Fractional Particle Swarm Optimization Research Articles

A coordinated modelling and control of modified pumped storage governor with unified power flow controller to damp low frequency oscillations in power system for stochastic renewable penetrations

A modern power system network is more susceptible to oscillatory instability problem due to power oscillations caused by uncertain penetration of renewable sources. Also, pumped storage hydro power generation (PSHPG) is going to take important role for energy storage and management with varying renewable penetrations, but their governors need to be utilized efficiently for power oscillation damping. In this work a coordinated modified pumped storage governor with fractional parallel unified power flow controller (FPUPFC) has been modelled and controlled to damp angular frequency variations in power system. The prime objective of this work is to enhance efficacy of pumped storage hydro turbine governors integrated into an expert system with UPFC to take part in electromechanical oscillation damping. Here the governor is modified by fractional order PID action with additional phase compensation and in FPUPFC both series and shunt converters are controlled simultaneously by parallel fractional lead-lag based control action. The power system considered here consists of hydro generators of 100 MVA with 90.94 MW rated turbine output power with different operating conditions for two area 4 generators and 39 bus 10 generators multimachine renewable integrated system. It has been observed that efficacy of pumped storage hydro turbine governor action has been much improved when coordinated with FPUFC control action. A new hybridized modified differential evolution with fractional particle swarm optimization (MDEFPSO) is proposed to tune the parameters of proposed controller, with a multi-objective function considering variation in speed and real power deviations, settling time, overshoot and undershoot of system response.

A new SOH estimation method for Lithium-ion batteries based on model-data-fusion

A new method for the estimation of the state-of-health (SOH) of lithium-ion batteries (LIBs) is proposed. The approach combines a LIB equivalent circuit model (ECM) and a deep learning network. Firstly, correlation analysis is performed between the LIB data and SOH and suitable portions are selected as health features (HFs). Simultaneously, a fractional-order RC ECM of the LIB is derived and a hybrid fractional particle swarm optimization with crisscross learning (FPSO-CL) strategy is used to identify the model parameters. Secondly, correlation analysis between the model parameters and SOH is conducted and those that best represent the battery health are selected as additional HFs. Thirdly, an improved vision transformer network (VIT) is designed by including a dimension transformation layer, multilayer perceptron and a trainable regression token. Finally, the VIT is trained with all determined HFs, yielding a compete framework for predicting the SOH of LIBs. Experimental verification is carried out on real LIBs data and the results show that the proposed scheme can achieve higher prediction accuracy than other alternative methods.

Optimal transmission voltage deviation based on Fractional Kinetic Gas Molecular Optimization Symbiotic Organism Search Algorithm for network operational stability improvement

This research applies Fractional Kinetic Gas Molecular Optimization Symbiotic Organism Search (FKGMOSOS) algorithm to optimize Total Voltage Deviation (TVD) while improving the security of an electricity transmission system. Hybrid KGMOSOS has excellent rate of convergence to optimum, but in order to avoid the possibility of getting stuck in local optimum, Fractional Differential Order (FDO) was integrated into the velocity term in the algebraic model of KGMOSOS leading to FKGMOSOS evolution. Using this algorithm, optimal TVD was accomplished by varying the levels of the grid control state variables to attain the finest set of optimal results in IEEE 30 – Bus, 13 – Variable standard network. The optimization competence of this new technique was verified for multiple runs through comparative investigation with that of Fractional Particle Swarm Optimization Gravitational Search Algorithm (FPSOGSA) for each FDO scenario. The results of simulation showed that for 250 successive iterations, FKGMOSOS outperformed FPSOGSA which also outperformed other literary techniques.

Variable Fractional-Order Equivalent Circuit Model for Lithium-Ion Battery via Chaotic Adaptive Fractional Particle Swarm Optimization Method

A variable fractional-order equivalent circuit model is proposed to accurately describe the dynamic characteristics of lithium-ion batteries (LIBs). Firstly, a fractional impedance model (FIM) is established, such that the fractional-order (FO) is a polynomial function of the LIB state of charge (SOC). Then, a chaotic adaptive fractional particle swarm optimization (CAFPSO) method is derived to identify the parameters of the FIM. Experiments reveal the reliability of the novel approach through the root-mean-squared error (RMSE) and the mean absolute error (MAE) of the LIB terminals voltage, yielding the values 8.99 mV and 4.56 mV, respectively. This translates into accuracy improvements of 22.5% and 34.4% for the particle swarm optimization (PSO) algorithm and 57.9% and 72.8% for the adaptive fractional particle swarm optimization (AFPSO) algorithm, respectively.

Fractional memetic computing paradigm for reactive power management involving wind-load chaos and uncertainties

Optimal reactive power dispatch (ORPD) with integration of renewable energy resources has a growing interest in research community due to its utmost requirements during the operation, planning and design of the modern electrical power networks. The objective of ORPD is to improve the performance of power network by means of reducing the losses in transmission line, improving the voltage profile, and decreasing the overall cost of operation through optimal tuning of the operational variables such as tap position of transformers, generator output voltages and capacitor banks. However, the nonlinear, non-stationary and complex nature of power network, presence of load uncertainties, and dynamic behavior of wind generation introduces a complex optimization task which cannot be readily solved in an efficient manner. In this research work, a new fractional memetic computing paradigm, i.e., the fractional particle swarm optimization gravitational search algorithm with entropy evolution (FPSOGSA-EE), is designed to solve the ORPD problems in power system adopting wind power plants (WPPs) and load uncertainties. The proposed optimization framework FPSOGSA-EE integrates the concept of fractional calculus and Shannon entropy to strengthen the optimization characteristics of canonical algorithm. The exhaustive experimentation endorse the efficacy of FPSOGSA-EE by providing minimum gauge of fitness evaluation function, namely, the line loss and voltage deviation index minimization, in IEEE 30 and 57 bus networks. The stability, consistency and reliability of proposed FPSOGSA-EE is ascertained through statistical interpretations by means of boxplots, probability measures for cumulative distribution function, and histogram illustrations.

Improvement and Application of Fractional Particle Swarm Optimization Algorithm

The convergence performance of existing fractional particle swarm optimization algorithm directly depends on a single fractional-order operator. When its value increases, the convergence speed of particles gets slower. When its value decreases, the probability of the particle swarm trapping into the local optimum increases. In order to solve this problem, an improved fractional particle swarm optimization (IFPSO) algorithm is proposed in this paper. New variables are introduced in this paper to redefine the formula. The fractional-order operator is added to the velocity update formula and the position update formula. The IFPSO algorithm has linearly decreasing inertia weights. Then the proposed IFPSO algorithm is used to optimize the parameters of support vector machine (SVM) and the clustering center of the K-means classifier is selected. Experimental results show that the IFPSO algorithm can effectively avoid falling into the local optimal solution. It has a faster convergence rate and better stability than the original algorithm, which proves the effectiveness of the algorithm. Examples verify that the IFPSO algorithm can improve the classification accuracy of SVM in practical application. The IFPSO algorithm effectively solves the problem that K-means algorithm is overly dependent on the initial center and may have empty classes.

Implementation of Tongue-Based Leukemia Detection Using Novel Fractional Particle Swarm Optimization and Compare with Particle Swarm Optimization Algorithm Using Matlab

The research is to perform tongue-based leukemia detection analysis with two different algorithms to identify the tumor cell from leukemia tongue images with high accuracy and high clarity image. Materials and Methods: Novel Fractional Particle Swarm Optimization (FPSO) with 10 sample datasets and particle Swarm Optimization (PSO) with 10 sample datasets are implemented to detect the tumor and leukemia tongue image. Result: The outcome result shows that FPSO achieves a high accuracy rate with 86% over PSO (79%). Attained significant accuracy rate (p < 0.251) in SPSS statistical analysis. Conclusion: The leukemia tongue images of FPSO have higher accuracy than the PSO algorithm.

Multi-Objective Power Distribution Network Reconfiguration using Chaotic Fractional Particle Swarm Optimization

Optimization of power distribution system reconfiguration is addressed as a multi-objective problem, which considers the system losses along with other objectives, and provides a viable solution for improvement of technical and economic aspects of distribution systems. A multi-objective chaotic fractional particle swarm optimization customized for power distribution network reconfiguration has been applied to reduce active power loss, improve the voltage profile, and increase the load balance in the system through deterministic and stochastic structures. In order to consider the prediction error of active and reactive loads in the network, it is assumed that the load behaviour follows the normal distribution function. An attempt is made to consider the load forecasting error on the network to reach the optimal point for the network in accordance with the reality. The efficiency and feasibility of the proposed method is studied through standard IEEE 33-bus and 69-bus systems. In comparison with other methods, the proposed method demonstrated superior performance by reducing the voltage deviation and power losses. It also achieved better load balancing.

Control Strategy for a Quadrotor Based on a Memetic Shuffled Frog Leaping Algorithm

This work presents a memetic Shuffled Frog Leaping Algorithm (SFLA) based tuning approach of an Integral Sliding Mode Controller (ISMC) for a quadrotor type of Unmanned Aerial Vehicles (UAV). Based on the Newton–Euler formalism, a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes. Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law, which are usually selected by repetitive and time-consuming trials-errors based procedures, a constrained optimization problem is formulated for the systematically tuning of these unknown variables. Under time-domain operating constraints, such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation- and swarm intelligence-based algorithms such as the Crow Search Algorithm (CSA), Fractional Particle Swarm Optimization Memetic Algorithm (FPSOMA), Ant Bee Colony (ABC) and Harmony Search Algorithm (HSA). Numerical experiments are carried out for various sets of algorithms’ parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization. Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence. Demonstrative results verified that the proposed metaheuristics-based approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.

The Siting and Sizing Problem of Distributed Generation Based on a Novel Fractional Particle Swarm Optimization Algorithm

Large-scale distributed generation grid-connection brings huge economic and environmental benefits, but also threatens the stability of the grid. To make the grid consume a higher proportion of distributed generation, it is necessary to optimize the location and capacity of the distributed generation connected to the grid. Firstly, the uncertainty analysis model of wind speed, illumination intensity, and load of grid is established. Secondly, a distributed generation location and capacity planning model with the lowest annual comprehensive cost as the objective function is constructed. Then, a novel fractional particle swarm optimization algorithm is proposed, and the performance of the algorithm on complex optimization problems is tested. Finally, the simulation results of the IEEE 33-bus system example verify the rationality of the established model and the effectiveness of the proposed algorithm.

A New Fractional Particle Swarm Optimization with Entropy Diversity Based Velocity for Reactive Power Planning.

Optimal Reactive Power Dispatch (ORPD) is the vital concern of network operators in the planning and management of electrical systems to reduce the real and reactive losses of the transmission and distribution system in order to augment the overall efficiency of the electrical network. The principle objective of the ORPD problem is to explore the best setting of decision variables such as rating of the shunt capacitors, output voltage of the generators and tap setting of the transformers in order to diminish the line loss, and improve the voltage profile index (VPI) and operating cost minimization of standard electrical systems while keeping the variables within the allowable limits. This research study demonstrates a compelling transformative approach for resolving ORPD problems faced by the operators through exploiting the strength of the meta-heuristic optimization model based on a new fractional swarming strategy, namely fractional order (FO)–particle swarm optimization (PSO), with consideration of the entropy metric in the velocity update mechanism. To perceive ORPD for standard 30 and 57-bus networks, the complex nonlinear objective functions, including minimization of the system, VPI improvement and operating cost minimization, are constructed with emphasis on efficacy enhancement of the overall electrical system. Assessment of the results show that the proposed FO-PSO with entropy metric performs better than the other state of the art algorithms by means of improvement in VPI, operating cost and line loss minimization. The statistical outcomes in terms of quantile–quantile illustrations, probability plots, cumulative distribution function, box plots, histograms and minimum fitness evaluation in a set of autonomous trials validate the capability of the proposed optimization scheme and exhibit sufficiency and also vigor in resolving ORPD problems.

FCM fuzzy clustering image segmentation algorithm based on fractional particle swarm optimization

FCM fuzzy clustering image segmentation algorithm based on fractional particle swarm optimization

Design of Fractional Particle Swarm Optimization Gravitational Search Algorithm for Optimal Reactive Power Dispatch Problems

In fact, optimal RPD is one of the most critical optimization matters related to electrical power stability and operation. The minimization of overall real power losses is obtained by adjusting the power systems control variables, for instance; generator voltage, compensated reactive power and tap changing of the transformer. In this search, a new heuristic computing method named as fractional particle swarm optimization gravitational search algorithm (FPSOGSA) is presented by introducing fractional derivative of velocity term in standard optimization mechanism. The designed FPSOGSA is implemented for the optimal RPD problems with IEEE-30 and IEEE-57 standards by attaining the near finest outcome sets of control variables along with minimization of two fitness objectives; active power transmission line losses ($P_{loss,}$ MW) and voltage deviation ($\text{V}_{\mathrm {D}}$ ). The superior performance of the proposed FPSOGSA is verified for both single and multiple runs through comparative study with state of art counterparts for each scenario of optimal RPD problems.

Energy Efficient Fractional Particle Swarm Optimization Based Power Allocation in MIMO-NOMA System

Non-Orthognal Multiple Access (NOMA) is a key technology used for improving the achievable rate in Multiple Input Multiple Output (MIMO) wireless networks of the next generation. In MIMO-NOMA systems the Energy Efficiency (EE) needs to be improved by a Fractional Particle Swarm Optimization Algorithm (FPSO) based on user ordering. To recommend capable energy and power allocation in the platform efficiently, the proposed optimization algorithm prioritizes the users based on satisfying the quality of service (QoS) and maximum power constraints. The FPSO algorithm prioritizes the users in optimal way by using objective function. The simulated results are analyze using the assessment metrics, like Bit Error Rate (BER), achievable rate, energy and spectral power. The performance of the FPSO-based power allocation approach is showing the higher spectral power, energy, achievable rate are 113.1915dB, 19.4898dB, 81.19153Mbps and lower BER of 0.0000152 respectively.

Deep neural network assisted diagnosis of time-frequency transformed electromyograms

Electromyograms (EMG) are recorded electrical signals generated from the muscles and these signals are closely interrelated with the muscle activity and hence are useful for the investigation of neuro-muscular disorders. The feature mining, feature collection and development of classification systems are greatly significant steps in the differentiation of normal and abnormal EMG signals to evaluate the abnormality. In this work, time-frequency domain based features of regular, myopathy and Amyotrophic Lateral Sclerosis (ALS) EMG signals were extracted from four different techniques namely Stockwell-Transform (ST), Wigner-Ville Transform (WVT), Synchro-Extracting Transform (SET) and Short-Time Fourier Transform (STFT). The Particle Swarm Optimization (PSO) with fractional velocity update technique was implemented for feature reduction. Further, the classifier based on the Deep Neural Networks (DNN) was developed by employing the features selected using fractional PSO. Finally, the performance of the DNN was compared with that of the Shallow Neural Network (SNN) classifier. Results of this work demonstrate that, the performance measure of the DNN classifiers is higher than that of the SNN classifier. This work appears to be of good clinical significance since efficient classification techniques are required for the development of robust neuro-muscular diagnosis systems.

Stalling-Free Control Strategies for Oscillating-Water-Column-Based Wave Power Generation Plants

The rising use of renewable power generation plants is highlighting the need for an integrated research combining multiple disciplines in order to achieve a commercially competitive technology stage. The demonstrative NEREIDA wave power plant installed in the northern coast of Spain constitutes a good example of this effort. This paper deals with the design, modeling, and control of the oscillating-water-column-based wave energy converter in order to maximize the power output of the NEREIDA power plant. The power optimization relies on two control strategies proposed to avoid the stalling behavior, a characteristic drawback of the Wells turbine, which limits the system's power. The first control strategy is an airflow control using a PID controller tuned by the particle swarm optimization algorithm and its recent memetic variant called fractional particle swarm optimization memetic algorithm. This controller will control a throttle valve to regulate the airflow in the turbine duct. The second one consist of adequately controlling the rotational speed of the generator by means of the rotor-side converter of the back-to-back converter connected to the doubly fed induction generator to provide a swift way to respond to the rapid variations in the turbine speed. The results show that both controls provide a higher power generation compared to the uncontrolled case.

Novel fractional order particle swarm optimization

In this paper, we provide a novel fractional particle swarm optimization (FPSO) algorithm. The traditional PSO is one of the most well-known bio-inspired algorithms used in optimization problems, which basically consists of a number of particles that collectively move in search of the global optimum. Nevertheless, despite its success over the past 20 years, the PSO is also known to be unable to converge, and even stagnate, in many complex problems with multiple local optima. In order to overcome this drawback, this paper proposes a modified version of the PSO algorithm, considering a fractional calculus approach. Stability results evaluation is carried out to analytically prove the convergence of the fractional extensions. This is naturally followed by simulation results to test the fractional-based PSOs under several well-known objective functions, thus highlighting the relationship between the fractional order velocity and position of particles with the convergence of the algorithm. Experimental results show that the FPSO and its variants significantly outperform the traditional PSO.