Browsing by Subject "state estimation"
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Item Improved measurement placement and topology processing in power system state estimation(2009-06-02) Wu, YangState estimation plays an important role in modern power system energy management systems. The network observability is a pre-requisite for the state estimation solution. Topological error in the network may cause the state estimation results to be seriously biased. This dissertation studies new schemes to improve the conventional state estimation in the above aspects. A new algorithm for cost minimization in the measurement placement design is proposed in this dissertation. The new algorithm reduces the cost of measurement installation and retains the network observability. Two levels of measurement place- ment designs are obtained: the basic level design guarantees the whole network to be observable using only the voltage magnitude measurement and the branch power flow measurements. The advanced level design keeps the network observable under certain contingencies. To preserve as many substation measurements as possible and maintain the net-work observability, an advanced network topology processor is introduced. A new method - the dynamic utilization of substation measurements (DUSM) - is presented. Instead of seeking the installation of new measurements in the system, this method dynamically calculates state estimation measurement values by applying the current law that regulates different measurement values implicitly. Its processing is at the substation level and, therefore, can be implemented independently in substations. This dissertation also presents a new way to verify circuit breaker status and identify topological errors. The new method improves topological error detection using the method of DUSM. It can be seen that without modifying the state estimator, the status of a circuit breaker may still be verified even without direct power flow measurements. Inferred measurements, calculated by DUSM, are used to help decide the CB status. To reduce future software code maintenance and to provide standard data ex- changes, the newly developed functions were developed in Java, with XML format input/output support. The effectiveness and applicability of these functions are ver-ified by various test cases.Item Measurement enhancement for state estimation(2009-05-15) Chen, JianAfter 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.Item Soft Sensors for Process Monitoring of Complex Processes(2012-10-19) Serpas, Mitchell RoySoft sensors are an essential component of process systems engineering schemes. While soft sensor design research is important, investigation into the relationships between soft sensors and other areas of advanced monitoring and control is crucial as well. This dissertation presents two new techniques that enhance the performance of fault detection and sensor network design by integration with soft sensor technology. In addition, a chapter is devoted to the investigation of the proper implementation of one of the most often used soft sensors. The performance advantages of these techniques are illustrated with several cases studies. First, a new approach for fault detection which involves soft sensors for process monitoring is developed. The methodology presented here deals directly with the state estimates that need to be monitored. The advantage of such an approach is that the nonlinear effect of abnormal process conditions on the state variables can be directly observed. The presented technique involves a general framework for using soft sensor design and computation of the statistics that represent normal operating conditions. Second, a method for determining the optimal placement of multiple sensors for processes described by a class of nonlinear dynamic systems is described. This approach is based upon maximizing a criterion, i.e., the determinant, applied to the empirical observability gramian in order to optimize certain properties of the process state estimates. The determinant directly accounts for redundancy of information, however, the resulting optimization problem is nontrivial to solve as it is a mixed integer nonlinear programming problem. This paper also presents a decomposition of the optimization problem such that the formulated sensor placement problem can be solved quickly and accurately on a desktop PC. Many comparative studies, often based upon simulation results, between Extended Kalman filters (EKF) and other estimation methodologies such as Moving Horizon Estimation or Unscented Kalman Filter have been published over the last few years. However, the results returned by the EKF are affected by the algorithm used for its implementation and some implementations may lead to inaccurate results. In order to address this point, this work provides a comparison of several different algorithms for implementation.