Volt/VAr Optimization of Distribution System with Integrated Distributed Generation

Mughees, Mariam; Awan, Fahim Gohar; Mughees, Anam

doi:10.22581/muet1982.1701.11

Cited by 2 publications

(2 citation statements)

References 14 publications

(22 reference statements)

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“…More shinyo synchronization in the iteration process, the work node first qualifying examination parameters obtained from the parameter server model, where each work node access is the same parameters, and then work nodes for training, after the completion of the training to the server sends parameters variation or gradient, parameters after the server to get all work node sends information to carry on the average, The global model on the parameter server is updated by gradient descent [15]. The synchronous update process is shown in Figure 1, and the formula is as follows:…”

Section: Figure1synchronously Update the Architecture Diagrammentioning

confidence: 99%

Collaborative Optimization Method for Distributed Systems based on Deep Learning

2020

DPS

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In the past decade, machine learning technology based on deep neural networks has made great progress, thanks to the continuous development of HPC hardware and software and practical applications. There are already a number of organizations and enterprises offering services to the public based on machine learning systems, such as face and speech recognition, photo optimization, and so on. Deep neural networks also require different computational power, so the demand for distributed neural network systems is also increasing. This paper mainly studies the collaborative optimization method of distributed systems based on deep learning. In this paper, a new distributed deep learning training system is designed and implemented by effectively combining cluster resource scheduling and distributed training based on the advantages of Pytorch in rapid neural network construction and parallel computing and cluster resource scheduling.

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Section: Figure1synchronously Update the Architecture Diagrammentioning

confidence: 99%

Collaborative Optimization Method for Distributed Systems based on Deep Learning

2020

DPS

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“…Celli et al [18] suggested Multi-Objective (MO) GA considering the ɛ-constraint method to find the feasible solutions of optimum DG capacity and site. Moreover, lot of research work is existed in the literature to find appropriate site and size of DG, that includes analytical approach [19], Genetic Algorithm (GA) [20], Bacterial Foraging Optimization (BFO) in [21], Artificial Neural Network (ANN) [22], Multi-Objective Particle Swarm Optimization (MOPSO) in [23]. Recently few numbers of studies have been accomplished to novelty simultaneously DG allocation and OFR, in which Adaptive CSA (ACSA) [24], Harmony Search Algorithm (HSA) [5], and Fireworks Algorithm (FWA) [25] are up-to-date few of the methods.…”

Section: Introductionmentioning

confidence: 99%

Constrained Composite Differential Evolution Search for Optimal Site and Size of Distributed Generation Along with Reconfiguration in Radial Distribution Network

Ali

Keerio

Mugheri

2020

Mehran Univ. res. j. eng. technol.

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This paper proposed a novel method to find the Optimal Feeder Reconfiguration (OFR) of radial distribution network along with optimal site and size of Distributed Generation (DG) with an objective of power loss reduction. OFR and DG allocation problems are highly non-linear and complex optimization problems, complexity of problem is further increased with the addition of distribution operation constraints. In the last two decades, Evolutionary Algorithms (EAs) have been implemented successfully to find the OFR and DG allocation considering different objective functions. However, search space adopted by EAs are unconstrained, therefore numerous methodologies are adapted to discard infeasible solutions. The penalty approach is most widely used in the literature, it requires appropriate selection of penalty parameters using a tedious trial and error method. In this paper, a new EA Constrained Composite Differential Evolution (C2oDE) is proposed find optimal capacity and site of DG along with reconfiguration problem. In the proposed algorithms three trial vectors are generated to strike balance among exploration and exploitation. Furthermore, to find the feasible solutions in the decision space two most widely used constraint handling techniques that include feasibility rule and ɛ-constrained method are added to select the trail vector and individuals respectively for the next generation. Proposed C2oDE method has been validated by considering IEEE 33-bus, 69-bus and 119-bus distribution networks at five different cases. Simulation results obtained shows better performance of proposed method against the most recent literature to find both OFR and DG allocation

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Volt/VAr Optimization of Distribution System with Integrated Distributed Generation

Abstract: ABSTRACT

Cited by 2 publications

References 14 publications

Collaborative Optimization Method for Distributed Systems based on Deep Learning

Collaborative Optimization Method for Distributed Systems based on Deep Learning

Constrained Composite Differential Evolution Search for Optimal Site and Size of Distributed Generation Along with Reconfiguration in Radial Distribution Network

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