. The design data are used in combination with the position(s) of the winding apparatus to determine the at least one image acquisition time. For illustration, the design data may be used to assess at which rotary and/or translatory positions of the winding apparatus shields, conductors, fillers, and/or crossovers and/or other components or structural features are suitably positioned in a field of view of the image acquisition device.

The system may be configured such that the at least one image acquisition time may be determined based on a stream of images acquired by the at least one image acquisition device. The computer vision system may use image processing techniques, such as edge detection techniques, Fourier-based techniques, and/or contrast-enhancing techniques, to identify the images in the stream that are most likely to provide information on whether a mistake was made in the winding process.

The system may be configured such that the computer vision system may use a first machine learning (ML) model for determining the at least one image acquisition time. The first ML model may have a first input layer that receives pixels of images of the stream. The first ML model may have a first output layer that outputs a value indicating whether or not further analysis is to be performed on any of the images and, if so, on which of the images. Alternatively or additionally, the first input layer may receive winding apparatus positions, such as a rotatory and/or translatory position of a winding support on which the transformer winding is being formed.

The system may be configured to identify a region of interest (ROI) in which the object classification is to be performed.

The system may be configured such that boundaries of the ROI may comprise edges of shields of the transformer winding.

Alternatively or additionally, the system may be configured such that boundaries of the ROI may comprise inner and outer circumferential edges of a layer formed by conductors and fillers.

The system may be configured to perform an edge detection to detect the ROI.

The system may be configured to apply at least one ROI classifier to the at least one image. Several ROI classifiers may be applied to the at least one image. Each of the one or more ROI classifiers may indicate, for each pixel, whether the respective pixel is classified as being within the ROI or outside the ROI.

The system may be configured such that the object classification may comprise performing at least one third ML model. The at least one third ML model may have a third input layer that receives pixels located within the ROI. The at least one third ML model may have a third output layer that outputs object classifiers indicating to which one of several object classes the respective pixel in the ROI is assigned.

The system may be configured such that the object classification may classify at least conductors, fillers, and shields in the at least one image, and/or other components and/or structural features of the winding.

Alternatively or additionally, the system may be configured such that the object classification may classify crossovers. The object classification may classify inner and outer crossovers.

The system may be configured such that processing the at least one image may comprise using one or several position measurements of the winding apparatus. The one or several position measurements may comprise a rotatory and/or translatory position measurement. The one or several position measurements may be received from sensos. Alternatively, the one or several position measurements may be received from a controller of the winding apparatus.

The system may be configured to use the one or several position measurements for determining which images are to be analyzed further, e.g., by performing ROI determination and object classification. Alternatively or additionally, the system may be configured to use the one or several position measurements in assisting the ROI determination.

The system may be configured such that processing the at least one image may comprise executing at least one ML model having an input layer that receives at least part of the at least one image and/or winding apparatus positions, wherein the at least one ML model has an output layer, wherein the output layer provides at least one of: at least one image acquisition time defining the at least one image that is to be processed; an ROI in which the object classification is to be performed; and/or object classifiers defining one of several predetermined object classes.

The system may be configured to determine, based on the design data, a target sequence of objects in a radial direction of the transformer winding.

The design data may define the winding (e.g., transformer winding) as a three-dimensional volumetric component. The design data may have various formats, such as one or several crossover diagrams, without being limited thereto.

The system may be configured such that determining the target sequence of objects may comprise generating, based on the at least one crossover diagram, a machine readable representation indicating the target sequence. The machine readable representation may comprise an XML representation, without being limited thereto.

The system may be configured such that detecting the discrepancy may comprise comparing the target sequence and the result of the object classification.

The computer vision system may be configured to determine the target sequence. Alternatively, system may further comprise a processing system communicatively coupled to the computer vision system and configured to determine the target sequence and provide the target sequence to the computer vision system.

The system may be configured to monitor a winding process for a winding of an electric power system (such as a transformer winding) that comprises several layers (also referred to as discs in the art). The at least one image may show at least one of the several layers at an upper face of the transformer winding as it is being formed on the winding apparatus. The image of the uppermost layer at the upper face of the transformer winding may be processed in the method to detect the discrepancy.

The system may be configured to repeat processing at least one image and comparing the result of the object classification to the design data may be repeated in an ongoing basis (i.e. continually) during the winding process.

When the system is configured to use one or several ML models (such as one, several, or all of the first, second, and third ML models mentioned above), the ML model may be trained using labeled image data. The labeled image data may comprise synthetically generated images which are generated from real-world images that show an error during the winding process. Generation of the synthetically generated images may comprise one or several image modification operations, such as rotations, mirroring, adding distortions, etc..

The transformer winding may be a transformer winding of a power transmission system, such as a high-voltage a medium voltage power transmission system, of a power distribution system, of a railway transformer, or of a reactive or inductive system (such as a shunt reactor and/or a variable reactor).

According to another aspect of the invention, there is provided a manufacturing system for manufacturing a transformer winding. The system comprises a winding apparatus and the system configured to monitor a winding process of forming a transformer winding according to an embodiment.

There is also disclosed machine-readable instruction code which, when executed by at least one programmable circuit, causes the programmable circuit to perform the method according to an embodiment.

There is also disclosed a non-transitory storage medium having stored thereon machine-readable instruction code which, when executed by at least one programmable circuit, causes the programmable circuit to perform the method according to an embodiment.

There is also disclosed a transformer winding, transformer core, or transformer manufactured using the method according to an embodiment.

The effects attained by the systems according to embodiments correspond to the effects disclosed in detail in association with the methods according to embodiments.

Various effects and advantages are attained by embodiments of the invention. The systems and methods allow mistakes to be detected by a winding process is still ongoing. This allows corrective and/or a mitigating actions to be taken during the winding process. Even if the mistake is such that it cannot be repaired, the systems and methods provide advantages, as the winding process can be terminated early (i.e. prior to completion of the transformer winding), thereby reducing based of material.

The systems and methods also are capable of detecting mistakes that would be located in the interior of the transformer winding if the winding process were completed. This provides a considerable advantage over techniques that analyze the transformer winding after completion of the winding process.

The systems and methods are configured such that they can monitor the winding process automatically, using images acquired by one or several image acquisition devices such as cameras.

The systems are configured for use with the formation of components of transformers that have a complex configuration and structure, such as transformers of electric power transmission systems or power distribution systems.

Embodiments of the invention will be described with reference to the drawings in which similar or identical reference signs designate elements with similar or identical configuration and/or function.

Embodiments of the invention will be described with reference to the drawings. In the drawings, similar or identical reference signs designate elements with similar or identical configuration and/or function.

While embodiments will be described in association with transformer windings configured to be installed in transformers of an electric power transmission or distribution system, the embodiments are not limited thereto. While embodiments will be described in association with certain winding configurations, such as a transformer windings that comprises several discs in which spacers are arranged between the discs, the embodiments are not limited thereto.

The features of embodiments may be combined with each other unless specifically stated otherwise.

<FIG> shows a system <NUM> according to an embodiment. The system <NUM> is configured to perform a winding process of forming a transformer winding. The system <NUM> is configured to monitor the winding process in which a transformer winding <NUM> is being formed. The system <NUM> comprises a computer vision system <NUM> that is used to monitor the winding process to detect mistakes that might have happened during the winding process.

The system <NUM> comprises a winding apparatus <NUM>. The winding apparatus <NUM> comprises a winding support on which the transformer winding is being supported during its winding process. The winding apparatus <NUM> may be configured to rotate the transformer winding <NUM> around an axis <NUM> while the transformer winding is being formed. The winding apparatus <NUM> may be configured to shift the already formed portion of the transformer winding <NUM> along the axis <NUM> in a translatory manner. A shift along the axis <NUM> may be performed respectively after a layer (or disc) of the transformer winding <NUM> has been completed. A distance by which the already formed portion of the transformer winding <NUM> is shifted may correspond to a height of each disc formed during one full rotation of the winding apparatus, possibly including spacers that are interposed between adjacent layers.

The winding apparatus <NUM> may comprise a first motor <NUM> configured to rotate the transformer winding during the winding process.

The winding apparatus <NUM> may comprise a second motor <NUM> configured to displace the transformer winding <NUM> in a translatory manner during the winding process. Translatory displacement may be effected intermittently, e.g., in an incremental, step-wise manner. Translatory displacement may be effected upon completion of a full revolution about the axis <NUM>. As will be described in more detail below, the translatory displacement along the axis <NUM> also provides the effect that images captured by an image acquisition device <NUM> for use in visual quality monitoring remains at approximately a same height above a topmost face <NUM> of the partially wound transformer coil <NUM> during the winding process.

The winding apparatus <NUM> may comprise one or several position sensors <NUM>. The one or several position sensors <NUM> may be configured to measure a rotational and/or translatory position of the winding apparatus <NUM>. The position measurements may be used by an image processing system <NUM> of the computer vision system <NUM> to determine, e.g., which images warrant further processing, and/or to perform ROI detection.

In addition or as an alternative to receiving position measurements from the position sensor(s) <NUM>, the computer vision system may receive position information from a winding apparatus controller <NUM>.

The computer vision system comprises an image processing system <NUM>. The image processing system <NUM> will be described in more detail below.

The image processing system <NUM> generally is configured to process images <NUM> captured by one or several image acquisition devices <NUM>. The one or several image acquisition devices <NUM> may be mounted in a stationary manner relative to a frame of the winding apparatus <NUM>. Translatory displacement effected by the winding apparatus along the axis <NUM> prevents excessive variations in angle between an optical axis of the image acquisition device <NUM> one and the topmost layer <NUM> of the transformer winding <NUM> that is being wound.

The image processing system <NUM> is configured to at least perform an object classification. The object classification may specify a series of different objects such as conductors and fillers seen in a radial direction from a radially inner edge to a radially outer edge of the topmost layer <NUM> of the formed portion of the transformer winding <NUM>.

The image processing system <NUM> may optionally be configured to perform additional processing of the images <NUM>. For illustration, the image processing system <NUM> may be configured to perform a preprocessing which attains one or several of: adjusting for perspective effects; contrast enhancement; identification of periodic features; elimination of light reflections; etc..

Alternatively or additionally, the image processing system <NUM> may be configured to perform a region of interest (ROI) determination. The ROI determination may be implemented as a classifier. The classifier may provide a binary output indicating, for any pixel, whether the respective pixel is included in the ROI. Object classification may then be limited to the ROI. When the transformer winding <NUM> comprises several layers with shields arranged therebetween, the ROI classifier may be operative to distinguish pixels representing a conductor or filler from pixels representing a shield.

The image processing system <NUM> uses the results of the object classification and design data <NUM> specifying a target design of the transformer coil <NUM> to detect a discrepancy, which indicates a mistake that happened during the winding process. The image processing system <NUM> or a separate computing system coupled thereto may process the design data <NUM> into a format which can be compared to the object classification result. For illustration, design data <NUM> that comprises one or several crossover diagrams may be converted automatically into a list specifying, for a desired rotational and translatory position of the winding apparatus <NUM>, which objects are expected to be present in any ROI within the field of view of the image acquisition device <NUM>.

When the image processing system <NUM> detects the discrepancy between the results of the object classification and what is expected in accordance with the design data, we image processing system <NUM> may trigger an action. The action may comprise an output via a human machine interface (HMI) <NUM>. The output may indicate that a discrepancy was detected, but may also include additional information, such as information indicating a location at which the discrepancy was detected and/or suggestions for how the identified discrepancy could be corrected, if such a correction is possible. Additionally alternatively, the output may affect operation of the winding apparatus <NUM>. For illustration, the image processing system <NUM> may trigger the winding apparatus controller <NUM> to halt rotatory and/or translatory displacement in response to the detected discrepancy. This facilitates a visual inspection by a human operator and possible corrective actions.

Additional features of the image processing system according to embodiments will be described in more detail next.

<FIG> shows an image processing system <NUM>. The image processing system <NUM> has at least one first interface <NUM> to receive images <NUM>. The received images <NUM> show at least part of a top end face <NUM> of the transformer winding <NUM> during various stages of the winding process. The at least one first interface <NUM> may be a data interface configured to receive the images <NUM> as digital data. The at least one first interface <NUM> may also be configured to receive the design data <NUM> that specifies the desired target configuration of the transformer winding <NUM>.

A result of the processing performed by the image processing system <NUM> may be output via the at least one first interface <NUM>. Alternatively, the image processing system <NUM> may have at least one second interface <NUM> configured to be communicatively coupled to the winding apparatus controller <NUM> and/or the HMI <NUM>. The image processing system <NUM> may generate and output a signal or command to trigger an action, such as providing information on the detected discrepancy by the HMI <NUM> and/or causing the winding apparatus controller <NUM> to react to the detected discrepancy.

The image processing system <NUM> may comprise a storage system <NUM>. Images, such as a stream of images received from the image acquisition device(s) <NUM>, may be temporarily stored in the storage system <NUM> for processing and further analysis. The storage system <NUM> may also include parameters used for processing the images <NUM>, such as parameters of a ROI classifier that has been trained to identify a ROI in an image and/or parameters of an object classifier that has been trained to distinguish different objects such as conductors, fillers, shields and/or crossovers and/or other components or structural features of the winding.

The image processing system <NUM> may comprise one or several processing circuits <NUM>. The one or several processing circuits <NUM> may be programmable circuits. The one or several processing circuits may comprise any one or any combination of integrated circuits, integrated semiconductor circuits, processors, controllers, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), circuit(s) including quantum bits (qubits) and/or quantum gates, without being limited thereto.

The image processing system <NUM> may comprise an image time selector <NUM>. The image time selector <NUM> may receive the winding apparatus position(s) and/or a stream of image included in the images <NUM> as input. The image time selector <NUM> may then determine which images are to be processed further to perform object classification. The image time selector <NUM> may select an already captured image from the stream of images for processing it in more detail. Alternatively or additionally, the image time selector <NUM> may predict at which image acquisition times images are to be taken in order to be particularly useful for detecting a potential discrepancy with the design data. The image processing system <NUM> may cause the image acquisition device(s) <NUM> to capture an image, in accordance with an output of the image time selector <NUM>.

The image processing system <NUM> may comprise a ROI detector <NUM>. The ROI detector <NUM> may comprise a first classifier, which is also referred to as ROI classifier herein. The ROI detector <NUM> receives pixels of an image, possibly of the preprocessing of the image. The ROI detector <NUM> may classify each pixel as being a pixel belonging to an ROI or being a pixel not belonging to any ROI. The ROI detector <NUM> may use edge detection techniques. Alternatively or additionally, the ROI detector <NUM> may comprise one or several trained ML models that are suitable for perform an image segmentation task. Deep learning models are one example of such ML models, but the implementation of the ROI detector <NUM> is not limited thereto.

The image processing system <NUM> may comprise an object classifier <NUM>. The object classifier <NUM> is configured to classify each pixel in at least an ROI as belonging to one of several object classes, which represent different physical component types included in the transformer winding <NUM>. For illustration, the object classes may comprise object classes for conductors (it being understood that the conductors may be covered with a dielectric material), for fillers that can be arranged between conductors within a layer (also referred to as a disc) of the transformer winding, or for shields that can be arranged between adjacent layers of the transformer winding. This list is not exhaustive. Additional or alternative object classes can be provided to reflect components and/or structural features of the winding.

More than one object classifiers <NUM> may be used. For illustration, the may be provided one object classifier that this specifically adapted to distinguish conductors, fillers, and shields, and another object classifier that this specifically adapted to identify a crossovers and/or to distinguish in a crossovers from order crossovers.

The image processing system <NUM> may comprise a discrepancy detector <NUM> configured to detect a discrepancy between the objects classified by the object classifier <NUM> and a sequence of objects expected, for example, in a radial direction of the transformer winding's topmost layer <NUM> in accordance with the design data <NUM>.

The image processing system <NUM> may comprise an output generator <NUM>. The output generator <NUM> may be configured to generate, responsive to an output of the discrepancy detector <NUM>, a command or signal that triggers an action. The action may comprise the provision of information related to the detected discrepancy, such as a warning or alarm, and/or a mitigating or corrective action.

<FIG> is a flow chart of a method <NUM>. The method <NUM> may be performed automatically by the computer vision system <NUM> or the image processing system <NUM> of the computer vision system <NUM>.

At process block <NUM>, image preprocessing is performed. The image preprocessing may comprise correcting perspective effects (e.g., by applying a transformer matrix to the image), edge enhancement, contrast enhancement, etc. The image preprocessing may comprise performing at least one transformation of the image, which depends on intrinsic and/or extrinsic camera parameters of a camera of the image acquisition device. The intrinsic and/or extrinsic camera parameters may be known (e.g., provided by a manufacturer of the camera) or determined in a calibration routine.

At process block <NUM>, an ROI is detected. Detecting the ROI may comprise at the edge detection. The ROI may be detected such that it is delimited by edges of shields on the topmost layer <NUM> end by radially inner and outer circumferences of the topmost layer <NUM>.

At process block <NUM>, object classification is performed. The object classification may discriminate different physical components present in the transformer winding <NUM>. The object classification may determine in which a sequence conductors and filler are arranged in a radial direction of the transformer winding <NUM>. The object classification may additionally or alternatively discriminate different structural features (such as crossovers) present in the transformer winding <NUM>.

At process block <NUM>, possible discrepancies between the results of the object classification and design data are detected. In response to detection of a discrepancy, an action may be performed.

Sensor signals may be used in various stages of the process. The sensor signals may be indicative of winding apparatus positions. The sensor signals may be indicative of a translatory and/or rotatory position of the winding apparatus.

The winding apparatus position may be used in various ways. For illustration, the sensor signals may determine which image preprocessing is to be applied to accommodate the respective height difference between the image acquisition device <NUM> and the topmost layer <NUM>. The winding apparatus position may also be used when performing ROI detection.

The winding apparatus position may also be used when determining, based on the design data, which sequence of objects is expected to be present in the image (e.g., in the ROI) for the given winding apparatus position.

At process block <NUM>, winding apparatus positions are obtained. The winding apparatus positions may be obtained from the sensor(s) <NUM> or the winding apparatus controller <NUM>.

At process block <NUM>, the sequence of objects, e.g. in the topmost layer <NUM>, is determined from one or several images <NUM>. The sequence of objects may be determined in a radial direction from an inner circumference to an outer circumferences of the topmost layer <NUM>. The sequence of objects may be determined as previously described using, for example, ROI detection and object classification.

At process block <NUM>, it is determined whether the determined sequence of objects and design data are consistent with each other. If there is consistency (as is usually the case), the process returns to process block <NUM>. The various process blocks may be repeated for a plurality of rotatory and a plurality of translatory winding apparatus positions.

At process block <NUM>, if there is an inconsistency between that detected sequence of objects in the design data, an action is triggered. As previously described, the action may comprise controlling an HMI and/or triggering a corrective and/or mitigating action.

The various classifiers that may be used in the computer vision system <NUM> and, more specifically, in the image processing system <NUM> may be implemented using the techniques such as: edge detection (for, e.g., detecting the ROI or detecting boundaries between different objects within the ROI), detecting periodicity (for, e.g., detecting a periodic arrangement of layers at an outer cylindrical surface of the transformer winding and/or for detecting a sequence of objects within the ROI),; but are not limited thereto.

One or several of the classifiers may be implemented using a trained ML model. More than one ML model may be used. For illustration, the image processing system <NUM> may comprise a first ML model configured to determine at which image acquisition times the most relevant images are captured that Walrond further analysis. Alternatively or additionally, the image processing system <NUM> may comprise a second ML model configured to detect the ROI. Alternatively or additionally, the image processing system <NUM> may comprise a third ML model configured to perform the object classification.

Several trained ML models or other classifiers may be deployed and used for performing any one of these functions. For illustration, several ROI classifiers may be applied to the same image data to attain an even more accurate detection of possible mistakes in the winding process.

<FIG> shows an image processing system <NUM> which comprises at least one ML model <NUM>. The ML model <NUM> may comprise an input layer <NUM>, an output layer <NUM>, and hidden layers <NUM>. The specific configuration of the ML model <NUM> may respectively depend on the function which it is intended to perform. The input layer <NUM> and output layer <NUM> similarly are dependent on the function which the ML model <NUM> has in the computer vision system.

The computer vision system may use a first ML model for determining at least one image acquisition time of an image which is to be processed to perform the object classification. In this case, the first ML model may have a first input layer <NUM> that receives pixels of images of a stream of images. The first ML model may have a first output layer <NUM> that outputs a value indicating whether or not further analysis is to be performed on any of the images. Alternatively or additionally, the first input layer may receive winding apparatus positions, such as a rotatory and/or translatory position of a winding support on which the transformer winding is being formed.

The one or several ROI classifiers may comprise at least one second ML model. The at least one second ML model may have a second input layer <NUM> that receives pixels of the at least one image and/or that receives the winding apparatus position(s). The at least one second ML model may have a second output layer <NUM> that outputs, for every pixel, a value indicating whether or not the respective pixel is in the ROI. Alternatively or additionally, the second input layer may receive winding apparatus positions, such as a rotatory and/or translatory position of a winding support on which the transformer winding is being formed.

The at least one second ML model <NUM> may comprise a deep learning ML model, such as a U-Net ML model.

The object classification may be performed using at least one third ML model. The at least one third ML model may have a third input layer <NUM> that receives pixels located within the ROI. The at least one third ML model may have a third output layer <NUM> that outputs object classification results indicating to which one of several object classes the respective pixel in the ROI is assigned. The third ML model may comprise a random forest (RF) model, without being limited thereto.

<FIG> shows an image <NUM> captured by the image acquisition device <NUM> while the transformer winding <NUM> is being formed on the winding apparatus <NUM>.

As mentioned above, optional preprocessing may be performed on the raw image data. The optional preprocessing may be used to take into account perspective effects caused by the arrangement of the optical axis relative to the topmost layer <NUM>. The optional preprocessing may also enhance certain features, such as edges and/or contracts and/or periodicity.

ROI detection identifies ROI <NUM> in the image <NUM>. The ROI <NUM> may be delimited by edges <NUM> of shields <NUM>. The ROI <NUM> may also be delimited, in a radial direction, by inner and outer circumferential boundaries of the innermost and outermost conductors seen in the image <NUM>.

Object classification is performed to classify objects such as conductors <NUM> and fillers <NUM>. A sequence of these object types may be determined in a radial direction (i.e., in a radially inward or outward direction). This sequence of object classes is then checked for consistency with the design data. A target sequence of object classes expected when the design data is complied with may be determined. The sequence of object classes determined from the image <NUM> may be compared to the target sequence.

<FIG> shows another image with an ROI <NUM> (determined, in this case, using a trained U-Net ML model as classifier). The ROI detection works reliably. In particular, the topmost layer <NUM> can be distinguished reliably from the outer cylindrical surface of the already wound transformer winding. Edges of the shields can be reliably determined.

Object classification does not need to be performed for every image obtained or obtainable by the image acquisition device <NUM>. Image acquisition times of images that are analyzed in detail, in particular by performing object classification, may be determined by the computer vision system <NUM>, e.g. by the image processing system <NUM> thereof.

<FIG> shows a functional block diagram <NUM> illustrating operation of the computer vision system <NUM> for determining image acquisition times for which they captured images around further analysis.

The winding apparatus positions <NUM> and, optionally, r a stream of images <NUM> serve as input to the determination <NUM> of the relevant image acquisition times. The design data may optionally be taken into account to determine for which winding apparatus position(s) <NUM> little occlusion of relevant features can be detected, which is then translated into corresponding image acquisition times during the winding process.

The determined image acquisition times of the images that are to be analyzed in detail can be used in various ways.

A camera control operation <NUM> may be performed to cause an image acquisition device <NUM> to perform image acquisition at the desired time. Alternatively or additionally, images may be selected from a stream of images at block <NUM> (which stream may be acquired continually), in accordance with the determined image acquisition time(s).

As an alternative to determining specific image acquisition times, the image processing may be performed on an ongoing basis. However, it is typically not required (but possible) that each image frame be analyzed.

<FIG> is a flow chart of a process <NUM>. The process <NUM> may be performed by the computer vision system <NUM> or separate computing system coupled to the computer vision system <NUM>. The process <NUM> translates the design data into a format that can be easily compared to a result of the object classification.

At process block <NUM>, the design data obtained. The design data may be retrieved from a database. The design data may be specific to the particular specification of the transformer in which the transformer winding is to be used. The design data may define a three-dimensional configuration of the transformer winding. The design data may comprise a crossovers diagram but may have alternative formats such as instruction code executed by the winding apparatus or winding apparatus controller.

At process block <NUM>, the design data are processed to infer a target sequence of object classes for the respective winding apparatus position, based on the design data and the status of the winding process (which determines the winding apparatus position).

The target sequence of object classes (also referred to as target sequence of objects herein) may be compared to a result of the object classification performed on the at least one image to detect possible discrepancies.

The computer vision system may comprise one or several ML models but may also use other processing techniques. One challenge encountered when using one or several ML models is the provision of a suitable training set, as the images showing errors during a winding process are, fortunately, rare compared to images that show a perfect operation of the winding process. Methods and systems disclosed herein may synthetically generate additional images showing winding errors, to thereby provide a more balanced training data set. This improves the accuracy during inference.

ML model training may be performed by the computer vision system. ML model training may be repeated during field use of the computer vision system. ML model training may also be performed by a computing system separate from the computer vision system.

<FIG> shows a flow chart of a method <NUM>. The method <NUM> may be performed to train one or several ML models that may be used in the methods or systems for monitoring a winding process.

At process block <NUM>, labeled training images are obtained. The labeled training images may but do not need to be captured on the same winding apparatus on which the ML model is used in the inference phase.

At process block <NUM>, training images labeled as showing a winding error are identified. Additional training images are synthetically generated. Generating the additional synthetic images with labels indicating a winding error may comprise a transformation, such as rotation, translation, distortion, and/or reflection, of an image labeled as showing a winding error.

At process block <NUM>, ML model training is performed using, inter alia, the synthetically generated images.

At process block <NUM>, the trained ML model may be deployed to a computer vision system for inference during field use of the computer vision system.

The ML model may be an ROI detector or an object classifier, without being limited thereto.

According to embodiments, a method of forming a transformer winding on a winding apparatus comprises monitoring the winding process while it is being performed, using the computer vision techniques described in detail herein.

The obtained winding may be assembled to form a transformer core or a transformer, such as a power transmission transformer for, e.g., a high-voltage or medium voltage transmission grid, or a power distribution transformer, a shunt reactor and/or a variable reactor.

<FIG> schematically shows a transformer <NUM> that comprises at least one transformer winding <NUM> formed using the method according to an embodiment. The at least one transformer winding <NUM> is assembled with a yoke <NUM> to form a transformer core. The transformer <NUM> may comprise additional components, such as a transformer tank <NUM>, insulation oil within the transformer tank <NUM>, bushings (not shown), a transformer breather (not shown), etc..

<FIG> is a block diagram of a manufacturing system <NUM> configured to manufacture a transformer core of a transformer. The manufacturing system <NUM> comprises the winding apparatus <NUM> and a yoke manufacturing system <NUM>. The manufacturing system <NUM> comprises the computer vision system <NUM> according to an embodiment.

The transformer winding formed on the winding apparatus <NUM> under the monitoring activity of the computer vision system <NUM> is used in association with other transformer components, such as the yoke, to assemble a transformer core or transformer. This assembly may be performed by an assembly system <NUM>.

<FIG> is a flow chart of a method <NUM>. The method <NUM> may be performed by the manufacturing system <NUM>.

At process block <NUM>, visual quality control is performed during a winding process, in which a transformer winding is formed on a winding apparatus.

At process block <NUM>, it is determined whether any discrepancy has been detected during the winding process, which discrepancy would indicate that the objects as seen by the computer vision system do not match or do not perfectly match what this expected in accordance with design data.

At process block <NUM>, the transformer winding may be used for assembling a transformer core or transformer if no discrepancy has been detected.

At process block <NUM>, the transformer winding may be discarded if a discrepancy has been detected.

If a discrepancy has been detected but has subsequently been rectified by a suitable corrective action, responsive to detection of the discrepancy, the transformer winding can potentially still be used at process block <NUM>.

While embodiments have been described with reference to the drawings, modifications and alterations may be implemented in other embodiments.

For illustration, while embodiments have been described in which a transformer winding may comprise several discs and shields between the discs, the techniques disclosed herein are not limited to monitoring the winding process of such transformer windings.

For further illustration, while embodiments have been described in which images captured by one image acquisition device are analyzed, the techniques are also applicable when images are captured by more than one image acquisition device. Use of several image acquisition devices may be beneficial to read use potentially adverse effects of a particular occlusions.

For further illustration, while embodiments have been described in which trained ML models may be used to perform certain processing operations, other processing techniques may be used.

For still further illustration, while embodiments have been described in which ROI detection is performed using a classifier, ROI detection may be performed using other techniques that do not involve a classifier, if the ROI detection is performed at all.

For still further illustration, while embodiments have been described in association with monitoring the winding process of a transformer winding, the techniques may also be used to monitor the manufacture of windings used in reactive or inductive systems, such as shunt or variable reactors.

Various effects and advantages are attained by embodiments of the invention. For illustration, embodiments allow mistakes to be detected by a winding process is still ongoing. This allows corrective and/or a mitigating actions to be taken during the winding process. The risk of transformer failure caused by winding errors is reduced.

Methods, systems, and devices are particularly suitable in association with power transformers of electric power transmission systems, such as high-voltage medium voltage transmission systems, or power distribution systems. The methods, systems, and devices are not limited to these applications. For illustration, the techniques disclosed herein may also be used with railway transformers.

This description and the accompanying drawings that illustrate aspects and embodiments of the present invention should not be taken as limiting-the claims defining the protected invention. In other words, while the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive. Various mechanical, compositional, structural, electrical, and operational changes may be made without departing from the claims. In some instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the invention. Thus, it will be understood that changes and modifications may be made by those of ordinary skill within the scope of the following claims.

The disclosure also covers all further features shown in the Figures individually although they may not have been described in the afore or following description. Also, single alternatives of the embodiments described in the Figures and the description and single alternatives of features thereof can be disclaimed from the subject matter of the invention or from disclosed subject matter. The disclosure comprises subject matter consisting of the features defined in the claims or the exemplary embodiments as well as subject matter comprising said features.

The term "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single unit or step may fulfil the functions of several features recited in the claims. Components described as coupled or connected may be electrically or mechanically directly coupled, or they may be indirectly coupled via one or more intermediate components.

Claim 1:
A method of monitoring a winding process of forming a transformer winding (<NUM>) on a winding apparatus (<NUM>), the method comprising:
processing, by a computer vision system (<NUM>), at least one image (<NUM>) showing at least part of the transformer winding (<NUM>) captured during the winding process, wherein processing the at least one image (<NUM>) comprises performing an object classification;
detecting, by the computer vision system (<NUM>), a discrepancy between a result of the object classification and design data (<NUM>) for the transformer winding (<NUM>);
effecting, by the computer vision system (<NUM>), at least one action responsive to the detected discrepancy; and
determining at least one image acquisition time of the at least one image (<NUM>) during the winding process based on both a translatory position and a rotary position of the winding apparatus (<NUM>) in combination with the design data (<NUM>).