Real Power Loss Research Articles

A Quasioppositional-Chaotic Symbiotic Organisms Search Algorithm for Distribution Network Reconfiguration with Distributed Generations

This study suggests an enhanced metaheuristic method based on the Symbiotic Organisms Search (SOS) algorithm, namely, Quasioppositional Chaotic Symbiotic Organisms Search (QOCSOS). It aims to optimize the network configuration simultaneously and allocate distributed generation (DG) subject to the minimum real power loss in radial distribution networks (RDNs). The suggested method is developed by integrating the Quasiopposition-Based Learning (QOBL) as well as Chaotic Local Search (CLS) approaches into the original SOS algorithm to obtain better global search capacity. The proposed QOCSOS algorithm is tested on 33-, 69-, and 119-bus RDNs to verify its effectiveness. The findings demonstrate that the suggested QOCSOS technique outperformed the original SOS and provided higher-quality alternatives than many other methods studied. Accordingly, the proposed QOCSOS algorithm is favourable in adapting to the DG placement problems and optimal distribution network reconfiguration.

Optimal sizing and sitting of electric vehicle charging station by using archimedes optimization algorithm technique

<p><span lang="EN-US">Increasing penetration of EV load into the electricity sector will result in generation imbalance, an increase in real power loss, a low voltage profile and consequently a decrease in the margin of stability of voltage. It is necessary for the coordination of charging stations (CSs) for EV at the relevant locations to minimize the effect of increased EV load penetration in radial systems. In this paper, a new optimization method named Archimedes optimization algorithm (AOA) is proposed; it determined the optimal location and size for EV-CS for reducing power losses and improved voltage profile. In this work we used the photo voltaic (PV) renewable source as a main feeder for the CSs. Many of Artificial Intelligence technique are applied to determine the optimal sizing and sitting of EV-CSs considering the objective of minimization of real power loss. IEEE 33-bus testing network conducts simulation tests. The results highlighted the need to refine the EV-CS allocation to improve the performance. The ability to solve complex, non-linear objective optimization issues using AOA and to compare the results with other algorithms, namely particle swarm optimization (PSO), Cuckoo search algorithm (CSA), shows its effectiveness in minimizing the power loss as required.</span></p>

HybridGA‐MCSbased optimization for distributed generation with dynamic voltage restorer planning in distribution networks

The distributed generations (DGs) and dynamic voltage restorer (DVR) play an important role in the context of environmental problems and sustainable development throughout the world. The traditional DG and DVR locating and sizing method is based on a fixed network structure, which is assumed to be unchangeable during the planning period. The research presented here discusses a hybrid approach working on Monte Carlo simulation (MCS) and genetic algorithm (GA), which can be used for enhancement of system power factor (SPF) and voltage profile (VP) by the integration of DGs with DVR planning in distribution networks with ZIP load models from the minimization of total real power loss of the system viewpoint. The proposed methodology was applied to a 75-bus distribution network. This article is useful for researchers as well as practitioners who are working in the fields of integration of DG with DVR for improvement of system performances such as power factors and voltage profile from minimizing the losses.

HybridGA‐MCSbased optimization for integration of DG, DVR, and DSTATCOM planning in distribution networks

The integration of distributed generations (DGs), dynamic voltage restorer (DVR), and distribution static compensator (DSC) plays an important role in the context of environmental problems and sustainable development throughout the world. The traditional DG, DVR, and DSC locating and sizing methods are based on a fixed network structure, which is assumed to be unchangeable during the planning period. The research presented here discusses a hybrid approach working on genetic algorithm (GA) and Monte Carlo simulation (MCS) which can be used for enhancement of system power factor (SPF) and voltage profile (VP) by the integration of DG, DVR, and DSC planning in distribution networks with constant impedance (Z), constant current (I), constant power (P) load models (ZIP-LMs) from the minimization of total real power loss of the system viewpoint. The proposed methodology is applied to a 75-bus distribution network. This article is useful for researchers as well as practitioners who are working in the fields of integration of DG, DVR, and DSC for the improvement of system performances such as SPF and voltage profile from the minimization of total real power loss of the system viewpoint.

Factual Power Loss Diminution by Enhanced Frog Leaping Algorithm

This paper proposes Enhanced Frog Leaping Algorithm (EFLA) to solve the optimal reactive power problem. Frog leaping algorithm (FLA) replicates the procedure of frogs passing though the wetland and foraging deeds. Set of virtual frogs alienated into numerous groups known as “memeplexes”. Frog’s position’s turn out to be closer in every memeplex after few optimization runs and certainly, this crisis direct to premature convergence. In the proposed Enhanced Frog Leaping Algorithm (EFLA) the most excellent frog information is used to augment the local search in each memeplex and initiate to the exploration bound acceleration. To advance the speed of convergence two acceleration factors are introduced in the exploration plan formulation. Proposed Enhanced Frog Leaping Algorithm (EFLA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.

Real Power Loss Minimization Considering Multiple DGs and Battery in Distribution System

The Distributed Generators (DGs) in the distribution systems play major roles, one of them being minimization of the network losses. The undesirable or intermittent nature is one of the challenges in maintaining the power between the DG and the grid. The main advantage of a battery energy storage system is to support the grid by storing the energy in it. The work articulated here is sizing and placement of DG in the distribution system and to support the grid, the battery storage is placed after the placement of the DGs. The heuristic approach namely Particle Swarm Optimization (PSO) is used for sizing and placement of DGs and the genetic algorithm is used for the placement of the battery energy storage system in the standard IEEE networks such as IEEE-33, IEEE-69, IEEE-118 radial node distribution systems. The operation of the battery in accordance with the state of charge is also presented. The MATLAB software is used for simulation and the results obtained are promising.

A two-stage method for optimal placement of distributed generation units and capacitors in distribution systems

This paper proposes a two-stage procedure to enhance the distribution system performance by determining the optimal sizes and locations of distributed generations (DGs) and capacitors considering single and multi-objective functions. In stage-1, two voltage sensitivity factors (VSFs) based on voltage deviation (VD), and voltage stability index (VSI) are proposed to reduce the search space (SS) by selecting the candidate buses for DGs and capacitors placement. In stage-2, the chaotic bat algorithm (CBA) is applied to find the optimal sizes and locations of DGs and capacitors, according to different objective functions (OFs) and system constraints. The considered OFs are real power loss reduction, total VD minimization, and total VSI maximization. The multi-OF, which aims to optimize these objectives simultaneously, is also considered. The load flow calculations are carried out using the backward/forward sweep (BFS) algorithm. The proposed methodology is evaluated and tested on small and large-scale standard test systems, namely 34-bus and 118-bus radial distribution systems, through different case studies. The numerical results obtained by the proposed procedure are compared with other methods in the literature to show the superiority of the proposed procedure for reducing the total real power loss and improving the voltage profile, especially at increasing the power system sizing.

Solving Optimal Reactive Power Dispatch Problem by Chaotic Based Brain Storm Optimization Algorithm

In this work Chaotic Predator-Prey Brain Storm Optimization (CPS) algorithm is proposed to solve optimal reactive power dispatch problem. Predator–Prey Brain Storm Optimization position cluster centers to execute as predators, accordingly it will progress towards enhanced positions, although the left over thoughts do as preys; consequently they move far from their neighboring predators. In the projected algorithm chaotic theory has been applied to enhance the quality of the exploration. Ergodicity and indiscretion are utilized in the CPS algorithm, such that projected algorithm will not get trapped in the local optimal solution. Chaotic predator-prey brain storm optimization (CPS) algorithm has been tested in standard IEEE 30 bus test system and results show the projected algorithm reduced the real power loss effectively.

Optimal Reactive Power Dispatch Using Multistage Artificial Immune System

Most countries over the past few decades have modernized their economies and become more reliant on electricity to run, so the electrical power system has also expanded greatly. Optimal Reactive Power Dispatch (ORPD) has a big influence on the reliability, security, and economic operation of the power system. Another thing to note is that ORPD has a few major targets and objectives which are to reduce the active or real power losses, to improve the voltage profile, to reduce transmission costs, and to increase system stability. Non-convex, non-linear, and multimodal problems make the development of intelligent algorithms to solve the reactive power dispatch problem highly relevant. Some researchers chose to compare and contrast optimization techniques from the past with each other in order to answer some remaining uncertainties such as the effectiveness and complexity of the technique toward the chosen objective function(s). Thus, this paper proposed applying the Multistage Artificial Immune System (MAIS) optimization method for solving the ORPD problem with the objective of reducing the power system losses. This algorithm was made by modifying and upgrading the classical AIS optimization method. Instead of only going through the process one time in the classical AIS algorithm, this MAIS method going through the processes more than one time in multiple stages of the same processes. This process includes cloning and mutation as well as selection. These modifications also aid in the development of new and unique solutions, as opposed to the classical AIS optimization process. Therefore, these enhancements could lead to a rise in the accuracy of the results' because there have been increased comparisons. This study confirms that MAIS optimization can deliver superior results in less time than AIS. Keywords—Optimal reactive power dispatch, computational intelligence, multistage artificial immune system, loss minimization.

Belleville Washer Search and enhanced Papilionoidea Optimization Algorithms for real power loss reduction

Belleville Washer Search and enhanced Papilionoidea Optimization Algorithms for real power loss reduction

Techno-economic strategy for reactive power planning using Series-shunt compensation in power transmission network

This research article proposes an optimization oriented planning strategy of reactive power (VAR) in power transmission system. Basically it is a qualitative effort to understand the mathematical principles, technical facets and economic aspects with their utility in reactive power planning (RPP) prior to execute the proposed strategy through a unique solution model. Initially, the objective is formulated by measuring different cost components such as VAR generation cost, line charging cost, FACTS (Flexible AC Transmission System) device operation cost and cost due to real power loss. The vulnerable positions in the system are defined for the placement of FACTS devices after computing a set of mathematical methods. The installation bottlenecks of these devices are wiped out depending upon some technical considerations and performances. Thereafter, differential evolutionary algorithm is hybridized with crow search algorithm to determine an optimal set of controlling variables. The code profiling of the proposed approach has been rendered on two inter-regional practical power networks (India’s 62 and 191 bus power network). Finally, the responses of the bench-mark functions and statistical outcomes are analyzed extensively to justify the efficiency and efficacy of the proposed solution model.

Real Time Implementation of Pollination Based Techniques for Radial Distribution Network Reconfiguration

The optimization of the distribution system is one of the most important operations for running an efficient power system. At the distribution stage, a wide variety of development frameworks are achieved, including optimum reconfiguration, optimal positioning of distributed generators and capacitors, and synthesis of all. The proposed approach for distribution system reconfiguration in the study eliminates the use of various types of radial distribution system (RDS) equipment to improve performance. In benchmark test systems, such as IEEE 33 bus RDS and 83 bus Taiwan power distribution company, a flower pollination algorithm is employed to reduce real power losses on the overall system. For the recommended technique, a prototype model of the distribution system for real-time implementation is also employed in the distribution network workstation. The proposed algorithm's performance reduces the search space, and the optimal solution is obtained with the least amount of running time. Abbreviations: RDS: radial distribution system; TPDC: Taiwan power distribution company; FPA: flower pollination algorithm; NFE: number of load flow executions; GA: genetic algorithm; RAG: refined genetic algorithm; ITS: improved TS algorithm; HSA: harmony search algorithm; COM: complementarity methods; SA: simulated annealing; PGSA: plant growth simulation algorithm; PTS1: proto type system 1; PTS2: proto type system 2; BM_TS1: benchmark test system 1; BM_TS2: benchmark test system 2

Real power loss reduction by Enhanced Electromagnetic Field and Lightning Process Optimization algorithms

In this research paper Enhanced Electromagnetic Field Optimization (EEFO) algorithm and Lightning Process Optimization (LPO) algorithm are projected to solve the problem. Real power loss reduction is the main objective of the work. In the algorithm population is alienated into three fields: positive, negative, and neutral. Based on the attraction–repulsion forces among these electromagnetic positive, negative, and neutral fields, a new-fangled electromagnetic particle will be engendered in iterations. In Electromagnetic field optimization algorithm, solution symbolized by Electromagnetic Particle (EMPE) and is made through electromagnets which equal to the total number of variables. Among the neighbor’s every electromagnet will imply the attraction and repulsion which possess’​ positive, negative polarity. Then in this paper Lightning Process Optimization (LPO) algorithm is used to solve optimal reactive power problem. Naturally happening lightning process has been imitated to formulate the algorithm. Test points situated on the cloud surface and the ground area are considered as solutions. Objective function is computed with all test points and taken as electric field. With reference to the function of head tip elevation, cloud altitude, channel charge subsequent bound of upward leader is interrelated to charge of the channel. With considering voltage stability index EEFO and LPO algorithms are verified in IEEE 30 bus system. Then EEFO and LPO algorithms evaluated in standard IEEE 14, 30, 57, 118 and 300 bus test systems without considering the voltage stability index. Minimization of real power loss with voltage stability index enhancement has been attained and voltage deviation also minimized.

Real Power loss reduction by hybrid pan troglodytes optimization: extreme learning machine based augmented sine: cosine algorithms

Real Power loss reduction by hybrid pan troglodytes optimization: extreme learning machine based augmented sine: cosine algorithms

Real Power Loss Reduction by Blue Noddy and European Night Crawler Optimization Algorithms

In this paper Blue noddy optimization (BNO) algorithm and European Night crawler optimization (ENO) algorithm are applied to solve the power loss reduction problem. Exodus and Preying behaviour of the Blue noddy has been imitated to formulate the algorithm. In the mathematical formulation of Exodus deed - collusion between the Blue noddy has been avoided and blue noddy will converge in the direction of most excellent companion. Position update of the Blue noddy is based on the most excellent explore agent. Preying behaviour is based on the line and angle of preying. Logically the angle, velocity will be transformed by the Blue noddy and it will do spiral act in the air to seizure the prey. Exploration and Exploitation is augmented through the Exodus and preying behaviour. In ENO algorithm reproduction nature of the European Night crawler is imitated to design the algorithm. European Night crawler population is created through the off-springs with two different kinds of reproduction. The dimension of the adolescent European Night crawler is alike to the parent. In the method Cross over operation has been implemented by considering the parent European Night crawler and Cauchy mutation has been included in order to elude the solution to be trapped under local optima. With and without L –index proposed BNO and ENO algorithms are verified in IEEE 30 Bus system. Active power loss reduction has been achieved with L-index improvement and voltage deviation minimized.

Real power loss reduction by enhanced Apple Maggot optimization algorithm

Real power loss reduction by enhanced Apple Maggot optimization algorithm

Real power loss reduction by monitor lizard optimization algorithm based on class room learning

Real power loss reduction by monitor lizard optimization algorithm based on class room learning

Capacity‐basedoptimization using whale optimization technique of a power distribution network

AbstractIn the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. Distributed generation (DG) and capacitor bank (QG) can be utilized to solve these issues. Therefore, this paper utilized optimally placed and sized DG and capacitor (QG) to minimize losses and improve the voltage profile. The overall problem is optimized using an upgraded method of the fitness assignment and solution chasing based on the aggregate approach called multi‐objective whale optimization algorithm (MWOA). Wind and solar photovoltaic sources with biomass are utilized as the DG sources with their probabilistic outputs. The developed method is tested using two practical feeders of Bahir Dar city distribution network, Ethiopia. The results of loss minimization and voltage profile enhancement with MWOA are compared with multi‐objective particle swam optimization (MPSO) with an equal number of iterations to show the superiority of the developed method.

Real Power Loss Reduction by Extreme Learning Machine Based Leontodon Algorithm

In this paper Extreme Learning Machine based Leontodon Algorithm (ELMLA) has been applied for solving the Real Power loss reduction problem. Key objectives of this paper are Real power loss reduction, Voltage stability enhancement and voltage deviation minimization. Leontodon Algorithm (LA) technique is an innovative swarm based optimization algorithm. In evolutionary procedure of LA, the eminence of the seeds engendered by Leontodon is rutted, and the outstanding seeds will be reserved and appraised, whereas the deprived seeds are rejected. In order to define whether a seed is tremendous or not, an enhancement of Leontodon algorithm with extreme learning machine (ELMLA) is projected in this paper. Based on fitness values the Leontodon population is segregated into outstanding and deprived Leontodons. Subsequently outstanding and deprived Leontodons are apportioned corresponding labels as +1 if outstanding or − 1 if deprived), and it has been considered as a training set, which built on extreme learning machine. Lastly, the design is applied to categorize the Leontodon seeds as excellent or deprived. Only outstanding Leontodon seeds are selected to take part in evolution procedure. Legitimacy of the Extreme Learning Machine based Leontodon Algorithm (ELMLA) is substantiated in IEEE 30 bus system (with and devoid of L-index). Actual Real Power loss reduction is reached. Proportion of actual Real Power loss reduction is augmented.

Opposition based red wolf algorithm for solving optimal reactive power problem

<p>This paper presents an opposition based red wolf optimization (ORWO) algorithm for solving optimal reactive power problem. Each red wolf has a flag vector in the algorithm, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). In this proposed algorithm, red wolf optimization algorithm has been intermingled with opposition-based learning (OBL). By this amalgamate procedure the convergence speed of the proposed algorithm will be increased. To discover an improved candidate solution, the concurrent consideration of a probable and its corresponding opposite are estimated which is closer to the global optimum than an arbitrary candidate solution. Proposed algorithm has been tested in standard IEEE 14-bus and 300-bus test systems. The simulation results show that the proposed algorithm reduced the real power loss considerably.</p>