Patricia Rousseaux

SIME: A hybrid approach to fast transient stability assessment and contingency selection

Abstract We propose an integrated scheme for transient stability assessment which in a sequence screens contingencies and scrutinizes only the selected ones. This scheme is based on a hybrid method, called SIME for SIngle Machine Equivalent. SIME relies on a particular direct method coupled with time-domain programs so as to combine the strengths of both, namely: the flexibility with respect to power system modelling of time-domain methods; the speed and richer information of the direct method. This paper lays the foundations of SIME, devises appropriate techniques for transient stability assessment per se and for contingency screening, and finally integrates these two techniques in a fully general function, i.e. able to comply with any power system modelling and stability scenario, and to assess any type of stability limits (critical clearing times or power limits). Throughout, real-world examples illustrate the proposed techniques and highlight their performances.

Whither dynamic state estimation

Abstract This paper aims to present some feasible directions along which investigations on dynamic state estimation have been carried out and could be developed in the future. It is shown that the benefits which could be encountered from dynamic state estimation are linked to its predictive ability which provides the necessary information to perform preventive analysis and control. Other benefits are improvements in observability analysis, identification of bad data and detection of topology errors. In practice, however, dynamic state estimation is faced with two problems: modelling of the system dynamics and algorithmic tractability within real-time requirements. To overcome these difficulties, two dynamic estimation schemes can be considered. The one combines a short term nodal load forecasting technique to model the system dynamics with a hierarchical extended Kalman filter. In the other, more pragmatic scheme, the Kalman filtering process is replaced by a static estimation algorithm.

Dynamic state prediction and hierarchical filtering for power system state estimation

Abstract Dynamic state estimation of large interconnected power systems poses two types of problems: modelling of the system dynamics and algorithmic tractability under stringent real-time requirements. The first problem has tentatively been encountered via the dynamic load prediction method, the second by means of a two-level technique. A unified approach is proposed in this paper to bridge the gap between these two techniques. It consists of an extended Kalman filter whose prediction step uses the dynamic load prediction method, and whose filtering step uses the hierarchical technique. Moreover, extensions of the initially developed two-level procedure to a multilevel are also proposed. The resulting method is extensively investigated under various operating conditions on the IEEE 118-node test system.

A time-scale decomposition-based simulation tool for voltage stability analysis

This paper proposes a time domain simulation tool combining full time scale (FTS) simulation in the short-term period following a contingency and quasi steady-state (QSS) approximation for the long-term phase. A criterion is devised to automatically switch from FTS to QSS simulation as soon as sufficient damping of short-term dynamics is reached. Various comparisons with FTS simulations have been performed on the Hydro-Quebec system. The proposed method is shown to combine accuracy of FTS with computational efficiency of QSS. Sensitivity of simulations to load model and the need for updating the whole model with frequency variations are also discussed.

Deconvolution of time-varying systems by Kalman filtering: its application to the computation of the active state in the muscle

Abstract A new representation of the active state in the muscle is proposed. It derives from the deconvolution of the intracavitary ventricles pressure, the heart muscle being modelled as a time-varying driven by the active state. This is achieved by using an appropriate deconvolution method, based on Kalman filtering technique, and it is able to treat both time-invariant and time-varying systems. It is shown that the active state representation thus obtained accounts for the recruitment of the cells undergoing activation.

Multi-level dynamic state estimation for electric power systems

In power system state estimation, dynamic schemes as compared to static ones are acknowledged to have attractive potentialities but also real-time implementation difficulties. The main attractiveness lies in their appropriateness for detecting-identifying gross errors and sudden unpredictable variations, whatever their nature; besides, they merely require low measurement redundancies. On the other hand, however, the computational burden they imply make their real-time use problematic; the difficulties amplify rapidly with the system size. This paper addresses essentially the latter problem, by investigating a recently proposed dynamic state estimator. It derives from the conjunction of an extended Kalman filter and of a hierarchical scheme, according to which the overall dynamic state estimation problem is decomposed into smaller, easier to handle, subproblems.

Quasi steady-state simulation diagnosis using Newton method with optimal multiplier

This paper deals with the quasi steady-state approximation of the long-term dynamics. This fast time simulation method assumes that the short-term dynamics are stable and can be replaced by their equilibrium equations. When the latter stop having a solution, the simulation undergoes a singularity. This paper proposes a method to identify which component(s) are responsible for the loss of equilibrium. The corresponding equations are identified using the Newton method with optimal multiplier. The method has been validated with respect to full time simulation. Very good results are shown on the Nordic-32 system, in cases where long-term voltage instability triggers loss of synchronism. The proposed method enhances time simulation at very low computational cost and can also help correcting model and/or operating point errors

Dynamic state estimator for large electric power systems

Abstract A method for performing dynamic state estimation in electric power systems isset forth. It derives from the conjunction of an extended Kalman filter and of a hierarchical scheme, according to which the overall dynamic state estimation problem is decomposed into smaller, easier to handle subproblems. Aside from making the solution of the large-scale problem at all feasible in practice, this leads to important computational savings. Used in real power systems, this structure succeeds indeed in keeping the advantages of the Kalman filtering while clearing it from its extremely demanding computer time and storage requirements. A simple but realistic example illustrates the method and shows its e ffectiveness.

A NEW HIERARCHICAL APPROACH FOR DYNAMIC STATE PREDICTION AND FILTERING IN ELECTRIC POWER SYSTEMS

Abstract Hierarchization is an interesting loophole in the extremely demanding problem of dynamic state estimation of electric power systems, built on an extended Kalman filter and called to work in real time. The first - and up to now only - hierarchical structure developed to this end uses a state variable-wise decomposition principle. This paper tentatively proposes a novel approach, based on a measurement-wise decomposition. If it is too early to make a comprehensive assessment of its advantages and intrinsic characteristics, we can however foresee its complementarity with the former hierarchical dynamic state estimation.

Hybrid Extended Equal-Area Criterion for Fast Transient Stability Assessment with Detailed Power System Models

Abstract The use of fast direct methods to evaluate the stability of power systems can benefit planning studies and operation planning. The existing Extended Equal-Area Criterion (EEAC), developed in 1988, enabled calculation much faster than timedomain simulations, but failed to deal with large and detailed dynamic systems. Based on it, we propose a new method, which is a Hybrid Extended Equal-Area Criterion (HEEAC), where time-domain simulations are used with a stability assessment provided by EEAC. HEEAC discriminates critical from non-critical machines, and yields a set of stability indicators such as critical clearing times, power transfer limits or power generation limits. The method has been applied to the French EHV system, with detailed generator and load models. The perfonnances are good both in tenns of computing speed and accuracy.