Th. Van Cutsem

Extended equal area criterion justifications, generalizations, applications

The extended equal area criterion (EEAC) for online transient stability analysis is considered with the following objectives. The first is to state systematically its main hypotheses and key conditions, justify the former, and suggest means to guarantee the latter. The identification and error analysis of critical machines are among the investigated issues. The second is to scan all possible types of instabilities likely to arise in practice and devise means to treat them. The extension of the EEAC to cases beyond the so-called first-swing stability makes it more robust than all direct methods developed up to now. The third objective is to extract essential information out of a large body of simulations and show that the above improvements and extensions enhance the EEAC accuracy and its capability to work properly even under stringent conditions. Possible EEAC applications are also discussed, and uses of the method as such or as an auxiliary technique for more sophisticated approaches are suggested. >

Hypothesis Testing Identification: A New Method For Bad Data Analysis In Power System State Estimation

The anomalous data identification procedures existing today in power system state estimation become problematic-if not totally unefficient-under stringent conditions, such as multiple and interacting bad data. The identification method presented in this paper attempts to alleviate these difficulties. It consists in :(i) computing measurement error estimates and using them as the random variables of concern;(ii) making decisions on the basis of a hypothesis testing which takes into account their statistical properties. Two identification techniques are then derived and further investigated and assessed by means of a realistic illustrative example. Conceptually novel, the identification methodology is thus shown to lead to practical procedures which are efficient, reliable and workable under all theoretically feasible conditions.

An Artificial Intelligence Framework for On-Line Transient Stability Assessment of Power Systems

Transient stability assessment (TSA) of a power system pursues a twofold objective: first to appraise the systems capability to withstand major contingencies, and second to suggest remedial actions, i.e. means to enhance this capability, whenever needed. The first objective is the concern of analysis, the second is a matter of control. For the time being, the on-line TSA is still a totally open question. Indeed, none of the existing two broad classes of methods (the time domain and the direct methods) are able to meet the on-line requirements of the analysis aspects, nor are they in the least appropriate to tackle control aspects. The methodology we are introducing aims at solving the above stated on-line problem by making use of decision rules, preconstructed off-line. To this end, an inductive inference method is developed, able to provide decision rules in the form of binary trees expressing relationships between static, pre-fault operating conditions of a power system and its robustness to withstand assumed disturbances. This paper concentrates on this latter problem, which is the most difficult task, and also the kernel of the overall methodology. The proposed inductive inference (II) method pertains to a particular family of Machine Learning from examples. It derives from ID3 by Quinlan [1], tailored to our problem, where the examples are provided by numeric (load flow and stability) programs [2, 3]. According to the method, a decision tree (DT) is built on the basis of a preanalyzed learning set (LS), composed of states or operating points (OPs).

Bad Data Identification Methods In Power System State Estimation-A Comparative Study

The identification techniques available today are first classified into three broad classes. Their behaviour with respect to selected criteria are then explored and assessed. Further, a series of simulations are carried out with various types of bad data. Investigating the way these identification techniques behave allows completing and validating the theoretical comparisons and conclusions.

Critical Survey of Hierarchical Methods for State Estimation of Electric Power Systems

This paper intends to give a unified survey of methods appropriate for solving the state estimation problem in large-scale electric power systems. After a first overview of the various approaches proposed up to now, the most suitable among them are described, examined and compared. The comparisons are carried out on the basis of selected criteria evolving estimation properties of the resulting algorithms, along with their organization possibilities and their capabilities of handling some important satellite functions.

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.

Inductive inference applied to on-line transient stability assessment of electric power systems

Abstract A decision tree methodology is proposed for the purpose of predicting the robustness of a power system in the occurrence of severe disturbances, and of discovering appropriate control actions, whenever needed. The decision trees are built off-line and used on-line. Their building calls upon an inductive inference method, which automatically extracts the relevant information from large bodies of simulation data and expresses it in terms of the system controllable variables. The trees provide a clear understanding of the intricate mechanisms of transient stability, and means to control. This paper focuses on fundamental issues relative to the inductive inference method. A real world power system comprising 14 generators, 112 branches and 92 nodes is used to illustrate it.

Hierarchical state estimation

Abstract A new approach for the state estimation of large-scale electric power systems is proposed. It consists in decomposing the overall system into subsystems and in performing a two-level calculation. In the lower hierarchical level, a conventional state estimation is carried out simultaneously but independently for all subsystems. These local estimations are then coordinated at the second hierarchical level which, by this means, receives and treats only a small number of variables. The potential advantages of this hierarchical procedure over the standard ‘integrated’ method include smaller subsystem state estimators, less transmitted information, higher reliability, easier bad data detection-identification, and shorter computing time.

Artificial Intelligence Applied to On-line Transient Stability Assessment of Electric Power Systems

Abstract A novel approach to fast transient stability assessment of power systems is presented. It aims at building decision rules, expressed in terms of static, directly or indirectly, controllable variables, appropriate for both on-line analysis and preventive control. The decision rules are constructed by using inductive inference in conjunction with large bodies of off-line simulation data. The proposed inductive inference procedure derives from ID3 by Quinlan, appropriately modified so as to handle the large number of continuous variables characterizing power system operating points. This modification is achieved by developing a new quantization procedure, based upon the entropy concept. The method is illustrated by means of an academic example of a one-machine-infinite-bus system.

Decision theory for fault diagnosis in electric power systems

Abstract In state estimation of electric power systems, anomalous measurement diagnosis is essential for guaranteeing a reliable database. More specifically, the identification of anomalous data is a very important but also very difficult task, especially in the case of multiple interacting ones. This work focuses primarily on the latter problem. The proposed solution is based on the classical hypothesis testing of decision theory and its application to the random vector of measurement error estimates; the latter are obtained via a perfect observations estimator which is proven to be optimal. Further, strategies able to treat any type of bad measurements are considered; the identification strategy under an upper bounded β risk is chosen and extensively exploited. The developments concern reliability aspects as well as on-line computational requirements for which many algorithmic improvements and useful formulae are derived. A practical example, coming from actual implementation experience, illustrates the overall methodology and shows the consistently good performances of the method.