Summary
The main objective of this paper is to determine the optimal sizing of a biomass and fuel cell micro‐grid. The biomass generator will be utilized as the main source of power generation for ...the study area, while the fuel cell generator will be used as a backup generator to be used if the biomass generator fails to meet the energy requirements of the study area. In this research, excess energy will be used to produce hydrogen which is to be used by the fuel cells to generate energy instead of using batteries. To achieve the goal of this paper, a multi‐objective particle swarm optimization (MOPSO) technique has been proposed to solve the sizing problem for the introduced micro‐grid via an economical perspective which is the cost of energy (COE). The MOPSO algorithm tries to mitigate the COE to the lower values by keeping the loss of power supply probability (LPSP) as minimal as possible. Likewise, statistical analysis has been concluded to study the accuracy of the outcomes of the introduced technique. Three indicators have been offered, which are the process capability indices, the normal probability, and the control chart. The final contribution of the consequences of the two presented objective functions clears that the algorithm process is under statistical control, stable, and very precise. The optimum system from the economic perspective consists of two biomass gensets, 31 fuel cells, 65 electrolyzers, and 186 H2 tanks with an NPC of $ 2 314 842, COE of 0.335 $/kWh at an LPSP of 1.929%.
Evolutionary feature selection (FS) methods face the challenge of "curse of dimensionality" when dealing with high-dimensional data. Focusing on this challenge, this article studies a variable-size ...cooperative coevolutionary particle swarm optimization algorithm (VS-CCPSO) for FS. The proposed algorithm employs the idea of "divide and conquer" in cooperative coevolutionary approach, but several new developed problem-guided operators/strategies make it more suitable for FS problems. First, a space division strategy based on the feature importance is presented, which can classify relevant features into the same subspace with a low computational cost. Following that, an adaptive adjustment mechanism of subswarm size is developed to maintain an appropriate size for each subswarm, with the purpose of saving computational cost on evaluating particles. Moreover, a particle deletion strategy based on fitness-guided binary clustering, and a particle generation strategy based on feature importance and crossover both are designed to ensure the quality of particles in the subswarms. We apply VS-CCPSO to 12 typical datasets and compare it with six state-of-the-art methods. The experimental results show that VS-CCPSO has the capability of obtaining good feature subsets, suggesting its competitiveness for tackling FS problems with high dimensionality.
In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where a sigmoid-function-based weighting strategy is developed to adaptively adjust the acceleration coefficients. ...The newly proposed adaptive weighting strategy takes into account both the distances from the particle to the global best position and from the particle to its personal best position, thereby having the distinguishing feature of enhancing the convergence rate. Inspired by the activation function of neural networks, the new strategy is employed to update the acceleration coefficients by using the sigmoid function. The search capability of the developed adaptive weighting PSO (AWPSO) algorithm is comprehensively evaluated via eight well-known benchmark functions including both the unimodal and multimodal cases. The experimental results demonstrate that the designed AWPSO algorithm substantially improves the convergence rate of the particle swarm optimizer and also outperforms some currently popular PSO algorithms.
State-of-charge (SOC) estimation of lithium-ion battery is one of the core functions of battery management system. In order to improve the estimation accuracy of SOC, this paper proposes a long ...short-term memory neural network based on particle swarm optimization (PSO-LSTM). Firstly, the key parameters of LSTM are optimized by PSO algorithm, so that the data characteristics of lithium-ion battery can match the network topology. In addition, random noise is added to the input layer of PSO-LSTM neural network to improve the anti-interference ability of the network. Finally, experiments show that the proposed method can achieve accurate estimation under different conditions. The estimates based on PSO-LSTM converge to the real state-of-charge within an error of 0.5%.
•A PSO-LSTM model is established for SOC estimation of lithium-ion battery.•PSO is applied to optimize the hyper-parameters of LSTM.•Random noises are added to the sampled data, so as to prevent over-fitting of the PSO-LSTM model.•Results show that the proposed method has high estimation accuracy and robustness.
This article presents a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms. The new PF-PSO algorithm consists of two ...steps: the first generates randomly the particle population;and the second zooms the search domain. An application of this algorithm to the optimal tuning of proportional-integral-fuzzy controllers for the position control of a family of integral-type servo systems is then presented as a second contribution. The reduction in PF-PSO algorithm's cost function allows for reduced energy consumption of the fuzzy control system. A comparison with other metaheuristic algorithms on canonical test functions and experimental results are presented at the end of this article.
This paper presents a new particle swarm optimizer for solving multimodal multiobjective optimization problems which may have more than one Pareto-optimal solution corresponding to the same objective ...function value. The proposed method features an index-based ring topology to induce stable niches that allow the identification of a larger number of Pareto-optimal solutions, and adopts a special crowding distance concept as a density metric in the decision and objective spaces. The algorithm is shown to not only locate and maintain a larger number of Pareto-optimal solutions, but also to obtain good distributions in both the decision and objective spaces. In addition, new multimodal multiobjective optimization test functions and a novel performance indicator are designed for the purpose of assessing the performance of the proposed algorithms. An effectiveness validation study is carried out comparing the proposed method with five other algorithms using the benchmark functions to prove its effectiveness.
Feature selection (FS) is an important data processing technique in the field of machine learning. There have been various FS methods, but all assume that the cost associated with a feature is ...precise, which restricts their real applications. Focusing on the FS problem with fuzzy cost, a fuzzy multiobjective FS method with particle swarm optimization, called PSOMOFS, is studied in this article. The proposed method develops a fuzzy dominance relationship to compare the goodness of candidate particles and defines a fuzzy crowding distance measure to prune the elitist archive and determine the global leader of particles. Also, a tolerance coefficient is introduced into the proposed method to ensure that the Pareto-optimal solutions obtained satisfy decision makers' preferences. The developed method is used to tackle a series of the UCI datasets and is compared with three fuzzy multiobjective evolutionary methods and three typical multiobjective FS methods. Experimental results show that the proposed method can achieve feature sets with superior performances in approximation, diversity, and feature cost.
This paper proposes a modified particle swarm optimization (MPSO) for fast convergence and harmonic minimization in three-phase eleven-level hybrid cascaded multilevel inverter (HC-MLI). Selective ...harmonic elimination pulsewidth modulation (SHE-PWM) technique implemented through MPSO has been used in the proposed work for synthesizing an eleven-level output voltage using two dc sources, a precharged capacitor, and twelve switches. The switching angles of the three-phase eleven-level HC-MLI has been computed for eliminating specified lower order odd harmonics such as 5th, 7th, 11th, and 13th from the output voltage of the HC-MLI. In the proposed MPSO optimized HC-MLI, capacitor voltage balance is also ensured even at higher modulation indices by utilizing the redundant switching states available at different switching instances of the HC-MLI. The performance of the MPSO optimized three-phase eleven-level HC-MLI has been verified through simulation and experimentation on a 1.5-kW prototype using closed-loop control. The results obtained through MPSO are compared with the results obtained through genetic algorithm (GA) and particle swarm optimization (PSO) in terms of convergence rate and harmonic content. It has been found that MPSO gives improved results in comparison to GA and PSO.
Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in ...various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.
Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high ...penetration level of photovoltaic (PV) generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM) technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC) maximum power point tracking (MPPT) technique that is based on proportional integral (PI) controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN), ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO) techniques and their performance are compared with existing models like back propagation (BP) forecasting model.
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