Transmission systems are prone to contingency conditions that would either occur using a generator outage or line outage. Placing and sizing the flexible ac transmission systems (FACTS) devices ...appropriately can reduce the effects of the contingency condition. This paper optimally locates FACTS devices in a transmission system under the N-1 contingency condition. The genetic algorithm (GA) technique is used to locate different, multiple FACTS devices (thyristor-controlled series capacitor and static VAR compensator) optimally in a power system. This optimization technique is used to locate FACTS devices on the IEEE 9 bus system. MATLAB simulation is developed and checked for both single and multiple FACTS placements. Simulation results obtained for generator outage and line outage are tabulated with the type of FACTS device/rating, location, and generation cost with line loss reduction. The optimized results observed for the cost-optimized FACTS placement problem are found to be satisfactory. The results obtained in the IEEE 9 bus system have shown improvement in a decrease of generation cost and system loss component while placement and sizing of both the FACTS devices.
Display omitted
•A hybrid artificial intelligence model is proposed for predicting coal strength alteration induced by CO2 adsorption.•BPNN, GA and AdaBoost algorithms are combined.•The ...GA-BPNN-AdaBoost model has good prediction accuracy and generalization ability.•The MIV is used to investigate the relative importance of each input variable.
CO2 geological sequestration in coal seams has gradually become one of the effective means to deal with the global greenhouse effect. However, the injection of CO2 into the coal seam can have an important impact on the physical and chemical properties of coal, which in turn affects the CO2 sequestration performance in coal seams and causes a large number of environmental problems. In order to better evaluate the strength alteration of coal in CO2 geological sequestration, a hybrid artificial intelligence model integrating back propagation neural network (BPNN), genetic algorithm (GA) and adaptive boosting algorithm (AdaBoost) is proposed. A total of 112 data samples for unconfined compressive strength (UCS) are retrieved from the reported studies to train and verify the proposed model. The input variables for the predictive model include coal rank, CO2 interaction time, CO2 interaction temperature and CO2 saturation pressure, and the corresponding output variable is the measured UCS. The predictive model performance is evaluated by correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The predictive results denote that the GA-BPNN-AdaBoost predictive model is an efficient and accurate method to predict coal strength alteration induced by CO2 adsorption. The simultaneous optimization of BPNN by GA and AdaBoost algorithm can greatly improve the prediction accuracy and generalization ability of the model. At the same time, the mean impact value (MIV) is used to investigate the relative importance of each input variable. The relative importance scores of coal rank, CO2 interaction time, CO2 interaction temperature and CO2 saturation pressure are 0.5475, 0.2822, 0.0373, 0.1330, respectively. The research results in this paper can provide important guiding significance for CO2 geological sequestration in coal seams.
The lifespan of a battery in battery energy storage systems (BESSs) is affected by various factors such as the operating temperature of the battery, depth of discharge, and magnitudes of the ...charging/discharging currents supplied to or drawn from the battery. In this study, the optimal location and size of a BESS are found for voltage regulation in a distribution system while increasing the lifespan of the battery. Various factors that affect the lifespan of a battery are considered and modelled. The problem is formulated as a multi-objective optimisation problem with two-objective functions. The first objective function calculates the energy losses in the system, whereas the second objective function represents the total investment cost of the distributed generator (DG) and BESS installations. Wind and solar DGs with uncertainties in their output powers are also considered with the BESSs. An elitist non-dominated sorting genetic algorithm-II with a utopian point method is used to solve the optimisation problem. Furthermore, an IEEE 906 bus European low-voltage test feeder and eight test cases are considered for this study. The results show reduced losses and cost, improvement in the voltage profile, and extended lifespan of the batteries.
A beamforming technique based on the genetic algorithm is proposed in this paper. This method reconstructs acoustic sources by exploiting the sparsity. To reduce the computational cost and speed up ...the implementation, a highly efficient algorithm is further developed by narrowing down the search domain for the genetic algorithm. The performance is numerically investigated through a quantitative analysis of the position and amplitude error of the recovered source, and experimentally demonstrated in acoustic imaging. For this newly established approach, the results indicate a higher resolution capability than the conventional beamforming, and better accuracy and robustness than the compressive sensing beamforming. Overall, the present work can enrich existing high-resolution beamforming methods and benefit relevant acoustic testing applications.
•Proposed new definition of interval order relations.•Proposed new definition of interval metric.•Theoretical development of multi-objective optimization in interval domain.•Developed Tournament ...Genetic Algorithm for solving multi-objective optimization problem in interval domain.
The aim of this paper is to discuss the optimality of interval multi-objective optimization problems with the help of different interval metric. For this purpose, we have proposed the new definitions of interval order relations by modifying the existing definitions and also modified different definitions of interval mathematics. Using the definitions of interval order relations and interval metric, the multi-objective optimization problem is converted into single objective optimization problem by different techniques. Then the corresponding problems have been solved by hybrid Tournament Genetic Algorithm with whole arithmetic crossover and double mutation (combination of non-uniform and boundary mutations). To illustrate the methodology, five numerical examples have been solved and the computational results have been compared. Finally, to test the efficiency of the proposed hybrid Tournament Genetic Algorithm, sensitivity analyses have been carried out graphically with respect to genetic algorithm parameters.
With the development of the information technology and logistics industry, industrial production models are more likely to be innovated than ever before. Therefore, there is a tendency for a large ...number of manufacturing enterprises to start outsourcing their manufacturing activities to more professional subcontractors so they could pay more attention to their core business. Cloud manufacturing (CMfg), as a supplement to cloud computing and big data, is also a new network manufacturing mode that is service-oriented. This mode makes it even more complex and impractical to organize and optimize manufacturing resources. Considering this problem, this paper proposes a manufacturing resource selection strategy based on an improved distributed genetic algorithm (DGA) for manufacturing resource combinatorial optimization (MRCO) in CMfg. We divided the DGA into several sections and distributed and optimized the process, which not only guaranteed algorithm speed but also expanded the search range and improved the accuracy. A case study, a performance comparison between a simple genetic algorithm (SGA) and a working procedure priority-based algorithm (WPPBA) is presented later in this paper. Experimental results showed that the proposed method is preferable and a more effective choice for searching for the optimal solution.
Typically, the battery stack temperature assumes constant in electrochemical or equivalent circuit models of the battery. However, experiments on a Vanadium Redox Flow Battery (VRFB) unit show that ...the temperature varies significantly during charge and discharge processes and considerably affects the value of the State of Charge (SoC) and other battery internal parameters. Therefore, monitoring and modeling the battery temperature is essential. An electrochemical based thermal model of VRFBs is adapted to the experimental data of a nine-cell VRFB unit in this study to find the optimal values of the coefficients and parameters of the VRFB's electrochemical and thermal models. The electrochemical model of VRFB and its thermal model include many unknown parameters and coefficients that need to be estimated. The objective of the current study is to determine the optimal value of all of these unknown parameters. According to the literature, the empirical methods are not sufficient to accurately determine the model's coefficients and parameters. An Optimization framework is defined to identify these coefficients by minimizing the mean square error between the measured experimental data of VRFB and the electrochemical model-based terminal voltage and electrolyte temperature. Moreover, a VRFB cell's electrochemical and thermal models are highly nonlinear; thus, excellent optimization techniques are needed to find these coefficients and parameters. A few metaheuristic optimizers are tested on the proposed optimization framework, where three of these algorithms have shown consistent and able to converge to reliable solutions. These algorithms are Genetic Algorithm (GA), Particle Swarm Optimizer (PSO), and Ant Lion Optimizer (ALO). The low RMS errors of the estimations by the metaheuristic-based algorithms compared to the measured data, show the accuracy of the proposed parameter identification approaches of VRFB's electrochemical and thermal model.
•The proposed method first employs NSGA III algorithm for an effective features extraction from power signals. The numbers of features required for detection are less in case of proposed NSGA III ...algorithm.•The NSGA III not only has exact localization of power quality events and stronger robustness but also reduces the computational time in comparison with the traditional NSGA II.•The obtained unique feature vectors are used for training of DAG-SVM to classify the power quality disturbances. In case of N-classes, the proposed DAG-SVM generates N(N-1)/2 classifiers, one for each pair of classes and makes the decision accurate and faster.•Virtex-5 FPGA based processor is used to test and validate the feasibility of the proposed method for real time analysis of power quality events.
This article proposes an application of non-dominated sorting genetic algorithm III (NSGA III) and directed acyclic graph support vector machine (DAG-SVM) based combined approach for recognition and classification of power quality disturbances. Power disturbances are non-stationary and non-linear by nature and require a large number of features vectors for detection, which results in high computation time and error. The proposed method first employs NSGA III algorithm for an effective features extraction from power signals. The numbers of features required for detection are less in case of proposed NSGA III algorithm. The NSGA III algorithm generates optimal solutions based on multi objective optimization and then fitness function is generated with the help of Pareto front to obtain unique features set from power signals. The NSGA III not only has exact localization of power quality events and stronger robustness but also reduces the computational time in comparison with the traditional NSGA II. The obtained unique feature vectors are used for training of DAG-SVM to classify the power quality disturbances. In case of N-classes, the proposed DAG-SVM generates N(N-1)/2 classifiers, one for each pair of classes and makes the decision accurate and faster. The short event detection, lesser computational timing, superior classification accuracy, and high anti-noise performance are the main advantages of the proposed method. Furthermore, Virtex-5 FPGA based processor is used to test and validate the feasibility of the proposed method for real time analysis of power quality events.
The paper deals with the multi-objective optimization problems of laminated composite beam structures. The objective function is to minimize the weight of the whole laminated composite beam and ...maximize the natural frequency. In particular, the simultaneous use of all the design variables such as fiber volume fractions, thickness and fiber orientation angles of layers is conducted, in which the fiber volume fractions are taken as continuous design variables with the constraint on manufacturing process while the thickness and fiber orientation angles are considered as discrete variables. The beam structure is subjected to the constraint in the natural frequency which must be greater than or equal to a predetermined frequency. For free vibration analysis of the structure, the finite element method is used with the two-node Bernoulli-Euler beam element. For solving the multi-objective optimization problem, the nondominated sorting genetic algorithm II (NSGA-II) is employed. The reliability and effectiveness of the proposed approach are demonstrated through three numerical examples by comparing the current results with those of previous studies in the literature.
PDF