Ankita Mohapatra

Economic Load Dispatch Using Hybrid Swarm Intelligence Based Harmony Search Algorithm

Abstract This article presents a novel stochastic optimization approach to solve the constrained economic load dispatch problem using a hybridization of the harmony search algorithm and particle swarm optimization, named hybrid harmony search based on the swarm intelligence principle. Harmony search is a recently developed derivative-free, meta-heuristic optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This work is an attempt to utilize the velocity-based particle updating process from particle swarm optimization in the improvisation process of the harmony search algorithm for a better convergence of the proposed algorithm. The proposed methodology also easily takes care of solving non-convex economic load dispatch problems along with different constraints, such as power balance, ramp rate limits of the generators, and prohibited operating zones. Simulations were performed over four various standard test systems with different numbers of generating units, and a comparative study is carried out with other existing relevant approaches. The findings affirm the robustness and proficiency of the proposed methodology over other existing techniques.

Neighborhood Search-Driven Accelerated Biogeography-Based Optimization for Optimal Load Dispatch

Lack of exploration capability of biogeography-based optimization (BBO) leads to slow convergence. To address this limitation, this paper presents a memetic algorithm (MA), namely, aBBOmDE, which is a new version of BBO to solve both complex and noncomplex economic load dispatch (ELD) problems of thermal plant. In aBBOmDE, the performance of BBO is accelerated by using a modified mutation and clear duplicate operators. Then, modified DE (mDE) is embedded as a neighborhood search operator to improve their fitness after a predefined threshold. mDE is used with mutation operator DE/best/1/bin to explore the search near the best solution. The length of local search is set to achieve a balance between the search capability and the excess computational cost. In aBBOmDE, migration mechanism is kept same as that of BBO to maintain its exploitation ability. Modified operators are utilized to enhance the exploration ability while a neighborhood search operator, further, enhances the search capability of the algorithm. This combination significantly improves the convergence characteristics of the original algorithm. The effectiveness of the proposed algorithm has been verified on five different test systems with varying degree of complexity. The results have been compared with other existing techniques. The results indicate that the proposed approach can efficiently solve practical ELD problems.

Economic load dispatch solution by improved harmony search with wavelet mutation

This paper presents a new evolutionary optimisation algorithm to solve economic load dispatch (ELD) problem with operational constraints using the improved harmony search algorithm. Harmony search algorithm is a recently developed derivative-free, meta-heuristic optimisation algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. In this paper we replace the random selection process in the classical harmony search method by wavelet theory based mutation process to improve the performance of the algorithm. The proposed methodology easily takes care of solving non-convex ELD problems along with different constraints like power balance, ramp rate limits of the generators and prohibited operating zones. Simulations were performed over various standard test systems with different number of generating units and a comparative study is carried out with other existing relevant approaches. The results obtained reveal the robustness and ability of the proposed methodology over other existing techniques.

Magnetic stimuli-responsive chitosan-based drug delivery biocomposite for multiple triggered release

Stimuli-responsive biomaterials offer a unique advantage over traditional local drug delivery systems in that the drug elution rate can be controllably increased to combat developing symptomology or maintain high local elution levels for disease treatment. In this study, superparamagnetic Fe3O4 nanoparticles and the antibiotic vancomycin were loaded into chitosan microbeads cross-linked with varying lengths of polyethylene glycol dimethacrylate. Beads were characterized using degradation, biocompatibility, and elution studies with successive magnetic stimulations at multiple field strengths and frequencies. Thirty-minute magnetic stimulation induced a temporary increase in daily elution rate of up to 45% that was dependent on field strength, field frequency and cross-linker length. Beads degraded by up to 70% after 3 days in accelerated lysozyme degradation tests, but continued to elute antibiotic for up to 8 days. No cytotoxic effects were observed in vitro compared to controls. These promising preliminary results indicate clinical potential for use in stimuli-controlled drug delivery.

A study on DG and capacitor placement in radial distribution system

This paper presents a meta-heuristic approach based on Differential Evolution (DE) algorithm for optimal placement of Distributed Generation (DG) and capacitor in an existing distribution system. The distribution system considered in the case study is a 69 bus radial distribution systems. The objective of the work is to find out the suitable size and the placement of the DG as well as the capacitor bank in the test system to minimize the total power loss while satisfying the operational constraints like voltage deviation and line flow limits of the system. In the existing 69 bus radial system, it is observed that maximum load (almost 32 %) is placed at 61 bus. The effect of the DG and capacitor placement in the proposed system is also studied by interchanging the maximum load to different bus at other laterals so as to study the effect of loading pattern.

Feature selection and accurate classification of single and multiple power quality events

In this paper an attempt has been made to classify the power quality disturbances more accurately. Wavelet Transform (WT) has been used to extract the useful features of the power system disturbance signal and optimal feature set is selected using Fuzzified Discrete Harmony Search (FDHS) to classify the PQ disturbances. Support Vector Machine (SVM) has been used to classify the disturbances. FDHS is used both for parameter selection of SVM and, feature dimensionality reduction to achieve high classification accuracy. Six types of PQ disturbances have been considered and simulations have been carried out which show that the combination of feature extraction by WT followed by feature dimension reduction and parameter selection of Gaussian kernel using FDHS increases the testing accuracy of SVM.

A Comparative Study of Signal Processing and Pattern Recognition Approach for Power Quality Disturbance Classification

Abstract This paper presents an extensive study of the advanced signal processing techniques for the classification of different Power Quality (PQ) disturbances. A detailed study of application of the signal processing techniques like Wavelet Transform (WT) and Wavelet Packet Transform (WPT) is carried out for the said purpose. These techniques are used to extract useful information from the raw signal in different frequency bands and give the time–frequency information. Hence, the statistical features are derived from this information and are used for the classification purpose. The features extracted are given to the Neural Network (NN) for training and subsequently it is tested for an effective classification. Three types of NN classifiers, namely, Multi Layer Feed Forward (MLFF), Probabilistic Neural Network (PNN) and Radial Basis Function (RBF) NN are analyzed for effective classification of PQ disturbances. For real-time implementation, one has to see the structural complexity of NN along with its capability of accurate classification. Hence, these NNs are compared with respect to classification performance and structural complexity. The simulation results show that the PNN offers acceptable classification accuracies.

Solution to Non-convex Electric Power Dispatch Problem Using Seeker Optimization Algorithm

This paper presents the application of Seeker Optimization Algorithm (SOA) to constrained economic load dispatch problem. Independent simulations were performed over separate systems with different number of generating units having constraints like prohibited operating zones and ramp rate limits. The performance is also compared with other existing similar approaches. The proposed methodology was found to be robust, fast converging and more proficient over other existing techniques.

A hybrid multi-objective improved bacteria foraging algorithm for economic load dispatch considering emission

In this paper, a hybrid multiobjective improved bacterial foraging algorithm with fuzzy dominance sorting FSIBF is proposed and used to solve environmental friendly economic load dispatch problem. The fuzzy dominance-based sorting procedure is used to select the non-dominated solutions in the Pareto front. The proposed algorithm is applied to nonlinear constrained multiobjective environmental friendly economic load dispatch problem. In the proposed work, we have considered the standard IEEE 30-bus six-generator test system with fuel cost and emission as two conflicting objectives to be optimised simultaneously satisfying the systems operational constraints. The constraints imposed from the operation point of view are the limits on generator real power and reactive power outputs, bus voltages and power flow in the transmission lines. The proposed work also includes the effect of having non-smooth cost characteristics of the thermal generators which arises because of the valve point loading effect. The practical generator constraints such as ramp rate limits and prohibited operating zones are also incorporated in this work suitably.

Optimal placement of capacitors in distribution networks using a modified teaching-learning based algorithm

This paper presents an efficient approach to determine the size of the capacitor bank and its placement in a radial distribution system. The objective of the above optimization is to place a proper size of the capacitor bank at a particular bus so as to reduce the power loss in the distribution system. Although the prime objective of the work in reduction of loss, during optimization other operational constraints of the system like, voltage limits at the distribution buses and the current limit of the lines have been considered. Teaching Learning Based Optimization (TLBO) algorithm is adopted in this work to find the optimal size of capacitors and its location in an existing radial distribution system. The proposed method is applied to 10 and 85-bus radial distribution systems and the obtained results are compared with other existing methods.