Hybrid PSOGSA Research Articles

An intelligent CPSOGSA-based mixed [formula omitted] robust controller for the multi-hydro-turbine governing system with sharing common penstock

In order to improve the stability of the multi-hydro-turbine governing system with sharing one penstock (MGSSP) during operation, an intelligent CPSOGSA-based mixed H2/H∞ robust controller with an integrator is proposed. The excellent control performance of the H2/H∞ robust controller is used to stabilize the system. Firstly, the mathematical model of the MGSSP is derived from the transfer function. Secondly, the robust controller is designed based on the linear matrix inequality (LMI), considered the optimal and robust performance. The modified hybrid PSOGSA with chaotic maps algorithm (CPSOGSA) is also introduced in the design process to ensure the controller is optimal. The designed robust controller is simple in structure, low in order, and easy to implement. Simulation experiments show that the CPSOGSA can complete the optimization of the controller with less than ten iterations. The designed robust controller can stabilize the system in less than 10 s for different operating conditions. The robust controller enables the controlled object to exhibit better dynamic performance than the conventional controller. The research results can provide a reference for the control application of the extended operation of the hydro-turbine governing system (HTGS).

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.

Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms

There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.

SOFC-Supported Hybrid PSOGSA-Optimized Dynamic Voltage Restorer for Power Quality Enhancement

A novel optimal method of a Dynamic Voltage Restorer (DVR) supported by solid oxide fuel cell (SOFC) and its simplified topological structure are proposed. DVR is a power-electronic converter-based device, and the objective of the DVR control system is to minimize supply voltage variations at the load terminals. This is attained by generating a compensating voltage at the series injection transformer. Conventional controllers are mathematical model-based; also, the particular system varies widely, and nonlinear factors make the PI controller tuning more challenging to some extent. As a result, an intelligent PI optimization control method is essential. This paper proposes Hybrid PSOGSA to search for optimal values of two PI control parameters for the [Formula: see text]–[Formula: see text]-axis components by considering a novel bi-objective function. The performance of the test system is analyzed for five test scenarios using the proposed PI controller with SPSO-optimized and Ziegler–Nichols tuning methods. DVR system provides an excellent control performance in the transient and steady states for compensating the sensitive load voltages with almost zero steady-state errors. Simulation results show that the proposed approach can provide improved performance than PSO-optimized and classical PI controllers for the power quality indices measured.

Hybrid PSO-GSA algorithm-based optimal control strategy for performance enhancement of a grid-connected wind generator

Hybrid PSO-GSA algorithm-based optimal control strategy for performance enhancement of a grid-connected wind generator

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.

A new compound wind speed forecasting structure combining multi-kernel LSSVM with two-stage decomposition technique

The aims of this study are to develop a novel compound structure that consists of two-stage decomposition (TSD), hybrid particle swarm optimization gravitational search algorithm (HPSOGSA) and multi-kernel least square support vector machine (MLSSVM) for improving forecasting accuracy. In the most previous wind speed forecasting studies, only one wind speed signal decomposition method is considered, which is insufficient. To better deal with the wind speed time series, TSD method combining complementary ensemble empirical mode decomposition with adaptive noise with wavelet transform is firstly employed in the proposed model to preprocess the wind speed samples; then, binary-valued particle swarm optimization gravitational search algorithm is exploited as feature selection to identify and eliminate the abnormal noise signal within the input candidate matrix that is determined by partial autocorrelation function. The kernel function and the kernel parameters have great influence on the regression performance of LSSVM. To solve these problems, integrations of radial basis function, polynomial (poly) and linear kernel functions by optimal weighted coefficients are constructed as multi-kernel function for LSSVM, namely MKLSSVM, and the parameter combination is tuned by conventional PSOGSA. The feature selection and parameter optimization are realized by hybrid PSOGSA (HPSOGSA) simultaneously. Finally, comprehensive comparison and analysis are carried out using the historical wind speed data from one wind farm of China to illustrate the excellent forecasting performance of TSD–HPSOGSA–MKLSSVM.

Hybrid PSOGSA technique for solving dynamic economic emission dispatch problem

In this paper, a new hybrid population-based algorithm is proposed with the combining of particle swarm optimization (PSO) and gravitational search algorithm (GSA) techniques. The main idea is to integrate the ability of exploration in PSO with the ability of exploration in the GSA to synthesize both algorithms’ strength. The new algorithm is implemented to the dynamic economic emission dispatch (DEED) problem to minimize both fuel cost and emission simultaneously under a set of constraints. To demonstrate the efficiency of the proposed algorithm, a 5-unit test system is used. The results show the effectiveness and superiority of the proposed method when compared to the results of other optimization algorithms reported in the literature.

Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network

The Cognitive Radio Network (CRN) in 5G heterogeneous network has trade-off between energy efficiency and spectrum sensing efficiency. Energy Efficiency is important for designing the battery-powered cognitive radio network. Existing methodologies are mainly focused on solving the optimization problem of energy efficiency in spectrum sensing using convex optimization. However, The real-time spectrum sensing is a non-convex optimization problem. In this paper, energy efficiency for various spectrum sensing scenarios is modeled as non-convex optimization problem. To detect spectrum holes with improved energy utilization, this paper proposes a novel hybridization of Particle Swarm Optimization (PSO) with Gravitational Search Algorithm (GSA) called as Hybrid PSO-GSA. With the proposed novel hybridization of PSO and GSA, it is possible to achieve balanced trade-off between exploration and exploitation abilities of PSO-GSA algorithm. In addition to that, with the incorporation of mutation and crossover factor in the PSO-GSA, the proposed algorithm is efficiently able to detect the spectrum holes with the optimized values of transmission power, sensing bandwidth and power spectral density. Thus, improving the energy efficiency of the spectrum sensing. Simulation results substantiate the efficiency of PSO-GSA in optimizing the energy efficiency for spectrum sensing in terms of transmission power, spectrum sensing bandwidth and power spectral density compared with existing PSO and Artificial Bee Colony (ABC) algorithms.

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.

Design and analysis of hybrid PSO–GSA tuned PI and SMC controller for DC–DC Cuk converter

In this Letter, a hybrid particle swarm optimisation and gravitational search algorithm (PSO–GSA) to tune the parameters of proportional–integral (PI) controller operating in cascade with sliding mode controller (SMC) is proposed for the design of Cuk converter for low-voltage electric vehicle application. The PI controller in the outer loop regulates the voltage and SMC in the inner loop regulates the current of converter operating in continuous conduction mode. The main objective of Cuk converter with PSO–GSA tuned PI & SMC controller is to track the reference voltage amidst disturbances, and to reduce the performance indices such as integral absolute error (IAE) and integral time absolute error. Initially, PI controller was tuned using Routh Hurwitz and root locus method. Then to improve its steady-state performance index, PSO–GSA technique is applied to tune the proportional and integral gains. To validate the effectiveness of the proposed scheme, the performance indices such as IAE, integral squared error was measured and compared. The results obtained reveal that IAE is reduced to minimal value and the solution converges at a faster rate with the proposed controller than the conventional control methods. Moreover, the stability analysis was carried out using the Lyapunov method of stability.

Design of fractional order PID controller for heat flow system using hybrid particle swarm optimisation and gravitational search algorithm

This paper uses the hybrid particle swarm optimisation (PSO) and gravitational search algorithm (GSA) for the design of fractional order proportional-integrator-derivative (FOPID) controller for a heat flow system. The social behaviour of PSO is combined with the motion technique of GSA. The objective of the algorithm is to obtain the optimal controller parameters for the heat flow system. To obtain the optimal computation, different performance indices such as integral absolute error (IAE), integral squared error (ISE), integral time absolute error (ITAE), and integral time squared error (ITSE) are considered for the optimisation. The performance of the hybrid PSO-GSA is compared with the IMC-PID, fractional order filter-PID, and PSO-FOPID. The proposed method performs comparatively better than the other published methods. Simulink/MATLAB environment is used for simulation purpose.

Design of fractional order PID controller for heat flow system using hybrid particle swarm optimisation and gravitational search algorithm

This paper uses the hybrid particle swarm optimisation (PSO) and gravitational search algorithm (GSA) for the design of fractional order proportional-integrator-derivative (FOPID) controller for a heat flow system. The social behaviour of PSO is combined with the motion technique of GSA. The objective of the algorithm is to obtain the optimal controller parameters for the heat flow system. To obtain the optimal computation, different performance indices such as integral absolute error (IAE), integral squared error (ISE), integral time absolute error (ITAE), and integral time squared error (ITSE) are considered for the optimisation. The performance of the hybrid PSO-GSA is compared with the IMC-PID, fractional order filter-PID, and PSO-FOPID. The proposed method performs comparatively better than the other published methods. Simulink/MATLAB environment is used for simulation purpose.

Integration of Renewable Distributed Generation in Distribution Networks Including a Practical Case Study Based on a Hybrid PSOGSA Optimization Algorithm

Integration of distributed generations (DGs) in distribution systems is deemed as effective and intelligent resolution to keep up with increased loads. Renewable sources are deemed as sources of DGs. In this article, a novel functional, and robust PSOGSA optimizer is demonstrated to discover the optimal allocations of DGs’ units for enhancing voltage stability in addition to minifying the power loss and operating cost. Such is accomplished in two divisions. (i) The loss-sensibility-factors (LSFs) are used to elect the convenient sensitive nodes for DGs’ installations. (ii) The PSOGSA is carried out to conclude the optimum allocation and capacity of DGs from the chosen nodes. The prepared methodology has been satisfied on IEEE distribution grids and 111 nodes distribution network of Moscow region. To check the validity of the prepared scheme, it has been demonstrated comprehensive analysis between PSOGSA and recent optimization mechanisms such as backtracking search, genetic, particle swarm, hybridized genetic algorithm and particle swarm, simulation annealing, and bacterial foraging. The numeral results have demonstrated the ability of the prepared algorithm to locate the optimum resolutions with high performance and speed.

Use of modified hybrid PSOGSA for optimum design of RC frame

ABSTRACTA realistic and optimum design of reinforced concrete structural frame, by hybridizing enhanced versions of standard particle swarm optimization (PSO) and standard gravitational search algorithm (GSA) is presented in this paper. PSO has been democratized by considering all good and bad experiences of the particles, whereas GSA has been made self-adaptive by considering a specific range for certain parameters like ‘gravitational constant’ and ‘set of agents with best fitness value.’ Optimal size and reinforcement of the members have been found by employing the technique in a computer-aided environment. Use of self-adaptive GSA together with democratic PSO technique has been found to provide two distinct advantages over standard PSO and GSA, namely better capability to escape from local optima and faster convergence rate. The entire formulation for optimal cost design of frame includes the cost of beams and columns. In this approach, variables of each element of structural frame have been considered as continuous functions and rounded off appropriately to imbibe practical relevance to the study. An example has been considered to emphasize the validity of this optimum design procedure and results have been compared with earlier studies.

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.

Researchon variable area hybrid system using optimized FractionalOrderControl andPassivity-BasedControl

Fractional Order (FO) calculus has been gaining increasing attention in research communities. Additionally, the concept of passivity for nonlinear systems has attracted new interest in nonlinear system control. This work aims to develop a hybrid PSOGSA-based Proportional Integral (PI), FO and Passivity Based Controller (PBCr) for a variable area hybrid system, namely, spherical–conical interacting tank system. A computer based control system is developed with data acquisition, processing/computation and for the generation of outputs. A real time model of the hybrid system is obtained experimentally. The controller response is obtained in MATLAB simulation and in real time through LABVIEW software and the performance of the controllers is compared in terms of error criteria. The results show that hybrid PSOGSA optimized PBCr works comparatively efficiently, with improved performance.