In the post-fault dynamic analysis of interconnected power systems, the critical fault clearing time (CCT) is one of the parameters of paramount importance. Critical clearing time is a complex function of pre-fault system conditions (operating point, topology, system parameters), fault structure (type and location) and post-fault conditions that are in part dependent on the protective relaying policy. To define analytically such a relationship would be highly desirable but diversity of variable involved makes this task extremely complicated. Our efforts focus on examination of that complex mapping and investigation of the influence of the various parameters on CCT. The evaluation of CCT involves elaborate computations that often include time-consuming solutions of nonlinear on-fault system equations. Existing conventional pattern recognition techniques are incapable of synthesizing such complex and transparent mappings. Thus, when a human operator tells the machine learning unit (that is the pattern recognizer) that system state belongs to a certain class, say "emergency", the pattern recognizer merely records that classification mindlessly and is not able to look at the pattern with insight and discover what underlies the "emergency" nature of pattern. It is, therefore, highly desirable to have a mechanism which when presented with a sequence of class labeled patterns not only learns an internal structure which allows it to generalize and to classify other and to classify other patterns correctly, but also is able to shed some light on what combination of features give rise to the particular class membership.

Artificial neural-net based dynamic security assessment for electric power systems

Transient stability analysis of a power system is concerned with the system's ability to remain in synchronism following a disturbance In utility planning, transient stability is studied by numerical simulation The long CPU run times for simulation preclude their use for on-line security analysis Interest has therefore shifted toward the Lyapunov direct method of stability analysis This paper provides a critical review of research on direct methods since 1970 Considerable progress has been made on both theoretical properties of energy functions and on criteria suitable for on-line implementation Current theory provides a satisfactory treatment of voltage-dependent reactive power demand, transfer conductances, and flux decay However, it cannot incorporate the exciter control Proposed on-line criteria appear to work very well on sample examples; but, they still lack rigorous justification Finally, recent work has shown that power systems can exhibit chaotic behavior This surprising fact demonstrates that our understanding of the dynamics of power systems remains incomplete

Direct methods for transient stability analysis of power systems: Recent results

The ability to rapidly acquire synchronized phasor measurements from around the system opens up new possibilities for power system protection and control. This paper demonstrates how decision trees can be constructed offline and then utilized online for predicting transient stability in real-time. Primary features of the method include building a single tree for all fault locations, using a short window of realistic-precision post-fault phasor measurements for the prediction, and testing robustness to variations in the operating point. Several candidate decision trees are tested on 40,800 faults from 50 randomly generated operating points on the New England 39 bus test system. >

Decision trees for real-time transient stability prediction

Artificial neural network (ANN) techniques are explored as a tool to assess the dynamic security of power systems. The basic role of ANNs is to provide assessment of the system's stability based on training examples from offline analysis. Such an assessment would be useful as an operations aid. In essence, ANNs interpolate among the planning analysis data. The authors present the results of a study to assess the capability of ANNs to learn from the offline stability analysis results and give accurate stability assessments when queried with data representing the current system status. The important feature of the result is that correct stability assessments are provided by the ANN not only when it is queried with an element of the training set of data but also under other operating conditions. >

Preliminary results on using artificial neural networks for security assessment (of power systems)

This paper presents applications of an efficient computerized technique for power system dynamic system security assessment using the transient energy function (TEF) method. The technique exploits the sensitivities of the unnormalized transient energy margin with respect to operating indices and critical interface flows in order to arrive at the stability limits for various system outage states and operating conditions, that would be required for on-line application. The paper describes the sensitivity technique and demonstrates a practical application to the dynamic security problem in one portion of the Ontario Hydro system including results of the validation and accuracy tests of the technique.

Dynamic Security Assessment Utilizing the Transient Energy Function Method

The growth of large interconnected power systems demands a high degree of security for normal operation. This requirement emerges from the fact that the possible disturbances in large power systems could have catastrophic results. Therefore it is the duty of power system control center to retain the operating point within secure boundaries. In order to control the security of system, one has to monitor and analyze the level of system security and enhance it in case it does not fulfill the system security requirement. The pattern recognition technique which is proposed in this paper is a method suitable for fast, on-line security assessment. Most of the literature in this area concerns the use of steady state variables for identification pruposes; the use of transient measurements, however, has been found to be better suited for the fast classification of operating points with minimal error. One of the salient conclusions of this paper is that the pattern recognition technique is a serious candidate for on-line applications. This favorable conclusion is based on the inclusion of transient state variables in the feature vector. The main objective of the pattern recognition method in transient security assessment is to reduce the computational requirement to a minimum. This is done at the expense of elaborate off-line computations. The classical methodology of pattern recognition consists of defining a pattern vector x whose components consist of all significant variables of the system.

Fast Transient Security Assessment

Planning electric energy resources for the future is complicated by uncertainties in future load,-hydro conditions in a predominantly hydro system and uncertainties associated with other resources. Additionally, there are unquantifiable costs and benefits associated with different technologies, e.g., environmental impacts. Political considerations can't be ignored in the planning decision process.

Hossein Hakimmashhadi

Papers

Fast Transient Security Assessment

A Flexible Generation Planning Approach Recognizing Long Term Load Growth Uncertainty