Sverre Aam

Bio: Sverre Aam is an academic researcher. The author has contributed to research in topics: Augmented matrix & Inflow. The author has an hindex of 5, co-authored 8 publications receiving 244 citations.

Comparison of different methods for state estimation

Abstract: Ill-conditioning in the gain matrix of the classical normal-equations-approach for state estimation has created a numerical stability problem for large power systems. Several methods have been proposed to circumvent the problem. A comparative study of five methods, namely, the normal equations method, the orthogonal transformation method, the hybrid method, normal equations with constraints, and Hachtel's augmented matrix method for state estimation has been conducted. The comparison is made in terms of their (i) numerical stability, (ii) computational efficiency, and (iii) implementation complexity. A theoretical analysis indicates that the orthogonal transformation method is numerically most stable. But the orthogonal transformation method cannot be implemented in the efficient fast decoupled version. It is shown that the hybrid method and Hachtel's method are both good compromises between numerical stability and computational efficiency. >

An implementation of estimation techniques to a hydrological model for prediction of run-off to a hydroelectric power station

Abstract: Parameter and state estimation algorithms have been applied to a hydrological model of a catchment area in southern Norway to yield improved control of the household of water resources and better economy and efficiency in the running of the power station, as experience proves since the system was installed on-line in the summer of 1978. A nonlinear conceptual state-space model of a Nordic hydrological system is presented in this paper. The model is transformed analytically to a piecewise linear time-discrete form in order to obtain straightforward updating of the gain matrix in the estimator through solution of the Riccati equation. Estimator gains can then be expressed approximately as a function of the state. Discharge coefficients (time constants) are estimated by the maximum likelihood method. Results are presented which show estimated and observed runoffs, and estimates of the state variables.

User experience with on-line predictive river flow regulation

Abstract: A control strategy based on adaptive prediction and a receding horizon controller is used to regulate the river flow rate in Suldalslagen in the Ulla-Forre production system on the southwest coast of Norway. The adaptive prediction of the unregulated inflow uses precipitation data and flow-rate data for a stream in the catchment area. A recursive estimation algorithm is used to estimate the stationary gains of the parallel transfer function models, which have the catchment area data as input and the unregulated inflow as output. The system is implemented on an IBM-PC and has been in operation since the summer of 1986. >

Regulation of River Water Level by Adaptive Control of a Reservoir Trap

Abstract: An ARMAX model is parameter-estimated using field measurement data. A generalized minimum-variance control strategy is designed to control adaptively the river water level at some location 20 km downstream from the reservoir trap.

Electric power system state estimation

Abstract: This paper discusses the state of the art in electric power system state estimation. Within energy management systems, state estimation is a key function for building a network real-time model. A real-time model is a quasi-static mathematical representation of the current conditions in an interconnected power network. This model is extracted at intervals from snapshots of real-time measurements (both analog and status). The new modeling needs associated with the introduction of new control devices and the changes induced by emerging energy markets are making state estimation and its related functions more important than ever.

Blind False Data Injection Attack Using PCA Approximation Method in Smart Grid

Abstract: Accurate state estimation is of paramount importance to maintain normal operations of smart power grids. However, recent research shows that carefully produced attacks with the knowledge of the grid topology, i.e., Jacobian matrix, can bypass the bad data detection (BDD) system. The BDD is used to ensure the integrity of state estimation to filter faulty measurements introduced by device malfunctions or malicious attacks. However, to construct the false data injection attack vectors, a common assumption in most prior works on false data injection attacks is that the attacker has complete knowledge about the power grid topology and transmission-line admittances. By contrast, this paper studies the general problem of blind false data injection attacks using the principal component analysis approximation method without the knowledge of Jacobian matrix and the assumption regarding the distribution of state variables. The proposed attack is proven to be approximately stealthy. 1 The performance of the proposed attack is analyzed. Simulations confirm the performance of the proposed method.

Adaptive feedback control

Abstract: Adaptive control is now finding its way into the marketplace after many years of effort. This paper reviews some ideas used to design adaptive control systems. It covers early ideas which primarily attempt to compensate for gain variations and more general methods like gain scheduling, model reference adaptive control, and self-tuning regulators. It is shown that adaptive control laws can be obtained using stochastic control theory. Techniques for analyzing adaptive systems are discussed. This covers stability and convergence analysis. Issues of importance for applications like parameterization, tuning, and tracking, as well as different ways of using adaptive control are also discussed. An overview of applications which includes feasibility studies as well as products based on adaptive techniques concludes the paper.

Modeling and Control of Hydrosystems

Abstract: This chapter gives an overview of the issue of water management. We describe open channel systems, why they are difficult to manage, and why control engineering may provide useful tools to better manage such systems. We also emphasize the difficulties involved in the design of efficient controllers for open channels: they are distributed systems, with large delays between inputs and outputs, few measurements along the system, subject to large and unmeasured perturbations, with a nonlinear behavior. Our approach is based on an engineering background, where pragmatic but efficient solutions can be found to solve practical problems. We limit our study to the linearized Saint-Venant equations, which describe open channel flow around a given equilibrium regime. This is a classical approach in the control engineering community, linked to the gain-scheduling method for designing a controller for a nonlinear system, based on its linearizations. 1.1 Water Management Issues for Hydrosystems Water is a key factor for life. We need freshwater for domestic, agricultural and industrial purposes. But we also need water to flow in natural streams, to preserve wildlife and biodiversity. Water is a renewable resource, but it is not distributed equally in time or in space. Since antiquity, human beings have used engineering techniques for water systems: Roman engineers were masters in designing aqueducts and sewer systems. Following these great ancestors, open channels have been built to transport water over long distances, either for irrigation purposes, or for drinking or domestic water supply. The hydrosystems considered in this book are those where operational water management is necessary and can be improved using control techniques. We briefly describe different types of hydrosystems falling into that category and explain why automatic control may improve their operational management. By operational management, we denote real-time operations on a time scale of minutes to hours, as