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    ThesisItemOpen Access
    ELECTRIC FORECASTING OPTIMIZATION BASED ON ARTIFICIAL INTELLIGENCE TECHNIQUES
    (DEPARTMENT OF ELECTRICAL ENGINEERING VAUGH INSTITUTE OF AGRICULTURAL ENGINEERING AND TECHNOLOGY SAM HIGGINBOTTOM UNIVERSITY OF AGRICULTURE, TECHNOLOGY AND SCIENCES, 2020) Singh, Anamika; SRIVASTAVA, DR. MANISH KUMAR
    Recently, utilization of nonlinear gadgets like power electronics, continuous power supplies, flexible speed drives, and delicate loads like personal computers, etc has expanded. It is seen that nonlinearity in the electric load profile increases with the use of these devices. Therefore, an accurate load forecasting is required to improve quality and quantity of power services. A significant truth about the power is that it cannot be stored for quite a long time in AC form; it is conceivable to store it in DC form, but it is restricted to a less amount comparing to demand and that too at an extreme high cost. Therefore, an accurate load forecasting is required. Lower accuracy level can be accomplished by utilizing any conventional technique however for higher accuracy; improved models are to be created. Therefore, the need for accurate and robust load forecasting model is evident in the current scenario of non linear electric load profile forecasting. Electric forecasting (EF) is an important tool for power system operation, planning, and control for decisions such as load management, generation scheduling, and system security assessment, etc. Most of the research is performed for short-term electric forecasting (STEF). It shows the importance of the STEF. In the literature, several robust and accurate forecasting models were developed such as auto-regressive, autoregressive integrated moving average and moving average and found capable of forecasting stationary time–series data but real-time series is never stationary. These models were failed to provide the desired level of accuracy with the nonlinearity present in electric load profile. Therefore, time-series models are not suitable for accurate shortterm load forecasting. STEF is related to operational tasks such as economic dispatch, fuel arrangement, load scheduling, etc. Thus, it becomes necessary to develop forecasting models with enhanced accuracy. The application of Artificial Intelligence (AI) techniques has been explored to solve the above problem. Additionally, various papers for electric forecasting exhibit that Artificial Neural Network (ANN) has the capability of learning the nonlinear behavior and ability to generalize. Other advantages of an ANN are parallel data processing, adaptability, fault tolerant, etc. Therefore, ANN-based models can forecast electricity generation, load with higher accuracy. Feed-Forward Neural Networks (FFNN) is commonly used architecture of ANN. The result of FFNN has been analyzed and vii compared for accuracy. Result shows that accuracy of forecasted models is not as desired, learning rate is slow and time consuming. In order to remove above problems, ANN based forecasting models is optimized by two optimization tools, viz., Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). These optimized models provide more accurate forecasting results. The experimentation shows that PSO algorithms better than GA for optimizing these models. This way this thesis fulfills its aim to develop an improved, modern STEF model with reduced complexity.
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    ThesisItemOpen Access
    Analyzing Fuzzy Control Model for Variable Speed Pitch Wind System Connected To Grid
    (FACULTY OF ENGINEERING AND TECHNOLOGY SAM HIGGINBOTTOM UNIVERSITY OF AGRICULTURE TECHNOLOGY AND SCIENCES NAINI, PRAYAGRAJ -211007, 2019) ALAMIN, AIMAN SALEH; SHRIVASTAVA, Dr. JYOTI
    Wind energy is playing a vital role in the world‟s energy markets nowadays, considering its striking growth rate in the last few years. Variable Speed wind turbines have many advantages over fixed-speed generation such as increased energy capture, operation at maximum power point, improved efficiency, and power quality. Fuzzy logic controller is employed for the control of pitch angle, real and reactive power flows of grid connected direct driven VSWT-PMSG system. The control system is done by planning the appropriate trajectories on components of the output variable vector of the system. The main advantage of the proposed method is control of the system even during the transient state as well as the high performance. The proposed system includes a three-bladed horizontal wind turbine and a permanent magnet synchronous generator (PMSG) which is connected to the grid through a back to back converter and a filter. To analyze the performance of the control strategy, a random profile of the wind speed has been used. A new nonlinear control method based on differential flatness is applied to a high-power wind energy conversion system connected to the grid is proposed in this paper. To prove the efficiency of the proposed control method, simulation results of a 5 MW wind turbine using the same model are presented.
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    ThesisItemOpen Access
    Design of Aircraft Control System
    (FACULTY OF ENGINEERING AND TECHNOLOGY SAM HIGGINBOTTOM UNIVERSITY OF AGRICULTURE, TECHNOLOGY AND SCIENCES Deemed-to-be-University (FORMERLY ALLAHABAD AGRICULTURAL INSTITUTE) NAINI, ALLAHABAD-211007, 2017) Elmajdub, Naser. F.AB.; Srivastava, Manish Kumar
    The two important stages of aircraft – takeoff and landing are to be designed in this thesis. In physical form, it is very difficult to study the takeoff and landing stages of aircraft. In this thesis, the new way is included to analyses the takeoff and landing of aircraft by SIMULINK which is the branch of MATLAB. On MATLAB simulator, it can see the takeoff and landing position of aircraft and analysis the graphs on different parameters. The take-off and landing of an aircraft is often the most critical and accident prone portion. This thesis describes the design of an aircraft take-off and landing algorithm implemented on an existing low-cost flight control system with six degree of freedom (6-DoF). This thesis also describes the takeoff and landing algorithm development and gives validation results from MATLAB in the loop simulation. The scope of the thesis is reaches to the best situation to the design of aircraft control systems in most common high risk phases at important two stages with use SIMULINK program, high performance in short runway and how change the classical design used the control system from mechanical to hydro mechanical into electrical control system as used in modern aircraft with Fly-By-Wire but in the future design technology Fly-By-Light may be used. The takeoff and landing control system is designed under constraints as degree of freedom and the equation of motion to be improved in many situations. The research is achieved by MATLAB/Simulink. The simulation results show that the control system performs well. The proposed approach gets the information of attitude and altitude by using aircraft model and various indicators shows the actual reading in aircraft model. The three classes of models and simulations are virtual, constructive and live.
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    ThesisItemOpen Access
    New Approach to Optimize Economic Load Dispatch by using Improved Environmental Adaption Method
    (FACULTY OF ENGINEERING AND TECHNOLOGY SAM HIGGINBOTTOM UNIVERSITY OF AGRICULTURE, TECHNOLOGY & SCIENCES NAINI, ALLAHABAD-211007 2017, 2017) Amahmad Ali, Youssef Nagem; Shrivastava, Jyoti
    The increasing demand for electrical energy has made the electric power systems large and complex, and because fuel cost is the major part of the variable cost in thermal electrical power generation, it has become necessary to optimize the methods to solve the economic load dispatch problem. The economic load dispatch is the process to calculate the generation of the generating units to minimize the total production cost with satisfying the equality and inequality constraints. The fuel cost of generation is represented as cost curves and overall calculation minimizes the operating cost by finding a point where total output of generators equals to total system load that must be delivered plus losses. The economic load dispatch problem is a highly non linear optimization problem with number of constraints, thus the economic load dispatch problem computationally became hard work where the traditional optimization methods were not able to reach the global minima so to get high quality solution and to get the global minima there must be used of Evolutionary optimization algorithms. In this thesis work, to investigate different methods and to improve the quality of the solution by improving the convergence rate and stability of the solution and reduce the of iteration or the time to reach the minimum cost, five different methods have been applied to solve economic load dispatch problem to minimize the cost by considering the generator units output and the control variable. By using the quadratic programming (QP), simulated annealing algorithm (SAA), Artificial bee colony (ABC), particle swarm optimization (PSO), Modified particle swarm optimization (MPSO),and Improved Environmental Adaption method(IEAM) with the help of MATLAB Programming, the simulations were carried out for IEEE standard six unit test system and twenty unit test system. From the comparative response it is clear that Improved Environmental Adaption Method (IEAM) is giving the best response by reducing the fuel cost corresponding to number of iterations, the solution is very high quality, stable convergence characteristics and very effective to capture global optimal solution in minimum time and very less number of iteration so it can be concluded that this optimization technique is the best to solve economic load dispatch.
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    ThesisItemOpen Access
    New Approach to Optimize Economic Load Dispatch by using Improved Environmental Adaption Method
    (FACULTY OF ENGINEERING AND TECHNOLOGY SAM HIGGINBOTTOM UNIVERSITY OF AGRICULTURE, TECHNOLOGY & SCIENCES NAINI, ALLAHABAD-211007, 2017) Amahmad A, Youssef Nagem; Srivastava, Jyoti
    ABSTRACT The increasing demand for electrical energy has made the electric power systems large and complex, and because fuel cost is the major part of the variable cost in thermal electrical power generation, it has become necessary to optimize the methods to solve the economic load dispatch problem. The economic load dispatch is the process to calculate the generation of the generating units to minimize the total production cost with satisfying the equality and inequality constraints. The fuel cost of generation is represented as cost curves and overall calculation minimizes the operating cost by finding a point where total output of generators equals to total system load that must be delivered plus losses. The economic load dispatch problem is a highly non linear optimization problem with number of constraints, thus the economic load dispatch problem computationally became hard work where the traditional optimization methods were not able to reach the global minima so to get high quality solution and to get the global minima there must be used of Evolutionary optimization algorithms. In this thesis work, to investigate different methods and to improve the quality of the solution by improving the convergence rate and stability of the solution and reduce the of iteration or the time to reach the minimum cost, five different methods have been applied to solve economic load dispatch problem to minimize the cost by considering the generator units output and the control variable. By using the quadratic programming (QP), simulated annealing algorithm (SAA), Artificial bee colony (ABC), particle swarm optimization (PSO), Modified particle swarm optimization (MPSO),and Improved Environmental Adaption method(IEAM) with the help of MATLAB Programming, the simulations were carried out for IEEE standard six unit test system and twenty unit test system. From the comparative response it is clear that Improved Environmental Adaption Method (IEAM) is giving the best response by reducing the fuel cost corresponding to number of iterations, the solution is very high quality, stable convergence characteristics and very effective to capture global optimal solution in minimum time and very less number of iteration so it can be concluded that this optimization technique is the best to solve economic load dispatch. Keywords— Economic Load Dispatch (ELD), Particle Swarm Optimization (PSO), Thermal