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This research paper reports a novel genetic algorithm (GA)‐based approach for reconfiguration of radial distribution networks for real loss minimization and power quality improvement.

A fuzzy controlled GA has been used for efficient reconfiguration of radial distribution systems for loss minimization and power quality improvement. The special features of the proposed algorithm are: an improved chromosome coding/decoding for network representation so as to preserve the radial property without islanding any load after reconfiguration and an efficient convergence characteristics attributed to fuzzy controlled mutation.

The proposed network reconfiguration algorithm is very much effective in arriving at the global optimal solution (minimum loss network structure) because of efficient search of the solution space. Also, no invalid chromosomes are generated in the genetic evolution because of appropriate coding/decoding. The algorithm is found to be very much suitable for real time implementations.

This research paper provides the power distribution engineers with a computationally efficient approach for optimal operation of distribution systems.

The algorithm proposed in this paper is computationally much faster compared to most of the present day mathematical programming approaches for distribution system operation. This makes it very much attractive for online implementations in any radial distribution network.

This paper has proposed a novel chromosome coding/decoding technique for radial distribution system and a fuzzy logic‐based mutation probability controller for efficient search of global solution space to be used in GA‐based optimal operation of radial distribution systems.

This study aims to propose a sensitivity-based methodology for the optimum accommodation of distributed generation (DG) units in meshed power networks with appropriate technologies. The effect of load variation is incorporated into the proposed methodology to identify the most trusted locations for DG placement.

The effectiveness of minimizing active power losses is considered a key criterion. A priority list comprising both sensitivity indexes and realistic indicators is deduced to rank the optimum sites for the placement of DG units. A sorting index for distinguishing the suitable DG type(s) for each candidate location is organized. Three common DG types are considered in this work. The modified IEEE 30-bus meshed system is chosen to perform the proposed methodology.

Results demonstrate that the obtained priority index can be used to achieve the best real loss minimization rates. Numerous load buses can be safely excluded as candidate locations using the proposed approach. Consequently, the methodology can minimize the computational process of diagnosing the optimum sites for DG accommodation.

The findings determine that instead of installing many DG units at various locations with one DG type, a few certain load buses can be used to accommodate more than one DG type and significantly reduce losses.

This paper aims to design an optimal shape for an annular S-duct, considering both energy losses and exit flow uniformity, starting from a given baseline design. Moreover, this paper seeks to identify the design factors that affect the optimal annular S-duct designs.

The author has carried out computational fluid dynamic (CFD)-based shape optimization relative to five distinct numerical objectives, to understand their interrelations in optimal designs. Starting from a given baseline S-duct design, they have applied control node-induced shape deformations and high-order polynomial response surfaces for modeling the functional relationships between the shape variables and the numerical objectives. A statistical correlation analysis is carried out across the optimal designs.

The author has shown by single-objective optimization that the two typical goals in S-duct design, energy loss minimization and exit flow uniformity, are mutually contradictory. He has presented a multi-objective solution for an optimal shape, reducing the total pressure loss by 15.6 per cent and the normalized absolute radial exit velocity by 34.2 per cent relative to a baseline design. For each of the five numerical objectives, the best optimization results are obtained by using high-order polynomial models.

The methodology is applicable to axisymmetric two-dimensional geometry models.

This paper applies a recently introduced shape optimization methodology to annular S-ducts, and, it is, to the author’s knowledge, the first paper to point out that the two widely studied design objectives for annular S-ducts are contradictory. This paper also addresses the value of using high-order polynomial response surface models in CFD-based shape optimization.

This paper initially presents the results of the analysis of a non linear on/off control system which is capable of generating a pulse width modulation (PWM). This technique can be used to design PWM choppers that can be dedicated to regulate fluctuating power supplies (photovoltaic, wind turbines, etc.). However, since the PWM losses mainly depend on the switching frequency, thus, the determination of an optimal frequency is required. Indeed, on the one hand, we seek to operate at high frequencies to reduce the residual noise by filtering. On the other hand, there is a limitation of the switching frequency due to the physical switching elements properties. Therefore, a compromise has to be made in order to determine an optimal switching frequency that minimizes the switching power losses. The main objective of this work is to present a technique that enables to sizing the chopper parameters based on the minimizing of the switching losses. An illustrative example of the proposed technique for sizing a PWM chopper is presented.

This paper initially presents the results of the analysis of a non linear on/off control system which is capable of generating a pulse width modulation (PWM). This technique can be used to design PWM choppers that can be dedicated to regulate fluctuating power supplies (photovoltaic, wind turbines, etc.). However, since the PWM losses mainly depend on the switching frequency, thus, the determination of an optimal frequency is required. Indeed, on the one hand, we seek to operate at high frequencies to reduce the residual noise by filtering. On the other hand, there is a limitation of the switching frequency due to the physical switching elements properties. Therefore, a compromise has to be made in order to determine an optimal switching frequency that minimizes the switching power losses. The main objective of this work is to present a technique that enables to sizing the chopper parameters based on the minimizing of the switching losses. An illustrative example of the proposed technique for sizing a PWM chopper is presented.

The purpose of this paper is to deal with a new dispatching and optimization of reactive power sources in power systems.

The methodology is first based on an optimal movement of existing reactive sources as a first phase, then an optimal investment in a second phase and finally a combination of the two previous phases as the third one. The methodology showed also the advantage of a two‐levels procedure, considering an initial minimal compensation before minimizing the active losses. The solution of the global non‐linear problem is performed using the projected and augmented Lagrange method associated with the reduced gradient and the DFP methods.

In waiting for new investment programs which are planned for limited periods, the study presents an alternative of optimizing the reactive power compensation by a movement of the power sources or some of them, satisfying all system constraints and minimizing also the active power losses.

The planners and operators are able to decide what cases are to be considered for reactive power dispatch; the proposed program gives a proposal solution to almost all changes that can occur to the power system (incident, contingency, load variation, development).

This paper aims to optimally plan distributed generation (DG) and capacitor in distribution network by optimizing multiple conflicting operational objectives simultaneously so as to achieve enhanced operation of distribution system. The multi-objective optimization problem comprises three important objective functions such as minimization of total active power loss (P^loss_total), reduction of voltage deviation and balancing of current through feeder sections.

In this study, a hybrid configuration of weight improved particle swarm optimization (WIPSO) and gravitational search algorithm (GSA) called hybrid WIPSO-GSA algorithm is proposed in multi-objective problem domain. To solve multi-objective optimization problem, the proposed hybrid WIPSO-GSA algorithm is integrated with two components. The first component is fixed-sized archive that is responsible for storing a set of non-dominated pareto optimal solutions and the second component is a leader selection strategy that helps to update and identify the best compromised solution from the archive.

The proposed methodology is tested on standard 33-bus and Indian 85-bus distribution systems. The results attained using proposed multi-objective hybrid WIPSO-GSA algorithm provides potential technical and economic benefits and its best compromised solution outperforms other commonly used multi-objective techniques, thereby making it highly suitable for solving multi-objective problems.

A novel multi-objective hybrid WIPSO-GSA algorithm is proposed for optimal DG and capacitor planning in radial distribution network. The results demonstrate the usefulness of the proposed technique in improved distribution system planning and operation and also in achieving better optimized results than other existing multi-objective optimization techniques.

The purpose of this paper is to enhance the line congestion and to minimize power loss. Transmission line congestion is considered the most acute trouble during the operation of the power system. Therefore, congestion management acts as an effective tool in using the available power without breaking the system hindrances or limitations.

Over the past few years, determining the optimal location and size of the devices have pinched a great deal of consideration. Numerous approaches have been established to mitigate the congestion rate, and this paper aims to enhance the line congestion and minimize power loss by determining the compensation rate and optimal location of a thyristor-switched capacitor (TCSC) using adaptive moth swarm optimization (AMSO) algorithm.

An AMSO algorithm uses the performances of moth flame and the chaotic local search-based shrinking scheme of the bacterial foraging optimization algorithm. The proposed AMSO approach is executed and discussed for the IEEE-30 bus system for determining the optimal location of single TCSC and dual TCSC.

In addition to this, the proposed algorithm is compared with various other existing approaches, and the results thus obtained provide better performances than other techniques.

In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems.

A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed.

Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions.

The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

The demand for electricity supply increases day by day due to the rapid growth in the number of industries and consumer devices. The electric power supply needs to be improved by properly arranging distributed generators (DGs). The purpose of this paper is to develop a methodology for optimum placement of DGs using novel algorithms that leads to loss minimization.

In this paper, a novel hybrid optimization is proposed to minimize the losses and improve the voltage profile. The hybridization of the optimization is done through the crow search (CS) algorithm and the black widow (BW) algorithm. The CS algorithm is used for finding some tie-line systems, DG locations, and the BW algorithm is used for finding the rest of the tie-line switches, DG sizes, unlike in usual hybrid optimization techniques.

The proposed technique is tested on two large-scale radial distribution networks (RDNs), like the 119-bus radial distribution system (RDS) and the 135 RDS, and compared with normal hybrid algorithms.

The main novelty of this hybridization is that it shares the parameters of the objective function. The losses of the RDN can be minimized by reconfiguration and incorporating compensating devices like DGs.