Hybrid PSOGSA Algorithm Research Articles

Efficient PID Control Design for Frequency Regulation in an Independent Microgrid Based on the Hybrid PSO-GSA Algorithm

Microgrids are a part of the power system that consists of one or more units of distributed generation and are expected to remain in operation after being disconnected from the system. Since they rely on overlying networks, frequency control is very important for network-independent operation. Some of the most common problems in independently operating microgrids are frequency sustainability and its fluctuations. The main purpose of this study is to control the frequency of a microgrid in island mode in different scenarios. The objective function is defined based on time and changes in the system frequency. Thus, the variable parameters of the PID controller are transformed into an optimization problem and are solved through the hybrid PSO-GSA algorithm. The study considers four scenarios: (a) a microgrid dynamic model and optimal PID controller coefficients; (b) variable velocity disturbance applied to the studied system in order to observe power changes and the microgrid frequency; (c) stepped load changes applied to the studied system; and (d) the proposed methods on the standard test function. Simulations under different operating conditions are performed, indicating improvements in the stability of microgrid frequency fluctuations by means of the proposed control method.

Feed-Forward Neural Networks Training with Hybrid Taguchi Vortex Search Algorithm for Transmission Line Fault Classification

In this study, the hybrid Taguchi vortex search (HTVS) algorithm, which exhibits a rapid convergence rate and avoids local optima, is employed as a new training algorithm for feed-forward neural networks (FNNs) and its performance was analyzed by comparing it with the vortex search (VS) algorithm, the particle swarm optimization (PSO) algorithm, the gravitational search algorithm (GSA) and the hybrid PSOGSA algorithm. The HTVS-based FNN (FNNHTVS) algorithm was applied to three datasets (iris classification, wine recognition and seed classification) taken from the UCI database (the machine learning repository of the University of California at Irvine) and to the 3-bit parity problem. The obtained statistical results were recorded for comparison. Then, the proposed algorithm was used for fault classification on transmission lines. A dataset was created using 735 kV, 60 Hz, 100 km transmission lines for different fault types, fault locations, fault resistance values and fault inception angles. The FNNHTVS algorithm was applied to this dataset and its performance was tested in comparison with that of other classifiers. The results indicated that the performance of the FNNHTVS algorithm was at least as successful as that of the other comparison algorithms. It has been shown that the FNN model trained with HTVS can be used as a capable alternative algorithm for the solution of classification problems.

Optimum design of 3R manipulator using hybrid PSOGSA algorithm

Optimum design of 3R manipulator using hybrid PSOGSA algorithm

Optimum design of 3R manipulator using hybrid PSOGSA algorithm

Larger workspace volume is one of the most important objectives in the optimum design of robot manipulator. In the present study, the workspace volume of a 3R manipulator has been maximised. The nonlinear constrained optimisation problem has been solved using the particle swarm optimisation (PSO) and a hybrid PSO and gravitational search algorithm (GSA). The proposed algorithm combines the search method of PSO and GSA with enhanced exploration ability to achieve higher workspace volume as compared to the established available results. Further, the total void cross section area has been estimated and a quantitative analysis of the results has been made to identify the key influencing kinematic parameters and prioritise the constraints. Applications of the hybrid algorithm on two industrial manipulators are presented as case studies. An important implication of this article is that the proposed hybrid algorithm provides encouraging result in terms of objective function values and CPU time.

Static and electromagnetic transient considerations incorporated into shunt capacitor allocation for distribution network using hybrid PSOGSA algorithm

Nowadays the majority of electrical power systems are stricken by some intricacies originating from their running near operational and physical limits due to the restructuring and liberalization. Deploying any apparatus within such a system is required all aspects of studies. Shunt capacitor is one of those equipment being utilized for a broad range of objectives including power factor correction, reactive power compensation, system stability improvement, and power loss reduction. So, it is required to investigate all dimensions of its allocation in the light of steady-state along with electromagnetic transients. Regarding a better response of a shunt capacitor bank (CB) to a distribution network, the steady-state study associated with its allocation is generally conducted while in many cases, consideration of electromagnetic transient is omitted. Consequently, it is more likely to cause transient overvoltage and extreme charging/discharging current damaging system equipment severely. In this paper, an integrated approach is proposed to make a compromise between two issues, steady-state study to find the optimal allocation of CBs and capacitor switching with minimum electromagnetic transients. In steady-state study, estimating the optimal allocation of CB is carried out through the hybrid PSOGSA algorithm. A certain privilege of this algorithm is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms’ strength. Moreover, the minimum electromagnetic transient of CB switching amongst the potential allocation sets is identified by a new index. The proposed method is verified by EMTP-RV and MATLAB software and the results show its applicability.

Hybrid PSOGSA Algorithm and Nonlinear Features for Prediction Body Mass Index (BMI) from Speech Signals

Hybrid PSOGSA Algorithm and Nonlinear Features for Prediction Body Mass Index (BMI) from Speech Signals

A solution to the ORPD problem and critical analysis of the results

One of the most important conditions for economic and secure operation of electric power system is the optimal reactive power dispatch (ORPD). The ORPD is achieving by appropriate coordination of the equipments which manage the reactive power flows to minimize the real power loss and/or improve the voltage profile of the power system. Mathematically, the ORPD problem can be formulated as a nonlinear optimization problem with constraints. In this paper, a hybrid PSOGSA algorithm is proposed for solving the ORPD problem. The results obtained by hybrid PSOGSA on standard IEEE 30-bus and IEEE 118-bus test systems are compared with other meta-heuristic optimization methods. In addition, a critical review of the ORPD results published in the recent literature is presented also.

A Solution to the Combined Economic and Emission Dispatch Using Hybrid PSOGSA Algorithm

ABSTRACTThis article presents a new hybrid algorithm based on particle swarm optimization (PSO) and the gravitational search algorithm (GSA) for solving the combined economic and emission dispatch (CEED) problem in power systems. Performance of this approach for the CEED problem is studied and evaluated on three test systems with 3, 6, and 40 generating units, with various cost curve nature and different constraints. The results obtained are compared to those reported in the recent literature. Those results show that the proposed algorithm provides an effective and robust high-quality solution of the CEED problem.

Simultaneous feature selection and parameter optimisation of support vector machine using adaptive particle swarm gravitational search algorithm

Particle swarm optimisation PSO and gravitational search algorithm GSA are two metaheuristics that have been used to solve both continuous and discrete problems. Furthermore, their hybridisation can enhance the algorithm performance in these two kinds of problems. However, their utilisation for mixed continuous-discrete problems has not been well investigated. One the other hand, feature selection and parameter optimisation are two important issues in machine learning. The aim of this work is to simultaneously explore both issues, proposing a mixed encoded population and variable representation of PSOGSA in order to select the relevant features and to optimise support vector machine parameters which has proved important predictive ability in feature selection. Furthermore, an adaptive mutation operator has been introduced into the hybrid PSOGSA algorithm. Experimental results on 10 benchmark data sets show that this proposed mixed approach can achieve high performance in both training and testing sets when comparing with PSO, GSA, PSOGSA and the genetic algorithm GA.

A comparative study of BA, APSO, GSA, hybrid PSOGSA and SPSO in dual channel speech enhancement

The present paper focuses on the suppression of background noise in speech signal by utilizing a powerful heuristic optimization algorithm called Bat algorithm (BA) for adaptive filtering in dual channel enhancement systems. Bat algorithm is a recently developed population based meta heuristic approach which is inspired by the hunting behavior of the bats. It is developed by Yang (2010). As a novel feature Bat algorithm is based on the echo location behavior of micro bats. BA uses the frequency tuning technique to increase the diversity of the solutions in the population, while at the same time it uses the automatic zooming and tries to balance the exploration and exploitation. A few studies have been carried out on the use of heuristics for ANC in speech enhancement by using standard particle swarm optimization (SPSO) and some of its variants till 2010. In order to extend these heuristic approaches to speech, accelerated particle swarm optimization (APSO) based enhancement approach has been proposed (Prajna et al. in Int J Speech Technol 17(4):341---351, 2014a; Prajna et al. in IJISA 6(4):1---10, 2014b, doi:10.5815/ijisa.2014.04.01; Prajna et al. in IEEE international conference on communications and signal processing (ICCSP), April 2014, pp 1457---1461, 2014c, doi:10.1109/ICCSP.2014.6950090). To overcome the problem of poor exploitation ability of APSO, another approach is proposed based on gravitational search algorithm (GSA) (Prajna et al. 2014a, b, c). To combine both the abilities of PSO and GSA algorithms, Hybrid PSOGSA algorithm is also proposed to ANC (Prajna et al. 2014a, b, c). To further improve the efficiency of adaptive filtering in ANC, by providing a dynamic balance between exploration and exploitation, BA is proposed to speech enhancement. This paper intends to present the Bat algorithm as an improved approach to ANC in speech enhancement when compared with that of SPSO, APSO, GSA and Hybrid PSOGSA based speech enhancement algorithms. The performance of all the algorithms is evaluated by computing four objective measures SNRI, PESQ, FAI and WSS, in two real world noise conditions Babble and Factory, at three different input SNR levels set at ?10, 0 and 5 dB. Simulation results prove that BA is the most successful algorithm of all the algorithms studied in this work, to suppress the background noise more effectively.

Time delay approach for PSS and SSSC based coordinated controller design using hybrid PSO–GSA algorithm

Abstract In this work we present a novel approach in order to improve the power system stability, by designing a coordinated structure composed of a power system stabilizer and static synchronous series compensator (SSSC)-based damping controller. In the design approach various time delays and signal transmission delays owing to sensors are included. This is a coordinated design problem which is treated as an optimization problem. A new hybrid particle swarm optimization and gravitational search algorithm (hPSO–GSA) algorithm is used in order to find the controller parameters. The performance of single-machine infinite-bus power system as well as the multi-machine power systems are evaluated by applying the proposed hPSO–GSA based controllers (PSS and damping controller). Various results are shown here with different loading condition and system configuration over a wide range which will prove the robustness and effectiveness of the above design approach. From the results it can be observed that, the proposed hPSO–GSA based controller provides superior damping to the power system oscillation on a wide range of disturbances. Again from the simulation based results it can be concluded that, for a multi-machine power system, the modal oscillation which is very dangerous can be easily damped out with the above proposed approach.

Comparative Analysis of Nature Inspired Algorithms Applied to Reactive Power Planning Studies

Reactive power optimization is one of the efficient ways of improving power system reliability and efficiency which is beneficial in power planning studies. ORPD problems are best solved using different algorithms to achieve optimization. This paper deals a comparative study of three algorithms PSO, GSA and hybrid PSOGSA algorithm. The ORPD multiobjective problem is tested over standard IEEE system. The test is performed under two conditions with and without penalty function added to the objective function. And all results obtained are summarized and best optimal solution is obtained from the hybrid PSOGSA algorithm. Keywords: ORPD (Optimal Reactive Power Dispatch) Problem, GSA (Gravitational Search algorithm), PSO (Particle swarm Optimization), Hybrid PSOGSA

Optimal Location and Sizing of Multiple Static VAr Compensators for Voltage Risk Assessment Using Hybrid PSO-GSA Algorithm

A new modified severity index is proposed, which when accounted with the probability of the occurrence of contingency, quantifies the risk that numerically describes how close the system is to voltage instability. Voltage instability is mainly due to insufficient reactive power support. FACTS devices are utilized to improve the voltage profile of the system during line outages. The static VAr compensator (SVC) is considered here, as the compensating device. This paper investigates the outage of all 820 possible combinations of N−2 line contingencies in the IEEE 30-bus system, and the most critical contingencies are ranked based on the voltage severity index and voltage risk index. A practical Indian utility 62-bus system is also investigated for all the possible 4,005 contingency states, which are then ranked. A hybrid combination of two heuristic algorithms, particle swarm optimization and gravitational search algorithm are used, which cartels the exploiting and exploring features of the two algorithms respectively, to achieve the objective of determining the optimal location and optimal size of the SVC and to minimize thereby the voltage deviation from the nominal value. This aids the system operator in deciding the effective control action, based on the real time severity index. The test results are compared with both the existing and proposed severity indices using the conventional PSO algorithm, and the proposed hybrid PSO-GSA algorithm. The computational time of the work substantiates the feasibility of the proposed method for real time online severity assessment and enhancement of the system’s security.