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Power system state estimation (SE) is one of the most fundamental applications at the control center, since it helps the system operator to monitor, control and optimize the performance of the power grid. Motivated by the advancements in synchronized phasor measurement units and the urgent need of a better state estimator to address the corresponding system complexity and computational burden, we focus on improving the resilience and efficiency of SE while incorporating synchrophasor measurements. Different types of cyber attacks are threatening the resilience of power system control and operations, while GPS spoofing attack (GSA) has been proved to be one of the most imminent threats to the recent modernization of the power grid. More specifically, it may greatly jeopardize the benefits brought by the pervasively installed phasor measurement units (PMUs). We consider the case where synchrophasor data from PMUs are compromised due to the presence of single or multiple GSAs, and show that this can be corrected by signal processing techniques. We introduce a statistical model for synchrophasor-based power system SE, then derive the spoofing-matched algorithms for GPS-spoofed synchrophasor data correction. Different testing scenarios based on IEEE 14-, 30-, 57-, 118-bus systems are simulated to show the proposed algorithms' performance on GSA detection as well as state estimation. Numerical results demonstrate that our proposed algorithms can consistently locate and correct the spoofed synchrophasor data with good accuracy. The accuracy of state estimation is significantly improved compared with the traditional weighted least square method and approaches the performance of Genie-aided method. To improve the efficiency of SE, we focus on a potential solution for the state estimation in the control room: decentralized multi-area state estimation (MASE) with synchrophasor measurements. A synchrophasor-assisted hybrid MASE algorithm has been proposed to tackle this problem, where the boundary bus state estimates generated from the tie-line based synchrophasor-only linear SE are transformed into the equality constraints imposed upon the local SE in each area, which makes it truly independent from others. Numerical simulations have been implemented in the IEEE 14-bus and 30-bus systems, and the simulation results show that the proposed algorithm can not only provide system state estimates with good accuracy, but also can speed up the computational process of SE for the entire system.
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.
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.
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.
After the deregulation of the power industry, power systems are required to be operated efficiently and economically in today's strongly competitive environment. In order to achieve these objectives, it is crucial for power system control centers to accurately monitor the system operating state. State estimation is an essential tool in an energy management system (EMS). It is responsible for providing an accurate and correct estimate for the system operating state based on the available measurements in the power system. A robust state estimation should have the capability of keeping the system observable during different contingencies, as well as detecting and identifying the gross errors in measurement set and network topology. However, this capability relies directly on the system network configuration and measurement locations. In other words, a reliable and redundant measurement system is the primary condition for a robust state estimation. This dissertation is focused on the possible benefits to state estimation of using synchronized phasor measurements to improve the measurement system. The benefits are investigated with respect to the measurement redundancy, bad data and topology error processing functions in state estimation. This dissertation studies how to utilize the phasor measurements in the traditional state estimation. The optimal placement of measurement to realize the maximum benefit is also considered and practical algorithms are designed. It is shown that strategic placement of a few phasor measurement units (PMU) in the system can significantly increase measurement redundancy, which in turn can improve the capability of state estimation to detect and identify bad data, even during loss of measurements. Meanwhile, strategic placement of traditional and phasor measurements can also improve the state estimation's topology error detection and identification capability, as well as its robustness against branch outages. The proposed procedures and algorithms are illustrated and demonstrated with different sizes of test systems. And numerical simulations verify the gained benefits of state estimation in bad data processing and topology error processing.
"Power distribution systems are faced with rising operational challenges and require continuous stability monitoring as the integration of renewable energy resources is increasing and the load demand is rapidly growing. In order to provide timely information about any impending grid problems to system operators, this thesis focuses on the analysis and monitoring of voltage stability on distribution system side. The implementation of synchrophasors in distribution systems enhances the situational awareness of the system. Synchrophasor measurement offers increased visibility, faster response time and more reliable state estimation, which provides a unique opportunity for developing new monitoring algorithms. This thesis proposes a voltage stability monitoring algorithm based on the synchrophasor-based linear state estimation method. Particularly, the voltage monitoring algorithm combines a set of early warning indicators with the BDS independence test (a statistical hypothesis test, after the initials of W. A. Brock, W. Dechert and J. Scheinkman). The early warning indicators are derived based on critical slowing down phenomenon in dynamical systems, and the BDS test serves as a diagnostic test to avoid false detections. The main advantage of the algorithm over other voltage stability indicators used in transmission side is that it can detect the onset of voltage instability in a faster and more accurate manner while avoiding false alarms when the system is still away from the stability boundary. Case studies conducted on a rural Quebec test feeder confirm the effectiveness of the proposed voltage stability monitoring algorithm. Reliable and fast detection of the proximity of the system states to voltage collapse conditions is achieved without false alarms." --
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.