New approach to design SVC‐based stabiliser using genetic algorithm and rough set theory

Fetouh, Tamer; Zaky, Mohamed S.

doi:10.1049/iet-gtd.2016.0701

Cited by 42 publications

(30 citation statements)

References 25 publications

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“…Rough set is a mathematical tool used to describe the imperfection and uncertainty systems and can effectively analyze and process various imprecise, inconsistent, incomplete information and find hidden knowledge or latent law that does not need any ancestor information . The theory of rough set is used to solve many difficult problems that depend on planning and classification as well as analysis of different types of data, especially uncertain, inaccurate, and nonunderstood data, related to the information system.…”

Section: Optimal Placement Of Cbs Dgs and Avrs For Tala Distributiomentioning

confidence: 99%

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Distribution system performance enhancement (Egyptian distribution system real case study)

Elsayed

Mishref²,

Farrag

2018

Int Trans Electr Energ Syst

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Higher load growth in the distribution system and its consequences on higher voltage deviation, feeders overloading, and distribution system loss increase have a bad effect on system quality. Global warming and the dwindling supply of fossil fuels also make it imperative to search for more effective alternatives that guarantee appropriate operating level of power losses and voltage profile at different points of the distribution networks. Capacitor banks (CBs), renewable distributed generators (DGs), and automatic voltage regulators (AVRs) can be considered the most suitable enhancement devices for distribution systems that reduce both pollution and power losses, improving the voltage profile, ..., etc, if they are optimally sized and sited. The objective of this paper is to investigate the optimal placement of each of the 3 mentioned solutions (CBs, DGs, and AVRs) on a part of real Egyptian distribution system. This system is an old, long, weak, and heavily loaded distribution system with high power losses (about 29% of system loading) and poor voltage regulation (about 30%). Each introduced solution has been evaluated according to power loss minimization and investment cost minimization. A new modification to the backward-forward power flow method for radial distribution system embedded AVRs has been proposed, which facilitates the placement procedure of AVRs and reduces computation time as compared to other methods. In this paper, an optimal placement of the combined CBs, DGs, and AVRs has also been introduced based on integrated particle swarm optimization technique and rough set theory. The proposed approach aims to minimize the problem complexity and reduce the storage capacity required for the optimization problem.

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Section: Optimal Placement Of Cbs Dgs and Avrs For Tala Distributiomentioning

confidence: 99%

“…Therefore, in this paper, a new technique for optimal placement of combined CBs, DGs, and AVRs in distribution systems has been introduced. The proposed technique is based on the integration between PSO and rough set theory . The importance of rough set is to select the most dominant number of each enhancement device connection points.…”

Section: Introductionmentioning

confidence: 99%

Distribution system performance enhancement (Egyptian distribution system real case study)

Elsayed

Mishref²,

Farrag

2018

Int Trans Electr Energ Syst

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“…To find attribute reduction sets quickly and efficiently, studies have applied the ant colony algorithm to attribute reduction [16], and some have combined particle swarm optimization and attribute reduction [17]. The genetic algorithm has been utilized to reduce the number of parameters and thereby obtain good results [18]. Although group-intelligent optimization algorithms could quickly obtain the attribute reduction sets of a decision table, they cannot ensure that the reduction sets are minimal.…”

Section: State Of the Artmentioning

confidence: 99%

Fault Diagnosis Based on the Optimization of Characteristic Parameters and Neural Networks of Gearboxes

Liu¹,

Zhang²

2019

JESTR

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Gearboxes are the most commonly used transmission components in heavy equipment such as helicopters, shearers, and ships. The failure rate of gearboxes is high, and the characteristic signals under faulty conditions tend to be extremely weak and are often overwhelmed by strong noise. Thus, extracting sensitive characteristic parameters is difficult. In order to optimize the characteristic parameters of gearboxes and improve diagnosis efficiency, this study proposed a method for fault diagnosis of gearboxes that combines empirical mode decomposition (EMD) with rough sets and neural networks. First, the principle of EMD was explored. The indicators for measuring characteristic parameters were identified to compare the feature set composed of energy values with those comprising approximate entropy parameters. Second, the conditional attribute reduction technique for rough sets was investigated. An algorithm for attribute reduction based on conditional equivalence classification was put forward for parameter optimization. Then, a neural network was employed to identify the feature sets before and after the attribute reduction. Results show that the energy characteristic set is the most sensitive to failures. The attribute reduction technique reduces the characteristic parameters from 6 to 4, thereby effectively lowering the input vectors of the neural network. The training time is also decreased from 1.024 s to 0.351 s, obviously promoting the efficiency of fault diagnosis. The study provides references for improving the performance of online real-time fault diagnosis.

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“…Similarly, many papers have been published in the field of designing dynamic var source controllers for power system stability improvements [15][16][17][18][19][20][21][22][23]. References [15][16][17][18][19][20] require linearization of the system as they are based on small disturbance analysis or optimize the SVC parameters with linearized model and check with dynamic simulation once merely for verification.…”

Section: Introductionmentioning

confidence: 99%

Optimization of SVC settings to improve post-fault voltage recovery and angular stability

Huang

Sun

2018

J. Mod. Power Syst. Clean Energy

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In this study, controller parameters of static var compensators (SVCs) at planned locations are optimized to mitigate fault-induced delayed voltage recovery issues and improve angular stability of a multi-machine power system. The problem is formulated as a nonlinear optimization problem involving constraints on post-fault trajectories of voltages and frequencies. This paper proposes a mesh adaptive direct search based algorithm interfaced with a power system simulator for optimization of SVC controller parameters. The proposed method is tested on the NPCC 140-bus system to optimize controller parameters for three SVCs. Simulations on critical contingencies verify that post-fault transient voltages and generator speeds can both quickly recover and transient stability of the system is improved.

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New approach to design SVC‐based stabiliser using genetic algorithm and rough set theory

Cited by 42 publications

References 25 publications

Distribution system performance enhancement (Egyptian distribution system real case study)

Distribution system performance enhancement (Egyptian distribution system real case study)

Fault Diagnosis Based on the Optimization of Characteristic Parameters and Neural Networks of Gearboxes

Optimization of SVC settings to improve post-fault voltage recovery and angular stability

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