Reactive Power Optimization Based on the Application of an Improved Particle Swarm Optimization Algorithm

Mourtzis, Dimitris; Angelopoulos, John

doi:10.3390/machines11070724

Cited by 7 publications

(5 citation statements)

References 61 publications

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“…Furthermore, it is evident from Table 4 that the ISSA-PSO method achieves lower network losses compared to the other two methods. Moreover, as indicated by the findings in reference [30], an enhanced PSO algorithm is able to reduce network losses by approximately 11%. In contrast, the implementation of the proposed optimization method in this paper results in a decrease of approximately 33% in network losses, showcasing a significantly superior optimization effect compared with reference [30].…”

Section: Ieee 33-node System Simulation Resultsmentioning

confidence: 88%

“…Moreover, as indicated by the findings in reference [30], an enhanced PSO algorithm is able to reduce network losses by approximately 11%. In contrast, the implementation of the proposed optimization method in this paper results in a decrease of approximately 33% in network losses, showcasing a significantly superior optimization effect compared with reference [30]. Therefore, the proposed approach demonstrates an excellent reactive power optimization capability.…”

Section: Ieee 33-node System Simulation Resultsmentioning

confidence: 88%

“…The PSO algorithm, known for its low memory requirements and fast convergence, has been widely adopted in the field of MORPO due to its advantages [29]. In reference [30], an improved RPO algorithm was proposed by considering the minimization of power loss as the primary objective function, which was achieved by enhancing the strategy of inertia weight and the acceleration coefficients. In reference [31], the L-index was incorporated to enhance the stability of static voltage in electrical power systems.…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved Sparrow Search Algorithm–Particle Swarm Optimization Algorithm

Wang,

Li,

Xiao

et al. 2024

Energies

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With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power in the power system becomes particularly important. The primary objective of this article is to create a multi-objective reactive power optimization (MORPO) model for distribution networks. The model aims to minimize reactive power loss, reduce the overall compensation required for reactive power devices, and minimize the total sum of node voltage deviations. To tackle the MORPO problems for distribution networks, the improved sparrow search algorithm–particle swarm optimization (ISSA-PSO) algorithm is proposed. Specifically, two improvements are proposed in this paper. The first is to introduce a chaotic mapping mechanism to enhance the diversity of the population during initialization. The second is to introduce a three-stage differential evolution mechanism to improve the global exploration capability of the algorithm. The proposed algorithm is tested on the IEEE 33-node system and the practical 22-node system. The results indicate a reduction of 32.71% in network losses for the IEEE 33-node system after optimization, and the average voltage of the circuit increases from 0.9485 p.u. to 0.9748 p.u. At the same time, optimization results in a reduction of 44.07% in network losses for the practical 22-node system, and the average voltage of the circuit increases from 0.9838 p.u. to 0.9921 p.u. Therefore, the proposed method exhibits better performance for reducing network losses and enhancing voltage levels.

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Section: Ieee 33-node System Simulation Resultsmentioning

confidence: 88%

Section: Ieee 33-node System Simulation Resultsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved Sparrow Search Algorithm–Particle Swarm Optimization Algorithm

Wang,

Li,

Xiao

et al. 2024

Energies

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“…Based on the PSO algorithm's global optimization ability and fast convergence speed, it changes the traditional BP neu ral network's tendency to fall into the problem of local minimization, and it improves the learning and prediction ability of the BP neural network. Since the PSO algorithm is in spired by the motion of birds as they form a flock, two hyperparameters, also known as acceleration coefficients or learning factors (c1 and c2), are utilized in an attempt to simu late the socialization and the instincts of the birds [20]. The PSO algorithm measures the current individual optimal solution of the particles and the current global optimal solution of the group through the fitness function in the multidimensional space, which is composed of initial weights and thresholds, to continuously update the position and speed of the particles, as well as to obtain the optimal global optimal solution of the group under the termination condition of the algorithm.…”

Section: Pso-bp Neural Network Algorithmmentioning

confidence: 99%

Section: Pso-bp Neural Network Algorithmmentioning

confidence: 99%

Prediction of Drilling Efficiency for Rotary Drilling Rig Based on an Improved Back Propagation Neural Network Algorithm

Jia,

Zhang,

Kong

et al. 2024

Machines

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Accurately predicting the drilling efficiency of rotary drilling is the key to achieving intelligent construction. The current types of principle analysis (based on traditional interactive experimental methods) and efficiency prediction (based on simulation models) cannot meet the requirements needed for the efficient, real-time, and accurate drilling efficiency predictions of rotary drilling rigs. Therefore, we adopted a method based on machine learning to predict drilling efficiency. The extremely complex rock fragmentation process in drilling conditions also brings challenges to predicting drilling efficiency. Therefore, this article went through a combination of mechanism and data analysis to conduct correlation analysis and to clarify the drilling characteristic parameters that are highly correlated with drilling efficiency, and it then used them as inputs for machine learning models. We propose a rotary drilling rig drilling efficiency prediction model based on the GA-BP neural network to construct an accurate and efficient drilling efficiency prediction model. Compared with traditional BP neural networks, it utilizes the global optimization ability of a genetic algorithm to obtain the initial weights and thresholds of a BP neural network in order to avoid the defect of ordinary BP neural networks, i.e., that they easily fall into local optimal solutions during the training process. The average prediction accuracy of the GA-BP neural network is 93.6%, which is 3.1% higher than the traditional BP neural network.

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Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients

Watanabe,

2024

Appl Intell

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In contemporary particle swarm optimization (PSO) algorithms, to efficiently explore global optimum solutions, it is common practice to set the inertia weight to monotonically decrease over time for stability, while allowing the two acceleration coefficients, representing cognitive and social factors, to adopt decreasing or increasing functions over time, including random variations. However, there has been little discussion on a unified design approach for these time-varying acceleration coefficients. This paper presents a unified methodology for designing monotonic decreasing or increasing functions to construct nonlinear time-varying inertia weight and two acceleration coefficients in PSO, along with a control strategy for exploring global optimum solutions. We first construct time-varying coefficients by linearly amplifying well-posed monotonic functions that decrease or increase over normalized time. Here, well-posed functions ensure satisfaction of specified conditions at the initial and terminal points of the search process. However, many of the functions employed thus far only satisfy well-posedness at either the initial or terminal points of the search time, prompting the proposal of a method to adjust them to virtually meet specified initial or terminal points. Furthermore, we propose a crossing strategy where the developed cognitive and social acceleration coefficients intersect within the search time interval, effectively guiding the search process by pre-determining crossing values and times. The performance of our Nonlinear Crossing Strategy-based Particle Swarm Optimization (NCS-PSO) is evaluated using the CEC2014 (Congress on Evolutionary Computation in 2014) benchmark functions. Through comprehensive numerical comparisons and statistical analyses, we demonstrate the superiority of our approach over seven conventional algorithms. Additionally, we validate our approach, particularly in a drone navigation scenario, through an example of optimal 3D path planning. These contributions advance the field of PSO optimization techniques, providing a robust approach to addressing complex optimization problems.

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Reactive Power Optimization Based on the Application of an Improved Particle Swarm Optimization Algorithm

Cited by 7 publications

References 61 publications

A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved Sparrow Search Algorithm–Particle Swarm Optimization Algorithm

A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved Sparrow Search Algorithm–Particle Swarm Optimization Algorithm

Prediction of Drilling Efficiency for Rotary Drilling Rig Based on an Improved Back Propagation Neural Network Algorithm

Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients

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