Patent ID: 12210049

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Parts which correspond to one another are provided with the same reference characters in the figures.

FIG.1shows a sequence diagram100of an exemplary embodiment of a method according to the invention with method steps101to109for the evaluation of partial discharge signals which, in the vicinity of an insulation of the electrical line conductor of a multiphase alternating current, of which the alternating currents flowing in one line conductor in each case have fixed phase shifts in relation to one another, are detected at only one location or are detected at multiple locations and overlaid on one another. The partial discharge signals are detected using an antenna, for example, and filtered using a frequency filter, of which the passband has typical frequencies for partial discharge signals. Any other sensor may also be used to detect the partial discharge signals, however, for example a capacitive sensor apparatus with at least one sensor capacitor or an inductive coupling apparatus with at least one sensor coil. This is irrelevant to the invention.

In a first method step101, at least one characteristic variable K is defined, on the basis of which partial discharge signals can be compared with one another. A characteristic variable of this kind may be a pulse duration, pulse height, electric charge, energy or repetition rate of a partial discharge signal, for example.

In a second method step102, each partial discharge signal is assigned a phase position of the alternating currents at the point in time of the detection of the partial discharge signal. The phase position is defined by a phase angle φ of one of the alternating currents.

In a third method step103, a characteristic variable value of each characteristic variable K is ascertained for each partial discharge signal.

In a fourth method step104, each partial discharge signal is assigned a numerical tuple that is formed of each characteristic variable value of the partial discharge signal and the phase position assigned to the partial discharge signal. If the characteristic variables K are a pulse duration and pulse height, for example, then the entries of the numerical tuple assigned to a partial discharge signal are the characteristic variable value of the pulse duration, the characteristic variable value of the pulse height and the phase position assigned to the partial discharge signal.

In a fifth method step105, for a predefined time window in each case, clusters C of the partial discharge signals detected in this time window are ascertained in a multidimensional space S with points V formed by the numerical tuples. The dusters C are ascertained, for example, using a partitioning cluster method (based on a k-means algorithm for example), a hierarchical duster method, a density-based duster method (DBSCAN for example) and/or using neural network methods.

In a sixth method step106, each cluster C is assigned a line conductor. To this end, each line conductor is assigned at least one phase angle interval I1to I6, for each cluster C a cluster centroid P is determined and a cluster-C is assigned to the line conductor that is assigned to the phase position of the cluster centroid P of the duster C (i.e. the value of the coordinate φ of the cluster centroid P in space S).

FIG.2shows, by way of example, for a three-phase angle current with alternating currents that have been phase-shifted in relation to one another by 120°, clusters C that have been ascertained in a time window and the cluster centroids P thereof, wherein the points V associated with a cluster C are represented by the same symbols and symbols that are different from other clusters C. The phase angle intervals I1and I4are assigned to a first line conductor. The phase angle intervals I2and I5are assigned to a second line conductor. The phase angle intervals I3and I6are assigned to the third line conductor. Each phase angle interval I1to I6has a width of 60°. The phase angle intervals I1and I3each have three cluster centroids P. The phase angle intervals I2and I4each have one cluster centroid P. The phase angle interval I5has no cluster centroid P. The phase angle interval I6has four cluster centroids P. Thus, in the time window, the first line conductor is assigned a total of four cluster centroids P, the second line conductor is assigned one cluster centroid P, and the third line conductor is assigned seven cluster centroids P.

In a seventh method step107, a partial discharge activity is ascertained in each time window from the ascertained clusters C for each line conductor. To this end, at least one activity variable A is defined for partial discharge signals that are detected in the respective time window and assigned to a line conductor. A number of partial discharge signals, which are detected in the time window and assigned to the line conductor, are defined as an activity variable A, for example. As an alternative or in addition, an activity variable A is formed from the characteristic variable values of the partial discharge signals which are detected in the time window and assigned to the line conductor. For example, the value of a characteristic variable K of the cluster centroid P is used as an activity variable A, and/or an activity variable A is formed of maxima, minima, standard deviations, ratios of maxima to average values and/or statistical moments of the characteristic variable values of the partial discharge signals associated with a cluster C.

In an eighth method step108, a temporal distribution of the activity values of at least one activity variable A of each line conductor is detected, for example a distribution over multiple months. Furthermore, for each of these temporal distributions, an anomaly detection (also referred to as outlier detection) is performed, with which what are known as anomalous activity values are ascertained, and anomalous activity values are removed from the respective distribution of the activity values. For anomaly detection, a temporal distribution of the activity values is analyzed, for example using a density-based cluster method such as DBSCAN. An anomaly detection of this kind is known for example from M. M. Breunig et al., LOF: identifying density-based local outliers, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp. 93-104, doi: 10.1145/342009.335388.

In a ninth method step109, for at least one activity variable A and each line conductor, a regression curve R, R1to R3for a progression of the activity variable A as a function of time t is ascertained from the temporal distribution of the activity values, for example using known so-called support vector machine regression methods. Furthermore, it may be provided to ascertain a first derivation of each regression curve R, R1to R3with respect to time. On the basis of the regression curves R, R1to R3as well as possibly the first derivations thereof, the temporal development of the partial discharge activities of each line conductor is monitored. For example, a warning and/or alarm signal is generated automatically if a regression curve R, R1to R3exceeds a predefined threshold value or the first derivation of a regression curve R, R1to R3exceeds a predefined threshold value.

The method described on the basis of method steps101to109may be expanded, for example, in that before the ascertaining of the clusters C in the fourth method step104an anomaly detection is performed in the multidimensional space S, in order to eliminate anomalous partial discharge signals.

FIG.3shows, by way of example, a temporal distribution of activity values of an activity variable A for a line conductor and a regression curve R for the progression of the activity variable A as a function of time t. Anomalous activity values lie further away from the regression curve R and are represented with a different symbol than the other activity values A.

By way of example,FIG.4shows regression curves R1to R3for temporal progressions of an activity variable A for the three line conductors of a three-phase alternating current. The regression curve R1of a first line conductor runs above the regression curve R2of a second line conductor and below the regression curve R3of the third line conductor, but has high gradients in a time interval Δt. From this it is concluded, for example, that the highest partial discharge activity occurs on the third line conductor, the lowest discharge activity occurs on the second line conductor and the partial discharge activity on the first line conductor increases very quickly during the time interval Δt.

The method steps101to109are performed by an evaluation unit for the evaluation of partial discharge signals, for example, on which a computer program is executed, which comprises commands which, when the computer program is executed by the evaluation unit, prompt it to carry out the method steps101to109. In particular, the evaluation unit may have at least one so-called neuromorphic integrated circuit. As an alternative, the method is carried out or partially carried out in at least one so-called “edge device” or in an application in a computer cloud.

Although the invention has been illustrated and described in detail on the basis of preferred exemplary embodiments, the invention is not restricted by the examples given and other variations can be derived therefrom by a person skilled in the art without departing from the protective scope of the invention.