Output Impedance Diffusion into Lossy Power Lines

Output impedances are inherent elements of power sources in the electrical grids. In this paper, we give an answer to the following question: What is the effect of output impedances on the inductivity of the power network? To address this question, we propose a measure to evaluate the inductivity of a power grid, and we compute this measure for various types of output impedances. Following this computation, it turns out that network inductivity highly depends on the algebraic connectivity of the network. By exploiting the derived expressions of the proposed measure, one can tune the output impedances in order to enforce a desired level of inductivity on the power system. Furthermore, the results show that the more "connected" the network is, the more the output impedances diffuse into the network. Finally, using Kron reduction, we provide examples that demonstrate the utility and validity of the method

PMU-based informational support of power system control tasks

Up-to-date wide area measurement systems (WAMS) based on phasor measurement units (PMU) appeared at the very end of the 20th century. Under present-day conditions, WAMS serve as the basis for information-measuring systems, which significantly improve power system control and operation. In practice, WAMS are mostly used for power system stability control and transient monitoring and visualization. This paper discusses the new opportunities for power system control quality improvement, resulting from PMU application for power system steady-state parameters' assessment. Firstly, better control is provided by online equivalent circuit parameters' identification using PMU measurement data and taking into account FACTS and other shunt and series compensation equipment. Secondly, the paper addresses the problems of "nodal" identification, which have taken on great importance recently due to the intensive development of small-scaled distributed generation. Based on PMU measurements of nodal voltages and incident transmission lines' electric currents, one can obtain online steady-state load characteristics, which can be used for dispatch control applications. Moreover, PMUs provide superaccelerated power flow calculations, which are of crucial importance for emergency automation, adjusted for prior operation. Such principles of emergency automation consist of the quick determination of control actions, aimed at power system stability maintenance in cases of any programmed faults' occurrence. It is known that such control is carried out by means of power flow calculations based on remote metering data. The proposed application and allocation of PMUs in the power system by means of combinatorial matrix transformation to triangle form give the possibility to perform accelerated node-voltage analysis without equivalent circuit simplification. All the calculations are verified using IEEE test networks. © 2014 WIT Press.International Journal of Safety and Security Engineering;International Journal of Sustainable Development and Planning;WIT Transactions on Ecology and the Environmen

Online Non-iterative Estimation of Transmission Line and Transformer Parameters by SCADA Data

Utilization of various measurements provided by supervisory control and data acquisition (SCADA) system has recently attracted increasing attention. Real-time estimation of transmission line parameters, utilizing voltage and power flow measurements provided by remote terminal units (RTUs) located at line terminals, has been investigated. This paper significantly improves the existing approaches by introducing a novel linear formulation of the problem, which can be solved in a closed form. The distributed-parameter model of long transmission lines is considered and its parameters are estimated in a noniterative manner using traditional SCADA measurements. The new method is further extended to estimate transformer series impedance and tap position using SCADA measurements, linearly. As such, the shortcomings associated with the previously proposed iterative approach, e.g. concern over convergence, for transmission line parameters are avoided. Moreover, the novel technique for estimating transformer parameters allows to determine the tap position as well as updated transformer series impedance. Furthermore, a thorough analysis is presented to take the measurement accuracy into account. Simulation results for different transmission lines and transformers in the IEEE 118-bus test system are reported. The results obtained indicate successful performance of the proposed algorithms

A Method for Accurate Transmission Line Impedance Parameter Estimation

Real-time estimation of power transmission line impedance parameters has become possible with the availability of synchronized phasor (synchrophasor) measurements of voltage and current. If sufficiently accurate, the estimated parameter values are a powerful tool for improving the performance of a range of power system monitoring, protection, and control applications, including fault location and dynamic thermal line rating. The accuracy of the parameter estimates can be reduced by unknown errors in the synchrophasors that are introduced in the measurement process. In this paper, a method is proposed with the aim of obtaining accurate estimates of potentially variable impedance parameters, in the presence of systematic errors in voltage and current measurements. The method is based on optimization to identify correction constants for the phasors. A case study of a simulated transmission line is presented to demonstrate the effectiveness of the new method, which is better in comparison with a previously proposed method. The results, as well as limits, and the potential extensions of the new method are discussed

Combined Unbalanced Distribution System State and Line Impedance Matrix Estimation

To address the challenges that the decarbonization of the energy sector is bringing about, advanced distribution network management and operation strategies are being developed. Many of these strategies require accurate network models to work effectively. However, distribution network data are known to contain errors, and attention has been given to techniques that allow to derive improved network information. This paper presents a novel method to derive line impedance values from smart meter measurement time series, with realistic assumptions in terms of meter accuracy, resolution and penetration. The method is based on unbalanced state estimation and is cast as a non-convex quadratically constrained optimization problem. Both line lengths and impedance matrix models can be estimated based on an exact nonlinear formulation of the steady-state three-phase network physics. The method is evaluated on the IEEE European Low Voltage feeder (906 buses) and shows promising results

Parameter estimation of three-phase untransposed short transmission lines from synchrophasor measurements

We present a new approach for estimating the parameters of three-phase untransposed electrically short transmission lines using voltage/current synchrophasor measurements obtained from phasor measurement units. The parameters to be estimated are the entries of the longitudinal impedance matrix and the shunt admittance matrix at the rated system frequency. Conventional approaches relying on the admittance matrix of the line cannot accurately estimate these parameters for short lines, due to their high sensitivity to measurement noise. Our approach differs from the conventional ones in the following ways: First, we model the line by the three-phase transmittance matrix that is observed to be less sensitive to measurement noise than the admittance matrix. Second, we compute an accurate noise covariance matrix using the realistic specifications of noise introduced by instrument transformers and phasor measurement units. This noise covariance matrix is then used in least-squares-based estimation methods. Third, we derive different least-squares-based estimation methods based on a statistical model of estimation and show that the weighted least-squares and the maximum likelihood methods, which make use of the noise covariance matrix produce the best estimates of the line parameters. Finally, we apply the proposed methods to a real dataset and show that our approach significantly outperforms existing ones

Phasor measuring unit calibration considering topology expansion of electric power utilities

Phasor Measuring Units (PMU) is considered as a feasible alternative for power system data communication and for the preparation of smart grid. To operate multiple PMUs in stable mode, synchronization is a much essential procedure. After examining the concise literature survey on topical PMU applications, this paper step ahead by considering prevailing objectives such as: extension of network topology and PMU calibration considering bias errors. The appropriate deployment of PMUs and intimidating situation influence the reliance on physical configuration. Hence, it is obligatory to extend the topology of the networks. Also, calibrating the PMUs in a definite trail is crucial as the phasor measurements will be corrupted by bias error. This paper proposes a novel framework for bias error detection and calibration of PMU while expanding the topology network. With a whale optimization algorithm based method to improve the accuracy of PMU measurements for enhancing power system state estimation, monitoring and control operations. Case studies on the standard test systems have been conducted to test the efficacy of the intended tool

Transmission Line Parameter Estimation using Synchrophasor Data

abstract: Transmission line parameters play an important role in state estimation, dynamic line rating, and fault analysis. Because of this, several methods have been proposed in the literature for line parameter estimation, especially using synchrophasor data. However, success of most prior research has been demonstrated using purely synthetic data. A synthetic dataset does not have the problems encountered with real data, such as invariance of measurements and realistic field noise. Therefore, the algorithms developed using synthetic datasets may not be as effective when used in practice. On the other hand, the true values of the line parameters are unknown and therefore the algorithms cannot be directly implemented on real data. A multi-stage test procedure is developed in this work to circumvent this problem. In this thesis, two popular algorithms, namely, moving-window total least squares (MWTLS) and recursive Kalman filter (RKF) are applied on real data in multiple stages. In the first stage, the algorithms are tested on a purely synthetic dataset. This is followed by testing done on pseudo-synthetic datasets generated using real PMU data. In the final stage, the algorithms are implemented on the real PMU data obtained from a local utility. The results show that in the context of the given problem, RKF has better performance than MWTLS. Furthermore, to improve the performance of RKF on real data, ASPEN data are used to calculate the initial estimates. The estimation results show that the RKF algorithm can reliably estimate the sequence impedances, using ASPEN data as a starting condition. The estimation procedure is repeated over different time periods and the corresponding results are presented. Finally, the significance of data drop-outs and its impact on the use of parameter estimates for real-time power system applications, such as state estimation and dynamic line rating, is discussed. To address the problem (of data drop-outs), an auto regressive integrated moving average (ARIMA) model is implemented. The ability of this model to predict the variations in sequence impedances is demonstrated.Dissertation/ThesisMasters Thesis Electrical Engineering 201