Coordinated stabilizing control for the exciter and governor loops using fuzzy set theory and neural nets

Djukanović, M.; Dobrijević, D. M.; Ćalović, M.S.; Novicevic, M; Šobajić, D.J.

doi:10.1016/s0142-0615(97)00020-3

Cited by 20 publications

(10 citation statements)

References 7 publications

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“…Djukanovic et al [42] presented adaptive fuzzy control based on unsupervised learning neural nets for improving the generating unit transient of a hydro power system. By using appropriate means, a linguistic control strategy of optimal state-feedback regulator design was converted to a variable control strategy.…”

Section: Hybrid Artificial Intelligent Controllermentioning

confidence: 99%

Power system stabilization based on artificial intelligent techniques; A review

Hassan

Moghavvemi

Mohamed

2009

2009 International Conference for Technical Postgraduates (TECHPOS)

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This paper reviews new approaches in modern research using Artificial Intelligent (AI) techniques to develop power system stabilizer (PSS). These techniques are Artificial Neural Network (ANN), fuzzy logic, hybrid artificial intelligent, expert systems, and optimization techniques base AI such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search (TS) algorithm, etc. Research showed controllers designed based on a conventional control theory, modern and adaptive control theories, suffer from some limitations. However, AI techniques proved to be able to overcome theses limits. Hence, more researchers preferred to utilize these approaches for the power systems. The review efforts geared towards PSS and excitation system stabilizer developed based on AI techniques, which effectively enhance both small signal stability and transient stability and equally provide superior performances. In addition, the dynamic performance of different AI based stabilizers are established and compared with other types of PSSs.

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Section: Hybrid Artificial Intelligent Controllermentioning

confidence: 99%

Power system stabilization based on artificial intelligent techniques; A review

Hassan

Moghavvemi

Mohamed

2009

2009 International Conference for Technical Postgraduates (TECHPOS)

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“…With output feedback applied to the system of eqs. (5a) to (5c) the following have been obtained: the gain output feedback vector k, i. e.: kT= [-0.0219 0.0167 -0.0412 0.48701, (7) and the matrix A'' from the relationship: xtt = A"-B"kT. Instead of solving a set of differential equations, the fuzzy PD controller provides an algorithm which converts the linguistic control strategy, founded on the fuzzy rule-based expert knowledge, into a selection of system control free parameters.…”

Section: -Case Iii: 8th-order Transformed Closed-loop Modelmentioning

confidence: 99%

Excitation control of a power‐generating system based on fuzzy logic and neural networks

Karnavas

Papadopoulos

2000

Int Trans Elec Energy Syst

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This paper presents a practical design of an intelligent controller (using fuzzy logic and neural network concepts) f o r the excitation control of an isolated power-generating system. The controller is suitable f o r realtime operation, with the aim of improving the dynamic characteristics of the generating unit by acting properly on the exciter input. Atfirst, digital simulations of high-and reduced-order models of the above system are performed using conventional control techniques on five system cases which are based on previous work. Then, a fuzzy logic proportional-plus-derivative controller (FPDC) is designed and the dynamic performances of the rnentionedfive system cases and the FPDCs are presented by comparison. Finally, an enhanced controller is designed which involves a pre-trained neural network as a model dynamics capture. The computer simulation results obtained clearly demonstrate that the performance of the developed fuzzy logic and neural network controller offers better damping effects on the generator oscillations over a bvider range of operating conditions, than the associated ones of the conventional excitation controller designs with output feedback applied to high-and reduced-order system models.

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“…Thus, it is necessary to effectively design control action in the plant with proper model representation of the system. Among the advanced techniques reported in the last decade we include using H ¥ optimal control [1], differential nonlinear geometric structure theory [2], the connectionist approach for tuning the control parameters [3], fuzzy and neural networks [4], nonlinear decentralized robust control [5], fuzzy logic [6], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…A nonlinear model with artificial neural network was also discussed in the study; however, the author did not consider a coordination between the speed control loop and the voltage control loop. Coordinated control of the low head turbine using a nonlinear model linearized around the reference operation region for the controller design is discussed in [4]. Kamwa et al [8] proposed a transient stability program to identify a small signal state-space model of the openloop hydro turbine governor system in normal operation.…”

Section: Introductionmentioning

confidence: 99%

Small hydro power plant identification using NNARX structure

Kishor

Saini

Singh

2005

Neural Comput & Applic

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A feedforward multi-layer perceptron neural network structure is developed to model the nonlinear dynamic relationship between input and output of a hydro power plant connected as single machine infinite bus system. Two independent second-order neural network nonlinear auto-regressive with exogenous signal models are used in the study. The structure selection of each independent model is based on various validation tests. The optimal brain surgeon pruning strategy adopted for optimizing the neural network structure. The network performance is studied for fixed and change in operating point.

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Coordinated stabilizing control for the exciter and governor loops using fuzzy set theory and neural nets

Cited by 20 publications

References 7 publications

Power system stabilization based on artificial intelligent techniques; A review

Power system stabilization based on artificial intelligent techniques; A review

Excitation control of a power‐generating system based on fuzzy logic and neural networks

Small hydro power plant identification using NNARX structure

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