This paper provides a thorough review of state-of-the-art current sensing techniques. It catalogues the current sensors according to the underlying physical principle in order to point out their strengths and weaknesses.

Current Sensing Techniques: A Review

A robust interferometric fiber-optic current sensor with inherent temperature compensation of the Faraday effect is presented. Sensor configurations based on Sagnac and polarization-rotated reflection interferometers are considered. The sensing fiber is residing and thermally annealed in a coiled capillary of fused silica. The capillary is embedded in silicone within a ring-shaped housing. It is theoretically and experimentally shown that the temperature dependence of the birefringent fiber-optic phase retarders of the interferometers can be employed to balance the temperature dependence of the Faraday effect (0.7/spl times/10/sup -4///spl deg/C). Insensitivity of the sensor to temperature within 0.2% is demonstrated between -35/spl deg/C and 85/spl deg/C. The influence of the phase retarders on the linearity of the sensor is also addressed. Furthermore, the sensitivity to vibration of the two configurations at frequencies up to 500 Hz and accelerations up to 10 g is compared. High immunity of the reflective sensor to mechanical perturbations is verified.

Temperature and vibration insensitive fiber-optic current sensor

Increasing interest in power quality has evolved over the past decade. This paper surveys the literature for current applications of advanced artificial intelligence techniques in power quality (PQ). Applications of some advanced mathematical tools in general, and wavelet transform in particular, in power quality are also reviewed. An extensive collection of literature covering applications of fuzzy logic, expert systems, neural networks, and genetic algorithms in power quality is included. Literature exposing the use of wavelets in power quality analysis as well as data compression is also cited.

Artificial Intelligence and Advanced Mathematical Tools for Power Quality Applications: A Survey

This paper presents a high impedance fault (HIF) detection method based on decision trees (DTs). The features of HIF, which are the inputs of DTs, are those well-known ones, including current [in root mean square (rms)], magnitudes of the second, third, and fifth harmonics, and the phase of the third harmonics. The only measurements needed in the proposed method are the current signals sampled at 1920 Hz. It will reduce the cost of hardware compared with methods that use high sampling rates. A new HIF model is also used. The data of current signals are from the simulation of Electromagnetic Transients Program (EMTP). The DT algorithm trained can successfully distinguish the HIFs from most normal operations on simulation data, including switching loads, switching shunt capacitors, and load transformer inrush currents. Testing on experimental data is recommended for future work.

Decision tree-based methodology for high impedance fault detection

Wavelets introduce new classes of basis functions for time-frequency signal analysis and have properties particularly suited to the transient components and discontinuities evident in power system disturbances. Wavelet analysis involves representing signals in terms of simpler, fixed building blocks at different scales and positions. This paper examines the analysis and subsequent compression properties of the discrete wavelet and wavelet packet transforms and evaluates both transforms using an actual power system disturbance from a digital fault recorder. The paper presents comparative compression results using the wavelet and discrete cosine transforms and examines the application of wavelet compression in power monitoring to mitigate against data communications overheads.

Wavelets for the analysis and compression of power system disturbances

Under certain conditions, one electrical parameter (independent variable) is not enough to detect high-impedance faults on certain surface conditions. These faults do not draw sufficient current for detection and may draw less current than similar faults on other soil surfaces. Moreover, because every electrical detection parameter displays characteristics of randomness, it is difficult to assign a probability that a given event is a high-impedance fault, rather than a switching event. It has been shown that detection by induction laws can improve the classification of faults and switching events. The second and third laws of induction are utilized with a minimum entropy method. Setting detection threshold values using induction methods is also proposed. The methods presented are taken from ongoing research in high-impedance fault detection. While the techniques have not been reduced to practice or field-tested, they hold promise for future improvements in the relaying of high-impedance faults. >

Classification of faults and switching events by inductive reasoning and expert system methodology

The authors discuss the need for the requirements of data compression techniques in a power-systems environment. Accuracy requirements from any compression technique are established, and two data compression techniques are evaluated. Observations regarding the performance of each technique are presented. The analysis is accomplished using both real and simulated data. The analysis is applicable to data from power system disturbances. >

Data compression for digital data from power systems disturbances: requirements and technique evaluation

An attempt is made to find an intelligent detection system. This intelligent system has two specific properties. First, it is capable of taking advantage of multiple detection parameters or variables. Second, it provides adaptability using inductive reasoning, incorporated with some critic modules in a detection system. Inductive reasoning which will minimize entropy was used to acquire the knowledge of fault class and nonfault class. This knowledge was used to make the intelligent system adaptive to surrounding environments. A learning detection system was implemented with inductive reasoning and an event detector. An example execution is shown with a decision rule which was derived using training sample data consisting of high impedance faults, switching events, and normal status. A complicated set of test data is used to test the performance of the learning detection system. It is found that, even when it met very complicated situations, the learning detection system made smart decisions and evolved to a new situation with a newly derived decision rule. >

A learning method for use in intelligent computer relays for high impedance faults

This paper presents experimental results comparing the susceptibility of two different optical fiber current sensors to environmental acoustic vibrations. Of the two sensors that are tested, one employs a uni-directional current detection scheme, and the other a bi-directional in-line Sagnac interferometer. Acoustic vibrations are applied to various optical components of the two different types of current sensors.

An Experimental Study of Acoustic Vibration Effects in Optical Fiber Current Sensors

We present experimental results from a comparison study of acoustic effects on in-line Sagnac and polarimetric type current sensors. Thermal transient response data of the in-line sensor is also presented.

B. D. Russell

Papers

Classification of faults and switching events by inductive reasoning and expert system methodology

Data compression for digital data from power systems disturbances: requirements and technique evaluation

A learning method for use in intelligent computer relays for high impedance faults

An Experimental Study of Acoustic Vibration Effects in Optical Fiber Current Sensors

Environmental Sensitivity Comparison of In-Line Sagnac and Polarimetric Type Current Sensors