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An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

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FOUNDATIONS OF COMPUTING AND DECISION SCIENCES 2020年第2期45卷 97-124页

作者： Puphasuk, Pikul Wetweerapong, Jeerayut Khon Kaen Univ Fac Sci Dept Math Khon Kaen 40002 Thailand

Designing an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at efficiency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and difficulties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions.

关键词： Continuous optimization enhanced differential evolution algorithm control parameter adaptation switching crossover strategy

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Load frequency control of time-delayed power systems using optimal IMC-PID design and model approximation approach

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INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2022年第5期42卷 725-742页

作者： Ganji, Vasu Ramraj, Ch Bharath Naga Raghu Engn Coll A Elect & Elect Engn Visakhapatnam Andhra Pradesh India Raghu Engn Coll Elect & Elect Engn Dept Visakhapatnam Andhra Pradesh India

In this paper, a new optimal and robust IMC-PID control design is proposed for the LFC of time-delayed power systems. The proposed design is a process reduction control approach. Primitively, a simple and approximate model (i.e. Reduced Order Model (ROM)) is determined for the large-scale power system by using the proposed enhanced differential evolution (EDE) algorithm-based Model Order Reduction (MOR) approach. Further, by considering the ROM as an internal plant model, the unknown IMC filter time constant is tuned to attain the optimal performance by minimizing the integral of error between the reference input and the actual output responses of the power system by using the EDE algorithm. Finally, the finest PID controller gains are determined by the least-square model matching with the optimal IMC. The proposed design is illustrated by applying to various Single-Area Power Systems (SAPS) with a delay between the power system sensing device to the control centre. The simulation results demonstrate the efficacy of the proposed IMC-PID design in improving the dynamic stability against consistent and inconsistent delays and the robustness towards the load disturbances, parameter uncertainties and model mismatches.

关键词： Time-delayed LFC systems internal model control PID control enhanced differential evolution algorithm model order reduction

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Optimal IMC-PID controller design for large-scale power systems via EDE algorithm-based model approximation method

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第1期43卷 59-77页

作者： Vasu, G. Sivakumar, M. Ramalingaraju, M. Raghu Engn Coll Dept EEE Dakamarri India Gudlavalleru Engn Coll Dept EEE Gudlavalleu Andhra Pradesh India JNTUK UCEK Dept EEE Kakinada Andhra Pradesh India

In this paper, the authors propose an optimal IMC-PID controller design for the Load Frequency Control (LFC) of large-scale power system via model approximation method. The model approximation method uses the enhanced differential evolution (EDE) algorithm to determine an optimal Reduced Order Model (ROM) for the considered large-scale power system by minimizing the performance measure called Integral Square Error (ISE) between their step responses. Later, the LFC design is carried out using an optimal ROM instead of processing with the large-scale power system model. Thus, this simplifies the design, reduces the computational efforts and also helps in determining the lower order controller. An optimal IMC design methodology is proposed by minimizing ISE between the actual output and the reference input responses of the large-scale power system using EDE algorithm. Further, PID controller gains are acquired by least square model matching with the optimal IMC transfer function. The proposed IMC-PID controller design allows a satisfied reference input tracking performance, robustness in disturbance rejection and improves the dynamic stability of the power system. The proposed method is validated by applying it to a single area power system of third-order SISO model and also extended to a centralized two-area thermal-thermal non-reheated power system of a seventh-order MIMO model. The simulation results and the comparison of error performance indices show the efficacy of the proposed method over the significant methods available in the literature.

关键词： Load frequency control model approximation optimal IMC-PID controller design enhanced differential evolution algorithm

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Internal model control design based on approximation of linear discrete dynamical systems

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APPLIED MATHEMATICAL MODELLING 2021年 97卷 683-700页

作者： Vasu, G. Kumar, M. Siva Raju, M. Ramalinga Raghu Engn Coll A Dept EEE Visakhapatnam Andhra Pradesh India Gudlavalleru Engn Coll A Dept EEE Gudlavalleru 521356 Andhra Pradesh India JNTUK UCEK A Dept EEE Kakinada 533001 Andhra Pradesh India

In this paper, a new direct discrete approximation based internal model control design is proposed to the linear discrete dynamical systems. The approximation method is used to determine an accurate and stable reduced-order model for the considered original higher-order discrete-time system. The method involves an enhanced differential evolution algorithm to ascertain the stable denominator polynomial coefficients, and the preferable reduced numerator polynomial coefficients are evaluated by using the improved discrete multi-point Pade approximation approach. The method deploys on discrete step integral square error minimization between the original dynamical system and the approximated model, together with retaining their discrete impulse response energy values. The approximated model has been considered an internal (predictive) model and proceeds with an optimal internal model controller design to improve the discrete dynamical system behaviour according to the reference input/the set point. The controller's best performance is attained by tuning the single filter parameter 'lambda' by minimizing the integral square error between the reference input and the actual output of the dynamical system using the enhanced differential evolution algorithm. The acceptability and applicability of the proposed process reduction-based controller design have been validated on a single-input single-output supersonic jet engine inlet dynamical model. The controller robust study is conducted by inserting 10% disruption uncertainty in the system dynamical model poles and zeros. The method has also been extended to the discrete multi-input multi-output dynamical model of the single machine infinite bus power system to develop an optimal internal model control-based power system stabilizer. The simulation results showing better reference input tracking, comparison of performance indices, and also highlight the efficacy of the proposed controller design. (C) 2021 Elsevier Inc. All righ

关键词： Model order reduction Discrete internal model control enhanced differential evolution algorithm Improved discrete Multi-Point Pade approximation Discrete-time systems

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Optimal Model Approximation of Linear Time-Invariant Systems Using the enhanced DE algorithm and Improved MPPA Method

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2020年第5期39卷 2376-2411页

作者： Ganji, Vasu Mangipudi, Sivakumar Manyala, Ramalingaraju Raghu Engn Coll Dept EEE Visakhapatnam Andhra Pradesh India Gudlavalleru Engn Coll Dept EEE Gudlavalleru India JNTUK Dept EEE UCEK Kakinada India

In this paper, the authors propose a novel model order reduction method integrating evolutionary and conventional approaches for higher-order linear time-invariant single-input-single-output (SISO) and multi-input-multi-output (MIMO) dynamic systems. The proposed method makes use of a differential evolution algorithm with enhanced mutation operation for the determination of reduced order model (ROM) denominator polynomial coefficients. In addition, an improved multi-point Pade approximation method is used to determine the optimal ROM numerator polynomial coefficients. The optimum property of the ROM is measured by minimising the integral square of the step response error between the original high-order dynamic system and the ROM. In the case of the MIMO system reduction approach, an optimal ROM transfer function matrix is determined by minimising a single objective function. This objective function is defined by a linear scalarising of the multi-step error function matrix components mml:mfenced close=")" open="("EijThe proposed method guarantees the preservation of the stability, passivity and accuracy of the original higher-order system in the ROM. The proposed method is validated by applying it to a ninth-order SISO system, as well as to the tenth- and sixth-order linearised single-machine infinite-bus power system model with and without an automatic excitation control system. The simulation results and the comparison of the integral square error and impulse response energy values of the ROM demonstrate the dominance of the proposed method over the latest reduction methods available in the literature.

关键词： Optimal model approximation enhanced differential evolution algorithm Improved multi-point Pade approximation SISO and MIMO systems

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Optimized semiblind sparse channel estimation algorithm for MU-MIMO OFDM system

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COMPUTER COMMUNICATIONS 2019年 146卷 103-109页

作者： Jeya, R. Amutha, B. SRM Inst Sci & Technol Dept Comp Sci & Engn Kattankulathur Tamil Nadu India

In Multiple Input Multiple Output - Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, the channel plays an imperative role as the multipath. The channel estimation frameworks specifically designed for the demanding channel conditions with faster time-varying characters are separately emphasized. The prevailing Channel Estimation (CE) methodologies are incredibly complex. To resolve such complexities, this paper proposed an Optimized Semi-Blind Sparse (OSBS) CE algorithm for MU-MIMO OFDM. On the transmitter block, initially, the QPSK modulation is implemented to modulate an input signal. Subsequently, the Pulse Shaping algorithm (PSA) used for mitigating the ISI (Inter-Symbol Interferences). For symbol mapping, an IFFT (Inverse Fast Fourier Transforms) operation performed at each transmitter. Next, transmit the symbols over the Multipath channel via transmitter's antennas towards the receiver's antennas by adding AWGN (Additive White Gaussian Noise). The operations in the transmitter inversely done on the receiver block. Then CE is done by utilizing the OSBS algorithm, and the cost function is lessened by employing EDE (enhanced differential evolution) algorithm. Lastly, the Channel Capacity (CC) is called gauge. Experiential result of the proposed system gives better results when contrasted with the other methods centered on Bit Error Rate (BER), PSNR, Symbol Error Rate (SER), LS, and MMSE.

关键词： Optimized semi-blind sparse Pulse shaping algorithm enhanced differential evolution algorithm Additive white Gaussian noise And quadrature pulse shaping key

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