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

This paper is directed towards the development of the Arithmetic and Visual Logic-based representations for classical nonlinear systems modeling and optimization The concept was originally proposed by Gabr and Dorrah for linear system using the notion of the normalized fuzzy matrices In this concept, the arithmetic fuzzy logic-based representation type is suggested based on dual cell representation, expressed by replacing each parameter with a pair of parentheses (Actual Value, Fuzzy Level) The visual fuzzy logic-based type is proposed based on colored cells representation expressed by replacing each parameter by its value and a coded corresponding to its fuzzy level For both representations, the theoretical foundations of the fuzzy logic algebra, different properties, and analogy with the Conventional Fuzzy Theory are elaborated for various cases of operations The suggested approach is generalized for the fuzzy case of the classical nonlinear modeling and optimization problems These problems are normally solved by the Lagrangean Function Method or the Jacobian Technique The two methods were then modified by incorporating the suggested fuzzy logic-based representations assuming the fuzziness of all the optimization formulation parameters Using a representative nonlinear optimization numerical example, the proposed fuzzy logic-based formulation is applied The results demonstrate the consistency and robustness of the developed approach for incorporation with nonlinear optimization problems Finally, it is shown that the presented concept provides a unified theory for various linear and nonlinear systems' modeling and optimization in fully fuzzy environments

Generalization of arithmetic and visual fuzzy logic-based representations for nonlinear modeling and optimization in fully fuzzy environment

The paper presents a new approach for the fuzzy modeling and analysis of automatic control systems in fully fuzzy environment. The proposed technique is based on the normalized fuzzy matrices and is an extension of the Arithmetic and Visual Fuzzy Logic-based Representations developed recently by Gabr and Dorrah. The approach is also suitable for determining the propagation of fuzziness in automatic control and dynamical systems where all system coefficients are expressed as fuzzy parameters. It is simply based on the assignment of corresponding fuzzy levels for parameters uncertainty that is made in a heuristic way circumventing the previous difficulties in assuming probabilistic or membership functions. Implementations of the approach are carried out for solving selected automatic control problems with parameters expressed in fully fuzzy environment. These problems cover fuzzy impulse response of systems, fuzzy Routh-Hurwitz stability criteria, fuzzy Controllability and Observability, and the stabilization of inverted pendulum through pole placement technique. The results demonstrated the robustness of the proposed formulation and illustrated in a systematic way how the system parameters fuzziness effect on output results can be effectively tracked for monitoring and control. Finally, it is pointed out that the suggested Arithmetic and Visual Fuzzy Logic-based Representations opens the door for a unified theory for fuzzy modeling, analysis and design of continuous and discrete automatic control systems operating in fully fuzzy environment.

New approach for automatic control modeling and analysis using Arithmetic And Visual Fuzzy Logic-based Representations in fully fuzzy environment

A recent study has shown that everywhere real systems follow common “fractals-general stacking behavior” during their change pathways (or evolutionary life cycles) This fact leads to the emergence of the new discipline “Fractals-General Science” as a mother-discipline (and acting as upper umbrella) of existing natural and applied sciences to commonly handle their fractals-general change behavior It is, therefore, the main targets of this short communication are to present the motives, objectives, relations with other existing sciences, and the development map of such new science It is discussed that there are many foreseen illustrative applications in geology, archeology, astronomy, life sciences, ecology, environmental science, hydrology, agronomy, engineering, materials sciences, chemistry, nanotechnology, biology, medicine, psychiatry, sociology, humanities, education, and arts that could effectively lead the implementation and experimentation of such new science It is highlighted that the new “Fractals-General Science” could provide through multi-stacking representations the necessary platforms for investigating interactions and mutual changes between real life systems belonging to several sciences and disciplines Examples are handling problems of the processing of basic formation and changes of matter and substances, propagation of combined corrosion, creep, fatigue and sedimentation of engineering and industrial systems, and the progressing of humans’ evolutionary life cycles

Toward a new ''Fractals-General Science''

The optimal power flow issue (OPFI) can be solved in this work using the recently developed algorithm, gorilla troops algorithm (GTA). The goal of OPFI is to reduce numerous functions such as minimizing fuel costs, emissions, and power losses and improving the voltage stability related to electric power networks (EPNs). The GTA is inspired by gorillas’ social habits, which include migration to a strange region, migration toward a specified spot, traveling to other gorillas, competing for adult females, and escorting the silverback. The developed GTA is tested with and without the inclusion of the Thyristor-Controlled Series Capacitor (TCSC) devices in the system. The proposed GTA is applied on a practical Egyptian West Delta-EPN (WD-EPN) and the standard IEEE 57-bus EPN and with and without the inclusion of the TCSC devices to appraise the GTA algorithm’s performance in the OPFI. In addition, the proposed GTA is applied on a large-scale IEEE 118 bus system with higher outperformance compared to particle swarm optimization. The results illustrate that the fuel costs, emissions, voltage stability, and power losses are reduced for the standard IEEE 57-bus EPN with and without TCSC devices by a percentage of (18.847% and 18.818%), (59% and 58.97%), (13.405% and 11.507%), and (64.337% and 65.178%), respectively, while fuel costs, emissions, voltage stability, and power losses are reduced for WD-EPN with and without TCSC devices by a percentage of (8.547%, 8.565%), (13.641%, 13.6%), and (61.949%, 61.954%), respectively. A comparison study is conducted to demonstrate the GTA’s effectiveness when compared with other recently developed algorithms such as improved Salp Swarm Algorithm, quasi-reflection jellyfish search, Salp Swarm Algorithm, improved heap-based algorithm, bat search algorithm, social network search algorithm, electromagnetic field optimization, and other well-known algorithms as well. According to the comparison with these algorithms, the GTA demonstrates the best results among the attained results.

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Optimal Power Flow Incorporating Thyristor-Controlled Series Capacitors Using the Gorilla Troops Algorithm

Use of wind energy as a renewable source of energy for electric utility systems is increasing around the world. The major challenges of wind energy generation are natural intermittency, unpredictability, and uncertainty due to wind variations. In this paper, five different adaptive neuro-fuzzy wind predictors are proposed and compared to forecast the speed of wind blowing in the East Coast of Egypt, a very promising location to generate more than 20 GW of wind power. The first and second proposed models are based on real wind-speed data of the selected site for the same month aided by the corresponding real wind-speed data for the same site for the past 4 and 6 years, respectively. The results that have been obtained from these models show more accuracy with respect to the previous work in the literature. Furthermore, three new models are proposed based on only 20% of the real data used for the first model obtaining similar accuracy to predict the average wind speed one day, half a day, and quarter day ahead.

Hassen T. Dorrah

Papers

Generalization of arithmetic and visual fuzzy logic-based representations for nonlinear modeling and optimization in fully fuzzy environment

New approach for automatic control modeling and analysis using Arithmetic And Visual Fuzzy Logic-based Representations in fully fuzzy environment

Toward a new ''Fractals-General Science''

Optimal Power Flow Incorporating Thyristor-Controlled Series Capacitors Using the Gorilla Troops Algorithm

Adaptive neuro-fuzzy short-term wind-speed forecasting for Egypt's East-Coast