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Wind power prediction enables advance prediction of the future capacity of wind farms to improve production, increase capacity and reduce costs, however, wind power generation data are highly unstable, making it difficult to achieve high‐precision prediction. Signal decomposition methods and optimization algorithms allow for data smoothing and optimization of parameter settings, but the limitations of parameter dependency and the slow optimization search process prevent existing research from being applied in a practical setting. To address these issues, this article proposes a multiscale ultra‐short‐term wind power prediction model based on particle swarm optimization based on greedy dynamic and integrated fitness evaluation method (GD‐IFEM‐PSO) and variational mode decomposition and back propagation network (VMD‐BP). First, this article improves the velocity update formula in the particle swarm optimization algorithm by the proposed GD weight and sets up a fitness function using multiple evaluation metrics by the proposed integrated fitness evaluation method (IFEM), so that the improved particle swarm optimization algorithm achieves high efficiency in the optimization search while ensuring the comprehensiveness of the evaluation. Second, the variable mode decomposition (VMD) decomposition algorithm is used to decompose the historical wind power data to achieve smoothing of the wind power data, and the improved particle swarm optimization algorithm is used to optimize the K and ⍺ values in the VMD decomposition algorithm to improve the comprehensive performance of the decomposition. Then, to achieve data reduction and fast model training, the components are divided into trend components, low‐frequency vibration components, high‐frequency vibration components, and random noise components according to the central frequency, so that the model can better grasp the data trend while reducing the number of components and achieving high prediction accuracy. Finally, the different components are predicted by the BP neural network and the predicted values of the different categories of components are reconstructed into the final wind power prediction values. To demonstrate the rationality and progressiveness of the proposed model, several models are compared on several data sets, and the results show that the proposed model has faster prediction speed and higher prediction performance, and is more suitable for application in real‐world environments. [ABSTRACT FROM AUTHOR]

In order to reduce the negative impact of disorderly charging of electric vehicles on the power grid, this article uses an improved particle swarm optimization algorithm to optimize the electric vehicle charging model. With full consideration of various constraints, load variance, load peak‐valley difference, and the charging cost of electric vehicle users are taken as optimization targets. The simulation results show that the orderly charging control strategy proposed in this article realizes the peak‐shaving and valley‐filling of the grid, reduces load fluctuations, improves the performance of the grid, and improves the safety and reliability of grid operation. The method proposed in this article provides a reference for electric vehicle charging scheduling. [ABSTRACT FROM AUTHOR]

With the rapid development of globalization economy, countries in the world are more and more closely connected in business, and international import and export trade has also achieved rapid development. Based on the analysis of the status quo of the country's import and export trade, this article combines the main national economic indicators to analyze and study the algorithm of import and export trade forecasting. RVM is a sparse probability model and a new supervised learning method. Firstly, this paper introduces the protection system of block chain technology for import and export trade data information. Then, a hybrid RVM model is constructed, which is optimized by PSO algorithm, and the import and export volume is predicted based on this model. Based on the analysis of the data, it is tested that the security protection coefficient of the blockchain security technology for import and export trade data can reach up to 99.9%, and the accuracy of the import and export trade forecast based on the PSO optimized hybrid RVM model can reach up to 85.79%. A series of experiments show that the import and export trade forecast model studied in this paper can accurately predict the volume of import and export trade, thus providing a new method for the country's import and export trade, with high practical significance. [ABSTRACT FROM AUTHOR]

In this paper, a method to forecast the volatility of crude oil market based on text mining technology and deep learning model is proposed. First, the text mining technology is used to construct the textual features hidden in the news headlines of crude oil market, including the emotional polarity score, investor sentiment classification, and risk factors. Then the textual features with other features reflecting fluctuations of the crude oil market are combined as inputs of the deep learning long short‐term memory (LSTM) model for better forecasting results. Furthermore, the particle swarm optimization (PSO) is used to optimize the hyper‐parameters of the deep learning LSTM model to further improve the forecasting ability. The results in this study show that the LSTM model optimized by PSO algorithm has reduced 7.66%, 8.15%, 8.2%, 10.93%, and 23.46% on the mean absolute error (MAE) of crude oil volatility forecasting compared with other models. And it is more stable in multiple‐step forecasting results compared with the econometric model. In addition, compared with the PSO‐LSTM model without textual features, the forecasting accuracy of the PSO‐LSTM model with textual features has reduced by 6.77% and 5.12% in the MAE and the mean square error (MSE), respectively. [ABSTRACT FROM AUTHOR]

As computer networks expand, attacks and intrusions into these networks have increased. In addition to firewalls and other intrusion prevention equipment, other systems, such as IDS (Metrics), are designed to provide enhanced security in computer systems, including the purpose of monitoring intrusive and intrusive activities. Intrusive allocation system can be considered effective if the high intrusion rate is slightly misleading, and in this article a new way to classify it is abnormal (infiltration) in the host or network. UNSW‐NB15 and KDD‐Cup'99 Datasets Introducing Random Forest and PSO algorithm. In this paper, training data and label data used with the random forest algorithm. After creating a random forest algorithm, provide test data. We use the data stored in train step, which is actually a copy of the data, so that when performing the test step, the same training data can be compared with categorize using a PSO algorithm. In in order to show the accuracy of the proposed method, an example of the confusion matrix formed in the code that showed performance of all methods and modes studied is compared based on accuracy and time that the PSO algorithm has always been able to take less time, which is quite acceptable and predictable. Improves correct diagnosis of correct detection rate in that report was 75.94% and in the proposed method in this article it reached 97%. With the proposed method, learning speed has been greatly increased and accuracy is acceptable. [ABSTRACT FROM AUTHOR]

Voltage unbalance is an important power quality issue, which occurs in electrical power systems (EPSs) and causes severe problems for them. In this work, a general mathematical model for EPS including its long transmission line is developed using the generalised circuit parameters method. Then, a hybrid Particle Swarm Optimisation–Artificial Neural Network (PSO–ANN) algorithm is proposed to overcome the voltage unbalance problem by controlling the firing angles of thyristor‐controlled reactor (TCR) which varies the amount of reactive power at the load side. PSO algorithm is responsible for determining the optimal set of TCR firing angles required to retrieve the balance conditions in offline mode for different load changes, employing the developed mathematical model of the EPS. Then, a dataset is taken as training samples for the ANN to be used in online mode. Aqaba Qatranah South‐Amman (AQSA) EPS is considered as a real case study and simulated in MATLAB environment to validate the proposed algorithm. The simulation results are compared with other ANN algorithms available in the literature. Finally, a laboratory prototype is built for AQSA EPS including its long transmission line to test the proposed hybrid PSO–ANN algorithm for real unbalance conditions acquired from the laboratory prototype. [ABSTRACT FROM AUTHOR]

Unlike conventional power plants, wind farm and solar photovoltaic (PV) module operations involved uncontrollable factors such as wind and solar irradiation, making the dispatch problem more complex. This study proposes optimal scheduling using a modification inertia weight of the particle swarm optimisation (PSO) algorithm and take into account unexpected forecast errors due to uncertainty in renewable distributed generators and loads in the day‐ahead market in a microgrid. Modified inertia weight makes the PSO algorithm have a strong global searching ability to solve the scheduling[AQ ID=Q1]. Meanwhile, the microgrid coordinates the realistic production of its power plant to get the optimal total cost. The uncertainty of renewable distributed generators is modelled based on forecast data. The Simulation result shows the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

The authors have proposed a new automatic classification scheme based on the conjoint use of the multi‐layer perceptron (MLP) neural network and an enhanced particle swarm optimisation (EPSO) algorithm for its training. In this work, six predominant categories of heartbeats from MIT‐BIH database are considered, which are: normal, premature ventricular contraction, atrial premature contraction, right bundle branch block, left bundle branch block and paced beats. First, the authors have applied the standard particle swarm optimisation (PSO) algorithm to select the network structure for each features vector. Then, the relevant electrocardiogram (ECG) features to the studied arrhythmias were chosen, which suited to the optimised training performance of the classifier. The recognition performance of the proposed EPSO‐MLP classification system is evaluated considering two different versions of the EPSO algorithm. In the first version (EPSOw), the inertia weight factor of the PSO algorithm is proposed to be a variable with iterations. However, two PSO parameters are taken to be variables in the second version of the improved learning algorithm (EPSOwc). The obtained experimental results prove the enhancement of the convergence ability of the MLP neural network and confirm the superiority of the proposed EPSO‐MLP classification scheme on comparison with the other last published classification systems. [ABSTRACT FROM AUTHOR]

Vector quantisation (VQ) is a widely used method for data compression or data clustering. This study proposes a fast codebook design method for image VQ. General particle swarm optimisation (PSO)‐K‐means hybrid methods for codebook design take too much time. To deal with this problem, the authors present a new clustering method, which takes a very short time and generates a pretty good codebook. This method contains four parts, i.e. pooling, PSO algorithm, reverse cast, and K‐means algorithm. Pooled images are used for the PSO process while original images are used for K‐means fine‐tuning, and these two processes connected by the reverse cast process. In the authors' experiment, this method can dramatically reduce the computation time by using big sizes of pooling window or enhance the codebook quality by using small sizes of pooling window. They can make the calculation time almost one‐tenth of that of the PSO‐K‐means hybrid method, and the scheme is also faster than the K‐means algorithm. Experimental results demonstrate that the main advantages of the proposed algorithm lie in the fact that it can reduce the computation time and enhance the quality of codebooks. [ABSTRACT FROM AUTHOR]

Based on the analysis on the uncertain nature of wind power output, the Weibull distribution parameters of regional wind speed at different periods are calculated to obtain the probability density function (PDF) and the cumulative distribution function (CDF) of the wind power. The spinning reserve (SR) requirements for wind power incorporation are determined according to the obtained PDF and CDF, and by converting the wind power into a chance constrained form, a model for dynamic economic emission dispatch with wind power is constructed. A hybrid multi‐objective algorithm that integrates differential evolution (DE) and particle swarm optimisation (PSO) algorithm is put forward to solve the proposed model. The algorithm is implemented based on the Pareto dominance theory and a dynamic external archive set, and fully exploits the advantages of DE and PSO. An improved calculation method of crowding distance and Pareto solution set reduction rule are also employed to enhance the performance of the proposed algorithm. Also, three performance indicators are introduced to evaluate the performance of the algorithm. Two distinct test systems are performed to verify the proposed model and algorithm, and the results show that they are effective and reasonable. [ABSTRACT FROM AUTHOR]

The generation of multiple‐path test cases can greatly enhance the efficiency of path‐wise testing. Various methods adopting meta‐heuristic algorithm to generate multiple‐path test cases have been proposed, but existing methods focus on improving the meta‐heuristic algorithm to get better test case generation efficiency, and test cases covering each path needs to be generated by meta‐heuristic algorithm searching. To improve efficiency, a test case generation method for multiple‐path coverage is proposed in this study, which combines a particle swarm optimisation (PSO) algorithm with metamorphic relations (MRs). The method first generates a test case using the PSO algorithm, and then generates new test cases by repeatedly using MRs between test cases. This method reduces evolving numbers of PSO algorithm. The experimental results show that the proposed method can significantly enhance the efficiency in terms of fitness evaluations and time consumption. [ABSTRACT FROM AUTHOR]

Supercapacitors present an attractive energy storage alternative for high‐performance applications due to their compact size and high‐power density. Therefore, the supercapacitors have broad prospects for the development in the field of electric vehicles and renewable energy. To describe the output characteristics of the supercapacitors with high accuracy for the simulation research and practical application, a dynamic modelling method is proposed for the supercapacitors based on support vector machine (SVM) and particle swarm optimisation (PSO) algorithm. In this study, the SVM is used to predict the output voltage of the supercapacitors with the key parameters (temperature, current and initial voltage). The PSO algorithm is adopted to optimise the parameters of the SVM to improve the performance of the dynamic modelling. An experimental platform is established, where an electric machine drive system powered by the supercapacitors is controlled to operate at frequent acceleration and deceleration modes, thus leading to the frequent charging and discharging of the supercapacitors. The experimental data is collected to validate the effectiveness of the proposed method. The results show that the proposed method can effectively predict the output voltage of the supercapacitors. [ABSTRACT FROM AUTHOR]

In this study, a clustering scheme based on the particle swarm optimisation (PSO) algorithm is used for probabilistic load flow calculation in the presence of wind generations. In this method, input random variables are first clustered in several groups according to their similarity and then a representative is assigned to each group by the PSO algorithm; finally, the deterministic load flow is performed. Using this technique, computational time is meaningfully decreased, while an acceptable level of accuracy is achieved. The IEEE 57‐bus and IEEE 118‐bus test systems were selected for the case study to demonstrate the performance of the proposed method. The results were compared with those of the Monte Carlo as well as K‐means clustering methods from the accuracy and computation time points of view. Simulation results show that the introduced method significantly reduced the computational burden while keeping a high level of accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a new technique to design fixed-structure controllers for linear unknown systems using a set of measurements. In model-based approaches, the measured data are used to identify a model of the plant for which a suitable controller can be designed. Due to the fact that real processes cannot be described perfectly by mathematical models, designing controllers using such models to guarantee some desired closed-loop performance is a challenging task. Hence, a possible alternative to model-based methods is to directly utilize the measured data in the design process. We propose an approach to designing structured controllers using a set of closed-loop frequency-domain data. The principle of such an approach is based on computing the parameters of a fixed-order controller for which the closed-loop frequency response fits a desired frequency response that describes some desired performance indices. This problem is formulated as an error minimization problem, which can be solved to find suitable values of the controller parameters. The main feature of the proposed control methodology is that it can be applied to stable and unstable plants. Additionally, the design process depends on a pre-selected controller structure, which allows for the selection of low-order controllers. An application of the proposed method to a DC servomotor system is presented to experimentally validate and demonstrate its efficacy. [ABSTRACT FROM AUTHOR]

A novel fuzzy proportional–integral derivative (PID) controller is proposed in this study for automatic generation control (AGC) of interconnected power systems. The optimum gains of the proposed fuzzy PID controller are optimised employing a hybrid differential evolution particle swarm optimisation (DEPSO) technique using an integral of time multiplied by absolute value of error criterion. The superiority of hybrid DEPSO algorithm over differential evolution and particle swarm optimisation (PSO) algorithm has also been demonstrated. The results are also compared with some recently published approaches such as artificial bee colony and PSO based proportional–integral/PID controllers for the same interconnected power systems. Furthermore, performance of the proposed system is analysed by varying the different parameters such as loading condition, system parameters and objective functions. It is observed that the optimum gains of the proposed fuzzy PID controller need not be reset even if the system is subjected to variation in loading condition and system parameters. Finally, the study is extended to a three area system considering generation rate constraint to demonstrate the ability of the proposed approach to cope with multiple interconnected systems. Comparison with previous AGC methods reported in the literature validates the significance of the proposed approach. [ABSTRACT FROM AUTHOR]

SUMMARY: Low encoding complexity is very important for quasi‐cyclic low‐density parity‐check (QC‐LDPC) codes used in wireless communication systems. In this paper, a new scheme is presented to construct QC‐LDPC codes with low encoding complexity. This scheme is called two‐stage particle swarm optimization (TS‐PSO) algorithm, in which both the threshold and girth distribution of QC‐LDPC codes are considered. The proposed scheme is composed of two stages. In the first stage, we construct a binary base matrix of QC‐LDPC code with the best threshold. The matrix is constructed by combining a binary PSO algorithm and the protograph extrinsic information transfer (PEXIT) method. In the second stage, we search an exponent matrix of the QC‐LDPC code with the best girth distribution. This exponent matrix is based on the base matrix obtained in the first stage. Consequently, the parity‐check matrix of the QC‐LDPC code with the best threshold and best girth distribution are constructed. Furthermore, bit error rate performances are compared for the QC‐LDPC codes constructed by proposed scheme, the QC‐LDPC code in 802.16e standard, and the QC‐LDPC code in Tam's study. Simulation results show that the QC‐LDPC codes proposed in this study are superior to both the 802.16e code and the Tam code on the additive white Gaussian noise (AWGN) and Rayleigh channels. Moreover, proposed scheme is easily implemented, and is flexible and effective for constructing QC‐LDPC codes with low encoding complexity. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A standalone wind/solar/battery hybrid power system, making full use of the nature complementarity between wind and solar energy, has an extensive application prospect among various newly developed energy technologies. The capacity of the hybrid power system needs to be optimised in order to make a tradeoff between power reliability and cost. In this study, each part of the wind/solar/battery hybrid power system is analysed in detail and an objective function combining total owning cost and loss of power supply probability is built. To solve the problems with non‐linearity, complexity and huge computation, an improved particle swarm optimisation (PSO) algorithm is developed, which integrates the taboo list to broaden the search range and introduces 'restart' and 'disturbance' operation to enhance the global searching capability. The simulation results indicate that the proposed algorithm is more stable and provides better results in solving the optimal allocation of the capacity of the standalone wind/solar/battery hybrid power system compared with the standard PSO algorithm. [ABSTRACT FROM AUTHOR]

SUMMARY The paper deals with common concepts of modern methods of train speed determination with minimal errors. Balise locations depend on a variety of parameters. With genetic algorithm and particle swarm optimization, as two new intelligent algorithms, and Kalman filtering concept used, the best locations are determined to reduce tachometer errors. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A hardware‐oriented lossless electrocardiogram compression algorithm is presented for very large‐scale integration (VLSI) circuit design. To achieve high performance and low complexity, a novel prediction method based on the fuzzy decision and particle swarm optimiser (PSO) was developed. The accuracy of prediction was advanced efficiently by using the PSO algorithm to find the optimal parameters, which provided 64 situations for the fuzzy decision. Moreover, a novel low‐complexity and high‐performance entropy‐coding algorithm based on Huffman coding was developed, which used one limited Huffman coding to encode a main region and five‐region codes to encode the extending regions. The average compression rate of the whole MIT‐BIH Arrhythmia database was up to 2.84 by combing the proposed fuzzy‐based PSO prediction and Huffman region entropy‐coding techniques. The VLSI architecture contained only a 1.9 K gate count and its core area was 5965 μm2 synthesised using a 90 nm CMOS process. It consumed 201 μW when operating at a 200 MHz processing rate. Compared with previous low‐complexity designs, the average compression rate is not only improved by more than 6.4% but also reduced the gate count by at least 8.2%. [ABSTRACT FROM AUTHOR]