Thesis (Ph. D.)--University of Hong Kong, 2006.

The objective of this thesis is to prove the validity of a multi-area state estimator and investigate the advantages it provides over a serial state estimator. This is done utilizing the IEEE 118 Bus Test System as a sample system. This thesis investigates the benefits that stem from utilizing a multi-area state estimator instead of a serial state estimator. These benefits are largely in the form of increased accuracy and decreased processing time. First, the theory behind power system state estimation is explained for a simple serial estimator. Then the thesis shows how conventional measurements and newer, more accurate PMU measurements work within the framework of weighted least squares estimation. Next, the multi-area state estimator is examined closely and the additional measurements provided by PMUs are used to increase accuracy and computational efficiency. Finally, the multi-area state estimator is tested for accuracy, its ability to detect bad data, and computation time.

State estimation is an important tool for power system monitoring and the present study involves integrating phasor measurement units in the state estimation process. Based on measurements taken throughout the network, the role of a state estimator is to estimate the state variables of the power system while checking that these estimates are consistent with the measurement set. In the case of power system state estimation, the state variables are the voltage phasors at each network bus.\ The classical state estimator currently used is based on SCADA (Supervisory Control and Data Acquisition) measurements. Weaknesses of the SCADA measurement system are the asynchronicity of the measurements, which introduce errors in the state estimation results during dynamic events on the electrical network.\ Wide-area monitoring systems, consisting of a network of Phasor Measurement Units (PMU) provide synchronized phasor measurements, which give an accurate snapshot of the monitored part of the network at a given time. The objective of this thesis is to integrate PMU measurements in the state estimator. The proposed state estimators use PMU measurements exclusively, or both classical and PMU measurements.\ State estimation is particularly useful to filter out measurement noise, detect and eliminate bad data. A sensitivity analysis to measurement errors is carried out for a state estimator using only PMU measurements and a classical state estimator. Measurement errors considered are Gaussian noise, systematic errors and asynchronicity errors. Constraints such as zero injection buses are also integrated in the state estimator. Bad data detection and elimination can be done before the state estimation, as in pre-estimation methods, or after, as in post-estimation methods. For pre-estimation methods, consistency tests are used. Another proposed method is validation of classical measurements by PMU measurements. Post-estimation is applied to a measurement set which has asynchronicity errors. Detection of a systematic error on one measurement in the presence of Gaussian noise is also analysed. \ The state estimation problem can only be solved if the measurements are well distributed over the network and make the network observable. Observability is crucial when trying to solve the state estimation problem. A PMU placement method based on metaheuristics is proposed and compared to an integer programming method. The PMU placement depends on the chosen objective. A given PMU placement can provide full observability or redundancy. The PMU configuration can also take into account the zero injection nodes which further reduce the number of PMUs needed to observe the network. Finally, a method is proposed to determine the order of the PMU placement to gradually extend the observable island. \ State estimation errors can be caused by erroneous line parameter or bad calibration of the measurement transformers. The problem in both cases is to filter out the measurement noise when estimating the line parameters or calibration coefficients and state variables. The proposed method uses many measurement samples which are all integrated in an augmented state estimator which estimates the voltage phasors and the additional parameters or calibration coefficients.

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In this thesis, we presented two case studies on vision-based moving objects measurement. In the first case, we used a monocular camera to perform ego-motion estimation for a robot in an urban area. We developed the algorithm based on vertical line features such as vertical edges of buildings and poles in an urban area, because vertical lines are easy to be extracted, insensitive to lighting conditions/shadows, and sensitive to camera/robot movements on the ground plane. We derived an incremental estimation algorithm based on the vertical line pairs. We analyzed how errors are introduced and propagated in the continuous estimation process by deriving the closed form representation of covariance matrix. Then, we formulated the minimum variance ego-motion estimation problem into a convex optimization problem, and solved the problem with the interior-point method. The algorithm was extensively tested in physical experiments and compared with two popular methods. Our estimation results consistently outperformed the two counterparts in robustness, speed, and accuracy. In the second case, we used a camera-mirror system to measure the swimming motion of a live fish and the extracted motion data was used to drive animation of fish behavior. The camera-mirror system captured three orthogonal views of the fish. We also built a virtual fish model to assist the measurement of the real fish. The fish model has a four-link spinal cord and meshes attached to the spinal cord. We projected the fish model into three orthogonal views and matched the projected views with the real views captured by the camera. Then, we maximized the overlapping area of the fish in the projected views and the real views. The maximization result gave us the position, orientation, and body bending angle for the fish model that was used for the fish movement measurement. Part of this algorithm is still under construction and will be updated in the future.

The ability to navigate in an unknown environment is an enabler for truly utonomous systems. Such a system must be aware of its relative position to the surroundings using sensor measurements. It is instrumental that these measurements are monitored for disturbances and faults. Having correct measurements, the challenging problem for a robot is to estimate its own position and simultaneously build a map of the environment. This problem is referred to as the Simultaneous Localization and Mapping (SLAM) problem. This thesis studies several topics related to SLAM, on-board sensor processing, exploration and disturbance detection. The particle filter (PF) solution to the SLAM problem is commonly referred to as FastSLAM and has been used extensively for ground robot applications. Having more complex vehicle models using for example flying robots extends the state dimension of the vehicle model and makes the existing solution computationally infeasible. The factorization of the problem made in this thesis allows for a computationally tractable solution. Disturbance detection for magnetometers and detection of spurious features in image sensors must be done before these sensor measurements can be used for estimation. Disturbance detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. There are two approaches to this problem. One is based on the traditional parity space method, where the influence of the initial state is removed by projection, and the other on combining prior information with data in the batch. An efficient parameterization of incipient faults is given which is shown to improve the results considerably. Another common situation in robotics is to have different sampling rates of the sensors. More complex sensors such as cameras often have slower update rate than accelerometers and gyroscopes. An algorithm for this situation is derived for a class of models with linear Gaussian dynamic model and sensors with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the Kalman filter is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems. Vision based target tracking is another important estimation problem in robotics. Distributed exploration with multi-aircraft flight experiments has demonstrated localization of a stationary target with estimate covariance on the order of meters. Grid-based estimation as well as the PF have been examined. / The third article in this thesis is included with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Linköping University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this material, you agree to all provisions of the copyright laws protecting it.Please be advised that wherever a copyright notice from another organization is displayed beneath a figure, a photo, a videotape or a Powerpoint presentation, you must get permission from that organization, as IEEE would not be the copyright holder.

Over the last 20 years, navigation has almost become synonymous with satellite positioning, e.g. the Global Positioning System (GPS). On land, sea or in the air, on the road or in a city, knowing ones position is a question of getting a clear line of sight to enough satellites. Unfortunately, since the signals are extremely weak there are environments the GPS signals cannot reach but where positioning is still highly sought after, such as indoors and underwater. Also, because the signals are so weak, GPS is vulnerable to jamming. This thesis is about alternative means of positioning for three scenarios where gps cannot be used. Indoors, there is a desire to accurately position first responders, police officers and soldiers. This could make their work both safer and more efficient. In this thesis an inertial navigation system using a foot mounted inertial magnetic mea- surement unit is studied. For such systems, zero velocity updates can be used to significantly reduce the drift in distance travelled. Unfortunately, the estimated direction one is moving in is also subject to drift, causing large positioning errors. We have therefore chosen to throughly study the key problem of robustly estimating heading indoors. To measure heading, magnetic field measurements can be used as a compass. Unfortunately, they are often disturbed indoors making them unreliable. For estimation support, the turn rate of the sensor can be measured by a gyro but such sensors often have bias problems. In this work, we present two different approaches to estimate heading despite these shortcomings. Our first system uses a Kalman filter bank that recursively estimates if the magnetic readings are disturbed or undisturbed. Our second approach estimates the entire history of headings at once, by matching integrated gyro measurements to a vector of magnetic heading measurements. Large scale experiments are used to evaluate both methods. When the heading estimation is incorporated into our positioning system, experiments show that positioning errors are reduced significantly. We also present a probabilistic stand still detection framework based on accelerometer and gyro measurements. The second and third problems studied are both maritime. Naval navigation systems are today heavily dependent on GPS. Since GPS is easily jammed, the vessels are vulnerable in critical situations. In this work we describe a radar based backup positioning system to be used in case of GPS failure. radar scans are matched using visual features to detect how the surroundings have changed, thereby describing how the vessel has moved. Finally, we study the problem of underwater positioning, an environment gps signals cannot reach. A sensor network can track vessels using acoustics and the magnetic disturbances they induce. But in order to do so, the sensors themselves first have to be accurately positioned. We present a system that positions the sensors using a friendly vessel with a known magnetic signature and trajectory. Simulations show that by studying the magnetic disturbances that the vessel produces, the location of each sensor can be accurately estimated.

State estimation plays an essential role in the monitoring and control of power transmission systems. In modern, highly inter-connected power systems the state estimation should be performed in a distributed fashion and requires information exchange between the control centers of directly connected systems. Motivated by recent reportson trojans targeting industrial control systems, in this paper we investigate how a single compromised control center can affect the outcome of distributed state estimation. We describe five attack strategies, and evaluate their impact on the IEEE 118 benchmark power system. We show that that even if the state estimation converges despite the attack, the estimate can have up to 30% of error, and bad data detection cannot locate theattack. We also show that if powerful enough, the attack can impede the convergence of the state estimation, and thus it can blind the system operators. Our results show that it is important to provide confidentiality for the measurement data in order to prevent the most powerful attacks. Finally, we discuss a possible way to detect and to mitigate these attacks. / <p>QC 20130522</p>

This thesis presents a novel state estimation structure, a hybrid extended Kalman filter/Kalman filter developed for a skid-steered, six-wheeled, ARGO® all-terrain vehicle (ATV). The ARGO ATV is a teleoperated unmanned ground vehicle (UGV) custom fitted with an inertial measurement unit, wheel encoders and a GPS. In order to enable the ARGO for autonomous applications, the proposed hybrid EKF/KF state estimator strategy is combined with the vehicle’s sensor measurements to estimate key parameters for the vehicle. Field experiments in this thesis reveal that the proposed estimation structure is able to estimate the position, velocity, orientation, and longitudinal slip of the ARGO with a reasonable amount of accuracy. In addition, the proposed estimation structure is well-suited for online applications and can incorporate offline virtual GPS data to further improve the accuracy of the position estimates. The proposed estimation structure is also capable of estimating the longitudinal slip for every wheel of the ARGO, and the slip results align well with the motion estimate findings.

A networked control system (NCS) is a feedback control system that has its control loop physically connected via real-time communication networks. To meet the demands of `teleautomation', modularity, integrated diagnostics, quick maintenance and decentralization of control, NCSs have received remarkable attention worldwide during the past decade. Yet despite their distinct advantages, NCSs are suffering from network-induced constraints such as time delays and packet dropouts, which may degrade system performance. Therefore, the network-induced constraints should be incorporated into the control design and related studies.<p> For the problem of state estimation in a network environment, we present the strategy of simultaneous input and state estimation to compensate for the effects of unknown input missing. A sub-optimal algorithm is proposed, and the stability properties are proven by analyzing the solution of a Riccati-like equation.<p> Despite its importance, system identification in a network environment has been studied poorly before. To identify the parameters of a system in a network environment, we modify the classical Kalman filter to obtain an algorithm that is capable of handling missing output data caused by the network medium. Convergence properties of the algorithm are established under the stochastic framework.<p> We further develop an adaptive control scheme for networked systems. By employing the proposed output estimator and parameter estimator, the designed adaptive control can track the expected signal. Rigorous convergence analysis of the scheme is performed under the stochastic framework as well.