Optimal Setting of Thyristor Controlled Series Compensator with Brain Storm Optimization Algorithms for Available Transfer Capability Enhancement

Applications of Flexible AC Transmission Systems (FACTS) devices for enhancement of Available Transfer Capability (ATC) is gaining attention due to economic and technical limits of the conventional methods involving physical network expansions. FACTS allocation which is sine-qua-non to its performance is a major problem and it is being addressed in recent time with heuristic algorithms. Brain Storm Optimization Algorithms (BSOA) is a new heuristic and predicting optimization algorithms which revolutionizes human brainstorming process. BSOA is engaged for the optimum setting of FACTS devices for enhancement of ATC of a deregulated electrical power system network in this study. ATC enhancement, bus voltage deviation minimization and real power loss regulation are formulated into multi-objective problems for FACTS allocation purposes. Thyristor Controlled Series Capacitor (TCSC) is considered for simulation and analyses because of its fitness for active power control among other usefulness. ATC values are obtained for both normal and N-1-line outage contingency cases and these values are enhanced for different bilateral and multilateral power transactions. IEEE 30 Bus system is used for demonstration of the effectiveness of this approach in a Matlab software environment. Obtained enhanced ATC values for different transactions during normal evaluation cases are then compared with enhanced ATC values obtained with Particle Swarm Optimization (PSO) set TCSC technique under same trading. BSO behaved much like PSO throughout the achievements of other set objectives but performed better in ATC enhancement with 27.12 MW and 5.24 MW increase above enhanced ATC values achieved by the latter. The comparative of set objectives values relative to that obtained with PSO methods depict suitability and advantages of BSOA technique.

Improved Arabic Alphabet Characters Classification Using Convolutional Neural Networks (CNN)

Handwritten characters recognition is a challenging research topic. A lot of works have been present to recognize letters of different languages. The availability of Arabic handwritten characters databases is limited. Motivated by this topic of research, we propose a convolution neural network for the classification of Arabic handwritten letters. Also, seven optimization algorithms are performed, and the best algorithm is reported. Faced with few available Arabic handwritten datasets, various data augmentation techniques are implemented to improve the robustness needed for the convolution neural network model. The proposed model is improved by using the dropout regularization method to avoid data overfitting problems. Moreover, suitable change is presented in the choice of optimization algorithms and data augmentation approaches to achieve a good performance. The model has been trained on two Arabic handwritten characters datasets AHCD and Hijja. The proposed algorithm achieved high recognition accuracy of 98.48% and 91.24% on AHCD and Hijja, respectively, outperforming other state-of-the-art models.

Study on the application of BP neural network optimized based on various optimization algorithms in storm surge prediction

Storm surge prediction is of great importance to disaster prevention and mitigation. In this study, four optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO), beetle antenna search (BAS), and beetle swarm optimization (BSO) are used to optimize the back propagation neural network (BPNN), and four optimized BPNNs for storm surge prediction are proposed and applied to Yulin station and Xiuying station at Hainan, China. The optimal model parameter combination is determined by trail-and-error method for the best prediction performance. Comparisons with the single BPNN indicate that storm surge can be efficiently predicted using the optimized BPNNs. BPNN optimized by BSO has the minimum prediction error, and BPNN optimized by BAS has the minimum time cost to reduce unit prediction error.

Formation of the composition of programs for the knowledge–intensive industry development using bacterial optimization algorithms and fuzzy logic methods

Today the knowledge–intensive industry development is carried out by the programs that combine a set of innovation and investment projects aimed at achieving a single goal and implemented in general constraints. The presence of a larger number of project characteristics (in particular, terms, resources, performers, etc.), which must be taken into account when forming the composition of the program, leads to the formulation of the problem of multicriteria optimization. As its solution, it is proposed to use an algorithm of bacterial optimization, supplemented by a procedure for forming initial positions using fuzzy logic methods.