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Convergence for IEEE 118 node power system with simple PSO algorithm
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In this paper, the chaotic particle swarm optimization (CPSO) algorithm is combined with MATPOWER toolbox and used as an optimization tool for attaining solving the optimal reactive power dispatch (RPD) problem, by finding the optimal adjustment of reactive power control variables like a voltage of generator buses (VG), capacitor banks (QC) and tra...
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... Suppose a multi-objective majorization tricky with M objective is set up, and m subpopulations enhance it, each subpopulation enhances one of the problems distinctly, and the procedure construction is exposed in Figure 4. The majorization procedure for a particular subpopulation is based on the expanded single-objective PSO procedure, so one of the populations is chosen to present the procedure [17]. ...
Exploring the majorization strategy of the power system (PS) dispatching operation is to achieve economic cost reduction and reduce environmental pollution. In this paper, starting from the PS dispatching model, the adaptive Corsi variance is introduced to get rid of the local optimum using particle swarm majorization procedure, and the adaptive Corsi variance multiple swarm coevolutionary procedure is constructed through coevolutionary strategy and information sharing strategy. The MCPSO-ACPM procedure is used to optimize the PS scheduling operation model, and experiments are conducted on both load and unit for the optimized scheduling model. From the load majorization results, the peak-to-valley variance is concentrated from 176.02KW to 110.51KW compared with the original load, and the peak-to-valley ratio is reduced by 0.718, which saves customers 98.63 yuan in electricity purchase cost. From the scheduling majorization prediction, the PS output power prediction value of 1 min during the day is closest to the actual measured value of output power, and its prediction deviation is about 2.67%. This shows that the use of a multi-objective majorization procedure can realize the optimal dispatch of PS and achieve the reduction of economic cost.
... Throughout each iteration, the (solution) particle will follow a path that is optimal for both itself or the group. Each particle defines a potential solution to the problem under consideration [26]. These particles move through the search space in search of the best optimal solution to a given problem. ...
Software defined networking (SDN) is the networking model which has completely changed the network through attempting to make devices of network programmable. SDN enables network engineers to manage networks more quickly, control networks from a centralized location, detect abnormal traffic, and distinguish link failures in efficient way. Aside from the flexibility introduced by SDN, also it is prone to attacks like distributed denial of service attacks (DDoS), that could bring the entire network to a halt. To reduce this threat, the paper introduces machine learning model to distinguish legitimate traffic from DDoS traffic. After preprocessing phase to dataset, the traffic is classified into one of the classes. We achieved an accuracy score of 99.95% by employing an optimized extremely randomized trees (ERT) classifier, as described in the paper. As a result, the goal of traffic flow classification using machine learning techniques was achieved.
Nowadays, there is a significant rise in electricity demand, posing challenges for power grid operators due to inaccurate forecasting, leading to excessive power losses and voltage instability. This paper addresses these issues by focusing on solving optimal reactive power dispatch (ORPD) while considering load demand uncertainty. The main objective of solving ORPD is to reduce power losses by adjusting generator voltage ratings, transformer tap ratio, and shunt capacitors' reactive power. Monte Carlo simulation (MCS) is employed to generate load scenarios using the normal probability density function, while a reduction-based technique is implemented to decrease the number of those scenarios. The improved gray wolf optimization (I-GWO) algorithm is introduced for the first time to address the stochastic ORPD problem. Experimentation is conducted on an IEEE-30 bus system when results are contrasted with conventional gray wolf optimization (GWO) and five other algorithms as stated in the literature. The I-GWO algorithm's performance is assessed with and without considering load demand uncertainty. Through Friedman's statistical tests, a significant decrease of 20.96% in active power losses and 63.06% in the summation of expected power losses is observed. The I-GWO algorithm's results on the ORPD problem demonstrate its effectiveness and robustness.
In recent years, the surge in continuously accelerating data generation has given rise to the prominence of big data technology. The MapReduce architecture, situated at the core of this technology, provides a robust parallel environment. Spark, a leading framework in the big data landscape, extends the capabilities of the traditional MapReduce model. Coping with big data, especially in the realm of clustering, requires more efficient techniques. Meta-heuristic-based clustering, known for offering global solutions within reasonable time frames, emerges as a promising approach. This paper introduces a parallel-distributed clustering algorithm for big data within the Spark Framework, named Spark, chaotic improved PSO (S-CIPSO). Centered on particle swarm optimization (PSO), the proposed algorithm is enhanced with a chaotic map and an efficient procedure. Test results, conducted on both real and artificial datasets, establish the superior performance and quality of clustering results achieved by the proposed approach. Additionally, the scalability and robustness of S-CIPSO are validated, demonstrating its effectiveness in handling large-scale datasets.
