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The testing of the LSE application has been carried out with different data sets: simulated steady state data, simulated RTDS data and field PMU data. Various conditions of testing of the LSE successfully demonstrate the correctness of the LSE algorithm and provide the useful practical information of integrating LSE application. All the implementation experience is also helpful on how to integrate "smart grid" applications for the future power grid.
This book brings together successful stories of deployment of synchrophasor technology in managing the power grid. The authors discuss experiences with large scale deployment of Phasor Measurement Units (PMUs) in power systems across the world, enabling readers to take this technology into control center operations and develop good operational procedures to manage the grid better, with wide area visualization tools using PMU data.
Offering an up-to-date account of the strategies utilized in state estimation of electric power systems, this text provides a broad overview of power system operation and the role of state estimation in overall energy management. It uses an abundance of examples, models, tables, and guidelines to clearly examine new aspects of state estimation, the testing of network observability, and methods to assure computational efficiency. Includes numerous tutorial examples that fully analyze problems posed by the inclusion of current measurements in existing state estimators and illustrate practical solutions to these challenges. Written by two expert researchers in the field, Power System State Estimation extensively details topics never before covered in depth in any other text, including novel robust state estimation methods, estimation of parameter and topology errors, and the use of ampere measurements for state estimation. It introduces various methods and computational issues involved in the formulation and implementation of the weighted least squares (WLS) approach, presents statistical tests for the detection and identification of bad data in system measurements, and reveals alternative topological and numerical formulations for the network observability problem.
This book builds on the cutting edge research presented in the previous edition that was the first of its kind to present the technology behind an emerging power systems management tool still in the early stages of commercial roll-out. In the intervening years, synchrophasors have become a crucial and widely adopted tool in the battle against electricity grid failures around the world. Still the most accurate wide area measurement (WAMS) technology for power systems, synchronized phasor measurements have become increasingly sophisticated and useful for system monitoring, as the advent of big data storage allows for more nuanced real-time analysis, allowing operators to predict, prevent and mitigate the impacts of blackouts with enhanced accuracy and effectiveness. This new edition continues to provide the most encompassing overview of the technology from its pioneers, and has been expanded and updated to include all the applications and optimizations of the last decade.
Wide-area monitoring for the power system is a key tool for preventing the power system from system wide failure. State Estimation (SE) is an essential and practical monitoring tool that has been widely used to provide estimated values for each quantity within energy management systems (EMS) in the control center. However, monitoring larger power systems coordinated by regional transmission operators has placed an enormous operational burden on current SE techniques. A distributed state estimation (DSE) algorithm with a hierarchical structure designed for the power system industry is much more computationally efficient and robust especially for monitoring a wide-area power system. Moreover, considering the deregulation of the power system industry, this method does not require sensitive data exchange between smaller areas that may be competing entities. The use of phasor measurement units (PMUs) in the SE algorithm has proven to improve the performance in terms of accuracy and converging speed. Being able to synchronize the measurements between different areas, PMUs are perfectly suited for distributed state estimation. This dissertation investigates the benefits of the DSE using PMU over a serial state estimator in wide area monitoring. A new method has been developed using available PMU data to calculate the reference angle differences between decomposed power systems in various situations, such as when the specific PMU data of the global slack bus cannot be obtained. The algorithms were tested on six bus, IEEE standard 30 bus and IEEE 118-bus test cases. The proposed distributed state estimator has also been implemented in a test bed to work with a power system real-time digital simulator (RTDS) that simulates the physical power system. PMUs made by SEL and GE are used to provide real-time inputs to the distributed state estimator. Simulation results demonstrated the benefits of the PMU and distributed SE techniques. Additionally a constructed test bed verified and validated the proposed algorithms and can be used for different smart grid tests.
Although the state estimator has been a regular application running in many utility control centers for over four decades, detection and identification of bad data (outliers) among the input measurements continues to be a difficult task. The widely adopted Weighted Least Square formulation of the power system static state estimation is known to be vulnerable to presence of bad data and even a single bad data can significantly impact the solution quality. Since the estimate of system state obtained from state estimation serves as starting point for many security and market related downstream applications that run within a control center, the problem of detection and identification of bad data is important. It has been shown that the traditionally used methods of detection and/or identification of bad data in power system static state estimation suffer from drawbacks like failing to detect mild to medium bad data in leverage measurements and excessive false detection rates. The traditional approach to process multiple bad data has been successive elimination (of single bad data) and re-estimation. This approach is highly computationally intensive and time consuming. The problem of computationally intensive multiple bad data processing has an even greater bearing on the Linear State Estimator, which is expected to run every second or potentially at sub-second intervals in the control centers.The work presented here focuses on the problem of multiple bad data processing in power system static state estimation in two ways. Firstly, use of Custom Thresholds is proposed for detection of bad data. The Custom Thresholds are shown to exhibit better false detection performance while being sensitive to mild bad data, even in leverage measurements. Secondly, a new algorithm is proposed to identify the culprit bad measurements. The proposed algorithm utilizes the very nature of bad data in different types of measurements, to accomplish the processing within few cycles of successive elimination and re-estimation. The efficiency and accuracy of the proposed algorithm is validated through thousands of simulations on various standard test systems. The proposed algorithm can be easily integrated in any commercial Weighted Least Square power system static state estimation - linear or iterative.
This book brings together successful case studies on the practical use of state estimators at both the transmission and distribution system levels in the power industry. Contributions are written by an international group of utility industry experts who have designed and implemented state estimators for managing their grid operations in real-time, providing readers with a solid background in the theoretical and functional aspects of running, supporting, and maintaining the operation of state estimators on an ongoing basis. Experiences on Use of State Estimator in Power System Operations provides a comprehensive picture of state estimators in a practical setting and is a valuable hands-on reference for system operators and engineers who need to enhance their understanding of the use of state estimation in utility operations.
The evolving complexity of electric power systems with higher levels of uncertainties is a new challenge faced by system operators. Therefore, new methods for power system prediction, monitoring and state estimation are relevant for the efficient exploitation of renewable energy sources and the secure operation of network assets. In order to estimate all possible operating conditions of power systems, this Thesis proposes the use of Gaussian mixture models to represent non-Gaussian correlated input variables, such as wind power output or aggregated load demands in the probabilistic load flow problem. The formulation, based on multiple Weighted Least Square runs, is also extended to monitor distribution radial networks where the uncertainty of these networks is aggravated by the lack of sufficient real-time measurements. This research also explores reduction techniques to limit the computational demands of the probabilistic load flow and it assesses the impact of the reductions on the resulting probability density functions of power flows and bus voltages. The development of synchronised measurement technology to support monitoring of electric power systems in real-time is also studied in this work. The Thesis presents and compares different formulations for incorporating conventional and synchronised measurements in the state estimation problem. As a result of the study, a new hybrid constrained state estimator is proposed. This constrained formulation makes it possible to take advantage of the information from synchronised phasor measurements of branch currents and bus voltages in polar form. Additionally, the study is extended to assess the advantages of PMU measurements in multi-area state estimators and it explores a new algorithm that minimises the data exchange between local area state estimators. Finally, this research work also presents the advantages of dynamic state estimators supported by Synchronised Measurement Technology. The dynamic state estimator is compared with the static approach in terms of accuracy and performance during sudden changes of states and the presence of bad data. All formulations presented in this Thesis were validated in different IEEE test systems.
State estimation plays a key role in the operation of power systems. This role becomes more important considering the increasing demand of emerging power market. Many methods have been proposed for power system state estimation, mostly based on Weighted Least Squares (WLS) approach. However, it is well known that Least Absolute Value (LAV) estimators are more efficient in terms of robustness and accuracy. For these estimators there is no closed form solution and each LAV estimator has its own criteria in choosing desired measurements. In this research, two novel LAV estimators are introduced for power system state estimation. The first estimator employs contraction mapping concepts for rejecting redundant measurements. The second estimator is introduced for systems where sparsity and ill-conditioning occur in the system matrix. In the second estimator, Singular Value Decomposition (SVD) method is combined with contraction mapping technique to find the appropriate equations for the estimation. The application of the new estimator is studied on different IEEE power systems for verification. The estimator shows a robust performance in all the test systems, and the estimation error remains comparatively small even in the presence of significant number of bad data points.