Nidul Sinha

Solution to automatic generation control problem using firefly algorithm optimized IλDµ controller

Present work focused on automatic generation control (AGC) of a three unequal area thermal systems considering reheat turbines and appropriate generation rate constraints (GRC). A fractional order (FO) controller named as I(λ)D(µ) controller based on crone approximation is proposed for the first time as an appropriate technique to solve the multi-area AGC problem in power systems. A recently developed metaheuristic algorithm known as firefly algorithm (FA) is used for the simultaneous optimization of the gains and other parameters such as order of integrator (λ) and differentiator (μ) of I(λ)D(µ) controller and governor speed regulation parameters (R). The dynamic responses corresponding to optimized I(λ)D(µ) controller gains, λ, μ, and R are compared with that of classical integer order (IO) controllers such as I, PI and PID controllers. Simulation results show that the proposed I(λ)D(µ) controller provides more improved dynamic responses and outperforms the IO based classical controllers. Further, sensitivity analysis confirms the robustness of the so optimized I(λ)D(µ) controller to wide changes in system loading conditions and size and position of SLP. Proposed controller is also found to have performed well as compared to IO based controllers when SLP takes place simultaneously in any two areas or all the areas. Robustness of the proposed I(λ)D(µ) controller is also tested against system parameter variations.

GA optimized PID controllers for automatic generation control of two area reheat thermal systems under deregulated environment

This paper proposes for a genetic algorithm (GA) tuned PID controllers for automatic generation control of two - area reheat thermal system under deregulated environment. A PID controller with its inherent superb capabilities of containing peak deviation, steady state errors and oscillatory behaviour of a dynamic system is an obvious choice for automatic generation control of interconnected power systems. The main issue towards the use of a PID controller is how to optimize the three gains of the controller. The trial and error method of finding the gains by indirect optimization using ISE technique with an appropriate performance index appears to be not wise enough because of its space complexity. GA appears to be the right choice in finding the optimum gains for the controllers. Hence, in this work, the gains of the PID controllers are optimized using floating point GA. The results of the GA optimized PID controllers on a two area reheat thermal system are compared with those with optimized through trial and error method. The GA optimized controllers are found to be superior in terms of peak transient deviation, settling times, and dynamic oscillations.

Meta Heuristic Search Algorithms for Short-Term Hydrothermal Scheduling

This paper presents the performances of meta heuristic search algorithms in solving short-term hydro thermal scheduling problems. Meta heuristic search algorithms like GA, CEP, FEP, IFEP and PSO have been developed for hydrothermal scheduling problems. The performances of the algorithms are demonstrated on a test case. It has been observed that all the algorithms are capable of finding very nearly global solutions within a reasonable time but PSO algorithm was found to be the most efficient amongst them in terms of convergence rate, solution time and success rate

Wavelet-GA-ANN Based Hybrid Model for Accurate Prediction of Short-Term Load Forecast

This paper proposes a hybrid model developed through wiser integration of wavelet transforms, floating point GA and artificial neural networks for prediction of short-term load. The use of wavelet transforms has added the capability of capturing of both global trend and hidden templates in loads, which is otherwise very difficult to incorporate into the prediction model of ANN. Auto-configuring RBF networks are used for predicting the wavelet coefficients of the future loads. Floating point GA (FPGA) is used for optimizing the RBF networks. The use of GA optimized RBF network has added to the model the online prediction capability of short-term loads accurately. The performance of the proposed model is validated using Queensland electricity demand data from the Australian National Electricity Market. Results demonstrate that the proposed model is more accurate as compared to RBF only model.

Optimal location and size determination of FACTS devices by using multiobjective optimal power flow

This paper proposes an algorithm based on nondominated sorting genetic algorithm (NSGA-II) with the feature of adaptive crowding distance for solving multiobjective optimal power flow (MOOPF) problem to find the optimal location and capacity of FACTS devices in power system. The problem is formulated as mixed integer one with both continuous and discrete control variables. Two types of FACTS devices (TCSC and SVC) are modeled and analyzed to improve the steady state performance of power system. The performance of the proposed algorithm has been tested on IEEE-30 bus systems. The results demonstrate that the proposed approach is quite efficient in finding optimal solutions.

Genetic Algorithms for non-convex combined heat and power dispatch problems

This paper investigates into performance of Genetic Algorithms (GA) for solving combined heat and power dispatch (CHPD) problems in power systems. Different algorithms in different combinations of crossover and mutation functions of GA are explored and tested on a test case of combined heat and power dispatch problem. The simulation results show that all the floating point GAs (FPGA) perform better than binary GA in solving non-convex CHPD problems. Amongst the FPGAs, the performance of the FPGA with heuristic crossover and multi-nonuniform mutation is the best in terms of the efficiency in achieving better quality solutions.

An Efficient Denoising Model based on Wavelet and Bilateral Filters

This paper investigates different models developed through hybridization of wavelet and bilateral filters for denoising of variety of noisy images. Hybridization between wavelet thresholding and bilateral filter is done in different configurations. The models are experimented on standard images like Lena, Barbara, Einstein and satellite as well as astronomical telescopic images and their performances are evaluated in terms of peak signal to noise ratio (PSNR) and image quality index (IQI). Out of number of trial models developed, only 25 models are reported as the performance of the rest models are too poor to be reported. Results demonstrate that use of bilateral filters in combination with wavelet thresholding filters in different ways on decomposed subbands deteriorates the performance. But the application of bilateral filter before or after or both before and after decomposition enhances the performance. Specifically, the filter developed with bilateral filter before decomposition of an image is found to give uniform and consistent results on all the images. General Terms Image Denoising

Optimal solution for non-convex combined heat and power dispatch problems using Differential Evolution

This paper investigates the performance of Differential Evolution (DE) for solving combined heat and power dispatch (CHPD) problems in power systems. Solution method based on Different Evolution is developed and its performance is tested on a test case of combined heat and power dispatch problem. The simulation results demonstrate that the performance of DE based algorithm is far better than the better known floating point Genetic Algorithm (FPGA) in terms of success rate, convergence rate and solution quality.

GA based algorithm for optimum allocation of reactive power under deregulated environment

An algorithm using floating point genetic algorithm (FPGA) was developed to solve the problem of optimum allocation of reactive power in power systems under open market environment. The performance of the proposed model is validated on IEEE-14 bus system with modifications to incorporate the varying working conditions of power systems like, change of tap settings of transformers, variable reactive power compensations, etc. Results of the FPGA demonstrate that the algorithm is well competent in achieving the near optimal allocation of reactive power under practical constraints and price based conditions.

Fuzzy logic based on-line fault detection and classification in transmission line.

This study presents fuzzy logic based online fault detection and classification of transmission line using Programmable Automation and Control technology based National Instrument Compact Reconfigurable i/o (CRIO) devices. The LabVIEW software combined with CRIO can perform real time data acquisition of transmission line. When fault occurs in the system current waveforms are distorted due to transients and their pattern changes according to the type of fault in the system. The three phase alternating current, zero sequence and positive sequence current data generated by LabVIEW through CRIO-9067 are processed directly for relaying. The result shows that proposed technique is capable of right tripping action and classification of type of fault at high speed therefore can be employed in practical application.