This thesis describes the algorithms and techniques developed for harmonic state estimation and transient state estimation, which can be used to identify the location of disturbance sources in an electrical power system. The previous harmonic state estimation algorithm is extended to include the estimation of time-varying harmonics using an adaptive Kalman filter. The proposed method utilises two covariance noise models to overcome the divergence problem in traditional Kalman filters. Moreover, it does not require an optimal covariance noise matrix of the Kalman filter to be used. The common problems faced in harmonic state estimation applications due to the influence of measurement bad data associated with measurements and the lack of measurement points, hence the system being partially observable, are investigated with reference to the Lower South Island of the New Zealand system. The state estimation technique is also extended to transient state estimation. Two formulation methods are outlined and the development of the proposed methodology is presented. Fault scenarios with reference to the Lower South Island of the New Zealand system are simulated to demonstrate the ability of transient state estimation in estimating the voltages and currents of the unmeasured locations, and applying the estimated results to search for the fault location. The estimation results are compared with PSCAD/EMTDC simulations to justify their accuracy.

Traditional state estimation whereby the state of the system is assessed based on a limited number of measurements is a well established tool for steady-state situations where the frequency of the system is 50 Hz. Previous contributions have looked at extending this concept to the power quality area. This area of research is called Power Quality State Estimation (PQSE) and represents a class of techniques. Under the umbrella of PQSE, the main contribution of this work is taking Transient State Estimation (TSE) on step further. A new three-phase formulation for TSE using the Numerical Integrator Substitution (NIS) will be detailed. NIS approach, also known as Dommel's method, gives a numerical solution to describe the transient behaviour of a dynamic system at discrete time points. The new transient state estimator is implemented and verified by applying the proposed algorithm to a real distribution test system. It's performance and accuracy are investigated in presence of measurement noise, background harmonics, multiple faults, etc. The conducted study has shown this technique has a great potential.

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.

No description available.

No description available.

After the deregulation of the power systems, the large-scale power systems may contain several areas. Each area has its own control center and each control center may have its own state estimator which processes the measurements received from its local substations. When scheduling power transactions, which involve several control areas a system-wide state estimation solution is needed. In this dissertation, an estimation approach which coordinates locally obtained decentralized estimates while improving bad data processing capability at the area boundaries is presented. It is assumed that synchronized phasor measurements from different area buses are available in addition to the conventional measurements provided by the substation remote terminal units. The estimator with hierarchical structure is implemented and tested using different measurement configurations for two systems having 118 and 4520 buses. Furthermore, we apply this multi-area solution scheme to the problem of Total Transfer Capability (TTC) calculation. In a restructured power system, the sellers and buyers of power transactions may be located in different areas. Computation of TTC will then require system-wide studies. We investigate a multi-area solution scheme, which takes advantage of the system-wide calculated Power Transfer Distribution Factors (PTDF) in order for each area to calculate its own TTC while a central entity coordinates these results to determine the final value. The proposed problem formulation and its solution algorithm are presented. 30 and 4520 bus test systems are used to demonstrate the approach and numerically verify the proposed TTC calculation method.

No description available.

A phasor-only estimator carries with it intrinsic improvements over its SCADA analogue with respect to performance and reliability. However, insuring the quality of the data stream which leaves the linear estimator is crucial to establishing it as the front end of an EMS system and network applications which employ synchrophasor data. This can be accomplished using a two-fold solution: the pre-processing of phasor data before it arrives at the linear estimator and the by developing a synchrophasor-only dynamic state estimator as a mechanism for bad data detection and identification. In order to realize these algorithms, this dissertation develops a computationally simple model of the dynamics of the power system which fits neatly into the existing linear state estimation formulation. The algorithms are then tested on field data from PMUs installed on the Dominion Virginia Power EHV network. / Ph. D.

No description available.

This thesis proposes a distributed observer design for a class of nonlinear systems that arise in the application of model reduction techniques. Distributed observer design techniques have been proposed in the literature to address estimation problems over sensor networks. In large complex sensor networks, an efficient technique that minimizes the extent of the required communication is highly desirable. This is especially true when sensors have problems caused by physical limitations that result in incorrect information at the local level affecting the estimation of states globally. To address this problem, scalable algorithms for a suitable distributed observer have been developed. Most algorithms are focussed on large linear dynamical systems and they are not directly generalizable to nonlinear systems. In this thesis, scalable algorithms for distributed observers are proposed for a class of large scale observable nonlinear system. Distributed systems models multi-agent systems in which each agents attempts to accomplish local tasks. In order to achieve global objectives, there should be agreement regarding some commonly known variables that depend on the state of all agents. These variables are called consensus states. Once identified, such consensus states can be exploited in the development of distributed consensus algorithms. Consensus algorithms are used to develop information exchange protocols between agents such that global objectives are met through local action. In this thesis, a higher order observer is applied in the distributed sensor network system to design a distributed observer for a class nonlinear systems. Fusion of measurement and covariance information is applied to the higher order filter as the first method. The consensus filter is embedded in the local nonlinear observer for fusion of data. The second method is based on the communication of state estimates between neighbouring sensors rather than fusion of data measurement and covariance. The second method is found to reduce disagreement of the states estimation between each sensor. The performance of these new algorithms is demonstrated by simulation, and the second method is effectively applied over the first method. / Thesis (Master, Chemical Engineering) -- Queen's University, 2012-12-12 11:22:49.113