Power System Security Assessment Research Articles

Estimation of line flows and bus voltages using decision trees

A machine learning method called the ID3 (Interative Dichotomizer 3) approach is presented for the estimation of line flows and bus voltages following an outage event. A decision tree which is capable of generating the desired line flows and bus voltages are created using the training patterns which are compiled from the historical operating records of Taiwan power system. The established decision tree contains the knowledge which is essential for line flow and bus voltage prediction. Thus, it can be applied to estimate line flows and bus voltages of a system in an efficient manner. The effectiveness of the proposed ID3 approach is demonstrated by security assessment of Taiwan power system which contains 170 buses and 207 lines.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Application of artificial neural networks in power system security and vulnerability assessment

In a companion paper by A.A. Fouad et al. the concept of system vulnerability is introduced as a new framework for power system dynamic security assessment. Using the transient energy function (TEF) method of transient stability analysis, the energy margin /spl Delta/V is used as an indicator of the level of security, and its sensitivity to a changing system parameter p (/spl delta//spl Delta/V//spl delta/p) as an indicator of its trend with changing system conditions. These two indicators are combined to determine the degree of system vulnerability to contingent disturbances in a stability-limited power system. Thresholds for acceptable levels of the security indicator and its trend are related to the stability limits of a critical system parameter (plant generation limits). Operating practices and policies are used to determine these thresholds. In this paper the artificial neural networks (ANNs) technique is applied to the concept of system vulnerability within the recently developed framework, for fast pattern recognition and classification of system dynamic security status. A suitable topology for the neural network is developed, and the appropriate training method and input and output signals are selected. The procedure developed is successfully applied to the IEEE 50-generator test system. Data previously obtained by heuristic techniques are used for training the ANN. >

Improvements on the line outage distribution factor for power system security analysis

This paper presents two sensitivity factors in terms of the generation shift distribution factor (GSDF) to improve some defects of the conventional formula for the line outage distribution factor (ODF). The sensitivity factors are established by using the concepts of generation change and power injection to simulate the outaged line flow and one of them is then applied to the case of line addition. A transfer factor is derived to calculate the power flow of the added line very quickly from the relationship between the ODF and the GSDF. The proposed method has been tested by means of a standard system. Compared with the conventional ODF in the line outage case and with DC load flow in the line addition case, the proposed method has good accuracy and a fast execution time. This method is very suitable for real-time security assessment and contingency analysis of power systems.

Dominant mode filter for enhancing power system stability

This paper proposes a new type of filter, that is, a dominant mode filter (DMF) which uses the rotor speed fluctuations for dynamic security assessment and stability enhancement of power systems. The proposed filter can be used in detecting the degradation of damping power of the system and in improving the stabilizing ability of a control device such as PSS. The basic concept of the DMF design is based on the inherent characteristics of the power system network and the application of principal component analysis to a covariance matrix of the rotor speed fluctuations. The coefficients of the DMF are easily computed from the eigenvector of the covariance matrix. The weak damping modes which are useful for generating the control signals of the stabilizing devices, are satisfactorily estimated from the rotor speed fluctuations by means of the DMF. In the computing process of the DMF coefficients the ‘mode energy’ and ‘mode profile’ are also obtained from the eigenvalue and the eigenvector of the covariance matrix respectively. The mode energy is the strength of each mode contained in the fluctuations. The mode profile expresses the distribution of the mode oscillation with the rotor speed responses of all machines. Since, in the DMF design, the on-line fluctuation data are used, the weak damping modes, the mode energy and the mode profile can be estimated adaptively according to the system situation. In this paper, applications of the DMF are also discussed. One application is to use the mode energy as an on-line stability index. If the damping power of the system becomes weak, a system operator can detect signs of an unstable condition by observing the time variation of the mode energy. Another is a disturbance identifier to find the location and severity of a disturbance. This identifier utilizes the mode profile and mode energy. The third application of the DMF is to supply the slower mode component as a global control signal for HPSS (Hybrid PSS) 11 . The mode energy can be also utilized for adapting the parameters of the HPSS.

Decision tree approach to power systems security assessment

An overview of the general decision tree approach to power system security assessment is presented. The general decision tree methodology is outlined, modifications proposed in the context of transient stability assessment are embedded, and further refinements are considered. The approach is then suitably tailored to handle other specifics of power systems security, relating to both preventive and emergency voltage control, in addition to transient stability. Trees are accordingly built in these various application domains, and their salient features are explored, assessed, compared. Among attractive aspects of the trees, we mention their ability to uncover the intrinsic mechanism governing physical processes, and to provide a clear description in terms of tractable system parameters. Further, their dual, ‘attribute space’ representation is shown to have complementary possibilities for more refined analysis and, in addition, for sensitivity assessment and control. Overall, the approach is characterized by its great flexibility with respect to tree structure, types of physical parameters driving the phenomena, and classes of potential applications. To illustrate and support the developments, real-world examples are reported, relating to transient and voltage stability issues simulated on several power systems.

Multi-contingency preprocessing for security assessment using physical concepts and CQR with classifications

A new method for preprocessing the outage lists for an expert system for security assessment of power systems is presented. This method uses disturbing capability, structure and location classifications and combining qualitative reasoning (CQR) with classifications (CQRC). Because this method avoids the traditional enumerative methods such as automatic contingency selection, the number of contingencies can be reduced very sharply with heuristic common sense and rules of CQRC before numerical computing. Therefore the double contingency problem or extremely heavy cost of computing can be tackled successfully. The proposed method along with an automatic contingency analysis and classification technique can assess the classified contingencies with basic physical concepts and without network simplification, minimize the number of missing harmful contingencies efficiently and give a clear accurate output. >

Design of a decision fusion rule for power system security assessment

An integrated decision support system is described based on sensor fusion techniques, used for assessing the security of power systems. The integrated decision support system fuses information from various approximated system performance (ASP) models to minimize the risk of making the wrong decision under changing operating conditions. It uses the classification decisions provided by different ASP models together with information about their statistical performance (e.g., probabilities of misclassifications) to synthesize the globally optimal decision in the Bayesian risk sense. This global decision is often superior (and in no case inferior) to the one obtained using any single ASP model. The design of the integrated decision support system is illustrated for detecting static voltage collapse by fusing the security information from a set of existing security indices. >

Power system static security assessment using the Kohonen neural network classifier

The authors present the application of an artificial neural network, Kohonen's self-organizing feature map, for the classification of power system states. This classifier maps vectors of an N-dimensional space to a two-dimensional neural net in a nonlinear way, preserving the topological order of the input vectors. Therefore, secure operating points-that is, vectors inside the boundaries of the secure domain-are mapped to a different region of the neural map than insecure operating points. The application of this classifier to power system security assessment is presented, and simulation results are discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Real-time data exchange for online security assessment-A report by the IEEE Task Force on Data Exchange for Security Assessment

The current status and near-term future goals of real-time data exchange for online security assessment of power systems in several regions of the United States are described from the viewpoint of the end-user application, namely, security assessment. An overview of the issues concerning data exchange for external network modeling is presented. Several data exchange systems are discussed. The following information is provided: description, justification of data exchange, uses for the data, types and quantities of the data exchanged, frequency of data exchange, impact of data exchange on system modeling detail, feasibility of implementing online security assessment without data exchange, exchange of supporting static data, efforts to establish and maintain data exchange, and problems/solutions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A conceptual ANN-ES scheme for power system security assessment and preventive control

Proposes a coupled artificial neural network-expert system (ANN-ES) scheme for on-line security assessment and preventive control of power systems. A conceptual scheme for this purpose is presented.

Linear power system equations and security assessment

A linear model of the load flow equations is developed. From this linear model a load flow linear solution is obtained and a linear method for load flow calculation is also deduced. The performance of the new load flow linear model is tested on standard test power systems. A linear method for power system security assessment is also obtained from the linear load flow model. Its performance is tested on a standard test power system.

Query-based learning applied to partially trained multilayer perceptrons

An approach is presented for query-based neural network learning. A layered perceptron partially trained for binary classification is considered. The single-output neuron is trained to be either a zero or a one. A test decision is made by thresholding the output at, for example, one-half. The set of inputs that produce an output of one-half forms the classification boundary. The authors adopted an inversion algorithm for the neural network that allows generation of this boundary. For each boundary point, the classification gradient can be generated. The gradient provides a useful measure of the steepness of the multidimensional decision surfaces. Conjugate input pairs are generated using the boundary point and gradient information and presented to an oracle for proper classification. These data are used to refine further the classification boundary, thereby increasing the classification accuracy. The result can be a significant reduction in the training set cardinality in comparison with, for example, randomly generated data points. An application example to power system security assessment is given.

Fast Dynamic Simulation of Power Systems for Security Assessment

Current techniques for dynamic security assessment in power systems rely on direct methods. While they provide a useful screening tool, the operator needs fast simulation of the system dynamics on-line to select a wide range of operating options. In this paper we propose two approaches, one based on trajectory approximation and the other based on time scale separation in the system. Slow dynamics are separated from the fast dynamics to accelerate the simulation using the integral manifold approach. The final goal of the project is to make both techniques suitable for parallel processing. With the rapidly changing computer technology, it is not inconceivable that on-line dynamic simulation may become feasible just like on-line load flow.

CQR: a hybrid expert system for security assessment

An offline prototype, called CQR, of a system for online security assessment has been built and tested in conjunction with a western Pennsylvania utility. CQR is a hybrid of rule-based programs written in OPS5 and numerical tools written in FORTRAN. CQR uses the utility's full power system model, accepts arbitrary operating conditions, evaluates the base case, evaluates a small number of AC contingency list using distribution factors, selects and evaluates a small number of AC contingencies, makes an explicit assessment of power system security, and presents results to the operator in a compact and understandable report. Security is decomposed into components that are evaluated independently, allowing separate criteria to be applied to the base case and contingencies. Transient stability is dealt with through dynamically imposed operating limits. CQR's strong points are the correctness, consistency, and compactness of its reports. Run times are acceptable for real time operation. >

A new approach to dynamic contingency selection

A method is proposed for contingency selection for the dynamic security assessment of large power systems. The proposed method iteratively computes the eigenvalues of the system under outage conditions. The initial values for the iterative procedure are the eigenvalues for the normal operating condition (base-case eigenvalues). To reduce the computational burden, the eigenvalues of the system under outage conditions from the first iteration are used for contingency ranking. From the results obtained from a study on the Taiwan power system, it is concluded that contingency ranking using the eigenvalues from the first iteration is both accurate and efficient. It is also found that only the eigenvalues for the worst-damped mode must be considered in contingency ranking. To further improve the efficiency of the proposed method, results obtained from the first iteration of the fast decoupled load flow (FDLF) are also used to model the operating conditions after contingencies. Again, it is observed that satisfactory dynamic contingency selection can be achieved by using the first iteration of FDLF with much less computational effort than that required by full AC power flow.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An efficient unified methodology for steady-state and dynamic security assessment of power systems

Abstract An approach to forming an efficient unified methodology and computer program for power system security analysis for the purposes of operational planning is presented. The proposed methodology, taking into consideration the usual aspects of steady-state security, also encompasses the aspects of dynamic security, so that either one or both types can be treated. Furthermore, an important characteristic is the developed efficient unified solution techniques of the system models, which secure the necessary speed and economy of simulation, as well as the corresponding unification of the computer program, a characteristic indispensible for realizing the application software support of energy management systems of modern electric power systems.

Discriminant Analysis in Security Assessment of Power Systems by Statistical Pattern Recognition

The method of pattern recognition for analyzing power system transient stability is particularly attractive. It gives the opportunity to assess the transient stability of power systems more directly and effectively than the conventional approach based on simulation. The success of the method depends on the choice of a pattern vector which contains two types of information:-information which permits to determine whether a state is stable or unstable,-redundant information which must be eliminated to reduce on line computational time.In this paper, we present the discriminant analysis methods used for extracting discriminant information contained in two pattern vectors. The first pattern vector consists of transient energy gained during the fault and a variable correlated to the critical energy. Its dimension is not governed by the number of generators and the discriminant information is extracted using a method based on within-class and between-class scatter matrices. The second pattern vector is derived from the first by decomposition of the transient energy into individual energy machines. Because the previous method is not suitable for this case, the redundant information is eliminated by projecting pattern space onto a set of discriminant vectors proposed by Foley and Sammon which are optimal for the BAHADUR criterion. The results are applied to a six machine system for a three phase fault.

Artificial Neural-Net Based Dynamic Security Assessment for Electric Power Systems

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.

Dynamic security assessment of power systems using structure-preserving energy functions

An application of direct methods to dynamic security assessment of power systems using structure-preserving energy functions (SPEF) is presented. The transient energy margin (TEM) is used as an index for checking the stability of the system as well as ranking the contigencies based on their severity. The computation of the TEM requires the evaluation of the critical energy and the energy at fault clearing. Usually this is done by simulating the faulted trajectory, which is time-consuming. In this paper, a new algorithm which eliminates the faulted trajectory estimation is presented to calculate the TEM. The system equations and the SPEF are developed using the centre-of-inertia (COI) formulation and the loads are modelled as arbitrary functions of the respective bus voltages. The critical energy is evaluated using the potential energy boundary surface (PEBS) method. The method is illustrated by considering two realistic power system examples.

An application of fuzzy concepts to transient stability evaluation (of power systems)

A transient stability index is presented that can be used for the security assessment of electric power systems. This index evaluates the system security level by considering the values of the accelerations and kinetic energies of the generators after the occurrence of a large disturbance. The derivation of the index is based on pattern recognition principles and fuzzy set theory. The results that are obtained from the application of the index are illustrated by the stability analysis of two power systems, the CIGRE test system and the Hellenic interconnected system. >