Pattern Search Technique Research Articles

Hybrid improved firefly-pattern search optimized fuzzy aided PID controller for automatic generation control of power systems with multi-type generations

Abstract This article deals with frequency control of five area power systems employing a technique which based on the hybridization of improved Firefly optimization Algorithm and Pattern Search technique (hIFA-PS) to tune the parameters of fuzzy aided PID controller. The performance of original firefly algorithm (FA) is improved by adding memory, newborn fireflies and using a new updating formula of fireflies which eliminate the wandering movement of the fireflies during the iteration process. The proposed hIFA-PS technique gets the benefits of FA's global explore capability and local search ability of PS. At first, an interconnected five area thermal power system with appropriate Generation Rate Constraints (GRC) and Dead Bands (DB) is considered and the integral constants are optimized by FA. To demonstrate the superiority of the proposed hIFA-PS algorithm results are compared with other soft computing approaches. To improve the dynamic performance, different controller structures are considered and a comparative study of hIFA-PS optimized I/PI/PID/Fuzzy aided PID is presented. The proposed design method is also applied to a five area ten unit system consisting of diverse generation sources such as thermal, hydro, wind, diesel, gas turbine. Performance analysis of the designed controller has been carried out for different system parameters and loading conditions. It has been observed that the designed hIFA-PS based fuzzy logic PID controller performs satisfactorily with varied conditions. The superiority of proposed AGC approach over some recently published AGC approaches is also demonstrated.

Dynamic model updating based on strain mode shape and natural frequency using hybrid pattern search technique

Aiming at providing a precise dynamic structural finite element (FE) model for dynamic strength evaluation in addition to dynamic analysis. A dynamic FE model updating method is presented to correct the uncertain parameters of the FE model of a structure using strain mode shapes and natural frequencies. The strain mode shape, which is sensitive to local changes in structure, is used instead of the displacement mode for enhancing model updating. The coordinate strain modal assurance criterion is developed to evaluate the correlation level at each coordinate over the experimental and the analytical strain mode shapes. Moreover, the natural frequencies which provide the global information of the structure are used to guarantee the accuracy of modal properties of the global model. Then, the weighted summation of the natural frequency residual and the coordinate strain modal assurance criterion residual is used as the objective function in the proposed dynamic FE model updating procedure. The hybrid genetic/pattern-search optimization algorithm is adopted to perform the dynamic FE model updating procedure. Numerical simulation and model updating experiment for a clamped-clamped beam are performed to validate the feasibility and effectiveness of the present method. The results show that the proposed method can be used to update the uncertain parameters with good robustness. And the updated dynamic FE model of the beam structure, which can correctly predict both the natural frequencies and the local dynamic strains, is reliable for the following dynamic analysis and dynamic strength evaluation.

Reduction of Total Harmonic Distortion in Cascaded H-Bridge Inverter by Pattern Search Technique

Pattern Search technique can be used to find the solution for the optimization problem. In this paper, pattern search algorithm has been utilized to calculate the switching angles for the cascaded H-bridge inverter with the consideration of minimizing total harmonic distortion. Mathematical equations for the optimization problem were formulated by fourier analysis technique. Lower order harmonics such as third, fifth, seventh, ninth and eleventh order harmonics were taken into account to mitigate the total harmonic distortion of the inverter. Simulations have been carried out for thirteen level, fifteen level and seventeen level cascaded H-bridge inverter using matlab software. Total harmonic distortion of voltage and current for resistive load, resistive-inductive load and motor load were analyzed.

Maximum Power Point Tracking of Photovoltaic Panels by Using Improved Pattern Search Methods

This paper deals with the optimization of maximum power point tracking when a photovoltaic panel is modelled as two diodes. The adopted control is implemented using a sliding mode control (SMC) and the optimization is implemented using an improved Pattern Search Method. Thus, the problem of maximum power point tracking is reduced to an optimization problem whose solution is implemented by Pattern Search Techniques, inheriting their convergence properties. Simulation examples show the effectiveness of the proposed technique in practice, being able to deal with different radiations. In addition, improved pattern search method (IPSM) is compared with other techniques such as perturb & observe and Particle Swarm optimization, after which IPSM presents lower energy losses in comparison with the other two algorithms, with the advantage of ensuring the location of the optimal power point in all cases.

Hybrid adaptive ‘gbest’‐guided gravitational search and pattern search algorithm for automatic generation control of multi‐area power system

In this research article, a maiden approach of hybrid adaptive ‘gbest’ guided gravitational search and pattern search (hGGSA-PS) optimization method are proposed for load frequency control (LFC) of multi-area interconnected power system considering the nonlinear effect of generation rate constraint (GRC). At first, the two area single stage thermal–thermal power system with conventional proportional integral derivative (PID) controller is analyzed and the parameters of the PID controller are optimized by the proposed technique. Initially, the ‘gbest’ guided gravitational search algorithm (GGSA) using integral time absolute error (ITAE) fitness function is used and then pattern search (PS) technique is employed to fine-tune the obtained best solution from the GGSA. The supremacy of the hGGSA-PS optimized PID controller is presented by comparing its results with other modern soft computing techniques. Later in order to demonstrate the robustness of the proposed controller, the sensitive analysis is performed. Finally, the proposed technique is extended to a two area multi-source power system. The parameters of the controller for each area are optimized using the novel hGGSA-PS technique. From the simulation results, it can be seen that the proposed technique has superior performance than the prior results with lesser settling time and different performance index values.

Automatic Generation Control by Hybrid Invasive Weed Optimization and Pattern Search Tuned 2-DOF PID Controller

A hybrid invasive weed optimization and pattern search (hIWO-PS) technique is proposed in this paper to design 2 degree of freedom proportionalintegral- derivative (2-DOF-PID) controllers for automatic generation control (AGC) of interconnected power systems. Firstly, the proposed approach is tested in an interconnected two-area thermal power system and the advantage of the proposed approach has been established by comparing the results with recently published methods like conventional Ziegler Nichols (ZN), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), particle swarm optimization (PSO), hybrid BFOA-PSO, hybrid PSO-PS and non-dominated shorting GA-II (NSGA-II) based controllers for the identical interconnected power system. Further, sensitivity investigation is executed to demonstrate the robustness of the proposed approach by changing the parameters of the system, operating loading conditions, locations as well as size of the disturbance. Additionally, the methodology is applied to a three area hydro thermal interconnected system with appropriate generation rate constraints (GRC). The superiority of the presented methodology is demonstrated by presenting comparative results of adaptive neuro fuzzy inference system (ANFIS), hybrid hBFOA-PSO as well as hybrid hPSO-PS based controllers for the identical system.

A hybrid stochastic fractal search and pattern search technique based cascade PI-PD controller for automatic generation control of multi-source power systems in presence of plug in electric vehicles

A hybrid Stochastic Fractal Search plus Pattern Search (hSFS-PS) based cascade PI-PD controller is suggested in this paper for Automatic Generation Control (AGC) of thermal, hydro and gas power unit based power systems in presence of Plug in Electric Vehicles (PEV). Firstly, a single area multi-source power system consisting of thermal hydro and gas power plants is considered and parameters of Integral (I) controller is optimized by Stochastic Fractal Search (SFS) algorithm. The superiority of SFS algorithm over some recently proposed approaches such as optimal control, differential evolution and teaching learning based optimization techniques is demonstrated by comparing simulation results for the identical power system. To improve the system performance further, Pattern Search (PS) is subsequently employed. The study is further extended for different controllers like PI, PID, and cascaded PI-PD controller and the superiority of cascade PI-PD controller over conventional controllers is demonstrated. Then, cascade PI-PD controller parameters of AGC searched using the proposed hSFS-PS algorithm in presence of plug in electric vehicles. The study is also extended to an interconnected power system. It is seen from the comparative analysis that hSFS-PS tuned PI-PD controller in single and multi-area with multi sources improves the system frequency stability in complicated situations. Lastly, a three area interconnected system with PEVs with dissimilar cascade PI-PD controller in each area is considered and proposed hSFS-PS algorithm is used to tune the controller parameters in presence of nonlinearities like rate constraint of units, dead zone of governor and communication delay.

HYBRID GREY WOLF OPTIMIZATION-PATTERN SEARCH (hGWO-PS) OPTIMIZED 2DOF-PID CONTROLLERS FOR LOAD FREQUENCY CONTROL (LFC) IN INTERCONNECTED THERMAL POWER PLANTS

The combination of Grey Wolf Optimization and Pattern Search Technique (hGWO-PS) has been introduced to optimize the parameters of two Degree of Freedom Proportional-Integral-Derivative Controller (2DOF-PID) for controlling the load frequency in Automatic Generation Control (AGC) for interconnected power system. The interconnected two area power system of non-reheat thermal power plants consisting of 2DOF-PID controller in each area has been considered for design and analysis. Firstly, the proposed approach has been implemented in the aforementioned standard test system and thereafter, the robustness of the system consisting 2DOF-PID controller optimized by proposed technique has been estimated using the sensitivity analysis for the same. The robustness of the system consisting of 2DOF-PID controller optimized by proposed scheme is examined by varying the parameters of standard test system, loading conditions during operation, size and location of the disturbances. The performance of the 2DOF-PID controller optimized by proposed approach has also been compared with recently published approaches in the literature. The simulation results show that the proposed hGWO-PS optimized 2DOF-PID controller shows far better performance than recently published approaches in the literature in terms of dynamic response. The simulation results also show that system performances hardly change when the operating load condition and system parameters are changed by ±50% from their nominal values, i.e. the proposed controllers are quite robust for a wide range of the system parameters and operating load conditions from their nominal values.

A PD-type Multi Input Single Output SSSC damping controller design employing hybrid improved differential evolution-pattern search approach

In this paper, a Proportional Derivative (PD)-type Multi Input Single Output (MISO) damping controller is designed for Static Synchronous Series Compensator (SSSC) controller. Both local and remote signals with associated time delays are chosen as the input signal to the proposed MISO controller. The design problem is formulated as an optimization problem and a hybrid Improved Differential Evolution and Pattern Search (hIDEPS) technique is employed to optimize the controller parameters. The improvement in Differential Evolution (DE) algorithm is introduced by changing two of its most important control parameters i.e. Scaling Factor F and Crossover Constant CR with an objective of achieving improved performance of the algorithm. The superiority of proposed Improved DE (IDE) over original DE and hIDEPS over IDE has also been demonstrated. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with DE and Particle Swarm Optimization (PSO) optimized Single Input Single Output (SISO) SSSC based damping controllers for both Single Machine Infinite Bus (SMIB) power system and multi-machine power system. It is noticed that the proposed approach provides superior damping performance compared to some approaches available in literature.

Efficient architecture of adaptive rood pattern search technique for fast motion estimation

This paper presents efficient VLSI architecture for fast Motion Estimation (ME) using Adaptive Rood Pattern Search (ARPS) technique. The proposed architecture uses a single processing element (PE) and simplified memory addressing to reduce the hardware complexity. The addressing logic, which is presently applied to 352×288 CIF frames, can be easily extended to frames of higher resolutions. The proposed architecture uses optimum area while satisfying speed requirements for real-time video processing. Implemented in Verilog HDL and mapped to Virtex 6 (XC6VLX75T-3) FPGA, the architecture uses only 165 slice registers and 273 slice LUTs. The architecture can process 240 frames per second while operating at a maximum frequency of 320MHz.

A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system

In this paper, a hybrid gravitational search algorithm (GSA) and pattern search (PS) technique is proposed for load frequency control (LFC) of multi-area power system. Initially, various conventional error criterions are considered, the PI controller parameters for a two-area power system are optimized employing GSA and the effect of objective function on system performance is analyzed. Then GSA control parameters are tuned by carrying out multiple runs of algorithm for each control parameter variation. After that PS is employed to fine tune the best solution provided by GSA. Further, modifications in the objective function and controller structure are introduced and the controller parameters are optimized employing the proposed hybrid GSA and PS (hGSA-PS) approach. The superiority of the proposed approach is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as firefly algorithm (FA), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), particle swarm optimization (PSO), hybrid BFOA-PSO, NSGA-II and genetic algorithm (GA) for the same interconnected power system. Additionally, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. Also, the proposed approach is extended to two-area reheat thermal power system by considering the physical constraints such as reheat turbine, generation rate constraint (GRC) and governor dead band (GDB) nonlinearity. Finally, to demonstrate the ability of the proposed algorithm to cope with nonlinear and unequal interconnected areas with different controller coefficients, the study is extended to a nonlinear three unequal area power system and the controller parameters of each area are optimized using proposed hGSA-PS technique.

Techno-Economic Analysis of a Grid-Connected Hybrid Energy System for Developing Regions

A B S T R A C T This paper examines the viability of a grid-connected hybrid energy system (HES) for domestic electricity generation in the developing world. It aims to determine the techno-economic benefits of operating a wind energy conversion system. The HES consists of the grid power supply, wind energy conversion, power electronics, and storage units. The grid supply unit incorporates a probabilitybased prediction technique. The wind energy system modelling is based on the piecewise third order polynomial, using wind turbine power profile supplied by the manufacturer. The formulated optimization problem was solved using a hybrid Genetic Algorithm and Pattern Search (h-GAPS) technique. The h-GAPS based approach constrains the generation and distribution of power to ensure efficient operation. Analysis performed for a typical residential area used meteorological data for six sites, which spread across Nigeria. Results showed that the proposed power system could bring benefits of cost saving and improve power reliability, but the range of financial benefits depends on the geographical coordinates. In particular, 10kW/5.40kWh capacity wind/battery system installed in Sokoto can deal with 95.4% of the total electricity demand, save more than 77% of electricity payments and increase the reliability by approximately 140%.

Optimal Design of a 3-Phase Core Type Distribution Transformer Using Modified Hooke and Jeeves Method

Hooke and Jeeves method is de facto a pattern search technique, which can be employed for getting an optimal solution. In this paper the method, in a modified form, has been applied for the design optimization of a distribution transformer. It is a constrained multi-variable optimization problem. The solution is obtained by choosing an initial point in the world map of the key variables and by making a local search (exploratory in all directions in the hyper surface formed by the variables. After recognizing the pattern, its advantage is taken by moving towards a lower cost point, using an acceleration factor for faster convergence. The step length is adjusted as we proceed to expedite improvement. The method has been applied to two different cost functions: the cost of production and the cost against production plus capitalized running losses. In both the cases, the problem has converged to a solution and the results are both interesting and illuminating.

A hybrid firefly algorithm and pattern search technique for SSSC based power oscillation damping controller design

In this paper, a novel hybrid Firefly Algorithm and Pattern Search (h-FAPS) technique is proposed for a Static Synchronous Series Compensator (SSSC)-based power oscillation damping controller design. The proposed h-FAPS technique takes the advantage of global search capability of FA and local search facility of PS. In order to tackle the drawback of using the remote signal that may impact reliability of the controller, a modified signal equivalent to the remote speed deviation signal is constructed from the local measurements. The performances of the proposed controllers are evaluated in SMIB and multi-machine power system subjected to various transient disturbances. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with some recently published approaches such as Differential Evolution (DE) and Particle Swarm Optimization (PSO). It is observed that the proposed approach yield superior damping performance compared to some recently reported approaches.

A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems

In this paper, a novel hybrid Firefly Algorithm and Pattern Search (hFA–PS) technique is proposed for Automatic Generation Control (AGC) of multi-area power systems with the consideration of Generation Rate Constraint (GRC). Initially a two area non-reheat thermal system with Proportional Integral Derivative (PID) controller is considered and the parameters of PID controllers are optimized by Firefly Algorithm (FA) employing an Integral Time multiply Absolute Error (ITAE) objective function. Pattern Search (PS) is then employed to fine tune the best solution provided by FA. The superiority of the proposed hFA–PS based PID controller has been demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and conventional Ziegler Nichols (ZN) based PI/PID controllers for the same interconnected power system. Furthermore, sensitivity analysis is performed to show the robustness of the optimized controller parameters by varying the system parameters and operating load conditions from their nominal values. Finally, the proposed approach is extended to multi area multi source hydro thermal power system with/without considering the effect of physical constraints such as time delay, reheat turbine, GRC, and Governor Dead Band (GDB) nonlinearity. The controller parameters of each area are optimized under normal and varied conditions using proposed hFA–PS technique. It is observed that the proposed technique is able to handle nonlinearity and physical constraints in the system model.

Hybrid Particle Swarm Optimization Based Local Search Technique for Reactive Power Compensation Problem

Abstract— This paper presents an enhanced particle swarm Optimization (PSO) algorithm, which applied to reactive power compensation (RPC) problem. It integrates the merits of both GAs and PSO and it has two characteristic features. Firstly, the algorithm is initialized by a set of random particles which traveling through the search space, during this travel; an evolution of these particles is performed by a PSO coupled with GA to get approximate nondominated solutions. Secondly, to improve the solution quality, dynamic version of pattern search technique is implemented as neighborhood search engine where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. Also In order to study the algorithm performance, the effect of change of the most significant parameters of the proposed approach was studied. The proposed approach is carried out on the standard IEEE 30-bus 6-generator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective RPC. Also the results declare that it is quite difficult to find fixed values for these significant parameters, thus we recommend to develop dynamic version of the proposed approach using any monitoring algorithm.

Geometry Design Optimization of Large-Scale Broadband Antenna Array Systems

The next generation radio telescopes such as the Square Kilometer Array (SKA) are expected to contain thousands of antenna array elements operating over a broad frequency range where the signals from each antenna element are combined and processed simultaneously providing high sensitivity with multiple beams providing a wide field of view. One crucial design aspect influencing both the performance and the cost of such systems is the array geometry. Due to the large bandwidth and number of broadband antenna elements, the optimization of such array system is difficult to achieve with the current array geometry optimization techniques which rely mainly on genetic algorithms and pattern search techniques. This paper provides a study of the effects of array geometry on the performance broadband array system. In addition, it provides a method where the array geometry can be more easily optimized for different applications. This is demonstrated for optimizing a typical SKA station in the frequency band between (70-450 MHz).

A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

Scarcity of energy resources, increasing power generation cost and ever-growing demand for electric energy necessitates optimal economic dispatch in today’s power systems. The main objective of economic dispatch is to reduce the total power generation cost, while satisfying various equality and inequality constraints. Traditionally in economic dispatch problems, the cost function for generating units has been approximated as a quadratic function which doesn’t provide accurate results. Moreover, to obtain accurate fuel cost, valve-point effect in thermal power plant has to be taken into account. The inclusion of valve-point effect makes the modeling of the fuel cost functions of generating units more practical. In this paper a new evolutionary algorithm called Pattern Search Technique, has been employed to solve economic dispatch problem with the valve-point effect. Using this technique the non-linear cost function is solved for three unit system and the results are compared with the traditional PSO and GA method. These results prove that Pattern Search method is capable of getting higher quality solution including mathematical simplicity, fast convergence, and robustness to solve hard optimization problems.

Unsupervised neural networks for solving Troesch's problem

In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm (GA) and pattern search (PS) techniques are used as the global search methods and the interior point method (IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM (GA-IPM) and PS hybridized with IPM (PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs.

Joint Estimation of Amplitude and Direction of Arrival for Far Field Sources using Intelligent Hybrid Computing

In this study, an intelligent hybrid computing technique is presented to estimate jointly the amplitude and Direction of Arrival (Elevation angle) of far field sources. In this intelligent hybrid scheme, Genetic Algorithm (GA) is hybridized with Pattern Search (PS), in which GA is working as a global optimizer while PS is used as local optimizer for further improvement of the results. GA and PS techniques are also applied independently to compare with GA hybridized with pattern search. The fitness evaluation function is formed by the Mean Square Error (MSE) of the desired response with the estimated one. This function is simple and requires a single snapshot to reach the optimum solution. A sufficient number of Monte-Carlo Simulations is used to evaluate the convergence rate, MSE and estimation accuracy of each scheme.