Multi-objective optimal power flow based gray wolf optimization method

Introduction. One of predominant problems in energy systems is the economic operation of electric energy generating systems. In this paper, one a new evolutionary optimization approach, based on the behavior of meta-heuristic called grey wolf optimization is applied to solve the single and multi-objective optimal power flow and emission index problems. Problem. The optimal power flow are non-linear and non-convex very constrained optimization problems. Goal is to minimize an objective function necessary for a best balance between the energy production and its consumption, which is presented as a nonlinear function, taking into account of the equality and inequality constraints. Methodology. The grey wolf optimization algorithm is a nature inspired comprehensive optimization method, used to determine the optimal values of the continuous and discrete control variables. Practical value. The effectiveness and robustness of the proposed method have been examined and tested on the standard IEEE 30-bus test system with multi-objective optimization problem. The results of proposed method have been compared and validated with hose known references published recently. Originality. The results are promising and show the effectiveness and robustness of proposed approach.Вступ. Однією з головних проблем енергетичних системах є економічна експлуатація систем виробництва електроенергії. У цій статті один новий підхід до еволюційної оптимізації, заснований на поведінці метаевристики, яка називається оптимізацією сірого вовка, застосовується для вирішення одно- та багатокритеріальних завдань оптимального потоку потужності та індексу викидів. Проблема. Оптимальний потік потужності - це нелінійні та неопуклі задачі оптимізації з дуже обмеженнями. Метою є мінімізація цільової функції, необхідної для найкращого балансу між виробництвом та споживанням енергії, яка представлена у вигляді нелінійної функції з урахуванням обмежень рівності та нерівності. Методологія. Алгоритм оптимізації сірого вовка - це натхненний природою комплексний метод оптимізації, що використовується для визначення оптимальних значень безперервних і дискретних змінних, що управляють. Практична цінність. Ефективність та надійність запропонованого методу були перевірені та протестовані на стандартній 30-шинній тестовій системі IEEE із завданням багатокритеріальної оптимізації. Результати запропонованого методу були зіставлені та підтверджені нещодавно опублікованими відомими посиланнями. Оригінальність. Результати є багатообіцяючими та показують ефективність та надійність запропонованого підходу.

A New Enhanced Hybrid Grey Wolf Optimizer (GWO) Combined with Elephant Herding Optimization (EHO) Algorithm for Engineering Optimization

Although the exploitation of GWO advances sharply, it has limitations for continuous implementing exploration. On the other hand, the EHO algorithm easily has shown its capability to prevent local optima. For hybridization and by considering the advantages of GWO and the abilities of EHO, it would be impressive to combine these two algorithms. In this respect, the exploitation and exploration performances and the convergence speed of the GWO algorithm are improved by combining it with the EHO algorithm. Therefore, this paper proposes a new hybrid Grey Wolf Optimizer (GWO) combined with Elephant Herding Optimization (EHO) algorithm. Twenty-three benchmark mathematical optimization challenges and six constrained engineering challenges are used to validate the performance of the suggested GWOEHO compared to both the original GWO and EHO algorithms and some other well-known optimization algorithms. Wilcoxon's rank-sum test outcomes revealed that GWOEHO outperforms others in most function minimization. The results also proved that the convergence speed of GWOEHO is faster than the original algorithms

Selected Papers from the ICEUBI2019 - International Congress on Engineering - Engineering for Evolution

Energies SI Book "Selected Papers from the ICEUBI2019 – International Congress on Engineering – Engineering for Evolution", groups six papers into fundamental engineering areas: Aeronautics and Astronautics, and Electrotechnical and Mechanical Engineering. ICEUBI—International Congress on Engineering is organized every two years by the Engineering Faculty of Beira Interior University, Portugal, promoting engineering in society through contact among researchers and practitioners from different fields of engineering, and thus encouraging the dissemination of engineering research, innovation, and development. All selected papers are interrelated with energy topics (fundamentals, sources, exploration, conversion, and policies), and provide relevant data for academics, research-focused practitioners, and policy makers

An improved grey wolf with whale algorithm for optimization functions

The Grey Wolf Optimization (GWO) is a nature-inspired, meta-heuristic search optimization algorithm. It follows the social hierarchical structure of a wolf pack and their ability to hunt in packs. Since its inception in 2014, GWO is able to successfully solve several optimization problems and has shown better convergence than the Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), and Evolutionary Programming (EP). Despite providing successful solutions to optimization problems, GWO has an inherent problem of poor exploration capability. The position-update equation in GWO mostly relies on the information provided by the previous solutions to generate new candidate solutions which result in poor exploration activity. Therefore, to overcome the problem of poor exploration in the GWO the exploration part of the Whale optimization algorithm (WOA) is integrated in it. The resultant Grey Wolf Whale Optimization Algorithm (GWWOA) offers better exploration ability and is able to solve the optimization problems to find the most optimal solution in search space. The performance of the proposed algorithm is tested and evaluated on five benchmarked unimodal and five multimodal functions. The simulation results show that the proposed GWWOA is able to find a fine balance between exploration and exploitation capabilities during convergence to global minima as compared to the standard GWO and WOA algorithms

Unit commitment by a fast and new analytical non-iterative method using IPPD table and “λ-logic” algorithm

Many different methods have been presented to solve unit commitment (UC) problem in literature with different advantages and disadvantages. The need for multiple runs, huge computational burden and time, and poor convergence are some of the disadvantages, where are especially considerable in large scale systems. In this paper, a new analytical and non-iterative method is presented to solve UC problem. In the proposed method, improved pre-prepared power demand (IPPD) table is used to solve UC problem, and then analytical “λ-logic” algorithm is used to solve economic dispatch (ED) sub-problem. The analytical and non-iterative nature of the mentioned methods results in simplification of the UC problem solution. Obtaining minimum cost in very small time with only one run is the major advantage of the proposed method. The proposed method has been tested on 10 unit and 40-100 unit systems with consideration of different constraints, such as: power generation limit of units, reserve constraints, minimum up and down times of generating units. Comparing the simulation results of the proposed method with other methods in literature shows that in large scale systems, the proposed method achieves minimum operational cost within minimum computational time

A Novel Hybrid Moth Flame Optimization with Sequential Quadratic Programming Algorithm for Solving Economic Load Dispatch Problem

The insufficiency of energy resources, increased cost of generation and rising load demand necessitate optimized economic dispatch. The real world ED (Economic Dispatch) is highly non-convex, nonlinear and discontinuous problem with different equality and inequality constraints. In this research paper, a novel hybrid MFO-SQP (Moth Flame Optimization with Sequential Quadratic Programming) is proposed to solve the ED problem. The MFO is stochastic searching algorithm minimizes by random search and SQP is definite in nature that refines the local search in vicinity of local minima. Proposed technique has been implemented on 6, 15 and 40 units test system with different constraints like valve point loading effect, transmission loss, prohibited zones, generator capacity limits and power balance. Results, obtained from proposed technique are compared with those of the techniques reported in the literature, are proven better in terms of fuel cost and convergence

Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection

Anomaly detection deals with identification of items that do not conform to an expected pattern or items present in a dataset. The performance of different mechanisms utilized to perform the anomaly detection depends heavily on the group of features used. Thus, not all features in the dataset can be used in the classification process since some features may lead to low performance of classifier. Feature selection (FS) is a good mechanism that minimises the dimension of high-dimensional datasets by deleting the irrelevant features. Modified Binary Grey Wolf Optimiser (MBGWO) is a modern metaheuristic algorithm that has successfully been used for FS for anomaly detection. However, the MBGWO has several issues in finding a good quality solution. Thus, this study proposes an enhanced binary grey wolf optimiser (EBGWO) algorithm for FS in anomaly detection to overcome the algorithm issues. The first modification enhances the initial population of the MBGWO using a heuristic based Ant Colony Optimisation algorithm. The second modification develops a new position update mechanism using the Bat Algorithm movement. The third modification improves the controlled parameter of the MBGWO algorithm using indicators from the search process to refine the solution. The EBGWO algorithm was evaluated on NSL-KDD and six (6) benchmark datasets from the University California Irvine (UCI) repository against ten (10) benchmark metaheuristic algorithms. Experimental results of the EBGWO algorithm on the NSL-KDD dataset in terms of number of selected features and classification accuracy are superior to other benchmark optimisation algorithms. Moreover, experiments on the six (6) UCI datasets showed that the EBGWO algorithm is superior to the benchmark algorithms in terms of classification accuracy and second best for the number of selected features. The proposed EBGWO algorithm can be used for FS in anomaly detection tasks that involve any dataset size from various application domains