State estimation (SE) is a critical must run successful unit within energy management system software. This is dictated by the high reliability requirements for system security and control and the need to represent the closest real-time model for market operations. There has been considerable emphasis in bringing phasor measurements into SE to improve performance. However, there are many practical problems in incorporating phasor measurements into SE. The higher reporting rates of phasor measurement units compared with supervisory control and data acquisition devices is one such problem. The disparity of the reporting rates raises the question of whether buffering the phasor measurements helps to improve the state estimates. This buffer is a subset of the entire phasor measurements set relevant to every particular instant at which SE is conducted. This paper describes the design of an optimal buffer, the use of the phasor measurements from that buffer, and the analysis of the impact on SE with the inclusion of these buffered phasor measurements. This hybrid SE, used for analysis purposes, is created from contemporary real-time system data and measurements from a utility in southwest USA.

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 prefiltering 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.

State estimation is applied to process a set of measurements which are assumed to be taken at the same snapshot in time to obtain a best estimate of the states of electric power systems. The beneficial impacts of incorporating phasor measurement unit (PMU) measurements into the state estimator have been studied by many researchers. However, the availability of PMU measurements and many practical implementation issues still need to be resolved. This paper addresses one specific and important issue when integrating PMU measurements into the state estimator, namely, determining the optimal buffer length for PMU data. Due to different reporting rates of the conventional measurements and PMU measurements, a memory buffer of PMU measurements is recommended to be processed ahead of the state estimation. The impact of PMU measurement buffer length on state estimation is discussed. A procedure to determine the optimal buffer length of PMU measurements is presented. Processing a buffer of PMU measurements is also helpful for determining the corresponding weights in state estimation.

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.

In this paper, the time skew problem observed in synchronized phasor measurement unit (PMU) measurements is illustrated and analyzed. The term `time skew' is used to describe the faulty sychronization of measurements from different PMUs which are expected to be synchronized based on the time stamps. The origin of the time skew is presented and a practical example is provided to depict the time skew existing in actual PMU measurements. A Kalman filter model is proposed to compensate for the time skew error.

This paper utilizes several mathematical techniques based on the method of minimizing least squares to integrate synchronized measurements and power system operations planning data. The intent is to use several measurements and system data to enhance the overall accuracy of measurements in a power transmission system, thus integrating the calibration process. The techniques used include a least squares estimator, the Newton Raphson method to attain low discrepancies between measurements, and the least squares method to match measurements made simultaneously (e.g., obtained from phasor measurement units). A novel process is proposed which is applicable in real-time synchronized measurements calibration.

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 © 2010 John Wiley & Sons, Ltd.

Within energy management systems, state estimation is a key function for building a network model. The performance of most other application programs strongly depends on the accuracy of data provided by the state estimator. A network real-time model is built from a combination of snapshots of measurements and static network parameter data. The measured data are subject to well known errors. Network parameters, in general, may be erroneous as a result of inaccurately provided data, transmission line sags on hot days, error in calibration and calculation. The performance of a state estimator, therefore, depends on the accuracy of the measured data as well as the parameters of the network model. Bad data processing is now a standard subroutine in state estimation algorithm. However, definitive research results on the processing of parameter errors are rare. This book describes an approach to network parameter estimation. An appropriate method for network parameter error detection, identification, and correction is developed. The method shown is found to be highly accurate for the cases of single as well as multiple parameter errors.

The subject of the identification of the equivalent circuit parameters of large synchronous generators is revisited. The equivalent circuit of Park is used. On-line measurements are used in a Kalman filter to estimate the machine parameters. The formulation is linear and the extended Kalman filter is not needed. The approach is similar to that of Harley (1992), but flux linkages are not used in the present formulation in the machine model vector. An example is shown for an actual machine in operation in the Southwest United States. A transition to real time data using synthetic synchronized measurements (e.g., from a phasor measurement unit) is proposed and demonstrated.

Synchronized measurements technology plays a vital role in the deployment of the Smart Grid at transmission level by revolutionizing the way power systems are operated and controlled. Utilizing very precise time reference enabled by the GPS system allows for measurement synchronization and ultimately optimizes the management of power network behavior and performance. This paper presents SRP experience with evolving synchrophasor technology.