Neural network based control for synchronous generators

Swidenbank, E.; McLoone, Seán; Flynn, Damian; Irwin, G.W.; Brown, Michael; Hogg, B.W.

doi:10.1109/60.815122

Cited by 45 publications

(13 citation statements)

References 13 publications

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“…The discussers would like to know the authors' comments on the following issues in the paper: 1 1) In the introduction the authors indicate the paper 1 as a maiden attempt to design an RBF network based adaptive PSS (RB-FAPSS). In [1] a RBF network is used to find the control parameters for an excitation controller with generalized minimum variance controller (GMV). In [2] a scheme for combining backpropagation neural network with conventional PID control is proposed.…”

Section: Discussion Of "Radial Basis Function (Rbf) Network Adaptive mentioning

confidence: 99%

“…This fact has been clearly stated in the paper. 1 Further, it may be noted that the results reported it Table II of the paper 1 have been obtained for SSE = 0.1 for all the cases studied. It is appropriate to highlight the fact that number of RBF centers chosen by OLS technique from the training set increases with the reduction in the value of SSE, which is pre-specified, for the training.…”

Section: ) the Training Time Of The Rbf Network Increases Steeply Wimentioning

confidence: 89%

“…1 The required number of operating points in the input space were generated by using the RAND function of the MATLA software. The RAND function generates random numbers uniformly distributed and hence represents the entire input-domain adequately.…”

Section: Discussion Of "Radial Basis Function (Rbf) Network Adaptive mentioning

confidence: 99%

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Discussion of "Radial basis function (RBF) network adaptive power system stabilizer" [and reply]

Ramakrishna

Malik²,

Segal³

et al. 2000

IEEE Trans. Power Syst.

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However, no-load consumption for the SCG is very low. This point must be taken into account in order to compare the efficiency of the two arrangements.SCG have other advantages different from efficiency, e.g., consumption of oil and maintenance costs are reduced, SCG installation is easier, etc.Nowadays, it is more appropriate to compare SCG with electronic solutions. For instance, a synchronous machine with an AC/AC converter coupled to the prime mover shaft. This kind of solutions is more complex and, so, its reliability is lower than that for mechanical systems. However, from the point of view of efficiency, installation, maintenance, etc., electronic schemes could be the most appropriate.2) As has been assumed, the prime mover power exceeds the electric power requirements, so the electric load variations must not affect the prime mover. In the case of a short-circuit, electric protections usually act in a few milliseconds. As a result, it should not affect the mechanical system. However, we agree with the discusser in that use of a SCG creates dependence between prime mover and electricity generation although an emergency group exists. And therefore, reliability of classical arrangements is a clear advantage with regard to SCG ones. 3) The above-mentioned dependence between Prime Mover and SCG also affects maintenance works. 4) In general, space requirements are higher for diesel units than for SCG. However, these requirements can be comparable in some cases. Nevertheless, situation of components is more flexible in the SCG scheme which implies additional space saving.

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Section: Discussion Of "Radial Basis Function (Rbf) Network Adaptive mentioning

confidence: 99%

Section: ) the Training Time Of The Rbf Network Increases Steeply Wimentioning

confidence: 89%

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Discussion of "Radial basis function (RBF) network adaptive power system stabilizer" [and reply]

Ramakrishna

Malik²,

Segal³

et al. 2000

IEEE Trans. Power Syst.

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“…A number of papers have been published in the last decade. An illustrative list of example papers include (Abido & Abdel-Magid, 1999;Changaroon, Srivastava, & Thukaram, 2000;Hiyama & Lim, 1989;Hosseinzadeh & Kalam, 1999;Segal, Kothari, & Madnani, 2000;Swidenbank et al, 1999;Yuan & Chao, 1991). Both of these techniques have their own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 96%

Generalized neuron-based PSS and adaptive PSS

Chaturvedi¹,

Malik²

2005

Control Engineering Practice

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“…Parameters of an adaptive stabilizer are adjusted on-line according to the operating conditions. Many years of intensive studies have shown that the adaptive stabilizer can provide good damping over a wide operating range (Pierre 1987;Shi-jie et al 1986a,b;Chen et al 1993;Segal et al 2000;Hosseinzadeh and Kalam 1999;Zhang et al 1993) and can also work in coordination with CPSSs (Chaturvedi et al 2004a;Swidenbank et al 1999).…”

mentioning

confidence: 97%

Experimental Studies of a Generalized Neuron Based Adaptive Power System Stabilizer

Chaturvedi

Malik

2006

Soft Comput

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Artificial neural networks trained as intelligent controllers can easily accommodate the non-linearities and time dependencies of non-linear, dynamic systems. However, they require large training time and large number of neurons to deal with complex problems. Taking benefit of the characteristics of a generalized neuron (GN), that requires much smaller training data and shorter training time, a generalized neuron based adaptive power system stabilizer (GNAPSS) is proposed. It consists of a GN as an predictor, that predicts the plant dynamics, and a GN as a controller to damp low frequency oscillations. Results show that the proposed GNAPSS can provide a consistently good dynamic performance of the system over a wide range of operating conditions.

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Neural network based control for synchronous generators

Cited by 45 publications

References 13 publications

Discussion of "Radial basis function (RBF) network adaptive power system stabilizer" [and reply]

Discussion of "Radial basis function (RBF) network adaptive power system stabilizer" [and reply]

Generalized neuron-based PSS and adaptive PSS

Experimental Studies of a Generalized Neuron Based Adaptive Power System Stabilizer

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