Dynamic reconfiguration of three-phase unbalanced distribution networks

Zhai, Hefeng; Yang, Ming; Chen, Bo; Kang, Ning

doi:10.1016/j.ijepes.2017.12.027

Cited by 74 publications

(39 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Protective constraints are not important in such networks and after determining the optimal configuration; the protection devices should be either replaces or retuned. In contrast, the operator of modern distribution networks may need to change the configuration of network several times in a day . According to number of protective devices and the location of them, changing or replacing them several times in a short period time is neither practical nor cost‐effective.…”

Section: Simulation Resultsmentioning

confidence: 99%

Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties

Fathi

Seyedi

Mohammadi‐Ivatloo

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Distribution network reconfiguration is a common method to achieve objectives such as minimization of power loss and voltage deviations. There exist several different algorithms to find the optimal configuration of network. Almost all of these algorithms consider the main operational constraints such as voltage, current, and power limits. Although it is crucially important to incorporate protective devices and their constraints in the reconfiguration problem, there are few works in network reconfiguration considering protection system. Accordingly, this paper presents a new reconfiguration algorithm considering protective constraints in the presence of distributed generation (DG) units. The objective function of this paper is power loss, which is minimized subject to both operation and protection constraints. In addition to the protection constraints, the effects of DG units on the reconfiguration are considered in this work. Uncertainties in loads and power generation of DG units are also investigated, using a new scenario‐based algorithm. Two test systems are utilized to illustrate the efficiency of the proposed method and to compare it with conventional approaches. Moreover, in order to evaluate the performance of new scenario‐based algorithm, it is compared with Monte Carlo simulations.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties

Fathi

Seyedi

Mohammadi‐Ivatloo

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…This method depends on distributing loads manually and automatically among the circuits to balance the distribution system using a feeder switch. However, this method balances the loads on an hourly basis . The method of unified power quality conditioner also provides a good performance in load balancing, but the operating cost of this method is quit high .…”

Section: Introductionmentioning

confidence: 99%

“…However, this method balances the loads on an hourly basis. 15,16 The method of unified power quality conditioner also provides a good performance in load balancing, but the operating cost of this method is quit high. 17 Also, it is mentioned in Mahela and Gafoor Shaik 17 that a more accurate and effective performance can be provided by flexible AC transmission system (FACTS) devices.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the response of thyristor‐controlled reactor using neural network

Ragab

Ghaeb

Al‐Naimi

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary In this paper, a neural network controller is proposed to retrieve the voltage balancing conditions in three‐phase power systems. The neural network is suggested to calculate the required set of firing angles for the thyristor‐controlled reactor accurately to balance the three‐load voltages quickly. The proposed controller is fed by different parameters within different feeding techniques, namely, root mean square (RMS) values of the three load voltages, RMS value of the space vector signal calculated from the three load voltages, and the RMS values of both the three load voltages and their associated space vector. The intentions of the proposed techniques are to combine between reducing the number of measured parameters and providing the controller with qualitative data about system status. The influence of the measured parameters on the neural network performance is examined by calculating the regression coefficients through several test cases. Accordingly, only the effective parameters are utilized to reduce the neural network complexity and to enhance the controller response time. Additionally, the calculations of the controller input parameters are made in terms of space vector cycle, which is half of system sinusoidal cycle. Consequently, the calculation time is reduced significantly. The Aqaba‐Qatrana‐South Amman power system is considered and modeled as a real case study. In addition, several test cases have been conducted to test and validate the ability of the proposed neural network controller in retrieving the voltage balance conditions precisely and quickly. The results have revealed the ability of the proposed neural network controller to calculate the firing angles quickly within 10 milliseconds and achieve very low voltage unbalance factor.

show abstract

“…Sectionalizing switches are normally closed switches placed within each feeder of the DN while tie switches are normally open switches connecting part of one feeder to another [7]. DNR is an effective way of improving the performance of power DNs such as reducing power losses [8,9], voltage profile (VP) improvement [10,11], feeder load balancing (LB) [9,12], increasing RDG penetration [13,14], mitigating operational constraint violations [12], service restoration [15,16], improving system security [17] and relieving primary feeders for the purpose of repairs on that feeder [18]. e DN reconfiguration changes the power flow direction which subsequently alters the line currents, bus voltages, level of symmetry and level of harmonic distortion on the bus voltages [19,20].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

Kunya

Shehu

Hassan

et al. 2019

J. Appl. Mat. Tech.

View full text Add to dashboard Cite

A reliable, eco- and nature-friendly operation has been the major concern of modern power system (PS). To improve the PS reliability and reduce the adverse environmental effect of conventional thermal generation facilities, renewable energy based distributed generation (RDG) are being enormously integrated to low and medium voltage distribution networks (DN). However, if these systems are not properly deployed, the reliability and stability of the PS will be endangered and its quality can be dreadfully jeopardized. Among the measures taken to avoid such is optimizing the location and size of each RDG unit in the DNs. These networks are generally operated in a radial configuration, though they can be reconfigured to other topologies to achieve certain objectives. Both RDG placement/sizing and DN reconfiguration are highly non-linear, multi-objective, constrained and combinatorial optimization problems. In this study, a hybrid of Particle Swarm Optimization (PSO) and real-coded Genetic Algorithm (GA) techniques is employed for DN reconfiguration and optimal allocation (size and location) of multiple RDG units in primary DNs simultaneously. The objectives of the proposed technique are active power loss reduction, voltage profile (VP) and feeder load balancing (LB) improvement. It is carried out subject to some technical constraints, with the search space being the set of DN branches, DG sizes and potential locations. To ascertain the effectiveness of the technique, it is implemented on standard IEEE 16-bus, 33-bus and 69-bus test DNs. The proposed algorithm is implemented in MATLAB and MATPOWER environments. It is observed the power loss, voltage deviation and LB are found to be reduced by 32.84%, 12.33% and 24.03% of their respective inherent values in the biggest system when the system is reconfigured only. With the optimized RDGs placed in the reconfigured systems, a further reductions of 46.27%, 25.92% and 36.65% are observed respectively.

show abstract

Dynamic reconfiguration of three-phase unbalanced distribution networks

Cited by 74 publications

References 29 publications

Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties

Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties

Enhancing the response of thyristor‐controlled reactor using neural network

Simultaneous Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

Contact Info

Product

Resources

About