Manas Kumar Mallick

Improved Harmony Search for Economic Power Dispatch

This paper presents a novel optimization approach using Improved Harmony Search (IHS) algorithm to solve economic power dispatch problem. The proposed methodology easily takes care of different equality and inequality constraints of the power dispatch problem to find the optimal solution. To show its efficiency, the proposed algorithm is applied to single area and multi area system of four area having 16 units with and without Prohibited Operating Zones (POZ). The results are compared with other existing relevant approaches. The result obtained by the proposed method confirmed that the robustness and efficiency of the algorithms over other reported methods.

Brain storming incorporated teaching-learning-based algorithm with application to electric power dispatch

This paper intends to incorporate a brain storming mechanism into the existing Teaching---Learning---Based Optimization (TLBO) algorithm. The potential solutions of TLBO evolve using the primitive steps that are maintained between the acts of teaching and learning. Another novel algorithm, Brain Storm Optimization (BSO) sticks to the philosophy of interchange of ideas by a team to develop as a whole. The brain storming methods from BSO are introduced into the working of TLBO and applied to a well---studied electric power dispatch problem of high intricacy. The results are compared to best of the existing solutions to demonstrate the efficacy of the proposed hybrid algorithm.

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.

On optimal feature selection using modified Harmony search for power quality disturbance classification

This paper presents a novel technique for power quality disturbance classification. Wavelet Transform (WT) has been used to extract some useful features of the power system disturbance signal and Discrete Harmony Search with Modified Differential mutation operator (DHS_MD) have been used for feature dimension reduction in order to achieve high classification accuracy. Next, a Probabilistic Neural Network (PNN) has been trained using the optimal feature set selected by DHS_MD for automatic PQ disturbance classification. Considering ten types of PQ disturbances, simulations have been carried out which show that the combination of feature extraction by WT followed by feature reduction using DHS_MD increases the testing accuracy of PNN while classifying PQ signals.

Dynamic routing algorithm in wireless mesh network

Wireless mesh networks (WMNs) have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broadband internet access. WMNs have been broadly accepted in the conventional application sectors of ad hoc networks because of their advantages over other wireless networks. Traffic routing has a critical role in determining the performance of wireless mesh networks. Routing protocol design for wireless mesh networks is critical to maintain the performance and reliability of wireless mesh networks. Thus a routing protocol or an algorithm for WMNs should be carefully designed taking into account the specific characteristics of that network. Traditional routing protocols send traffic along pre-determined paths and have been shown ineffective in coping with unreliable and unpredictable wireless medium. In this paper we propose to formulate the mesh network routing problem as an optimisation problem. In the formulation of the problem we have considered both the static and dynamic traffic demand. This paper proposes a dynamic routing algorithm that is able to provide the optimal performance on all possible traffic demands. Our simulation results show that the proposed method performs competitively.

A novel fuzzy logic controller for STATCOM to improve power system stability

This paper presents an application of Single-input Fuzzy Logic Controller (SFLC) to determine the control signal of a Static Compensator (STATCOM) synchronous for improvement of power system stability. This compensation scheme is relevant to Flexible AC Transmission systems (FACTSs) technology which is used worldwide to improve system dynamic performance. STATCOM improves the damping of electromechanical oscillations when used in transmission systems. The SFLC uses only one input variable which is called as signed distance. The SFLC has the advantages of reduced number of rules. Thus, generation, estimation and tuning of control rules are much easier while comparing with the existing conventional Fuzzy Logic Controllers (FLCs). The proposed control method is applied to control the AC and DC bus voltage of a STATCOM connected at a load bus in a Single Machine Infinite Bus (SMIB) System and also to improve the power system stability.

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.

Joint energy and spinning reserve dispatch in wind-thermal power system using IDE-SAR technique

This paper proposes an informative differential evolution with self adaptive re-clustering (IDE-SAR) technique to solve the optimal energy and spinning reserve scheduling problem of a wind-thermal power system. The goal of the paper is to solve an economic dispatch problem, and to find optimal allocation of energy and spinning reserves among the thermal and wind generators available to serve the demand. The stochastic behavior of wind speed and wind power is represented by Weibull probability density function. The total cost minimization objective includes cost of energy provided by conventional thermal generators and wind generators, cost of reserves provided by conventional thermal generators. It also includes costs due to over-estimation and under-estimation of available wind power. In order to show the effectiveness and feasibility of the proposed frame work, various case studies are presented for conventional and wind-thermal power system considering the provision of spinning reserves.

Optimal short-term hydrothermal generation scheduling using modified seeker optimisation algorithm

This paper presents a new evolutionary optimisation algorithm to solve the short-term hydrothermal generation problem with operational constraints using the modified seeker optimisation algorithm. Seeker optimisation algorithm is a recently developed empirical gradient based, meta-heuristic optimisation algorithm, which draws inspiration from the random process of human search strategy. In this paper, we improvise the step length determination strategy in the classical seeker optimisation method by considering an optimistically contracting step length calculation. The proposed methodology easily takes care of solving non-linear hydrothermal generation problem along with different constraints like power balance, capacity limits, valve-point loading and prohibited operating zones. Simulations were performed over various standard test cases and a comparative study is carried out with other existing relevant approaches. The result obtained reveals the robustness and ability of the proposed methodology over other existing techniques.