Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

Optimization techniques based on Swarm-intelligence has been reported to have significant benefits towards addressing communication issues in Wireless Sensor Network (WSN). We reviewed the most dominant swarm intelligence technique called as Bacteria Foraging Optimization (BFO) to find that there are very less significant model towards addressing the problems in WSN. Therefore, the proposed paper introduced a novel BFO algorithm which maintains a very good balance between the computational and communication demands of a sensor node unlike the conventional BFO algorithms. The significant contribution of the proposed study is to minimize the iterative steps and inclusion of minimization of both receiving / transmittance power in entire data aggregation process. The study outcome when compared with standard energy-efficient algorithm was found to offer superior network lifetime in terms of higher residual energy as well as data transmission performance

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Voltage stability maximization based optimal network reconfiguration in distribution networks using integrated particle swarm optimization for marine power applications

1949-1956This paper addresses a novel method to optimize network reconfiguration problem in radial distribution network considering voltage stability maximization and power loss reduction without violating the system constraints. In nature inspired population based standard particle swarm optimization (PSO) technique, the flight path of current particle depends upon global best and particle best position. However, if the particle flies nearby to either of these positions, the guiding rule highly decreases and even vanishes. To resolve this problem and to find the global best position, integrated particle swarm optimization (IPSO) is utilized for finding the optimal reconfiguration in the radial distribution network. The performance and effectiveness of the method are validated through IEEE 33 and 69 buses distribution networks and is compared with other optimization techniques published in recent literature for optimizing network reconfiguration problem. The simulated results simulate the fact that to attain the global optima, IPSO requires less numbers of iterations as compared to the simple PSO. The present method facilitates the optimization of modern electric power systems by empowering them with voltage stability

A hybrid algorithm for voltage stability enhancement of distribution systems

This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature

Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms will enhance ACS in terms of exploration mechanism and solution refinement by implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment, performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan

Optimal controllers design for voltage control in Off-grid hybrid power system

Generally, for remote places extension of grid is uneconomical and difficult. Off-grid hybrid power systems (OGHPS) has  renewable energy sources integrated with conventional sources. OGHPS is very significant as it is the only source of electric supply for remote areas. OGHPS under study  has Induction generator (IG) for wind power generation, Photo-Voltaic source with inverter, Synchronous generator (SG) for Diesel Engine (DE) and load. Over-rated PV-inverter has capacity to supply reactive power.  SG of  DE  has Automatic voltage regulator for excitation control to regulate terminal voltage. Load and IG demands reactive power, causes reactive power imbalance hence voltage fluctuations in OGHPS. To manage reactive power for voltage control, two control structures with Proportional–Integral controller(PI), to control  inverter reactive power and  SG excitation by automatic voltage regulator are incorporated.  Improper tuning of controllers lead  to oscillatory and sluggish response. Hence in this test system both controllers need to be tune optimally. This paper proposes novel intelligent computing algorithm , Enhanced Bacterial forging algorithm (EBFA) for optimal reactive power controller for voltage control in OGHPS. Small signal model of OGHPS with proposed controller is  tested for different disturbances. simulation results  are compared  with conventional  method , proved the effectiveness of EBFA

Optimal hybrid photovoltaic distributed generation and distribution static synchronous compensators planning to minimize active power losses using adaptive acceleration coefficients particle swarm optimization algorithms

The paper aims to identify the optimum size and location of photovoltaic distributed generation systems and distribution static synchronous compensators (DSTATCOMs) systems to minimize active power losses in the distribution network and enhance the voltage profile. The methodology employed in this article begins by thoroughly discussing various acceleration algorithms used in Particle Swarm Optimization (PSO) and their variations with each iteration. Subsequently, a range of PSO algorithms, each incorporating different variations of acceleration coefficients was verified to solve the problem of active power losses and voltage improvement. Simulation results attained on Standard IEEE-33 bus radial distribution network prove the efficiency of acceleration coefficients of PSO; it was evaluated and compared with other methods in the literature for improving the voltage profile and reducing active power. Originality. Consists in determining the most effective method among the various acceleration coefficients of PSO in terms of minimizing active power losses and enhancing the voltage profile, within the power system. Furthermore, demonstrates the superiority of the selected method over others for achieving significant improvements in power system efficiency. Practical value of this study lies on its ability to provide practical solutions for the optimal placement and sizing of distributed generation and DSTATCOMs. The proposed optimization method offers tangible benefits for power system operation and control. These findings have practical implications for power system planners, operators, and policymakers, enabling them to make informed decisions on the effective integration of distributed generation and DSTATCOM technologies.Метою статті є визначення оптимального розміру та розташування фотоелектричних систем розподіленої генерації та систем розподільних статичних синхронних компенсаторів (DSTATCOM) для мінімізації втрат активної потужності у розподільній мережі та покращення профілю напруги. Методологія, що використовується в цій статті, починається з детального обговорення різних алгоритмів прискорення, що використовуються в оптимізації рою частинок (PSO), та їх варіацій на кожній ітерації. Згодом було перевірено низку алгоритмів PSO, кожен з яких включає різні варіанти коефіцієнтів прискорення, для вирішення проблеми втрат активної потужності та покращення напруги. Результати моделювання, одержані на радіальній розподільній мережі шини стандарту IEEE-33, підтверджують ефективність коефіцієнтів прискорення PSO; він був оцінений та порівняний з іншими описаними в літературі методами покращення профілю напруги та зниження активної потужності. Оригінальність. Полягає у визначенні найбільш ефективного методу серед різних коефіцієнтів прискорення PSO з погляду мінімізації втрат активної потужності та покращення профілю напруги в енергосистемі. Крім того, демонструє перевагу обраного методу над іншими для досягнення значного підвищення ефективності енергосистеми. Практична цінність цього дослідження полягає у його здатності надати практичні рішення для оптимального розміщення та визначення розмірів розподіленої генерації та DSTATCOM. Запропонований метод оптимізації дає відчутні переваги для експлуатації та керування енергосистемою. Ці результати мають практичне значення для фахівців із планування енергосистем, операторів та розробників політики керування, дозволяючи їм приймати обґрунтовані рішення щодо ефективної інтеграції технологій розподіленої генерації та технологій DSTATCOM