Voltage profile Improvement Using Static Synchronous Compensator STATCOM

Static synchronous compensator (STATCOM) is a regulating device used in AC transmission systems as a source or a sink of reactive power. The most widely utilization of the STATCOM is in enhancing the voltage stability of the transmission line. A voltage regulator is a FACTs device used to adjust the voltage disturbance by injecting a controllable voltage into the system. This paper implement Nruro-Fuzzy controller to control the STATCOM to improve the voltage profile of the power network. The controller has been simulated for some kinds of disturbances and the results show improvements in voltage profile of the system. The performance of STATCOM with its controller was very close within 98% of the nominal value of the busbar voltage

Wide Area Signals Based Damping Controllers for Multimachine Power Systems

Nowadays, electric power systems are stressed and pushed toward their stability margins due to increasing load demand and growing penetration levels of renewable energy sources such as wind and solar power. Due to insufficient damping in power systems, oscillations are likely to arise during transient and dynamic conditions. To avoid undesirable power system states such as tripping of transmission lines, generation sources, and loads, eventually leading to cascaded outages and blackouts, intelligent coordinated control of a power system and its elements, from a global and local perspective, is needed. The research performed in this dissertation is focused on intelligent analysis and coordinated control of a power system to damp oscillations and improve its stability. Wide area signals based coordinated control of power systems with and without a wind farm and energy storage systems is investigated. A data-driven method for power system identification is developed to obtain system matrices that can aid in the design of local and wide area signals based power system stabilizers. Modal analysis is performed to characterize oscillation modes using data-driven models. Data-driven models are used to identify the most appropriate wide-area signals to utilize as inputs to damping controller(s) and generator(s) to receive supplementary control. Virtual Generators (VGs) are developed using the phenomena of generator coherency to effectively and efficiently control power system oscillations. VG based Power System Stabilizers (VG-PSSs) are proposed for optimal damping of power system oscillations. Herein, speed deviation of VGs is used to generate a supplementary coordinated control signal for an identified generator(s) of maximum controllability. The parameters of a VG-PSS(s) are heuristically tuned to provide maximum system damping. To overcome fallouts and switching in coherent generator groups during transients, an adaptive inter-area oscillation damping controller is developed using the concept of artificial immune systems - innate and adaptive immunity. With increasing levels of electric vehicles (EVs) on the road, the potential of SmartParks (a large number of EVs in parking lots) for improving power system stability is investigated. Intelligent multi-functional control of SmartParks using fuzzy logic based controllers are investigated for damping power system oscillations, regulating transmission line power flows and bus voltages. In summary, a number of approaches and suggestions for improving modern power system stability have been presented in this dissertation

Vertical transportation in buildings

Nowadays, the building industry and its associated technologies are experiencing a period of rapid growth, which requires an equivalent growth regarding technologies in the field of vertical transportation. Therefore, the installation of synchronised elevator groups in modern buildings is a common practice in order to govern the dispatching, allocation and movement of the cars shaping the group. So, elevator control and management has become a major field of application for Artificial Intelligence approaches. Methodologies such as fuzzy logic, artificial neural networks, genetic algorithms, ant colonies, or multiagent systems are being successfully proposed in the scientific literature, and are being adopted by the leading elevator companies as elements that differentiate them from their competitors. In this sense, the most relevant companies are adopting strategies based on the protection of their discoveries and inventions as registered patents in different countries throughout the world. This paper presents a comprehensive state of the art of the most relevant recent patents on computer science applied to vertical transportationConsejería de Innovación, Ciencia y Empresa, Junta de Andalucía P07-TEP-02832, Spain

Bidirectional optimization of the melting spinning process

This is the author's accepted manuscript (under the provisional title "Bi-directional optimization of the melting spinning process with an immune-enhanced neural network"). The final published article is available from the link below. Copyright 2014 @ IEEE.A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.National Nature Science Foundation of China, Ministry of Education of China, the Shanghai Committee of Science and Technology), and the Fundamental Research Funds for the Central Universities

Performance investigation of stand-alone induction generator based on STATCOM for wind power application

Self-Excited induction generators (SEIG) display a low voltage and frequency regulation due to variable applied load and input rotation speed. Current work presents a simulation and performance analysis of a three-phase wind-driven, SEIG connect to a three-phase load. In addition, an investigation of the dynamic operation of the induction generator from starting steady state until no-load operation. It is assumed that the input mechanical power is constant where the rotor of the SEIG rotates at a constant speed. The value of the excitation capacitance which is necessary to the operation of the induction generator also computed to ensure a smooth and self-excitation starting. The output voltage of the generator is adjusted by varying the reactive power injected by STATCOM. A 3-phase IGBT voltage source inverter with a fuel cell input supply is connected as STATCOM which is used to compensate for the reduction in the supply voltage and its frequency due to variation occurred in the applied loads. This work includes introducing a neuro-fuzzyy logic controller to enhance the performance of the SEIG by regulation the generated voltage and frequency The dynamic model of SEIG with STATCOM and loads are implemented using MATLAB/SIMULIN

Voltage profile enhancement in distribution network using static synchronous compensator STATCOM

STATCOM is one of FACTS devices that used as regulator for transmission and distribution systems which works for reactive power compensation. STATCOM utilisation in distribution system mostly for enhancing the profile of voltage, where used for adjusting the disturbance voltage by injecting into the system a controllable voltage. This paper present a Fuzzy controller based on STATCOM to enhance the voltage profile in distribution network. The controller of STATCOM has simulated for different types of abnormal load conditions of balance and unbalance load. The results of simulation show ability of proposed design to enhance the load voltage which was 96% of the nominal value

Maximum power point tracking for brushless DC motor-driven photovoltaic pumping systems using a hybrid ANFIS-FLOWER pollination optimization algorithm

In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to amend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets the Luo converter occupied brushless DC motor (BLDC)-directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. The Luo converter, a newly developed DC-DC converter, has high power density, better voltage gain transfer and superior output waveform and can track optimal power from PV modules. For BLDC speed control there is no extra circuitry, and phase current sensors are enforced for this scheme. The most recent attempt using adaptive neuro-fuzzy inference system (ANFIS)-FPA-operated BLDC directed PV pump with advanced Luo converter, has not been formerly conferred

Hybrid optimization techniques based automatic artificial respiration system for corona patient

Artificial ventilation is widely used for various respiratory problems of human beings. The oxygen level of the corona patients has to be maintained for smooth breathing which is very difficult. For achieving this state, the air pressure should be controlled in the respiration system that has a piston mechanism driven by a motor. An Automatic respiration system model is designed and controller parameters are tuned using hybrid Optimization techniques. Hybrid Controllers like genetic algorithm based Fractional Order Proportional Integral Derivative controller (FOPID), Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller, and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) controllers were designed and verified. Integral Square Error is considered as the objective function of the optimization technique to find the controller parameters. The output responses of all three hybrid controllers are compared based on the error indices, time domain specifications, set-point tracking and Convergence speed graph. The genetic algorithm-based FOPID controller gives better results when compared with the Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) for the proposed artificial ventilation system

Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator

A novel hybrid strategy combining a spiral dynamic algorithm (SDA) and a bacterial foraging algorithm (BFA) is presented in this article. A spiral model is incorporated into the chemotaxis of the BFA algorithm to enhance the capability of exploration and exploitation phases of both SDA and BFA with the aim to improve the fitness accuracy for the SDA and the convergence speed as well as the fitness accuracy for BFA. The proposed algorithm is tested with the Congress on Evolutionary Computation 2013 (CEC2013) benchmark functions, and its performance in terms of accuracy is compared with its predecessor algorithms. Consequently, for solving a complex engineering problem, the proposed algorithm is employed to obtain and optimise the fuzzy logic control parameters for the hub angle tracking of a flexible manipulator system. Analysis of the performance test with the benchmark functions shows that the proposed algorithm outperforms its predecessor algorithms with significant improvements and has a competitive performance compared to other well-known algorithms. In the context of solving a real-world problem, it is shown that the proposed algorithm achieves a faster convergence speed and a more accurate solution. Moreover, the time-domain response of the hub angle shows that the controller optimised by the proposed algorithm tracks the desired system response very well