Seyyed Soheil Sadat Hosseini

Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect

The growing costs of fuel and operation of power generating units warrant improvement of optimization methodologies for economic dispatch (ED) problems. The practical ED problems have non-convex objective functions with equality and inequality constraints that make it much harder to find the global optimum using any mathematical algorithms. Modern optimization algorithms are often meta-heuristic, and they are very promising in solving nonlinear programming problems. This paper presents a novel approach to determining the feasible optimal solution of the ED problems using the recently developed Firefly Algorithm (FA). Many nonlinear characteristics of power generators, and their operational constraints, such as generation limitations, prohibited operating zones, ramp rate limits, transmission loss, and nonlinear cost functions, were all contemplated for practical operation. To demonstrate the efficiency and applicability of the proposed method, we study four ED test systems having non-convex solution spaces and compared with some of the most recently published ED solution methods. The results of this study show that the proposed FA is able to find more economical loads than those determined by other methods. This algorithm is considered to be a promising alternative algorithm for solving the ED problems in practical power systems.

Combined heat and power economic dispatch by mesh adaptive direct search algorithm

Research highlights? In this study, an optimization method, namely mesh adaptive direct search (MADS) is introduced to solve combined heat and power (CHP) economic dispatch problem. ? MADS is a recently developed algorithm that is supported by a thorough convergence analysis. The MADS method is illustrated using three test cases taken from the literature. ? Latin hypercube sampling (LHS), particle swarm optimization (PSO) and design and analysis of computer experiments (DACE) algorithms are employed as effective search strategies in MADS to solve the CHPED problems. The results clearly demonstrate that the MADS-based methods are practical and valid for CHPED applications. ? The MADS-DACE algorithm performs superior than or as well as the other recent methods in terms of solution quality, handling constraints and computation time. The optimal utilization of multiple combined heat and power (CHP) systems is a complex problem. Therefore, efficient methods are required to solve it. In this paper, a recent optimization technique, namely mesh adaptive direct search (MADS) is implemented to solve the combined heat and power economic dispatch (CHPED) problem with bounded feasible operating region. Three test cases taken from the literature are used to evaluate the exploring ability of MADS. Latin hypercube sampling (LHS), particle swarm optimization (PSO) and design and analysis of computer experiments (DACE) surrogate algorithms are used as powerful SEARCH strategies in the MADS algorithm to improve its effectiveness. The numerical results demonstrate that the utilized MADS-LHS, MADS-PSO, MADS-DACE algorithms have acceptable performance when applied to the CHPED problems. The results obtained using the MADS-DACE algorithm are considerably better than or as well as the best known solutions reported previously in the literature. In addition to the superior performance, MADS-DACE provides significant savings of computational effort.

Short-term load forecasting of power systems by gene expression programming

Short-term load forecasting is a popular topic in the electric power industry due to its essentiality in energy system planning and operation. Load forecasting is important in deregulated power systems since an improvement of a few percentages in the prediction accuracy will bring benefits worth of millions of dollars. In this study, a promising variant of genetic programming, namely gene expression programming (GEP), is utilized to improve the accuracy and enhance the robustness of load forecasting results. With the use of the GEP technique, accurate relationships were obtained to correlate the peak and total loads to average, maximum and lowest temperatures of day. The presented model is applied to forecast short-term load using the actual data from a North American electric utility. A multiple least squares regression analysis was performed using the same variables and same data sets to benchmark the GEP models. For more verification, a subsequent parametric study was also carried out. The observed agreement between the predicted and measured peak and total load values indicates that the proposed correlations are capable of effectively forecasting the short-term load. The GEP-based formulas are relatively short, simple and particularly valuable for providing an analysis tool accessible to practicing engineers.

Optimized FPGA based implementation of particle filter for tracking applications

Particle filter has been proven to be a very effective method for identifying targets in non-linear and non-Gaussian environment. However, particle filter is computationally intensive. So, particle filter has been implemented on FPGA by exploiting parallel and pipelining approaches to reduce the computational burden. Our optimized FPGA implementation improves up to twelve times speed up. Also more speed ups are achieved with increasing number of particles.

Solutions of Non-smooth Economic Dispatch Problems by Swarm Intelligence

The increasing costs of fuels and operations of power generating units necessitate the development of optimization methods for economic dispatch (ED) problems. Classical optimization techniques such as direct search and gradient methods often fail to find global optimum solutions. Modern optimization techniques are often meta-heuristic, and they are very promising in solving nonlinear programming problems. This chapter presents a novel method to determine the feasible optimal solutions of the ED problems utilizing the newly developed Bat Algorithm (BA). The proposed BA is based on the echolocation behavior of bats. This technique is adapted to solve non-convex ED problems under different nonlinear constraints such as transmission losses, ramp rate limits, multi-fuel options and prohibited operating zones. Parameters are tuned to give the best results for these problems. To describe the efficiency and applicability of the proposed algorithm, we will use four ED test systems with non-convexity. We will compare our results with some of the most recently published ED solution methods. Comparing with the other existing techniques, the proposed approach can find better solutions than other methods. This method can be deemed to be a promising alternative for solving the ED problems in real systems.

Discussion of “Economic Load Dispatch—A Comparative Study on Heuristic Optimization Techniques With an Improved Coordinated Aggregation-Based PSO”

This paper aims to introduce an improved coordinated aggregation-based particle swarm optimization (ICA-PSO) algorithm for solving the optimal economic load dispatch (ELD) problem in power systems. In the ICA-PSO algorithm each particle in the swarm retains a memory of its best position ever encountered, and is attracted only by other particles with better achievements than its own with the exception of the particle with the best achievement, which moves randomly. Moreover, the population size is increased adaptively, the number of search intervals for the particles is selected adaptively and the particles search the decision space with accuracy up to two digit points resulting in the improved convergence of the process.

Reactive Power and Voltage Control Based on Mesh Adaptive Direct Search Algorithm

This is a pioneer study that presents a new optimization algorithm called mesh adaptive direct search (MADS) to solve optimal steady-state performance of power systems. MADS is utilized to specify the optimal settings of control variables, i.e. transformer taps and generator voltages for optimal reactive power and voltage control of IEEE 30-bus system. Covariance matrix adaptation evolution strategy (CMAES) algorithm is utilized as a strong search strategy in the MADS technique to enhance its effectiveness. The results acquired by the hybrid search algorithm coupling MADS and CMAES, called MADS-CMAES, and the MADS algorithm itself without any search method are compared with multi-objective evolutionary and particle swarm optimization algorithms, demonstrating the superiority of MADS. The proposed MADS-based techniques are very robust against their parameters and changing the search space because of their inherent adaptive tuning.

Target tracking via combination of particle filter and optimisation techniques

Particle filters (PFs) have been used for the nonlinear estimation for a number of years. However, they suffer from the impoverishment phenomenon. It is brought by resampling which intends to prevent particle degradation, and therefore becomes the inherent weakness of this technique. To solve the problem of sample impoverishment and to improve the performance of the standard particle filter we propose a modification to this method by adding a sampling mechanism inspired by optimisation techniques, namely, the pattern search, particle swarm optimisation, differential evolution and Nelder-Mead algorithms. In the proposed methods, the true state of the target can be better expressed by the optimised particle set and the number of meaningful particles can be grown significantly. The efficiency of the proposed particle filters is supported by a truck-trailer problem. Simulations show that the hybridised particle filter with Nelder-Mead search is better than other optimisation approaches in terms of particle diversity.

Parralelization of non-linear & non-Gaussian Bayesian state estimators (Particle filters)

Particle filter has been proven to be a very effective method for identifying targets in non-linear and non-Gaussian environment. However, particle filter is computationally intensive and may not achieve the real time requirements. So, its desirable to implement it on parallel platforms by exploiting parallel and pipelining architecture to achieve its real time requirements. In this work, an efficient implementation of particle filter in both FPGA and GPU is proposed. Particle filter has also been implemented using MATLAB Parallel Computing Toolbox (PCT). Experimental results show that FPGA and GPU architectures can significantly outperform an equivalent sequential implementation. The results also show that FPGA implementation provides better performance than the GPU implementation. The achieved execution time on dual core and quad core Dell PC using PCT were higher than FPGAs and GPUs as was expected.

Application of Genetic Programming for Electrical Engineering Predictive Modeling: A Review

The purpose of having computers automatically resolve problems is essential for machine learning, artificial intelligence and a wide area covered by what Turing called‘machine intelligence’. Genetic programming (GP) is an adaptable and strong evolutionary algorithm with some features that can be very priceless and adequate to get computers automatically to address problems starting from a high-level statement of what to do. Using the concept from natural evolution, GP begins from an ooze of random computer programs and improve them progressively through processes of mutation and sexual recombination until solutions appear. All this without the user needing to know or determine the form or structure of solutions in advance. GP has produced a plethora of human-competitive results and applications, involving novel scientific discoveries and patent-able inventions. The goal of this paper is to give an introduction to the quickly developing field of GP. We begin with a gentle introduction to the basic representation, initialization and operators utilized in GP, completed by a step by step description of their utilization and application. Then, we progress to explain the diversity of alternative representations for programs and more advanced specializations of GP. Despite the fact that this paper has been written with beginners and practitioners in mind, for completeness we also provide an outline of the theoretical aspect available to date for GP.