Life-cycle assessment of high-voltage (HV) circuit breakers (CB) in transmission systems, if efficiently done, can lead to an optimal decision on when, where, and how to perform maintenance. This paper elaborates a new approach on the identification of CB's deterioration/recovery states, i.e., the so-called life-cycle assessment, using its control circuit condition monitoring data. Reliability-oriented performance indicators, which can assess the condition of different physical parts of an HV CB in real time, are introduced first. Then, a quantitative methodology to define the probability of the CB falling into each class of deterioration/recovery states, i.e., healthy, vulnerable, troubled, and failed, is proposed. Using this approach, maintenance decisions can be effectively made on different parts of an HV CB, the impact of maintenance can be quantified, and system-wide maintenance optimization with respect to the condition-based distinction of CBs can be made possible. Field condition monitoring data recorded at different time intervals during the CB operation is utilized to evaluate the applicability and effectiveness of the proposed approach.

Real-Time Life-Cycle Assessment of High-Voltage Circuit Breakers for Maintenance Using Online Condition Monitoring Data

A new method is proposed for locating multiple harmonic sources in distribution systems. The proposed method first determines the proper locations for metering measurement using fuzzy clustering. Next, an artificial neural network based on the back-propagation approach is used to identify the most likely location for multiple harmonic sources. A set of systematic algorithmic steps is developed until all harmonic locations are identified. The simulation results for an 18-busbar system show that the proposed method is very efficient in locating the multiple harmonics in a distribution system.

Application of algorithms and artificial-intelligence approach for locating multiple harmonics in distribution systems

Applications of artificial neural networks (ANN) to power systems is an area of growing interest. The main reasons are the ability of ANN to learn complex non-linear relations, and their modular structure, which allows parallel processing. This paper presents an extended bibliography of ANN application to power systems. Brief discussion on applications of ANN to various power systems problems have also been presented in this paper.

Overview and literature survey of artificial neural networks applications to power systems (1992 - 2004)

Propagation characteristics of partial discharge (PD) signal in gas-insulated switchgear (GIS) were investigated in a laboratory and at a 300 kV underground substation. The laboratory test indicates that signal damping can be attributed to two major mechanisms: reflection due to irregularity of characteristic impedance such as spacers and the mode shift from TEM to TE or TM. Tests at the substation show that the signal damping phenomena are independent of propagation direction and the mode shift gives rise to a large signal damping. The major higher mode seems to be TE11. Amplitudes of observed waveforms using narrow-band filters that agree well with the corresponding frequency components observed with a spectrum analyzer show irregular or random behavior depending upon their observation point. This indicates that narrow-band filters do not seem to be suitable for a PD location system by a frequency component comparison, and the reliability of the system using broadband filters instead is discussed.

PD Signal Propagation Characteristics in GIS and Its Location System by Frequency Components Comparison

This paper presents a practical approach to evaluation of the operational health and reliability of circuit breakers in substations. Motivated by the recent failure surveys conducted by CIGRE working groups, circuit breaker condition data, obtained by monitoring critical parameters such as operational timings, tank gas pressure and temperature, mechanism traveling time and speed, etc., is used to evaluate the proposed health index. The proposed measure would help identify the transmission lines available for switching actions from the circuit breaker reliability view point. The proposed health index also provides a valuable input for substation maintenance personnel. The applicability of the proposed methodology is explored and validated using the actual circuit breaker condition data collected in the field.

Circuit breaker operational health assessment via condition monitoring data

A study was carried out to detect partial discharge signals generated in a gas-insulated substation (GIS) subjected to an insulation spacer method (internal electrode method), and to determine whether or not partial discharges were generated and then locate them with a pattern recognition method using a neural network (NN). This paper describes the results, while also explaining the equivalence between simulation pulse injection and partial discharges as web as the determination using the NN mentioned above and then presenting applications using field data.

Development of partial discharge monitoring technique using a neural network in a gas insulated substation

A study was carried out to detect partial discharge signals generated in a gas insulated substation (GIS) subjected to an insulation spacer method (internal electrode method), and to determine whether or not partial discharges were generated and then locate them with a pattern recognition method using a neural network (NN). This paper describes the results, while also explaining the equivalence between simulation pulse injection and partial discharges as well as the determination using the NN mentioned above and then presenting applications using field data.

I. Oki

Papers

Development of partial discharge monitoring technique using a neural network in a gas insulated substation

Development of partial discharge monitoring technique using a neural network in a gas insulated substation