An Application of Fuzzy Concepts to Transient Stability Evaluation

Souflis, J.L.; Machias, A.V.; Papadias, B.C.

doi:10.1109/mper.1989.4310887

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…It can also provide the margin of system stability under a given operating condition. The application of fuzzy concepts in TSA was introduced for the first time in [14], where an index providing an evaluation of the system level of security by considering the values of generator accelerations and kinetic energies after the occurrence of a large disturbance was presented.…”

confidence: 99%

Transient Stability Assessment using Decision Trees and Fuzzy Logic Techniques

Abdelaziz¹,

El-Dessouki²

2013

IJISA

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Abstract-Many techniques are used for Transient Stability assessment (TSA) of synchronous generators encompassing traditional time domain state numerical integration, Lyapunov based methods, probabilistic approaches and Artificial Intelligence (AI) techniques like pattern recognition and artificial neural networks. This paper examines another two proposed artificial intelligence techniques to tackle the transient stability problem. The first technique is based on the Inductive Inference Reasoning (IIR) approach which belongs to a particular family of machine learning from examples. The second presents a simple fuzzy logic classifier system for TSA. Not only steady state but transient attributes are used for transient stability estimation so as to reflect machine dynamics and network changes due to faults.The two techniques are tested on a standard test power system. The performance evaluation demonstrated satisfactory results in early detection of machine instability. The advantage of the two techniques is that they are straightforward and simple for on-line implementation.

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confidence: 99%

Transient Stability Assessment using Decision Trees and Fuzzy Logic Techniques

Abdelaziz¹,

El-Dessouki²

2013

IJISA

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“…The crisp output of the fuzzy system, defined above, (also in Fig. 1) is given by (5) where (6) gives the degree of fulfillment of rule by the input vector and is given in (3). The fuzzy system whose crisp output is defined by (5) can be represented by a three layer network as shown in Fig.…”

Section: B Description Of Fuzzy Neural Networkmentioning

confidence: 99%

“…Fuzzy logic is a research area of AI which is receiving increased attention lately. Fuzzy systems have been successfully applied in system control [2], system identification [3], [4], power systems transient stability [5], optimal load flow [6], and short-term load forecasting [7].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic for scaling finite element solutions of electromagnetic fields

1997

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Artificial intelligence (AI) has been used to determine the quasi-stationary two-dimensional electromagnetic fields within rectangular boundaries. Amplitude and phase of magnetic vector potential have been calculated in an iron slot with an embedded current carrying conductor. A suitable fuzzy neural network (FNN) for scaling finite elements electromagnetic field calculations has been developed. FNN has been trained, using finite elements calculations within rectangular boundaries. Then, FNN has been used to calculate the field in a new geometry differing significantly from the geometries used for training. It was concluded that FNN may be used to scale results from one geometry to another with negligible errors.Index Terms-Artificial intelligence, eddy currents, finite element methods, fuzzy neural networks.

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“…The pioneering work of Zadeh [I31 in fuzzy set theory has inspired work in many research areas with excellent results. Fuzzy systems have been successfully applied in process control, system identification and pattern recognition [14-181. In power systems fuzzy systems have been applied in many diverse areas among which transient stability evaluation [19], optimal power flow [20] and dynamic dispatch 1211. A fuzzy expert system for STLF is developed in [22].…”

Section: A Paper Recommended and Approvedmentioning

confidence: 99%

Short term load forecasting using fuzzy neural networks

Bakirtzis

Theocharis

Kiartzis

et al. 1995

IEEE Trans. Power Syst.

175

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This paper presents the development of a fuzzy system for short term load forecasting. The fuzzy system has the network structure and the training procedure of a neural network and is called Fuzzy Neural Network (FNN). A FNN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that the output of the FNN adequately matches the available historical load data. Once trained, the FNN can be used to forecast future loads. Test results show that the FNN can forecast future loads with an accuracy comparable to that of neural networks, while its training is much faster than that of neural networks.

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An Application of Fuzzy Concepts to Transient Stability Evaluation

Cited by 14 publications

References 12 publications

Transient Stability Assessment using Decision Trees and Fuzzy Logic Techniques

Transient Stability Assessment using Decision Trees and Fuzzy Logic Techniques

Fuzzy logic for scaling finite element solutions of electromagnetic fields

Short term load forecasting using fuzzy neural networks

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