Evolutionary techniques, a sensitivity-based approach for handling discrete variables in reactive power planning

Ionescu, Cristiana; Bulac, Constantin; Eremia, Mircea; Roscia, Mariacristina

doi:10.1109/ichqp.2012.6381296

Cited by 4 publications

(4 citation statements)

References 8 publications

(7 reference statements)

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“…Evolutionary optimization algorithm are global search techniques, thus they can overcome the drawbacks of the traditional optimization methods: in fact, they can face nonlinear and discontinuous problems, with a great number of variables [14,15]. Among the main evolutionary optimization approaches it is worth mentioning the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO).…”

Section: Gso Algorithmmentioning

confidence: 99%

Using design optimization in radio frequency energy harvesting systems

Roscia

Zaninelli

Lazaroiu³

et al. 2013

2013 International Conference on Clean Electrical Power (ICCEP)

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RF-harvesting energy is the process of scavenging power from electromagnetic fields at radio frequency band. By RF-harvesting technology, we are able to recycle EM-fields and use them as power supply for electrical devices. The significant advantage of this technology is the reduction of electromagnetic pollution existence in the free space from wireless radio networks, cell phone towers, TV signals, and etc. An important component in RF harvesting system is the "rectenna". The rectenna is composed of a receiving antenna and a RF/DC converter. Since the receiving antenna provides the RF power as input for the converter, the design of a receiving antenna with high efficiency represents the objective of the present paper.In order to design antenna and to improve the link coupling, a hybrid optimization method in this study, called Genetical Swarm Optimization (GSO), is proposed and applied. The GSO has already been successfully applied to the optimization of antennas, wireless systems, and energy harvesting devices.

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Section: Gso Algorithmmentioning

confidence: 99%

Using design optimization in radio frequency energy harvesting systems

Roscia

Zaninelli

Lazaroiu³

et al. 2013

2013 International Conference on Clean Electrical Power (ICCEP)

Self Cite

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“…The authors in [20] discussed a hierarchical reactive power planning that optimizes a set of curative controls, such that solution satisfies a given voltage stability margin. Evolutionary algorithms (EAs) Like Genetic Algorithm (GA), Differential Evolution (DE), and Evolutionary planning (EP) [19] have been extensively demoralized during the last two decades in the field of engineering optimization. They are computationally competent in result the global finest solution for reactive power planning and will not to be get attentive in local minima.…”

Section: Introductionmentioning

confidence: 99%

“…They are computationally competent in result the global finest solution for reactive power planning and will not to be get attentive in local minima. Such intelligence modified new algorithms are used for reactive power planning recent works [18,19].Despite of several positive features, it has been observed that DE sometimes does not perform as good as the expectations. Empirical analysis of DE has shown that it may stop proceeding towards a global optimum even through the population has not converged to a local optimum.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Optimization based optimal Reactive Power Planning Using Improved Differential Evolution Incorporating FACTS

Vadivelu¹,

Marutheeswar²

2015

ijeei

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Optimal reactive power planning is one of the major and important problems in electrical power systems operation and control. This is nothing but multi-objective, nonlinear, minimization problem of power system optimization. This paper presents the relevance of New Improved Differential Evolution (NIDE) algorithm to solve the Reactive Power Planning (RPP) problem based on Multi-objective optimization. Minimization of total cost of energy loss and cost of F A C T c o n t r o l e r s installments are taken as the objectives incorporating (RPP) problem. With help of New Voltage Stability Index (NVSI), the critical lines and buses are identified to install the FACTS controllers. The optimal settings of the control variables of the generator voltages, transformer tap settings and provision and parameter settings of the FACT controllers SVC, TCSC, and UPFC are considered for reactive power planning. The approach applied to IEEE 30 and 72-bus Indian system for minimization of active power loss. Simulation results are compared with other optimization algorithm.

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“…Heuristic methods often use sensitivity analysis to calculate static first order sensitivity of power loss to node reactive power under primary distribution system before installing capacitors to select the candidate buses of capacitors [11][12][13]. In a feeder line maybe there are several nodes with higher sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Optimal Capacitors Placement of Distribution Systems Using 2<sup>nd</sup> Order Power Loss Sensitivity Analysis and Hierarchical Clustering

Gao

Zhang

2014

AMR

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Heuristic methods by first order sensitivity analysis are often used to determine location of capacitors of distribution power system. The selected nodes by first order sensitivity analysis often have virtual high by first order sensitivities, which could not obtain the optimal results. This paper presents an effective method to optimally determine the location and capacities of capacitors of distribution systems, based on an innovative approach by the second order sensitivity analysis and hierarchical clustering. The approach determines the location by the second order sensitivity analysis. Comparing with the traditional method, the new method considers the nonlinear factor of power flow equation and the impact of the latter selected compensation nodes on the previously selected compensation location. This method is tested on a 28-bus distribution system. Digital simulation results show that the reactive power optimization plan with the proposed method is more economic while maintaining the same level of effectiveness.

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Evolutionary techniques, a sensitivity-based approach for handling discrete variables in reactive power planning

Cited by 4 publications

References 8 publications

Using design optimization in radio frequency energy harvesting systems

Using design optimization in radio frequency energy harvesting systems

Multi Objective Optimization based optimal Reactive Power Planning Using Improved Differential Evolution Incorporating FACTS

Optimal Capacitors Placement of Distribution Systems Using 2<sup>nd</sup> Order Power Loss Sensitivity Analysis and Hierarchical Clustering

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