Active power generation pattern via considering voltage stability margin improvement

In this study, an efficient approach is proposed to find best real power generation participation for improving static voltage stability and reducing total generation cost. Therefore, loadability limit index is used to assess static voltage stability security margin, which is associated to the point of voltage collapse. Based on it, a toolbox is developed to recognize the loadability margin by using Lagrangian method. Finally, the mentioned problem is modeled as a non-linear and multiobjective optimization problem. In this paper, Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) and Particle Swarm Optimization (PSO) algorithms are applied to find out best generation direction. This problem is performed as a hierarchical optimization problem. In first stage, an algorithm is used to search possible solution area and reach tradeoff between secure and economic direction of real power generations. In second stage of proposed approach, respectively to each generation pattern, power system voltage stability margin and generation cost are evaluated during search algorithm. The simulations are performed on IEEE 14 and IEEE 30 bus test systems to find best generation direction.

Metaheuristic techniques are enormously being used nowadays for their applications in field like Power system and are being applied to many optimization problems of power system due to difficult approach of classical methods. Transmission expansion planning (TEP) is a challenging task to deal in power system. A new approach, hybrid genetic algorithm particle swarm optimization (HGAPSO) for solving TEP problem, is introduced to eliminate the drawback of GA and PSO. Problems of immature convergence in particle swarm optimization (PSO) and low convergence speed in genetic algorithm (GA) mitigate to the hybridization of both techniques. Proposed HGAPSO Algorithm is tested for three standard electric test systems for TEP problem in MATLAB tool. Experimental results found by HGAPSO are compared with GA and PSO Algorithm to test its performance for TEP problem.

In recent years, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid genetic algorithm particle swarm optimization (HGAPSO) have attracted considerable attention among various modern heuristic optimization techniques. In this study the HGAPSO, PSO and GA optimization techniques are used for to search the optimal placement and sizing of static VAR compensator (SVC) in power system. The objective function is defined for reducing power loss, voltage deviation and investment costs of SVC. The effectiveness of the proposed hybrid based approach is applied and demonstrated on IEEE 30 Bus network. The results show that the proposed hybrid HGAPSO compared with PSO and GA optimization for performs and giving better solution.

Evolutionary Algorithms are good optimizing agents that use the evolutionary concepts to evolve systems. The complex fitness landscapes are a problem that restricts the performance of these algorithms. Besides specifying the correct parameter is always very important. In this chapter we study the means by which we can execute a number of evolutionary algorithms in a variety of models to give an overall optimal performance to the entire network. We first discuss the Island Model Algorithm where the individuals are evolved in multiple islands with regular interaction. We then study a related algorithm called the Hierarchical Fair Competition algorithm. Here we would separate the various individuals into classes as per their fitness values. Each of these would have a separate evolution. We would then discuss the Nested Evolutionary Strategies where we fix the parameters of an Evolutionary Strategy by some technique. Towards the end we discuss the Hybrid Genetic Algorithm Particle Swarm Optimization. This algorithm generates individuals by twin usage of Genetic Algorithm and Particle Swarm Optimization.

A novel method to solve the problem of reconfiguration and distributed generation (DG) planning in distribution systems is proposed in this paper. By a multistage hieratical optimization problem, this problem has been modeled as a nonlinear and multi-objective optimization problem based on to improve power system voltage stability margin, investment cost, and reduce losses. As the first stage, the ability of developed harmony search is used to reach the best network configuration and DGs location under variable load patterns. This reconfiguration algorithm starts with a radial topology by a theoretical approach based on the Graph concept and Matroid theory. The presented approach is useful to avoid tedious mesh checks for the topology constraint validation. In the second stage of proposed approach, the voltage stability index is calculated to evaluate static voltage stability security margin. This calculation is done for each time variable reconfiguration pattern with defined DGs planning based on a developed toolbox which evaluate the loadability limit and other terms of task function. Finally, the simulations are carried out on 69 and 119 bus IEEE distribution systems and have been compared with particle swarm optimization. The results show good enhancement to reach the optimum solution with respect to the other studies.

Text compression is a necessity for efficient data storage and transmission. Especially in the digital era, volumes of digital text have increased incredibly. Traditional text compression methods, including Huffman coding and Lempel-Ziv-Welch, have certain limitations regarding their adaptability and efficiency in dealing with such complexity and diversity of data. In this paper, we propose a hybrid method that combines Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) to optimize the compression of text using the broad exploration capabilities of GA and fast convergence properties of PSO. The experimental results reflect that the proposed hybrid approach of GA-PSO yields much better performance in compression ratio than the standalone methods by reducing the size to about 65% while retaining integrity in the original content. The proposed method is also highly adaptable to various text forms and outperformed other state-of-the-art methods such as the Grey Wolf Optimizer, the Whale Optimization Algorithm, and the African Vulture Optimization Algorithm. These results support that the hybrid method GA-PSO seems promising for modern text compression.

Two-echelon logistics distribution region partitioning problem based on a hybrid particle swarm optimization–genetic algorithm

This paper studies the two-stage assembly flow shop problem (TAFSP) considering aging effects of the machines and preventive maintenance activities. At the first stage, m-1 parallel machines process parts of each jobs, and at the second stage, related parts of the jobs are assembled by one assembly machine. As the machines work on the jobs, their tools get aged. Aging effects on the machines causes that they will not be able to complete the jobs in the same time could as they were new or when they are operating jobs immediately after their preventive maintenance activity. Processing times of the job are related to the positions, in which it is located after the last preventive maintenance. The job that is operated in a position immediately after the preventive maintenance activity on a machine has its standard processing time. However, the processing time of the jobs operated in the further positions increase based on the number of the positions. The machines return to the initial condition after each preventive maintenance activity. The objective is to schedule the jobs on the machines and determine when the preventive maintenance activities get done on them in order to minimize the total weighted tardiness and maintenance costs. An integer mathematical model is presented for the problem and its validation is shown by solving an example in small scale. Since two-stage assembly flow shop problem is NP-hard, in order to solve the problem in medium and large scale two meta-heuristic algorithms, hybrid genetic algorithm (HGA) and hybrid particle swarm optimization (HPSO) are proposed. These algorithms are the hybrid version of genetic algorithm and particle swarm optimization representatively with simulated annealing. The algorithms are tuned by using Taguchi method, and are used to solve many numerical examples. Finally, the statistical analysis illustrates that the performance of HPSO is better than HGA.

This paper presents an optimal load shedding approach to enhance voltage stability employing a combination of modal analysis and particle swarm optimization (PSO). At first as a preventive control action the best transformers tap setting is indicated by PSO optimization to get the most possible voltage stability margin then as a corrective control action after contingencies the proposed approach is organized as a multi-objective optimization problem which reveals the best location and the lowest level of load shedding for special protection systems (SPS) in the direction of improving the voltage stability margin as well as the voltage profile. The proposed approach is applied to Gharb and Bakhtar areas of the Iranian transmission network for its annual peak load at 2009. Simulation results verify the effectiveness of the proposed scheme in comparison with the current scheme.

A reactive power planning method based on improved particle swarm optimization (IPSO) is presented. This method can improve the quality of initial particles and eliminate the boundary oscillation in traditional particle swarm optimization. The voltage stability margin index (VSI) is used to rank the load buses. Then reactive power compensators are installed on the buses with weak voltage stability. The maximization of voltage stability margin is included in the objective function of reactive power planning to improve system voltage stability. Its application in IEEE 30‐bus system illustrates that the real power loss is depressed with voltage stability margins increased simultaneously. Copyright © 2009 John Wiley & Sons, Ltd.

Urbanization and increasingly extreme weather patterns have significantly heightened the strain on underground drainage systems. The cumulative impacts of prolonged pipeline aging and continuous ground loading further exacerbate leakage issues. These leaks trigger soil erosion and structural degradation under combined hydraulic pressure and surface loads, eventually leading to ground collapse. To address these challenges, this study developed an advanced intelligent calibration model that integrates the Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) with a Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO) framework to analyze the complex interactions involved. The research established a comprehensive soil particle volume loss equation that incorporates key hydraulic and loading variables. Following the validation of CFD-DEM parameters, the complete erosion-to-collapse process was simulated, enabling the construction of an HGAPSO-based predictive model. The results demonstrated that increased flow velocity enhances pressure gradient forces and Magnus lift effects, thereby disrupting the soil's natural pressure arch mechanism and accelerating particle displacement and collapse. The proposed model achieved an accuracy of 0.87 and a correlation coefficient of 0.931, confirming its robustness and predictive reliability. These findings provide valuable insights into the mechanisms underlying ground collapse and offer practical implications for urban infrastructure maintenance and risk management strategies.

Voltage instability and voltage collapse have been considered as a major threat to present power system networks due to their stressed operation. It is very important to do the power system analysis with respect to voltage stability. Flexible AC Transmission System (FACTS) is an alternating current transmission system incorporating power electronic-based and other static controllers to enhance controllability and increase power transfer capability. A FACTS device in a power system improves the voltage stability, reduces the power loss and also improves the load ability of the system. FACTS have made the power systems operation more flexible and secure. Amongst the several FACTS controllers, the Unified Power Flow Controller (UPFC) is most effective to improve the enhancement of voltage stability and reduces the power loss. This study investigates the application of Particle Swarm Optimization (PSO) to find sizing of Unified Power Flow Controller (UPFC) device to minimize the voltage stability index, total power loss, load voltage deviation, and cost of FACTS devices to improve voltage stability in the power system. A new model is proposed in this thesis to improve existing power-based model by using the Norton Equivalent Theorem. The proposed model can be integrated with the Equivalent Current Injection (ECI) power flow model easily. By ECI algorithm, it is much quickly and precisely to implement power flow calculations. Finally It is observed from the results that the voltage stability margin is improved, the voltage profile of the power system is increased and real power losses also reduced by optimally sizing UPFC device in the power system. Index Terms: Voltage stability, Unified Power Flow Controller (UPFC), Equivalent -Current Injection (ECI), Modified particle swarm optimization (MPSO)

A hybrid genetic-particle swarm optimization algorithm for multi-constraint optimization problems

To address the low efficiency in the Assembly Order (AO) preparation process of aircraft assembly wing-body docking station, characterized by an excessively long critical path and frequent resource conflicts, we propose an AO optimization framework integrating multi-process reorganization. High-frequency process combinations are mined from the historical AO data to construct a standardized process sequence library, enabling intelligent reorganization of the original AO. A hybrid approach integrating the Migration Hybrid Genetic-Particle Swarm Optimization algorithm (MHGAPSO) with the Critical Path Method (CPM) is employed to perform multi-objective optimization of the reorganized AO process, aiming to minimize the total critical path duration and balance resource utilization. The proposed method is validated by using 30 assembly cases of a specific aircraft assembly line. The results demonstrate that, compared with the conventional approaches, the AO restructuring time is reduced by 65%, the total critical path duration decreases by 15%, and the equipment load fluctuations are controlled within 20%. This work provides a scientific and practical basis for supporting the multi-batch aircraft assembly production.

Cloud computing provides the computational machines as a support of the clients utilizing cloud organize. In cloud computing, the user inputs are executed with required machines to convey the administrations. Numerous task scheduling methods are utilized to plan the client tasks to the machines. In this paper, another successful hybrid task scheduling is proposed to minimize the total execution time using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. In hybrid Genetic Algorithm - Particle Swarm Optimization (GA-PSO) algorithm, PSO helped GA to obtain better results compare to a standard genetic algorithm, Min-Min, and Max-Min algorithms results.