Controlling Techniques for STATCOM using Artificial Intelligence

The static synchronous compensator (STATCOM) is a power electronic converter designed to be shunt-connected with the grid to compensate for reactive power. Although they were originally proposed to increase the stability margin and transmission capability of electrical power systems, there are many papers where these compensators are connected to distribution networks for voltage control and power factor compensation. In these applications, they are commonly called distribution static synchronous compensator (DSTATCOM). In this paper we have focussed on STATCOM and the controlling techniques which are based on artificial intelligence

Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming

© 2019, Springer-Verlag London Ltd., part of Springer Nature. In this paper, a novel application of biologically inspired computing paradigm is presented for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by exploiting the competency of accurate modeling with feed forward artificial neural network (FF-ANN), global search efficacy of genetic algorithms (GA) and rapid local search with sequential quadratic programming (SQP). The fitness function for IVP of associated nonlinear RL circuit is developed by exploiting the approximation theory in mean squared error sense using an approximate FF-ANN model. Training of the networks is conducted by integrated computational heuristic based on GA-aided with SQP, i.e., GA-SQP. The designed methodology is evaluated to variants of nonlinear RL systems based on both AC and DC excitations for number of scenarios with different voltages, resistances and inductance parameters. The comparative studies of the proposed results with Adam’s numerical solutions in terms of various performance measures verify the accuracy of the scheme. Results of statistics based on Monte-Carlo simulations validate the accuracy, convergence, stability and robustness of the designed scheme for solving problem in nonlinear circuit theory

Heat transfer and simulated coronary circulation system optimization algorithms for real power loss reduction

In this paper, the heat transfer optimization (HTO) algorithm and simulated coronary circulation system (SCCS) optimization algorithm has been designed for Real power loss reduction. In the projected HTO algorithm, every agent is measured as a cooling entity and surrounded by another agent, like where heat transfer will occur. Newton’s law of cooling temperature will be updated in the proposed HTO algorithm. Each value of the object is computed through the objective function. Then the objects are arranged in increasing order concerning the objective function value. This projected algorithm time “t” is linked with iteration number, and the value of “t” for every agent is computed. Then SCCS optimization algorithm is projected to solve the optimal reactive power dispatch problem. Actions of human heart veins or coronary artery development have been imitated to design the algorithm. In the projected algorithm candidate solution is made by considering the capillaries. Then the coronary development factor (CDF) will appraise the solution, and population space has been initiated arbitrarily. Then in the whole population, the most excellent solution will be taken as stem, and it will be the minimum value of the Coronary development factor. Then the stem crown production is called the divergence phase, and the other capillaries’ growth is known as the clip phase. Based on the arteries leader’s coronary development factor (CDF), the most excellent capillary leader’s (BCL) growth will be there. With and without L-index (voltage stability), HTO and SCCS algorithm’s validity are verified in IEEE 30 bus system. Power loss minimized, voltage deviation also reduced, and voltage stability index augmented

FCC algorithm for power loss diminution

In this work, the FCC algorithm has been applied to the power problem. Real power loss reduction, voltage deviation minimization, and voltage stability enhancement are the key objectives of the proposed work. The proposed FCC algorithm has been modeled based on the competition, communication among teams, and training procedure within the team. The solution has been created based on the team, players, coach, and substitution tactic. A preliminary solution of the problem is produced, and the initialization of the teams depends on the team’s formation with substitute tactics. Mainly fitness function for each solution is computed, and it plays an imperative role in the process of the algorithm. With the performance in the season, promotion and demotion of the teams will be there. Most excellently performed teams will be promoted to a senior division championship, and the most poorly performed team will be demoted to the top lower division league. Ideas and tactics sharing procedure, repositioning procedure, Substitution procedure, seasonal transmit procedure, Promotion and demotion procedure of a team which plays in the confederation cup has been imitated to solve the problem. Similar to an artificial neural network, a learning phase is also applied in the projected algorithm to improve the quality of the solution. Modernization procedure employed sequentially to identify the best solution. With and without voltage stability (L-index) FCC algorithm is evaluated in IEEE 30, bus system. Then the Proposed FCC algorithm has been evaluated in standard IEEE 14, 57,118,300 bus test systems without Lindex. Power loss minimization and voltage stability index improvement have been achieved with voltage deviation minimization

Ubicación óptima de dispositivos de compensación reactiva para la mejora del perfil de voltaje y reducción de pérdidas en el sistema eléctrico de potencia utilizando técnicas de clusterización

En este artículo se expone un método para localizar de forma óptima compensadores SVC en el SEP, utilizando una metodología de clusterización. Para lo cual, se empieza por cuantificar el flujo óptimo de potencia (OPF) donde se determina la matriz de sensibilidad del sistema a partir del jacobiano del mismo. En lo sucesivo, se genera el arreglo de distancia eléctrica y de atenuación. Ulteriormente, se emplea el primer criterio de identificación de clústeres junto con la técnica de agrupación k-Means. De este modo se obtienen las zonas de control de tensión (VCA). Asimismo, se emplea el criterio de minimización de los costos en función de la capacidad de compensación, para determinar la mejor disposición de los compensadores. El enfoque descrito se prueba en el sistema de 14 y 39 nodos. Además, los resultados conseguidos se comprueban en un software comercial (Power Factory) y con investigaciones similares. En conclusión, el enfoque sugerido demuestra ser útil para la ubicación de compensadores en los sistemas de prueba, puesto que mejora el perfil de voltaje tendiendo a 1 en por unidad en cada caso. Además, un valor excesivo de la capacidad del SVC provoca una mayor cantidad de pérdidas.This paper presents a method to locate SVC compensators in the SEP, using a clustering methodology. For which, we start by quantifying the OPF where the sensitivity matrix of the system is determined from the Jacobian of the system. Subsequently, the electrical distance and attenuation array is generated. Subsequently, the first cluster identification criterion calculated together with the k-Means clustering technique is used. In this way, the voltage control zones (VCA) are obtained. Also, the SVC installation cost minimization criterion is used to determine the best arrangement of the compensators. The described approach is tested on the 14-node and 39-node system. Furthermore, the obtained results are compared with the Power Factory software and similar research. In conclusion, the suggested approach is useful for the creation of VCA, as well as for finding the best arrangement of SVC equipment. However, it is critical to consider the capacity of the compensators, since an excessive value of the SVC capacity causes a higher number of losses

A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility

STATCOM Estimation Using Back-Propagation, PSO, Shuffled Frog Leap Algorithm, and Genetic Algorithm Based Neural Networks

Different optimization techniques are used for the training and fine-tuning of feed forward neural networks, for the estimation of STATCOM voltages and reactive powers. In the first part, the paper presents the voltage regulation in IEEE buses using the Static Compensator (STATIC) and discusses efficient ways to solve the power systems featuring STATCOM by load flow equations. The load flow equations are solved using iterative algorithms such as Newton-Raphson method. In the second part, the paper focuses on the use of estimation techniques based on Artificial Neural Networks as an alternative to the iterative methods. Different training algorithms have been used for training the weights of Artificial Neural Networks; these methods include Back-Propagation, Particle Swarm Optimization, Shuffled Frog Leap Algorithm, and Genetic Algorithm. A performance analysis of each of these methods is done on the IEEE bus data to examine the efficiency of each algorithm. The results show that SFLA outperforms other techniques in training of ANN, seconded by PSO

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words