Optimal control of distributed generators and capacitors by hybrid DPSO

In this paper, a comprehensive planning methodology is proposed that can minimize the line loss, maximize the reliability and improve the voltage profile in a distribution network. The injected active and reactive power of Distributed Generators (DG) and the installed capacitor sizes at different buses and for different load levels are optimally controlled. The tap setting of HV/MV transformer along with the line and transformer upgrading is also included in the objective function. A hybrid optimization method, called Hybrid Discrete Particle Swarm Optimization (HDPSO), is introduced to solve this nonlinear and discrete optimization problem. The proposed HDPSO approach is a developed version of DPSO in which the diversity of the optimizing variables is increased using the genetic algorithm operators to avoid trapping in local minima. The objective function is composed of the investment cost of DGs, capacitors, distribution lines and HV/MV transformer, the line loss, and the reliability. All of these elements are converted into genuine dollars. Given this, a single-objective optimization method is sufficient. The bus voltage and the line current as constraints are satisfied during the optimization procedure. The IEEE 18-bus test system is modified and employed to evaluate the proposed algorithm. The results illustrate the unavoidable need for optimal control on the DG active and reactive power and capacitors in distribution networks.

Optimal allocation of a cross-connection and sectionalizers in distribution systems

In this paper, the placement of sectionalizers, as well as, a cross-connection is optimally determined so that the objective function is minimized. The objective function employed in this paper consists of two main parts, the switch cost and the reliability cost. The switch cost is composed of the cost of sectionalizers and cross-connection and the reliability cost is assumed to be proportional to a reliability index, SAIDI. To optimize the allocation of sectionalizers and cross-connection problem realistically, the cost related to each element is considered as discrete. In consequence of binary variables for the availability of sectionalizers, the problem is extremely discrete. Therefore, the probability of local minimum risk is high and a heuristic-based optimization method is needed. A Discrete Particle Swarm Optimization (DPSO) is employed in this paper to deal with this discrete problem. Finally, a testing distribution system is used to validate the proposed method.

Optimal allocation and sizing of capacitors to minimize the transmission line loss and to improve the voltage profile

To allocate and size capacitors in a distribution system, an optimization algorithm, called Discrete Particle Swarm Optimization (DPSO), is employed in this paper. The objective is to minimize the transmission line loss cost plus capacitors cost. During the optimization procedure, the bus voltage, the feeder current and the reactive power flowing back to the source side should be maintained within standard levels. To validate the proposed method, the semi-urban distribution system that is connected to bus 2 of the Roy Billinton Test System (RBTS) is used. This 37-bus distribution system has 22 loads being located in the secondary side of a distribution substation (33/11 kV). Reducing the transmission line loss in a standard system, in which the transmission line loss consists of only about 6.6 percent of total power, the capabilities of the proposed technique are seen to be validated.

Optimal allocation and sizing of DGs in distribution networks

In this paper, the placement and sizing of Distributed Generators (DG) in distribution networks are determined optimally. The objective is to minimize the loss and to improve the reliability. The constraints are the bus voltage, feeder current and the reactive power flowing back to the source side. The placement and size of DGs are optimized using a combination of Discrete Particle Swarm Optimization (DPSO) and Genetic Algorithm (GA). This increases the diversity of the optimizing variables in DPSO not to be stuck in the local minima. To evaluate the proposed algorithm, the semi-urban 37-bus distribution system connected at bus 2 of the Roy Billinton Test System (RBTS), which is located at the secondary side of a 33/11 kV distribution substation, is used. The results finally illustrate the efficiency of the proposed method.

Maximum loadability achievement in SWER networks using optimal sizing and locating of batteries

This paper presents an optimisation algorithm to maximize the loadability of single wire earth return (SWER) by minimizing the cost of batteries and regulators considering the voltage constraints and thermal limits. This algorithm, that finds the optimum location of batteries and regulators, uses hybrid discrete particle swarm optimization and mutation (DPSO + Mutation). The simulation results on realistic highly loaded SWER network show the effectiveness of using battery to improve the loadability of SWER network in a cost-effective way. In this case, while only 61% of peak load can be supplied without violating the constraints by existing network, the loadability of the network is increased to peak load by utilizing two battery sites which are located optimally. That is, in a SWER system like the studied one, each installed kVA of batteries, optimally located, supports a loadability increase as 2 kVA.

Diphenyl sulphoxide complexes of some oxocations

Diphenyl sulphoxide (DPSO) complexes of TiO2+, ZrO2+, VO2+ and UO22+ have been prepared and characterized by physicochemical methods. The complexes have the formulae: [TiO(DPSO)5]2 (ClO4)4, [ZrO(DPSO)6] (ClO4)2, [VO(DPSO)5](ClO4)2, [VO(DPSO)3Cl2], [UO2-(DPSO)4] (ClO4)2, [UO2(DPSO)2Cl2],[UO2(DPSO)2(NO3)2]and[UO2(DPSO)2(CH3COO)2]. The i.r. spectra show the coordination through the oxygen of the sulphoxide in all the complexes. The spectroscopic, conductivity and crysoscopic studies indicate the ionic nature of the perchlorate, while the chloride, nitrate and acetate are coordinated, the last two being bidentate. The probable stereochemistry of the complexes is discussed. The complexes decompose exothermally.

Diphenyl sulphoxide complexes of some divalent metal ions*1

Diphenyl sulphoxide (DPSO) complexes of some divalent metal perchlorates and chlorides are prepared The perchlorates of Mn, Co, Ni, Zn and Cd have the general formula [M(DPSO)6](CIO4)2. The Cu(II) complex is found to have the composition [Cu(DPSO)4] (CIO42. The chloro complex having the formula ZnCl2. 2DPSO, CdCl2.DPSO, HgCl2. DPSO and PdCl2. 2 DPSO have also been obtained. Infrared spectra indicate that the DPSO complexes of Mn, Co, Ni, Cu and Zn are oxygen-bonded while those of Cd, Hg and Pd are sulphur-bonded. The magnetic susceptibility and the optical spectral data reveal octahedral coordination for Mn, Co and Ni complexes. From the electronic spectra of Co and NI complexes, the ligand field parameters, Dq and β, are calculated.

Some sulphoxide complexes of iron

Diphenyl sulphoxide(DPSO) and dimethyl sulphoxide(DMSO) complexes of iron(II) having the composition [Fe(DPSO)6](ClO4)2, Fe(DPSO)2Cl2, Fe(DPSO)3Br2, Fe(DPSO)4I2, [Fe (DMSO)3Cl2]. DMSO and [Fe(DMSO)3Br2]. DMSO and DPSO complexes of iron(III), Fe(DPSO)2 Cl3 have been prepared and their physico-chemical properties studied. Their magnetic moments at room temperature show them to be spin-free complexes. The i.r. spectra reveal that oxygen is the donor atom in all the complexes. The electronic spectra of iron(II) complexes indicate octahedral coordination for the metal ion. A salt like structure [Fe(DPSO)4Cl2][FeCl4], is suggested for the iron (III) complex, where the cationic species has distorted octahedral structure while the anionic species has tetrahedral structure.

Diphenyl sulphox1de complexes of thorium(IV)

Thorium(IV) is known to form high coordination-number complexes. An attempt has therefore been made to determine the effect of anions on the coordination complexes of diphenyl sulphoxide (DPSO) with thorium(IV). The complexes formed have the formulae [Th(DPSO)6](ClO4)4, [Th(DPSO)4Cl4], [Th(DPSO)4Br4], [Th(DPSO)6I2]I2, [Th(DPSO)4(NCS)4]and [Th(DPSO)3(NO3)4]. In all the complexes, DPSO is coordinated to the metal ion through its oxygen. The electrical conductances in nitrobenzene and in nitromethane, and ebullioscopic molecular weights in acetonitrile, show that the perchlorate and iodide complexes behave as 1:4 and 1:2 electrolytes, respectively; while the other complexes are monomeric and non-electrolytes. The infrared spectra of the solid complexes indicate the ionic nature of the perchlorate, the bidentate nature of the nitrate and the coordination of the thiocyanate through its nitrogen. [Th(DPSO)4Cl4], [Th(DPSO)4Br4]and [Th-(DPSO)3 (NO3)4]decompose endothermically while [Th(DPSO)6](ClO4)4 and [Th(DPSO)4(NCS)4]decompose exothermically, both in air and in nitrogen. The perchlorate complex has octahedral symmetry around the thorium, the halo- and the thiocyanato complexes are 8-coordinate, probably with square antiprismatic structures, while the nitrate complex is 11-coordinate

Stochastic Algorithms for Discrete Parameter Simulation Optimization

We present two efficient discrete parameter simulation optimization (DPSO) algorithms for the long-run average cost objective. One of these algorithms uses the smoothed functional approximation (SFA) procedure, while the other is based on simultaneous perturbation stochastic approximation (SPSA). The use of SFA for DPSO had not been proposed previously in the literature. Further, both algorithms adopt an interesting technique of random projections that we present here for the first time. We give a proof of convergence of our algorithms. Next, we present detailed numerical experiments on a problem of admission control with dependent service times. We consider two different settings involving parameter sets that have moderate and large sizes, respectively. On the first setting, we also show performance comparisons with the well-studied optimal computing budget allocation (OCBA) algorithm and also the equal allocation algorithm. Note to Practitioners-Even though SPSA and SFA have been devised in the literature for continuous optimization problems, our results indicate that they can be powerful techniques even when they are adapted to discrete optimization settings. OCBA is widely recognized as one of the most powerful methods for discrete optimization when the parameter sets are of small or moderate size. On a setting involving a parameter set of size 100, we observe that when the computing budget is small, both SPSA and OCBA show similar performance and are better in comparison to SFA, however, as the computing budget is increased, SPSA and SFA show better performance than OCBA. Both our algorithms also show good performance when the parameter set has a size of 10(8). SFA is seen to show the best overall performance. Unlike most other DPSO algorithms in the literature, an advantage with our algorithms is that they are easily implementable regardless of the size of the parameter sets and show good performance in both scenarios.

Multi-objective optimization of satellite image registration using discrete Pparticle swarm optimisation

A new multi-sensor image registration technique is proposed based on detecting the feature corner points using modified Harris Corner Detector (HDC). These feature points are matched using multi-objective optimization (distance condition and angle criterion) based on Discrete Particle Swarm Optimization (DPSO). This optimization process is more efficient as it considers both the distance and angle criteria to incorporate multi-objective switching in the fitness function. This optimization process helps in picking up three corresponding corner points detected in the sensed and base image and thereby using the affine transformation, the sensed image is aligned with the base image. Further, the results show that the new approach can provide a new dimension in solving multi-sensor image registration problems. From the obtained results, the performance of image registration is evaluated and is concluded that the proposed approach is efficient.

Multiobjective discrete particle swarm optimization for multisensor image alignment

A new technique is proposed for multisensor image registration by matching the features using discrete particle swarm optimization (DPSO). The feature points are first extracted from the reference and sensed image using improved Harris corner detector available in the literature. From the extracted corner points, DPSO finds the three corresponding points in the sensed and reference images using multiobjective optimization of distance and angle conditions through objective switching technique. By this, the global best matched points are obtained which are used to evaluate the affine transformation for the sensed image. The performance of the image registration is evaluated and concluded that the proposed approach is efficient.

Accurate point matching based on multi-objective Genetic Algorithm for multi-sensor satellite imagery

This paper investigates a novel approach for point matching of multi-sensor satellite imagery. The feature (corner) points extracted using an improved version of the Harris Corner Detector (HCD) is matched using multi-objective optimization based on a Genetic Algorithm (GA). An objective switching approach to optimization that incorporates an angle criterion, distance condition and point matching condition in the multi-objective fitness function is applied to match corresponding corner-points between the reference image and the sensed image. The matched points obtained in this way are used to align the sensed image with a reference image by applying an affine transformation. From the results obtained, the performance of the image registration is evaluated and compared with existing methods, namely Nearest Neighbor-Random SAmple Consensus (NN-Ran-SAC) and multi-objective Discrete Particle Swarm Optimization (DPSO). From the performed experiments it can be concluded that the proposed approach is an accurate method for registration of multi-sensor satellite imagery. (C) 2014 Elsevier Inc. All rights reserved.