Fault Recorder Data Research Articles

Applying Multi-Agent System Technology in Practice: Automated Management and Analysis of SCADA and Digital Fault Recorder Data

This paper reports on the use of multi-agent system technology to automate the management and analysis of SCADA and digital fault recorder (DFR) data. The multi-agent system, entitled Protection Engineering Diagnostic Agents (PEDA), integrates legacy intelligent systems that analyze SCADA and DFR data to provide data management and online diagnostic information to protection engineers. Since November 2004, PEDA agents have been intelligently interpreting and managing data online at a transmission system operator in the U.K. As the results presented in this paper demonstrate, PEDA supports protection engineers by providing access to interpreted power systems data via the corporate intranet within minutes of the data being received. In this paper, the authors discuss their experience of developing a multi-agent system that is robust enough for continual online use within the power industry. The use of existing agent development toolsets and standards is also discussed.

An algebraic approach for identifying operating point dependent parameters of synchronous machines using orthogonal series expansions

This paper presents a method for identifying armature and field parameters of synchronous machines from digital fault recorder (DFR) data. The method uses operational properties of orthogonal series expansions such as the Hartley, Walsh and Fourier series to transform a set of differential equations into linear algebraic equations. The algebraic formulation and use of operational calculus reduce the problem of identifying parameters to the manipulation of matrices that may be easily performed in such computational packages as Matlab. The variation of machine parameters with operating point is considered.

Synchronous machine parameter estimation using the Hartley series

This paper presents a novel alternative to estimate armature circuit parameters of large utility generators using real time operating data. The proposed approach uses the Hartley series for fitting operating data (voltage and currents measurements). The essence of the method is the use of linear state estimation to identify the coefficients of the Hartley series. The approach is tested for noise corruption likely to be found in measurements. The method is found to be suitable for the processing of digital fault recorder data to identify synchronous machine parameters.

Parameter Estimation Using An Orthogonal Series Expansion

This paper presents an innovative alternative to estimate parameters of a system for which a dynamic model is known. The focus of this paper is the estimation of the armature circuit parameters of large utility generators using real time operating data. Other applications are possible. The alternatives considered are the use of orthogonal series expansions, in general, and the Hartley series, in particular. The main idea considers the use of orthogonal series expansions for fitting operating data (e.g., voltage and currents measurements). This allows writing a set of linear algebraic equations that can be “ solved” in the least squares sense for the unknown parameters. The method shown utilizes the pseudoinverse in the solution. The essence of the approach is linear state estimation. Several alternative types of orthogonal expansions are briefly discussed. Although solutions are the same in all domains, one wishes to employ the expansion that gives the most efficient computation. The approach may be used for static as well as dynamic problems. The approach is tested for noise corruption likely to be found in measurements. The method is found to be suitable for the processing of digital fault recorder data to identify synchronous machine parameters.

The use of fault-recorder data for diagnosing timing and other related faults in electricity transmission networks

This paper describes a model-based diagnostic system for diagnosing faults in electrical transmission systems (Timely's off-line system). This diagnostic system uses data available from digital fault recorders which are collected after a network event (such as a short circuit) has occurred. The data are used to detect incipient faults in network equipment by comparing their operation against that predicted by extended finite state automaton known as augmented reactive models (ARM). Thus Timely's off-line system combines signal processing and model based diagnostic techniques to provide a practical model-based system that aids the analysis of the performance of protective equipment after a network event has occurred. In particular, its use of data derived directly from fault recorder files (such as voltage and impedance magnitudes) means that the system can diagnose much more subtle faults (e.g. timing related faults).

Model-based analysis of protection system performance

Following a power system fault, protection engineers have to analyse the protection scheme activity to ensure correct operation. To aid them in this task, data gathering systems are increasingly being fitted to power systems. However, during extreme operating conditions, the volume of data made available by these systems can be overwhelming. To help overcome this, knowledge-based systems have been developed and installed at ScottishPower's corporate headquarters to extract the relevant `information' from the supervisory control and data acquisition (SCADA) system `data'. The paper reports on the next stage of decision support for the protection engineers which builds upon the diagnosis provided by the knowledge-based systems. This enhanced support is provided by a model-based diagnostic system which provides automatic analysis of the available fault recorder data. This system utilises the most appropriate techniques employed by existing model-based diagnostic systems. A novel approach for handling tolerances associated with the operating times of protection scheme components is introduced, thus enabling modelling measurement inaccuracies to be dealt with

Improved algorithm for detecting arcing faults using random fault behavior

High impedance distribution feeder faults have been shown to generate considerable high frequency current when arcing is present. A new algorithm is presented which takes advantage of this phenomenon and its random behavior. Using recorded fault data, parametric analysis has been performed to pseudo-optimize the detection capability while maintaining a high degree of insensitivity to normal system events. A discussion of possible implementation constraints is included.

A Digital Distributed System for Measurement, Protection and Control

This paper describes a distributed digital system under development at CEPEL, integrating measurement, control and protection of an electric power substation. In this system, the signals are digitalized and transmitted to the inputs of digital processors connected by a local network. This network, together with the application software developed, allows synchronization of the sampling processes at different nodes of the network. The data banks obtained by this procedure allow the distributed system to easily adapt to the substation topology. This is the case, for instance, of power evaluated as a function of voltages and currents available at sometimes far apart terminals of CTs and VTs. The digital processors generate the measures of currents, voltages, active and reactive powers, frequency, etc. based on the received data banks. Results of a laboratory prototype system are presented. Although allowing only on/off commands in this first version, the distributed system is able to be transformed into an hierarchical distributed control network. The paper also describes the principles by which microprocessor based protection relays in a dedicated protection network would be connected to the first distributed system, using it for security purposes and for transmitting fault recording data whenever necessary.

Fault location for transmission lines with current-transformer saturation

The paper presents a fault-location method which is insensitive to current-transformer saturation. The method requires less than one cycle of recorded fault data, which do not need filtering. A parameter estimation technique is applied where the linear part of the reference model response is described by instantaneous symmetrical components and the nonlinear part, caused by transformer saturation, described by phase variables. The parameters identified by the estimator are fault distance, fault resistance and saturation parameters. Accuracy is demonstrated by digital simulation studies.