Acceleration Coefficients Particle Swarm Optimization Research Articles

Optimal hybrid photovoltaic distributed generation and distribution static synchronous compensators planning to minimize active power losses using adaptive acceleration coefficients particle swarm optimization algorithms

The paper aims to identify the optimum size and location of photovoltaic distributed generation systems and distribution static synchronous compensators (DSTATCOMs) systems to minimize active power losses in the distribution network and enhance the voltage profile. The methodology employed in this article begins by thoroughly discussing various acceleration algorithms used in Particle Swarm Optimization (PSO) and their variations with each iteration. Subsequently, a range of PSO algorithms, each incorporating different variations of acceleration coefficients was verified to solve the problem of active power losses and voltage improvement. Simulation results attained on Standard IEEE-33 bus radial distribution network prove the efficiency of acceleration coefficients of PSO; it was evaluated and compared with other methods in the literature for improving the voltage profile and reducing active power. Originality. Consists in determining the most effective method among the various acceleration coefficients of PSO in terms of minimizing active power losses and enhancing the voltage profile, within the power system. Furthermore, demonstrates the superiority of the selected method over others for achieving significant improvements in power system efficiency. Practical value of this study lies on its ability to provide practical solutions for the optimal placement and sizing of distributed generation and DSTATCOMs. The proposed optimization method offers tangible benefits for power system operation and control. These findings have practical implications for power system planners, operators, and policymakers, enabling them to make informed decisions on the effective integration of distributed generation and DSTATCOM technologies.

Applying Particle Swarm Optimization Variations to Solve the Transportation Problem Effectively

The Transportation Problem (TP) is a special type of linear programming problem, where the objective is to minimize the cost of distributing a product from a number of sources to a number of destinations. Many methods for solving the TP have been studied over time. However, exact methods do not always succeed in finding the optimal solution or a solution that effectively approximates the optimal one. This paper introduces two new variations of the well-established Particle Swarm Optimization (PSO) algorithm named the Trigonometric Acceleration Coefficients-PSO (TrigAc-PSO) and the Four Sectors Varying Acceleration Coefficients PSO (FSVAC-PSO) and applies them to solve the TP. The performances of the proposed variations are examined and validated by carrying out extensive experimental tests. In order to demonstrate the efficiency of the proposed PSO variations, thirty two problems with different sizes have been solved to evaluate and demonstrate their performance. Moreover, the proposed PSO variations were compared with exact methods such as Vogel’s Approximation Method (VAM), the Total Differences Method 1 (TDM1), the Total Opportunity Cost Matrix-Minimal Total (TOCM-MT), the Juman and Hoque Method (JHM) and the Bilqis Chastine Erma method (BCE). Last but not least, the proposed variations were also compared with other PSO variations that are well known for their completeness and efficiency, such as Decreasing Weight Particle Swarm Optimization (DWPSO) and Time Varying Acceleration Coefficients (TVAC). Experimental results show that the proposed variations achieve very satisfactory results in terms of their efficiency and effectiveness compared to existing either exact or heuristic methods.

A Thermal Displacement Prediction System With an Automatic LRGTVAC-PSO Optimized Branch Structured Bidirectional GRU Neural Network

Considering technology’s rapid development, traditional manufacturing methods are insufficient to achieve the high accuracy demanded by aerospace, national defense, and numerous leading-edge engineering projects. Thermal displacement is a significant source of manufacturing errors, and accurately correcting such errors is difficult or even impossible using traditional machining methods. This paper proposes a machine learning method for high-accuracy error prediction that nonprofessionals can easily implement. An optimized automatic Logistic Random Generator Time Varying Acceleration Coefficient Particle Swarm Optimization (LRGTVAC-PSO) method is proposed to optimize a branch structured bidirectional Gated Recurrent Unit (GRU) neural network. The accuracy of the proposed method (with a three-axis average of 0.945) is superior to that of the other optimized algorithms evaluated in this study. The method serves as a means not only of accurately predicting thermal displacement but also of autotuning the hyperparameters of machine learning algorithms.

Intelligent fault diagnosis for power distribution system-comparative studies

Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-to-ground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double line-to-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.

TVACPSO-assisted analysis of the effects of temperature and irradiance on the PV module performances

The time-varying acceleration coefficients particle swarm optimization method is employed to extract the physical parameters of the single diode PV model. Under the condition of low radiation and high temperature, there is a slight deviation between the calculated and the published data, but this model still has good accuracy in most ranges. The time-varying mechanism is introduced to mitigate the premature convergence problem and establish an appropriate tradeoff between the explorative and exploitative capabilities. The method provides the lowest cost function in the extraction of parameters. Although the experimental conditions of the curves cannot fully represent the working environment of the photovoltaic module, the validity of the model can be verified by comparing the calculated curves with the released curves. Analytical solutions of short-circuit current, open-circuit voltage, maximum power point current and voltage are also derived by the Lambert W function. The variation of maximum power, fill factor, energy efficiency, exergy efficiency and power conversion efficiency of the multi-crystalline and thin-film modules in the range of 10–70 °C were studied. The electrical performance of PV modules is more sensitive to temperature than irradiance. The output performance of thin-film modules is better than crystalline modules at high temperature conditions.

Optimal Parameters of OUPFC and GUPFC Under Renewable Energy Power Variation Using Cuckoo Search Algorithm Variants

In this paper, improved variants of cuckoo search algorithm are proposed for optimizing the controlling variables of flexible ac transmission system devices towards voltage stability enhancement and active power loss minimization by considering renewable energy sources intermittency in the network. Primarily, the optimal location for flexible ac transmission system devices is determined using line stability index and later the control parameters of generalized unified power flow controller and optimal unified power flow controller are optimized at different intermittency levels of renewable energy sources using three cuckoo search algorithm variants. The case studies are performed on standard IEEE 14-, 30-, 57-bus test systems. The superiority of proposed cuckoo search algorithm variants (linearly increased switching parameter, exponentially increased switching parameter and increased switching parameter in a power of three) in solving the multi-objective, non-linear complex optimization problem over time varying acceleration coefficient—particle swarm optimization variants is presented by illustrating various case studies.

Optimal energy management system for distribution systems using simultaneous integration of PV-based DG and DSTATCOM units

The energy management system (EMS) of an electrical distribution system (EDS), with the integration of distributed generation (DG) and distribution static compensator (DSTATCOM), provides numerous benefits and significantly differs from the existing EDSs. This paper presents an optimal integration of DG based on photovoltaic (PV) solar panels and DSTATCOM in EDS. A single objective function, based on maximizing the active power loss level (APLL) in EDS, is deployed to find the optimal size and location of photovoltaic DG and DSTATCOM simultaneously in different study cases using various particle swarm optimization (PSO) algorithms. These PSO algorithms are the basic PSO, adaptive acceleration coefficients PSO (AAC-PSO), autonomous particles groups for PSO (APG-PSO), nonlinear dynamic acceleration coefficients PSO (NDAC-PSO), sine cosine acceleration coefficients PSO (SCAC-PSO), and time-varying acceleration PSO (TVA-PSO). These algorithms are applied to the standard IEEE 33- and 69-bus EDSs, which are used as test systems to verify the effectiveness of the proposed algorithms. Simulation results prove that the TVA-PSO algorithm exhibits higher capability and efficiency in finding optimum solutions. Comparing the simulation results attained for different study cases leads to the conclusion that DG and DSTATCOM were optimally-allocated simultaneously, which resulted in a significant reduction of power losses and an enhancement of the voltage profile.

Day-ahead profit-based reconfigurable microgrid scheduling considering uncertain renewable generation and load demand in the presence of energy storage

Due to the penetration of renewable energy sources (RESs) with probabilistic natures into microgrids (MGs), optimal scheduling and reconfiguration (RG) processes are associated with uncertainty. This paper presents the stochastic profit-based optimal day-ahead scheduling of a reconfigurable microgrid (RMG) as a new generation of the conventional microgrid. The proposed algorithm finds the optimal RMG's topology from the profit maximization point of view, the optimal hourly MG's unit set-points like micro-turbines (MTs) and energy storage, and power exchange with the main grid, simultaneously. The generated power of wind turbine (WT) and PV panel, as well as load demand are considered as uncertain parameters. To solve the profit maximization problem of RMG, time-varying acceleration coefficients particle swarm optimization (TVAC-PSO) algorithm is employed. Also, to ensure simulation accuracy in the presence of high-level uncertainties, the autocorrelation model is used based on actual data for the uncertainty of renewable power output. The feasibility and applicability of the proposed framework are demonstrated on a 69-bus radial RMG with various distributed generators in different cases. The results show the effectiveness of the proposed model.

Intelligent Control Strategy for Robotic Arm by Using Adaptive Inertia Weight and Acceleration Coefficients Particle Swarm Optimization

This paper proposes an intelligent control strategy for enabling a robotic arm to grasp and place water-filled bottles without spilling any of the water. First, the system architecture of a five-degree-of-freedom robotic arm and its mechanical design are introduced. Second, both the forward and inverse kinematics of the robotic arm are derived. The study conducted an experiment in which the designed and implemented robotic arm could grasp a bottle of water and move it to another place. However, if the acceleration or the orientation of the robotic arm were inappropriate, the water in the bottle may be spilled during the movement. Therefore, the proposed strategy applies an inertial measurement unit for obtaining relevant information. According to the obtained information, the velocity curves of each joint could be optimized by adaptive inertia weight and acceleration coefficients particle swarm optimization. Finally, the experimental results demonstrated the feasibility and effectiveness of the proposed method.

A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units

With increasing penetration of renewable energy sources, it is necessary to analyze its impact on the allocation of optimal power generation schedule. In this work, the pumped storage hydrothermal (PSHT) system incorporating solar units has been undertaken. The novel integrated heuristic approach named as time varying acceleration coefficient particle swarm optimization with mutation strategies (TVAC-PSO-MS) has been proposed. In this approach, an initial solution has been updated by the TVAC-PSO approach and then local best solutions are updated by using the successive mutation strategies namely Cauchy, Gaussian, and opposition based mutations. The Cauchy mutation strategy is applied to enhance the search capability and the Gaussian, as well as the opposition based mutation strategies are used to improve the exploitation capability of the algorithm. In order to validate the proposed approach, a standard test system of hydrothermal generation scheduling has been undertaken and the results have been compared with other state of art algorithms. Further, the proposed approach has been applied to optimize the cost of the PSHT system incorporating solar units and validate it through statistical test.

Economic dispatch of multiple energy carriers

Energy hubs can provide high flexibility in the operation of the integrated systems. This paper presents Time Varying Acceleration Coefficients Particle Swarm Optimization (TVAC-PSO) algorithm to optimize the Multiple Energy Carriers Economic Dispatch (MECED) problem for hybrid electrical and natural gas networks. The simulation results on two case studies are reported. The first one verifies an introduced gas loss formula and another one is devoted to a modified version of IEEE 14-bus test system. The results are compared with PSO, Genetic Algorithm (GA), and Differential Evolution (DE) technique. They show that a hybrid system can operate at a lower cost than independently-operated systems; CHP units can supply more electrical demand than the electrical network while their contribution in supplying the heat demand may be smaller than that of gas furnaces. In a hybrid system, energy efficiency can be reached, producing electrical power and heat locally; CHP units have a great role in reduction of operational cost; In the coupled mode, electrical power losses are decreased, while the gas losses are increased; The simple proposed gas loss formula can provide acceptable results; Cost reductions due to employing CHPs and applying the proposed optimization technique are greater than the other methods.

Combined Heat and Power Dispatch using Hybrid Genetic Algorithm and Biogeography-based Optimization

This paper explores the performance of biogeography-based optimization (BBO) algorithm for solving combined heat and power dispatch (CHPD) or cogeneration problem of power system. BBO is a type of evolutionary algorithm which is based on the theory of biogeography and is inspired by the two concepts, namely migration of species between “islands” via flotsam, wind, flying, swimming, etc. and mutation. To improve the convergence property and solution quality, blended crossover strategy of genetic algorithm (GA) is integrated with conventional BBO algorithm in this study. The effect of valve-point in cost function is considered by adding an absolute sinusoidal term with the conventional polynomial cost function. The potential of the proposed BBO and GA based BBO (GABBO) algorithms are assessed by means of an extensive comparative study of the solutions obtained for small and medium CHPD problems of power systems. To show the priority of the proposed algorithm, comparative studies are carried out to examine the effectiveness of the proposed BBO and GABBO approaches over evolutionary programming (EP), differential evolution (DE), particle swarm optimization (PSO) and time varying acceleration coefficients PSO (TVAC-PSO reported in the literature. The experimental results and comparison with other algorithms demonstrate that the proposed GABBO algorithm can be a proficient substitute lying on solving combined heat and power dispatch problems.

Grasping Posture Control Design for a Home Service Robot Using an ABC-Based Adaptive PSO Algorithm

In this paper, a grasping posture control for a robotic arm is developed based on novel adaptive particle swarm optimization (PSO) for the home service robot. To grasp an object using the robotic arm of the home-service robot, both the spatial coordinates of the target and the appropriate collocation of the grasping posture should be examined. In this paper, we present another method for dealing with this problem, which integrates the artificial bee colony (ABC) algorithm into the adaptive particle swarm optimization (APSO) algorithm, where the mutation concept of the scout bee in the ABC algorithm is used to increase the diversity of the particles. In addition, adaptive acceleration coefficients and adaptive inertia weight are presented to ameliorate the convergence rate of the PSO algorithm. We name this control scheme AIWCPSO-S, which represents Adaptive Inertia Weight and acceleration Coefficients PSO with the aid of the Scout bee. Performance comparisons of existing ABC, global ABC, adaptive inertia weight PSO, low-discrepancy sequence initialized PSO algorithm with high-order nonlinear time-varying inertia weight (LHNPSO), oscillating triangular inertia weight PSO (OTIWPSO) and AIWCPSO-S algorithms are conducted by computer simulations. The experiment results show that the presented algorithm gives the most correct and fastest convergence capability.

A new heuristic method to solve unit commitment by using a time-variant acceleration coefficients particle swarm optimization algorithm

Unit commitment is one of the most important problems in power system operation. Because of the large amount of parameters and constraints, it contains a high level of complexity. In this paper a new method based on a time-variant acceleration coefficients particle swarm optimization algorithm has been proposed to solve the unit commitment problem. Integer coding (for satisfying minimum up/down constraints) and binary coding (for satisfying spinning reserve constraint) have been utilized in the proposed method. Simulations in the different cases have been done with different sizes. Numerical results have shown the superiority and better convergence of the proposed method in comparison with other methods.

Modeling and Optimization of Aggregate Production Planning by Time Varying Acceleration Coefficients PSO

The aim of this paper is to apply Time-varying Acceleration Coefficients PSO (TVACPSO) for modeling and optimization of the aggregate production planning problem. TVACPSO is a modified and updated form of Particle Swarm Optimization (PSO). Most practical decisions made to solve APP problems usually consider total costs; we have eliminated other objective functions of APP in this case. There was several variables problem with constraints which was solved in MATLAB. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, and overtime, subcontracting and backordering levels. An industrial case is presented to demonstrate the feasibility of applying the proposed method to real APP problems. Consequently, the proposed TVACPSO approach yields an efficient APP compromise solution.

Repulsive Self-Adaptive Acceleration Particle Swarm Optimization Approach

Abstract Adaptive Particle Swarm Optimization (PSO) variants have become popular in recent years. The main idea of these adaptive PSO variants is that they adaptively change their search behavior during the optimization process based on information gathered during the run. Adaptive PSO variants have shown to be able to solve a wide range of difficult optimization problems efficiently and effectively. In this paper we propose a Repulsive Self-adaptive Acceleration PSO (RSAPSO) variant that adaptively optimizes the velocity weights of every particle at every iteration. The velocity weights include the acceleration constants as well as the inertia weight that are responsible for the balance between exploration and exploitation. Our proposed RSAPSO variant optimizes the velocity weights that are then used to search for the optimal solution of the problem (e.g., benchmark function). We compare RSAPSO to four known adaptive PSO variants (decreasing weight PSO, time-varying acceleration coefficients PSO, guaranteed convergence PSO, and attractive and repulsive PSO) on twenty benchmark problems. The results show that RSAPSO achives better results compared to the known PSO variants on difficult optimization problems that require large numbers of function evaluations.

Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients

In this paper a novel time varying acceleration coefficients particle swarm optimization (TVAC-PSO) algorithm is implemented to solve combined heat and power economic dispatch (CHPED) problem. The CHPED problem is a challenging non-convex and non-linear optimization problem. The acceleration coefficients in PSO algorithm are varied adaptively during iterations to improve solution quality of original PSO and avoid premature convergence. The effect of valve-point in cost function considered with adding an absolute sinusoidal term to conventional polynomial cost function. The proposed method is applied to five test cases with different characteristics. Also, a new large scale test case considering valve-points effects is proposed in this paper. The obtained results demonstrate the superiority of the proposed method in solving non-convex and constrained CHPED problem.