Daniel J. Tylavsky

Parallel processing in power systems computation

The availability of parallel processing hardware and software presents an opportunity and a challenge to apply this new computation technology to solve power system problems. The allure of parallel processing is that this technology has the potential to be cost effectively used on computationally intense problems. The objective of this paper is to define the state of the art and identify what the authors see to be the most fertile grounds for future research in parallel processing as applied to power system computation. As always, such projections are risky in a fast changing field, but the authors hope that this paper will be useful to the researchers and practitioners in this growing area.

Generalization of the matrix inversion lemma

A generalized form of the matrix inversion lemma is shown which allows particular forms of this lemma to be derived simply. The relationships between this direct method for solving linear matrix equations, lower-diagonal-upper decomposition, and iterative methods such as point-Jacobi and Hotellings method are established. The generalized form is used to derive a new factorization scheme and a new matrix inversion algorithm with a high degree of parallelism.

Parallel Newton type methods for power system stability analysis using local and shared memory multiprocessors

Both the very dishonest Newton (VDHN) and the successive over relaxed (SOR) Newton algorithms have been implemented on the iPSC/2 and Alliant FX/8 computers for power system dynamic simulation using complex generator and nonlinear load models. The main thrust is to explore the match between the algorithms, their implementation, and the machine architectures. For example, the less parallel but sequentially faster VDHN runs faster on the hypercube (iPSC/2) whereas the more parallel SOR-Newton requires data sharing more often because of the extra iterations and does better on the Alliant. The implementation on the hypercube requires significant manual programming to schedule the processors and their communication whereas the compiler in the Alliant recognizes parallel steps but only if the software is properly coded. The authors present these various considerations together with the results. >

A highly parallel method for transient stability analysis

A simple but powerful method for solving the transient stability problem with a high degree of parallelism is implemented. The transient stability is seen as a coupled set of nonlinear algebraic and differential equations. By applying a discretization method such as the trapezoidal rule, the overall algebraic-differential set of equations is transformed into a unique algebraic problem at each time step. A solution that considers every time step, not in a sequential way, but concurrently, is suggested. The solution of this set of equations with a relaxation-type indirect method gives rise to a highly parallel algorithm. The parallelism consists of a parallelism in space (that is in the equations at each time step) and a parallelism in time. Another characteristic of the algorithm is that the time step can be changed between iterations using a nested iteration multigrid technique from a coarse time grid to the desired fine time grid to enhance the convergence of the algorithm. The method has been tested on various size power systems, for various solution time periods, and various types of disturbances. It is shown that the method has good convergence properties and can significantly increase computational efficiency in a parallel-processing environment.<<ETX>>

Identification of short transmission-line parameters from synchrophasor measurements

Accurate knowledge of transmission line impedance parameters helps to improve accuracy in relay settings, post-event fault location and transmission power flow modeling. Four methods are presented in this paper to identify transmission line impedance parameters from synchronized measurements for short transmission lines. Estimates of parameters for short transmission lines is more challenging than for long transmission lines since measurement noise often causes large errors in the estimates. The effectiveness of these methods is verified through simulations. These simulations incorporate two types of measurement errors: biased and non-biased noise. The different effects of bias errors and random noise on the accuracy of the calculated impedance parameters are quantified. Last, some complicating factors and challenges inherent in real world measurements are discussed.

Prediction of top-oil temperature for transformers using neural networks

Artificial neural networks represent a growing new technology as indicated by a wide range of proposed applications. At a substation, when the transformers windings get too hot, either load has to be reduced as a short-term solution, or another transformer bay has to be installed as a long-term plan. To decide on whether to deploy either of these two strategies, one should be able to predict the transformer temperature accurately. This paper explores the possibility of using artificial neural networks for predicting the top-oil temperature of transformers. Static neural networks, temporal processing networks and recurrent networks are explored for predicting the top-oil temperature of transformers. The results using different networks are compared with the auto regression linear model.

Coarse grain scheduling in parallel triangular factorization and solution of power system matrices

Two new coarse-grain scheduling schemes, the levelwise and factorization path scheduling schemes, are examined. These schemes differ significantly from fine-grain scheduling schemes which have been proposed in the past. If a fine-grain scheduling scheme at the floating-point-operation level is an appropriate scheduling method for the iPSC hypercube parallel processing computer, then the levelwise scheduling scheme presented should have gain comparable to that obtained using the factorization path scheduling scheme. Since this is not the case, it may be concluded that a fine-grain scheduling scheme is not appropriate for parallel LU factorization using an iPSC hypercube. Furthermore, the parallel LU factorization implementation using factorization path scheduling was found to perform significantly better than levelwise scheduling. The maximum speedup of 2.08 was obtained by using four processors on the 494 bus system. The efficiency at maximum speedup was 52.1%. >

Transmission line parameter identification using PMU measurements

Accurate knowledge of transmission line (TL) impedance parameters helps to improve accuracy in relay settings and power flow modeling. To improve TL parameter estimates, various algorithms have been proposed in the past to identify TL parameters based on measurements from Phasor Measurement Units (PMUs). These methods are based on the positive sequence TL models and can generate accurate positive sequence impedance parameters for a fully transposed TL when measurement noise is absent; however, these methods may generate erroneous parameters when the TLs are not fully transposed or when measurement noise is present. PMU field-measure data are often corrupted with noise and this noise is problematic for all parameter identification algorithms, particularly so when applied to short TLs. This paper analyzes the limitations of the positive sequence TL model when used for parameter estimation of TLs that are untransposed and proposes a novel method using linear estimation theory to identify TL parameters more reliably. This method can be used for the most general case: short/long lines that are fully transposed or untransposed and have balanced/unbalance loads. Besides the positive/negative sequence impedance parameters, the proposed method can also be used to estimate the zero sequence parameters and the mutual impedances between different sequences. This paper also examines the influence of noise in the PMU data on the calculation of TL parameters. Several case studies are conducted based on simulated data from ATP to validate the effectiveness of the new method. Through comparison of the results generated by this novel method and several other methods, the effectiveness of the proposed approach is demonstrated. Copyright

An Adaptive Method for Detection and Correction of Errors in PMU Measurements

PMU data are expected to be GPS-synchronized measurements with highly accurate magnitude and phase angle information. However, this potential accuracy is not always achieved in actual field installations due to various causes. It has been observed in some PMU measurements that the voltage and current phasors are corrupted by noise and bias errors. This paper presents a novel method for detection and correction of errors in PMU measurements with the concept of calibration factors. The proposed method uses nonlinear optimal estimation theory to calculate calibration factor using a traditional model of an untransposed transmission line with unbalanced load. This method is intended to work as a pre-filtering scheme that can significantly improve the accuracy of the PMU measurement for further use in system state estimation, transient stability monitoring, wide area protection, etc. Case studies based on simulated data are presented to demonstrate the effectiveness and robustness of the proposed method.

Sources of error in substation distribution transformer dynamic thermal modeling

When a transformers windings get too hot, either load has to be reduced (in the short term) or another transformer bay needs to be installed (in the long run). To be able to predict when either of these remedial schemes must be used, we need to be able to predict the transformers temperature accurately. Our experimentation with various discretization, schemes and models, convinced us that the linear and nonlinear semiphysical models we were using to predict transformer temperature were near optimal and that other sources of input-data error were frustrating our attempts to reduce the prediction error further. In this paper we explore some of the sources of error that affect top-oil temperature prediction. We show that the traditional top-oil rise model has incorrect dynamic behavior and show that another model proposed corrects this problem. We show that the input error caused by database quantization, remote ambient temperature monitoring and low sampling rate account for about 2/3 of the error experienced with field data. It is the opinion of the authors that most of this difference is due to the absence of significant driving variables, rather than the approximation used in constructing a linear semiphysical model.