• Advances in Energy Research
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• v.4 no.4
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• pp.255-274
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• 2016

This paper presents a novel symbiotic organisms search (SOS) algorithm to optimize both real power loss (RPL) and voltage stability limit (VSL) of a transmission network by controlling the variables such as unified power flow controller (UPFC) location, UPFC series injected voltage magnitude and phase angle and transformer taps simultaneously. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained multi objective, multi variable optimization problem with a fitness function integrating both RPL and VSL. The symbiotic organisms search (SOS) algorithm is a nature inspired optimization method based on the biological interactions between the organisms in ecosystem. The advantage of SOS algorithm is that it requires a few control parameters compared to other meta-heuristic algorithms. The proposed SOS algorithm is applied for solving optimum control variables for both single objective and multi-objective optimization problems and tested on New England 39 bus test system. In the single objective optimization problem only RPL minimization is considered. The simulation results of the proposed algorithm have been compared with the results of the algorithms like interior point successive linear programming (IPSLP) and bacteria foraging algorithm (BFA) reported in the literature. The comparison results confirm the efficacy and superiority of the proposed method in optimizing both single and multi objective problems.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.11
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• pp.568-576
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• 2002

This paper presents a new algorithm for the enhancement of voltage stability by optimal routing (OR) technique. A new voltage stability index (VSI) for optimal routing is also proposed by using theories of critical transmission path based on voltage phasor approach and equivalent impedance method. Furthermore, the proposed algorithm automatically detect the critical transmission path to critical transmission path to critical load which are faced to voltage collapse due to additional real or reactive loading. We also adopt a improved branch exchange (IBE) algorithm based on a tie branch power (TBP) flow equation to apply the OR technique. The proposed IBE algorithm for the VSI maximizing can effectively search the optimal topological structures of distribution feeders by changing the open/closed states of the sectionalizing and tie switches. The proposed algorithm has been evaluated with the practical IEEE 32, 69 bus test systems and KEPCO 148 bus test system to show favorable performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.227-232
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• 2007

The autoreclosing is applied to power system for maintaining system stability and continuity of supply. Developments on distributed generation(DG) grows significantly by environmental issues and economical issues. If the DG is connected to distribution system, the DG influences the technical aspects such as power quality, protection and stability. It causes the challenges to protection, especially to reclosing. In order to achieve reliability and safety of the distribution system, the rules and guidelines suggest that the DG units should be rapidly disconnected from the network before the reclosing. If the DG is disconnected whenever the fault occurs, it cannot be utilized effectively. This paper presents the adaptive reclosing algorithm considering the DG. The algorithm consists of angle oscillation's judgment, EEEAC(Emergency Expanded Equal-Area Criterion), calculation of optimal reclosing time and re-connection algorithm. The simulation is implemented for the DG technology by using EMTP MODELS. The simulation results show that the transient stability is maintained and the DG is protected against disturbance.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.268-272
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• 2007

These days many scientists make studies of feedback control system for stability on non-linear state and for the maneuver of flying objects. These feedback control systems have to satisfy trajectory condition and angular conditions, that is to say, controllability and stability simultaneously to achieve mission. In this paper, a design methods using model based control system which consists of spiral predictive model, Q-function included into generalized-work function is shown. It is made a clear that the proposed algorithm using SPM maneuvers for controllability and stability at the same time is successful in attaining our purpose. The feature of the proposed algorithm is illustrated by simulation results. As a conclusion, the proposed algorithm is useful for the control of moving objects.

• Smart Media Journal
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• v.11 no.5
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• pp.82-90
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• 2022

Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.1
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• pp.14-17
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• 2015

This paper presents a analysis method for power system stability using a Modified Genetic-based Power System Stabilizer(MGPSS). The proposed MGPSS parameters are optimized using Modified Genetic Algorithm(MGA) in order to maintain optimal operation of generator under the various operating conditions. To improve the convergence characteristics, real variable string is adopted. The results tested on a single machine infinite bus system verify that the proposed controller has better power system stability than conventional controller.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.1
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• pp.1-6
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• 2006

The emergence of competitive power market makes dispatch algorithm with transient stability constraints increasingly important for the transparent power system operation. Heuristic and off-line evaluation for the operation point can produce a discrimination among market players in the deregulated power system. In this paper, a dispatch algorithm with transient stability constraints is proposed. Energy margin under the TEF(Transient Energy Function) structure is adopted as a measure for the stability index. Implementation issues and simulation results are discussed in the context of a 10-bus system

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.52-57
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• 2006

In this paper, we have proposed a new adaptive fuzzy control algorithm based on Takagi-Sugeno fuzzy model. The regulation problem for the uncertain SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the parameter approximation error is proved. Unlike the existing feedback linearization based methods, the proposed algorithm can guarantee the global stability in the presence of the singularity in the inverse dynamics of the plant. The performance of the proposed algorithm is demonstrated through the problem of balancing and swing-up of an inverted pendulum on a cart.

• Nonlinear Functional Analysis and Applications
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• v.26 no.4
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• pp.749-780
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• 2021

In this paper, we give the notion of M(., .)-𝜂-proximal mapping for a nonconvex, proper, lower semicontinuous and subdifferentiable functional on Banach space and prove its existence and Lipschitz continuity. As an application, we introduce and investigate a new system of variational-like inclusions in Banach spaces. By means of M(., .)-𝜂-proximal mapping method, we give the existence of solution for the system of variational inclusions. Further, propose an iterative algorithm for finding the approximate solution of this class of variational inclusions. Furthermore, we discuss the convergence and stability analysis of the iterative algorithm. The results presented in this paper may be further expolited to solve some more important classes of problems in this direction.