An artificial intelligence framework for online transient stability assessment of power systems
TL;DR: 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.
Abstract: A framework is proposed to tackle the online transient stability problem of power systems. Based on artificial intelligence, it successively makes use of an inductive inference method to build decisions automatically and a deductive inference method to apply them online. The authors lay the foundations of an inductive inference method, where the rules explicitly relate a system's stability with relevant parameters of it. A simple but realistic power system is treated to illustrate important features of the method and to suggest how the derived decision rules could be used online. >
Citations
More filters
TL;DR: The proposed scheme is able to identify key security indicators and give reliable and accurate online dynamic security predictions and is tested on a 2100-bus, 2600-line, 240-generator operational model of the Entergy system.
Abstract: This paper describes an online dynamic security assessment scheme for large-scale interconnected power systems using phasor measurements and decision trees. The scheme builds and periodically updates decision trees offline to decide critical attributes as security indicators. Decision trees provide online security assessment and preventive control guidelines based on real-time measurements of the indicators from phasor measurement units. The scheme uses a new classification method involving each whole path of a decision tree instead of only classification results at terminal nodes to provide more reliable security assessment results for changes in system conditions. The approaches developed are tested on a 2100-bus, 2600-line, 240-generator operational model of the Entergy system. The test results demonstrate that the proposed scheme is able to identify key security indicators and give reliable and accurate online dynamic security predictions.
345 citations
Cites methods from "An artificial intelligence framewor..."
...The subject of online dynamic security assessment by machine learning techniques has been addressed in [ 19 ]‐[27]....
[...]
TL;DR: In this paper, the transient stability status of a power system following a large disturbance such as a fault can be early predicted based on the measured post-fault values of the generator voltages, speeds, or rotor angles.
Abstract: The paper first shows that the transient stability status of a power system following a large disturbance such as a fault can be early predicted based on the measured post-fault values of the generator voltages, speeds, or rotor angles. Synchronously sampled values provided by phasor measurement units (PMUs) of the generator voltages, frequencies, or rotor angles collected immediately after clearing a fault are used as inputs to a support vector machines (SVM) classifier which predicts the transient stability status. Studies with the New England 39-bus test system and the Venezuelan power network indicated that faster and more accurate predictions can be made by using the post-fault recovery voltage magnitude measurements as inputs. The accuracy and robustness of the transient stability prediction algorithm with the voltage magnitude measurements was extensively tested under both balanced and unbalanced fault conditions, as well as under different operating conditions, presence of measurement errors, voltage sensitive loads, and changes in the network topology. During the various tests carried out using the New England 39-bus test system, the proposed algorithm could always predict when the power system is approaching a transient instability with over 95% success rate.
293 citations
TL;DR: In this paper, a decision tree is constructed offline and then utilized online for predicting transient stability in real-time, using a short window of realistic-precision postfault phasor measurements for the prediction, and testing robustness to variations in the operating point.
Abstract: 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. >
238 citations
TL;DR: In this paper, the authors developed a transient stability assessment system based on the long short-term memory network by proposing a temporal self-adaptive scheme, which aims to balance the trade-off between assessment accuracy and response time.
Abstract: Online identification of postcontingency transient stability is essential in power system control, as it facilitates the grid operator to decide and coordinate system failure correction control actions Utilizing machine learning methods with synchrophasor measurements for transient stability assessment has received much attention recently with the gradual deployment of wide-area protection and control systems In this paper, we develop a transient stability assessment system based on the long short-term memory network By proposing a temporal self-adaptive scheme, our proposed system aims to balance the trade-off between assessment accuracy and response time, both of which may be crucial in real-world scenarios Compared with previous work, the most significant enhancement is that our system learns from the temporal data dependencies of the input data, which contributes to better assessment accuracy In addition, the model structure of our system is relatively less complex, speeding up the model training process Case studies on three power systems demonstrate the efficacy of the proposed transient stability as sessment system
203 citations
TL;DR: The approach generalizes the idea of energy methods, and extends the concept of energy function to a more general Lyapunov functions family (LFF) constructed via semidefinite programming techniques, and shows that they can certify stability of a broader set of initial conditions in comparison to the energy function in the closest-UEP method.
Abstract: Analysis of transient stability of strongly nonlinear post-fault dynamics is one of the most computationally challenging parts of dynamic security assessment. This paper proposes a novel approach for assessment of transient stability of the system. The approach generalizes the idea of energy methods, and extends the concept of energy function to a more general Lyapunov functions family (LFF) constructed via semidefinite programming techniques. Unlike the traditional energy function and its variations, the constructed Lyapunov functions are proven to be decreasing only in a finite neighborhood of the equilibrium point. However, we show that they can still certify stability of a broader set of initial conditions in comparison to the energy function in the closest-UEP method. Moreover, the certificates of stability can be constructed via a sequence of convex optimization problems that are tractable even for large scale systems. We also propose specific algorithms for adaptation of the Lyapunov functions to specific initial conditions and demonstrate the effectiveness of the approach on a number of IEEE test cases.
197 citations
References
More filters
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Abstract: The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.
17,177 citations
TL;DR: This paper proposed a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization, which is illustrated in the context of several example problems, and used to contrast several existing systems for explanation based generalization.
Abstract: The problem of formulating general concepts from specific training examples has long been a major focus of machine learning research. While most previous research has focused on empirical methods for generalizing from a large number of training examples using no domain-specific knowledge, in the past few years new methods have been developed for applying domain-specific knowledge to formulate valid generalizations from single training examples. The characteristic common to these methods is that their ability to generalize from a single example follows from their ability to explain why the training example is a member of the concept being learned. This paper proposes a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization. The EBG method is illustrated in the context of several example problems, and used to contrast several existing systems for explanation-based generalization. The perspective on explanation-based generalization afforded by this general method is also used to identify open research problems in this area.
1,220 citations
TL;DR: In this article, a method for online transient stability assessment of large power systems is proposed, which consists of replacing the multimachine system by a two-machine dynamic equivalent, further amenable to a one-machine-infinite-bus system.
Abstract: A method for online transient stability assessment of large power systems is proposed. It consists of: replacing the multimachine system by a two-machine dynamic equivalent, further amenable to a one-machine-infinite-bus system; reducing the stability problem to a sole algebraic equation, devised from the equal area criterion, or equivalently from the Lyapunov direct criterion; and using this equation to derive one-shot stability analysis strategies. A technique for system admittance matrix reduction is developed that proves efficient, especially for large systems and multiple-contingency evaluation. The method's main appeal is rapidity: it is about one order of magnitude faster than the most efficient direct criterion. Other attractive features are flexibility and ability to encompass various simulation conditions. Extensions to online sensitivity analysis and control are suggested. >
257 citations
01 May 1987
TL;DR: For measuring the degree of association or correlation between two nominal variables, a measure based on informational entropy is presented as being preferable to that proposed recently by Horibe.
Abstract: For measuring the degree of association or correlation between two nominal variables, a measure based on informational entropy is presented as being preferable to that proposed recently by Horibe [1]. Asymptotic developments are also presented that may be used for making approximate statistical inferences about the population measure when the sample size is reasonably large. The use of this methodology is illustrated using a numerical example.
214 citations
TL;DR: This paper integrates a number of notions, mainly from artificial intelligence, but also from pattern recognition and cognitive psychology, to create a synthetic view which exploits uncertainty, task-guidance, and biases such as language restriction.
Abstract: This paper has two major parts. The first is an extensive analysis of the problem of induction, and the second part is a detailed study of selective induction. Throughout the paper we integrate a number of notions, mainly from artificial intelligence, but also from pattern recognition and cognitive psychology. The result is a synthetic view which exploits uncertainty, task-guidance, and biases such as language restriction. Some of the main themes and contributions are as follows. (1) Practical induction is really a problem of efficacy and efficiency (power). (2) Search in a space of hypothetical concepts is governed by a credibility function which combines various knowledge sources in a single subjective probability or belief measure μ. (3) The amount of knowledge supplied by various sources can often be quantifieds these sources include various biases and the learning system itself. (4) Induction is equivalent to discovery of a utility function u, which captures the purpose or goal of induction. (5) The difficulty of induction may be characterized by the form of u. Smooth or coherent functions mean selective induction, which has had the most attention in machine learning. (6) Systems for selective induction are more similar than commonly understood. By juxtaposing them we can discover similarities and improvements. (7) Our analysis suggests a number of incipient principles for powerful induction.
144 citations