Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
1999-03-24
2001-06-12
Powell, Mark R. (Department: 2122)
Data processing: artificial intelligence
Neural network
Learning task
C700S048000, C361S036000
Reexamination Certificate
active
06247003
ABSTRACT:
TECHNICAL FIELD
This invention relates to correcting for current transformer signal distortions.
BACKGROUND
Iron-core toroidal current transformers (CTs) are widely used in the electric power industry to measure line current for protection and metering purposes. The line current is applied to a primary coil of the CT, and a reduced-magnitude version of the line current is produced on a secondary coil of the CT. This reduced-magnitude version of the line current is used as a measurement for protection and metering purposes.
One advantage of using an iron core CT is that most of the magnetic flux produced by a current in the primary winding passes through the secondary winding. Thus, iron-core CTs provide good flux linkage between the primary and secondary windings. Other advantages of using an iron-core toroidal CT include low production cost, inherent galvanic isolation, reliability, and ease of application.
However, a major disadvantage of iron-core toroidal CTs is that they are prone to current saturation. Such saturation occurs when currents exceeding a dynamic operating range of the CT cause magnetization of the core to be independent of the current, and thus produce distortion in the secondary signal. Saturation in these CTs is due mainly to two factors. First, the relationship between a magnetizing current (i.e., a current which produces the flux required to induce a voltage for transformer action) and a voltage applied to the secondary winding is non-linear. Second, iron-core toroidal CTs are able to retain a large magnetic flux density, or remanence, in their cores after removal of a current applied to the primary winding.
SUMMARY
The invention provides techniques for correcting for saturation in a current transformer used to provide a current measurement. To this end, a current measurement received from a current transformer is provided to an artificial neural network. The artificial neural network is trained to implement an inverse transfer function of the current transformer and produces an output that accounts for saturation of the current transformer.
Embodiments may include one or more of the following features. For example, the output of the artificial neural network may be converted to a projected current measurement using an ideal transfer function for the current transformer. The projected current measurement is provided to a protective device which signals a relay to trip if the projected current measurement is greater than a predetermined value.
The current measurement may be provided to one of two artificial neural networks, with the particular artificial neural network used depending on whether the current measurement is greater than a predetermined threshold. Both artificial neural networks are trained to implement inverse transfer functions of the current transformer, but under different operating conditions (e.g., different current levels).
The artificial neural network may be bypassed if the current measurement is less than a first threshold. When the artificial neural network is bypassed, the current measurement may be provided directly to a protective device which signals a relay to trip if the current measurement is greater than a predetermined value.
The artificial neural network may be trained using data from Electro Magnetic Transient Program simulations. The artificial neural network also may be trained using data from actual current transformers.
The current measurement from the current transformer may be converted into a sequence of digital samples. An input of the artificial neural network may be a digital sample from a current cycle. Another input of the artificial neural network may be based on digital samples from a previous current cycle.
The current measurement may be monitored to determine within which of several ranges the measurement falls. If the measurement falls in a first range, the artificial neural network may be bypassed and the current measurement may be provided directly to a protective device. If the measurement falls in a second range, the current measurement may be provided to a first artificial neural network. If the measurement falls in a third range, the current measurement may be provided to another artificial neural network.
Other features and advantages will be apparent from the following description, including the drawings, and from the claims.
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Cummins James C.
Kojovic Ljubomir A.
Stone David T.
Yu David C.
Fish & Richardson P.C.
McGraw-Edison Company
Powell Mark R.
Starkes Wilbert
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