Matlab (the language of technical computing): using matlab graphics ver.5

Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks

In the bacteria foraging optimization algorithm (BFAO), the chemotactic process is randomly set, imposing that the bacteria swarm together and keep a safe distance from each other. In hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA-PSO) algorithm the principle of swarming is introduced in the framework of BFAO. The hBFOA-PSO algorithm is based on the adjustment of each bacterium position according to the neighborhood environment. In this paper, the effectiveness of the hBFOA-PSO algorithm has been tested for automatic generation control (AGC) of an interconnected power system. A widely used linear model of two area non-reheat thermal system equipped with proportional-integral (PI) controller is considered initially for the design and analysis purpose. At first, a conventional integral time multiply absolute error (ITAE) based objective function is considered and the performance of hBFOA-PSO algorithm is compared with PSO, BFOA and GA. Further a modified objective function using ITAE, damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients is proposed to increase the performance of the controller. Further, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, tie-line power in the range of +50% to -50% as well as size and position of step load perturbation to demonstrate the robustness of the proposed hBFOA-PSO optimized PI controller. The proposed approach is also extended to a non-linear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of craziness based particle swarm optimization (CRAZYPSO) approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different PI coefficients and comparison between ANFIS and proposed approach has been provided.

Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems

Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II

The existence of sunlight, air and other resources on earth must be used in an appropriate way for human welfare while still protecting the environment and its living creatures. The exploitation of sunlight and air as a substantial Renewable Energy (RE) source is an important research and development domain over past few years. The present and future overtaking in RE mainly comprises of (i) the development of novel technology for optimum production from the available natural resources (ii) environmental awareness, and (iii) the better management and distribution system. Like other domains (food, health, accommodation, safety, etc.), Artificial Intelligence (AI) could assist in achieving the future goals of the RE. Statistical and biologically inspired AI methods have been implemented in several studies to achieve common and future aims of the RE. The present study summarizes the review of reviews and the state-of-the-art research outcomes related to wind energy, solar energy, geothermal energy, hydro energy, ocean energy, bioenergy, hydrogen energy, and hybrid energy. Particularly, the role of single and hybrid AI approaches in research and development of the previously mentioned sources of RE will be comprehensively reviewed.

Renewable energy: Present research and future scope of Artificial Intelligence

Networked sensors in smart grids allow techniques like sensor fusion including: sensor similarities, as well as, sensor complementarities to be integrated to obtain new information or feature that is not measured directly. On the other hand, these techniques can be extended to get trusted readings at different correlated areas based on historical observations and their corresponding probabilistic distributions of sensors at these areas. In this paper a stochastic modelling of multivariate within the platform of cyber-physical systems has been discussed. A proposed multivariate Weibull distribution (WD) modeling is adopted to predict wind speed (WS) at a certain site given data at other correlated place(s). The proposed methodology has been implemented on some cases of study to illustrate the effectiveness of the adopted technique using bivariate or trivariate models. It has been revealed that the same methodology could be extended to any multivariate WD for any stochastic modeling problem. In this paper a comparison between the proposed trivariate, and bivariate Weibull is established to show their efficiency on estimating WS at a location that has a faulty sensor, that fails to deliver its data.

A Generalized Cascaded Approach to Estimate Missing Wind Data Using Multivariate Weibull Distribution Network

Advancements in the field of artificial intelligence (AI) made during this decade have forever changed the way we look at automating spacecraft subsystems including the electrical power system. AI have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. In this paper, a mathematical modeling and MATLAB–SIMULINK model for the different components of the spacecraft power system is presented. Also, a control system, which includes either the Neural Network Controller (NNC) or the Fuzzy Logic Controller (FLC) is developed for achieving the coordination between the components of spacecraft power system as well as control the energy flows. The performance of the spacecraft power system is evaluated by comparing two control systems using the NNC and the FLC.

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Artificial Intelligence Techniques for Controlling Spacecraft Power System

Most types of networks depend on fixed resources in their configuration, but these fixed resources are not effective, especially in the case of a problem in any of these resources. This may lead to a defect in the entire network until the problem is resolved and therefore there must be an alternative to replace the fixed resources in such cases. In this paper, the concept of addressing optimal operations of flexible reconfigurable networks with movable and changeable resources is thoroughly presented. The methodology is represented in a mathematical form by solving the optimization problems of quadratic programming, nonlinear programming and multi-objective programming. Implementation of the proposed technique is carried out to several illustrative examples with a single or multiple movable symbolic-based resources. The implemented examples demonstrate the efficacy of the proposed technique and its strong capability in presenting many scenarios for system operation under movable resources. Finally, the applications of the new concept could be extended to real life systems of the electricity, water, oil and gas, communication, computer, transportation, and service networks.

Optimal Analysis of Flexible Reconfigurable Networks Using Movable and Changeable Components

Modelling is important in studying systems. It shows the system from different angles, help in analysing and understanding the system, analysis the causes and effects, can discover the defects in the system and treat it, illustrates the alternatives and studies their effects. Research looks for appropriate models of low, medium, high and very high wind speed. ARMA can be used in making time series models. It can get models with similar properties of original data. This research develops several ARMA models for wind speed data classified according to its value in four groups. The least mean square error (MSE) is the criteria to choose between models, for each wind speed model gets the appropriate wind turbine model that tells the predicted output power. The original data is taken to its group according to its value and taken to its appropriate power model. Turbine stops for low and very high speed. MPPT is used for medium speed. High speed models need pitch angle control. The model is implemented to Zafranaa wind speed data. It is divided into three groups. There were no data points for fourth group, Max point was 17m/s. Third group has appropriate amount of data and most of data are in second group, Finally the implementation procedure has demonstrated the applicability of the preferred method only

Wind-Based Multi-Regional Modelling and Control of Wind Turbines Generation

Temperature is one of the most principle factors affects aquaculture system. The water temperature is very important parameter for shrimp growth. It can cause stress and mortality or superior environment for growth and reproduction. The required temperature for optimal growth is 34oC, if temperature increase up to 38oC it causes death of the shrimp, so it is important to control water temperature. Solar thermal water heating system is designed to supply an aquaculture pond with the required hot water in Mersa Matruh in Egypt as presented in this paper. This paper introduces a complete mathematical modeling and MATLAB SIMULINK model for the solar thermal aquaculture system. Moreover the paper presents the control of pond water temperature using artificial intelligence technique. Neural networks are massively parallel processors that have the ability to learn patterns through a training experience. Because of this feature, they are often well suited for modeling complex and non-linear processes such as those commonly found in the heating system. They have been used to solve complicated practical problems. The simulation results indicate that, the control unit success in keeping water temperature constant at the desired temperature by controlling the hot water flow rate.

Hassen T. Dorrah

Papers

A Generalized Cascaded Approach to Estimate Missing Wind Data Using Multivariate Weibull Distribution Network

Artificial Intelligence Techniques for Controlling Spacecraft Power System

Optimal Analysis of Flexible Reconfigurable Networks Using Movable and Changeable Components

Wind-Based Multi-Regional Modelling and Control of Wind Turbines Generation

Solar Thermal Aquaculture System Controller Based on Artificial Neural Network