Smart Electric Grids Three-Phase Automatic Load Balancing Applications using Genetic Algorithms

Goudah, Ahmad A.; Schramm, D.; El-Habrouk, Mohmed; Dessouky, Yasser G.

doi:10.21622/resd.2020.06.1.018

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…Series compensation systems can be used for treating voltage and current imbalance conditions beside the treatment of harmonic issues [15]. Separate compensating susceptances connected in delta and star forms are capable of accomplishing more flexible compensation for voltage and current imbalance conditions compared to systems using lumped compensation systems like DSTATCOMs [1], [3], [5], [8]- [11], [14]- [19], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Load current balancing for 4-wire systems using harmonic treated TCR based SVCs

Obais

Mukheef²

2022

IJPEDS

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<p>In this paper, a harmonic-treated thyristor-controlled reactor TCR is presented as a linearized harmonic-free compensating susceptance controllable in inductive and capacitive modes. The harmonic-treated TCR is a traditional TCR conditioned in such a manner that it can respond continuously and linearly to capacitive and inductive reactive current demands without noticeable harmonic association or active power contribution. The conditioned configuration is produced by equipping the TCR with self-harmonic suppressing and filtering circuitries, which guarantee harmonic cancellation with minimal no load operating losses. The harmonic treated TCR avoids the need to high power harmonic filters required to treat the harmonics of the traditional TCR. The devised susceptances are used to build a load current balancing system for grounded loads in a 380-V, 50Hz power distribution system. Both the compensating susceptances and the load current balancing system were designed and tested on PSpice. The simulation results have demonstrated the linearity, control continuity, and harmonic cancellation of the proposed harmonic-treated TCR as a fast response compensating susceptance reliable for load current balancing purposes. The proposed load current balancing system revealed superior treatment to various unbalance conditions, thus it is deservedly promoted to have the feasibility of supporting grids having fast varying loads.</p>

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Section: Introductionmentioning

confidence: 99%

Load current balancing for 4-wire systems using harmonic treated TCR based SVCs

Obais

Mukheef²

2022

IJPEDS

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“…Optimal planning and design of the power distribution systems involves network reconfiguration for distribution loss minimization, load balancing under normal operating conditions, and fast restoration of supply to minimize zones without power under failure conditions [1]. Most of the distribution networks are configured radially [2,3] which simplifies overcurrent protection of the feeders; hence the manual or automatic switching operations is performed to vary the configurations. Feeder reconfiguration in the distribution system has different needs: from balancing the loads in the network, to minimizing the losses in the system and as well as to correct the voltage profile of supply phases, and so forth [4].…”

Section: Introductionmentioning

confidence: 99%

Feeder Load Balancing Using Genetic Algorithms and Artificial Neural Network

Suleiman

Shobowale²,

Ter³

et al. 2022

MEKATRONIKA

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Low voltage power distribution system problems such as system planning, energy loss minimization and restoration usually involve proper load balancing or network reconfiguration procedures. To achieve an appreciable level of load phase balance, feeder reconfiguration using appropriate switching control strategy such as: Simulated Annealing, Tabu Search, Particle Swarm Optimization, and heuristic algorithms are viable preferences. However, the systematic solution to load phase balancing can be greatly enhanced optimally through implementation of an appropriate combinatorial optimization procedure such as Genetic Algorithms and Artificial Neural Network. Accordingly, this paper presents a genetic algorithms procedure to enhance the load phase balancing optimization and then train an artificial neural network to automate the reconfiguration of the distribution network loads, thus ensuring an optimal phase balancing in the system. An Intel® 2.0 GHz, 4GB RAM HP255 computer-based MATLAB® 14 was used for the neural network training, testing, and the implementation of the genetic algorithms. The outputs of the algorithms are the switching sequence for a balanced network. The parameters ΔIph (max - min) and Δ(Iph – Imax) which is the maximum difference between the phase currents, which are ideally zero if there are no imbalances in the network, shows considerable improvement in the balancing when compared with other literatures. This work presents the application examples of the proposed methods using real test data.

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“…The swift surge in the load demands throughout the day contributes to an unbalanced load state. Owing to the different categories of load, there exits voltage unbalancing that can cause serious damage to the equipment and load [1,2]. Unbalancing of load on a domestic level is a common problem that leads the lines to overload by experiencing an extremely high voltage which is often disastrous for the appliances.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Load Sharing at Domestic Level Using the Internet of Things

Rashid¹,

Ahmad²,

Talha³

et al. 2020

IJIE

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At domestic level, the load management plays a vital role as the consumer line gets overloaded due to the various load categories. The fully loaded line consumes more energy units, which increases the electricity bill of the consumer. To circumvent the issue of load unbalancing, an automated load management system is developed that shifts the load from one line to another. The proposed system is developed by making use of current and potential sensing transformers and Arduino Mega board. Moreover, the proposed system also provides an internet of things (IoT) based monitoring facility to the user. The sensed information from the system is uploaded to the web server by the help of Wireless Fidelity (Wi-Fi) from where the user can access this information at any time through appropriate applications. Based on the monitoring data, the user can take necessary decisions regarding the switching off unnecessary load. Both simulation and hardware results demonstrate the effectiveness of the proposed system.

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Smart Electric Grids Three-Phase Automatic Load Balancing Applications using Genetic Algorithms

Cited by 6 publications

References 1 publication

Load current balancing for 4-wire systems using harmonic treated TCR based SVCs

Load current balancing for 4-wire systems using harmonic treated TCR based SVCs

Feeder Load Balancing Using Genetic Algorithms and Artificial Neural Network

Dynamic Load Sharing at Domestic Level Using the Internet of Things

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