B.L. Agrawal

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.

Application of the Global Positioning System to the Measurement of Overhead Power Transmnission Conductor Sag

This article describes a method to directly measure the physical sag of overhead electric power transmission conductors. The method used relies on the global positioning system (GPS) used in the differential mode. The direct measurement of sag is a main advantage of the concept. The digital signal processing required is described in detail in a four-level configuration. Typical accuracy, response time, problems, strengths, and weaknesses of the method are also described.

Synchronous generator model identification and parameter estimation from operating data

A novel technique to estimate and model parameters of a 460-MVA large steam turbine generator from operating data is presented. First, data from small excitation disturbances are used to estimate linear model armature circuit and field winding parameters of the machine. Subsequently, for each set of steady state operating data, saturable inductances L/sub ds/ and L/sub qs/ are identified and modeled using nonlinear mapping functions-based estimators. Using the estimates of the armature circuit parameters, for each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). The developed nonlinear models are validated with measurements not used in the estimation procedure.

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.

State-of-the-Art Technique for Power System Stabilizer Tuning

Power System Stabilizers (PSS) have broad application throughout the world. PSS application requires careful tuning which is usually accomplished in the field with the generator and power system in an abnormal condition. Recently developed equipment which combines fast Fourier transform capability with digital computer technique provides a means of PSS tuning which is faster and more accurate than was previously obtainable. This paper describes a PSS tuning test conducted using the new technique. The advantages will be apparent to the reader. The described test is very poignant as evidenced by two unexpected occurrences of instability.

Identification of armature, field, and saturated parameters of a large steam turbine-generator from operating data

This paper presents a step by step identification procedure of armature, field and saturated parameters of a large steam turbine-generator from real time operating data. First, data from a small excitation disturbance is utilized to estimate armature circuit parameters of the machine. Subsequently, for each set of steady state operating data, saturable mutual inductances L/sub ads/ and L/sub aqs/ are estimated. The recursive maximum likelihood estimation technique is employed for identification in these first two stages. An artificial neural network (ANN) based estimator is used to model these saturated inductances based on the generator operating conditions. Finally, using the estimates of the armature circuit parameters, the field winding and some damper winding parameters are estimated using an output error method (OEM) of estimation. The developed models are validated with measurements not used in the training of ANN and with large disturbance responses.

Methodology development for estimation of armature circuit and field winding parameters of large utility generators

This paper presents a methodology to estimate armature circuit and field winding parameters of large utility generators using the synthetic data obtained by the machine natural abc frame of reference simulation. First, a one-machine infinite bus system including the machine and its excitation system is simulated in abc frame of reference by using parameters provided by the machine manufacturer. A proper data set required for estimation is collected by perturbing the field side of the machine in small amounts, The recursive maximum likelihood (RML) estimation technique is employed for the identification of armature circuit parameters. Subsequently, based on the estimates of armature circuit parameters, the field winding and some damper parameters are estimated using an output error estimation (OEM) technique. For each estimation case, the estimation performance is also validated with noise corrupted measurements. Even in case of remarkable noise corruption, the agreement between estimated and actual parameters is quite satisfactory.

Results of residential air conditioner testing in WECC

This paper summarizes the key results of testing work performed by three organizations (EPRI, SCE, and BPA) on a total of twenty seven air conditioning units in order to better understand and thus characterize their behavior for power system simulations. The diversity of the tested air conditioner units included sizes (tonnage), compressor technology (reciprocating and scroll), type of refrigerant (R-22 and R-410A), efficiencies (between 10 and 13 SEER), and vintage (new and old). A common test plan was developed by the three organizations. The tests were then performed independently by each of the three organizations. The EPRI work was sponsored by APS and SRP. This effort was part of the current load modeling effort going on in WECC under the load modeling task force. The key findings of this work are presented here together with a description of the testing methodology. All three organizations found very similar results despite testing a variety of different sizes and manufacturer units. The key results presented are associated with the stalling behavior of the units at different outdoor temperatures, the behavior of thermal overload tripping, contactor dropout, and the behavior of the units in response to different emulated types of system events.

Model Validation Studies for a Disturbance Event That Occurred on June 14 2004 in the Western Interconnection

The reliable operation of the interconnected electric system heavily depends upon the accuracy of the dynamic simulation studies. The simulation studies contain models of complex components such as generators, exciter, governors, loads, etc. Many of these models are at best approximate. Hence, the simulation studies must be validated against actual system disturbance from time to time to have confidence in the study results. Such model validation was conducted for a very complex event in WECC interconnected system. It was found necessary to adjust generator saturation and over-excitation models of the units in a large nuclear power plant to achieve a satisfactory correlation. The validated data was then used to run several what if scenarios.

Neural network based modeling of a large steam turbine-generator rotor body parameters from on-line disturbance data

A novel technique to estimate and model rotor-body parameters of a large steam turbine generator from real time disturbance data is presented. For each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). Artificial neural network (ANN)-based estimators are later used to model the nonlinearities in the estimated parameters based on the generator operating conditions. The developed ANN models are then validated with measurements not used in the training procedure. The performance of estimated parameters is also validated with extensive simulations and compared against the manufacturer values.