This paper aims to study distribution system operations by the ant colony search algorithm (ACSA). The objective of this study is to present new algorithms for solving the optimal feeder reconfiguration problem, the optimal capacitor placement problem, and the problem of a combination of the two. The ACSA is a relatively new and powerful swarm intelligence method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as ldquogreedyrdquo search. The ACSA was inspired from the natural behavior of ants in locating food sources and bring them back to their colony by the formation of unique trails. Therefore, through a collection of cooperative agents called ldquoants,rdquo the near-optimal solution to the feeder reconfiguration and capacitor placement problems can be effectively achieved. In addition, the ACSA applies the state transition, local pheromone-updating, and global pheromone-updating rules to facilitate the computation. Through simultaneous operation of population agents, process stagnation can be effectively prevented. Optimization capability can be significantly enhanced. The proposed approach is demonstrated using two example systems from the literature. Computational results show that simultaneously taking into account both feeder reconfiguration and capacitor placement is more effective than considering them separately.

Reconfiguration and Capacitor Placement for Loss Reduction of Distribution Systems by Ant Colony Search Algorithm

As a direct consequence of power systems restructuring on one hand and unprecedented renewable energy utilization on the other, the uncertainties of power systems are getting more and more attention. This fact intensifies the difficulty of decision making in the power system context; therefore, the uncertainty analysis of the system performance seems necessary. Generally, uncertainties in any engineering system study can be represented probabilistically or possibilistically. When sufficient historical data of the system variables is not available, a probability density function (PDF) might not be defined, they must be represented in another manner i.e. using possibilistic theory. When some of the system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution is needed. This paper gives a complete review on uncertainty modeling approaches for power system studies making sense about the strengths and weakness of these methods. This work may be used in order to select the most appropriate method for each application.

A comprehensive review on uncertainty modeling techniques in power system studies

Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse coding is applied to sub-elements ( i.e. patches) of the signal, where such an assumption is invalid. Convolutional sparse coding explicitly models local interactions through the convolution operator, however the resulting optimization problem is considerably more complex than traditional sparse coding. In this paper, we draw upon ideas from signal processing and Augmented Lagrange Methods (ALMs) to produce a fast algorithm with globally optimal sub problems and super-linear convergence.

Fast convolutional sparse coding

A power distribution system reconfiguration methodology considering the reliability and the power loss is developed in this paper. Probabilistic reliability models are used in order to evaluate the reliability at the load points. An algorithm for finding the minimal cut sets is utilized to find the minimal set of components appearing between the feeder and any particular load point. The optimal status of the switches in order to maximize the reliability and minimize the real power loss is found by a binary particle swarm optimization-based search algorithm. The effectiveness of the proposed methodology is demonstrated on a 33-bus and a 123-bus radial distribution system.

Reconfiguration of Power Distribution Systems Considering Reliability and Power Loss

A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using Fireworks Algorithm

A new method based on a fuzzy mutated genetic algorithm for optimal reconfiguration of radial distribution systems (RDS) is presented. The proposed algorithm overcomes the combinatorial nature of the reconfiguration problem and deals with noncontinuous multi-objective optimization. The attractive features of the algorithm are: preservation of radial property of the network without islanding any load point by an elegant coding scheme and an efficient convergence characteristic attributed to a controlled mutation using fuzzy logic.

Optimal reconfiguration of radial distribution systems using a fuzzy mutated genetic algorithm

In this letter, the probabilistic distribution-based interval arithmetic approach is used to incorporate uncertainty in load demand. The variation of load is represented as Gaussian distribution function. It is suggested that the proposed power flow could be useful for planning and expansion planning of radial distribution systems.

Use of interval arithmetic to incorporate the uncertainty of load demand for radial distribution system analysis

The purpose of this paper is to expose students to a simple and efficient load flow algorithm for a radial distribution system (RDS). The basic structure and properties of the RDS are discussed, followed by an illustrative description of the load flow algorithm. The proposed load flow algorithm exploits the tree-type structure of the RDS and uses data structures efficiently. The flowchart and pseudo-codes of the algorithm have been included to help the students in writing their own program. The time and space complexity of the algorithm have also been discussed. The treatment and analysis will be useful for students' understanding of RDS, use of data structures in programming, and approximate complexity analysis of algorithms. The instructors can effectively use the contents of the paper for a basic lecture on RDS (PDF files of the Microsoft PowerPoint slides and C language program code can be downloaded from the website http://www1.mmu.edu.my/similar to nirod/).

A Simple Approach for Branch Current Computation in Load Flow Analysis of Radial Distribution Systems

The significant issues in remote sensing image fusion are enhancing the spatial details and preserving the essential spectral information. The classical pan-sharpening methods often incur spectral distortion and still striving to produce the fused images with prominent spatial and spectral attributes. Motivated by the desirable results of sparse representation (SR) theory, a novel pan-sharpening method is developed based on SR of high frequency (HF) components over a multi-scale learned dictionary (MSLD). MSLD technique acquires the capability of extracting the intrinsic characteristics of images, wherein, it possess the features of both multi-scale representation and learned dictionaries. In this paper, the dictionaries are adaptively learned from HF sub-images derived from the two versions of panchromatic image, realized at different spatial resolutions. A fast and computationally efficient algorithm is used for dictionary learning. The notion of SR together with patch recurrence over different scales is incorporated to estimate the high frequency details. The fused image is reconstructed by injecting the band specific spatial details into the up-sampled multi-spectral images. The performance of the proposed method is appraised with the datasets from different satellite sensors namely, QuickBird, IKONOS, WorldView-2 and Pleiades. The observations inferred from visual perception and quality indices analysis manifest the efficiency of proposed method over several well-known methods for the datasets considered at reduced-scale and full-scale resolutions. Further, the quantitative analysis of obtained performance measures confirms the efficacy of the proposed method for the reduced-scale and full-scale data sets. Especially, at a reduced-scale, proposed method yields an optimal value of Correlation coefficient, Structural similarity and Q4. In a comparative sense, usage of the proposed method at full-scale results in 4% and 2.56% improvement in the Spatial distortion index for QuickBird and WorldView-2 data respectively contrary to the best reported outcome obtained from Sparse Representation of injected details (SR-D) scheme. Invariably, for full-scale data, the QNR attains its optimal value.

Sparsity inspired pan-sharpening technique using multi-scale learned dictionary

The overall performance and thus the achievable efficiency of wireless sensor networks (WSNs) rely on the type of protocols deployed to support particular application in hand. In WSN, network's lifetime depends upon the residual energy of individual nodes. In this paper; a brief survey comprises of various reported methodologies that make uses of geographical location attributes for routing in WSN is presented. To assist the routing algorithm on location aspect of the source and destination as well as other intermediate nodes; a location index is formulated based on binary encoded spatial frames. The merit of proposed spatial encoding scheme is its supportiveness on scalability aspects, so it can be easily tailored to accommodate hierarchy based network architectures. Further, the scheme incorporates an effective mechanism to select cluster heads for each cluster in such a way so as to avoid the localization of hot spot effects right from cluster level to network level.

Ashvini Chaturvedi

Papers

Optimal reconfiguration of radial distribution systems using a fuzzy mutated genetic algorithm

Use of interval arithmetic to incorporate the uncertainty of load demand for radial distribution system analysis

A Simple Approach for Branch Current Computation in Load Flow Analysis of Radial Distribution Systems

Sparsity inspired pan-sharpening technique using multi-scale learned dictionary

Geographical location based hierarchical routing strategy for wireless sensor networks