جستجوی مقالات مرتبط با کلیدواژه « distributed generator » در نشریات گروه « برق »

This paper presents an intelligent meta-heuristic algorithm, named improved equilibrium optimizer (IEO), for addressing the optimization problem of multi-objective simultaneous integration of distributed generators at unity and optimal power factor in a distribution system. The main objective of this research is to consider the multi-objective function for minimizing total power loss, improving voltage deviation, and reducing integrated system operating costs with strict technical constraints. An improved equilibrium optimizer is an enhanced version of the equilibrium optimizer that can provide better performance, stability, and convergence characteristics than the original algorithm. For evaluating the effectiveness of the suggested method, the IEEE 69-bus radial distribution system is chosen as a test system, and simulation results from this method are also compared fairly with many previously existing methods for the same targets and constraints. Thanks to its ability to intelligently expand the search space and avoid local traps, the suggested method has become a robust stochastic optimization method in tackling complex optimization tasks.

In recent years, DC microgrid has attracted considerable attention of the research community because of the wide usage of DC power-based appliances. However, the acceptance of DC microgrid by power utilities is still limited due to the issues associated with the development of a reliable protection scheme. The high magnitude of DC fault current, its rapid rate of rising and absence of zero crossing hinders achieving reliable protection in DC microgrid. Further, the intermittency associated with the non-conventional distributed generators demands adaptiveness under varying weather conditions. In this paper, the above-mentioned issues are addressed by developing a bagging tree-based protection approach for a multi-terminal DC microgrid. The proposed scheme addresses the intermittency associated with renewable sources. It performs the functions of mode detection, fault detection/classification, and faulty section identification using local information of current and voltage signals only. The same avoids the communication network related drawbacks like data loss and latency.

Distribution system supplies power to variety of load depending upon the consumer’s demand, which is increasing day by day and lead to high power losses and poor voltage regulation. The increase in demand can be met by integrating Distributed Generators (DG) into the distribution system. Optimal location and capacity of DG plays an important role in distribution network to minimize the power losses. Some researchers have studied this important optimization problem with constant power load which is independent of voltage. However, majority of consumers at load center uses voltage dependent load models, which are primarily dependent on magnitude of supply voltage. In practical distribution network, the assumption of constant power load can significantly affect the location and size of DG, which in turn can lead to higher power losses and poor voltage regulation. In this study, an investigation has been performed to find the increase in power loss due to the use of inappropriate load models, while solving the optimization problem. Furthermore, an attempt has been made in this study to reduce power losses occurring in large test bus systems with loads being dependent on voltage rather than the constant power load. Different test cases are created to analyse the power losses with appropriate load model and in-appropriate load model (constant power load model). The load at distribution network is not mainly dependent on any single type of load model, it is a combination of all load models. In this study, a class of mix load viz., combination of residential, industrial, constant power, and commercial load, is also considered. In order to solve this critical combinatorial optimization problem with voltage dependent load model, which requires an extensive search, Adaptive Quantum inspired Evolutionary Algorithm (AQiEA) is used. The proposed algorithm uses entanglement and superposition principles, which does not require an operator to avoid premature convergence and tuning parameters for improving the convergence rate. A Quantum Rotation inspired Adaptive Crossover operator has been used as a variation operator for a better convergence. The effectiveness of AQiEA is demonstrated and computer simulations are carried out on two standard benchmark large test bus systems viz., 85 bus system and 118 bus system. In addition to AQiEA, four other algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), and Ecogeography-based Optimization (EBO) with Classification based on Multiple Association Rules (CMAR)) have also been employed for comparison. Tabulated results show that the location and size of DGs determined using in-appropriate load model (constant power load model) has significantly high power losses when applied in distribution system with different load model (other voltage dependent load models) as compared with the location and size of DGs determined using the appropriate load model. Experimental results indicate that AQiEA has a better performance compared to other algorithms which are available in the literature.

Basic Reactive power compensation is provided by capacitor banks and active power compensation is provided by Distributed Generators (DGs) like, Solar, Wind and Mini-hydro power plants.  For obtaining optimal placement of Mini Hydro Power Plants and capacitor banks Water Cycle Algorithm (WCA) is intended. The optimal sizes of Capacitors and DGs chosen are of continuous sizes.   33-Bus and 69-Bus radial  distribution systems are chosen for testing this algorithm to identify potential locations and sizes of Mini hydro and capacitors.   It is found that with the proposed WCA, maximum loss reduction of  79 % is obtained for 33 Bus system and 83.6% for 69 Bus system.  Cost analysis is also presented with three different load levels along with above DGs and Capacitors.  In addition to cost analysis, for the benefit of environment carbon emissions are also calculated and found that there is a reduction of 4,034.57 tons of CO2 emission per year on 33 bus system and 4,607.88 tons of CO2 emission per year on 69 bus system.  It is proved that WCA offers highest loss reduction with minimum sizes of DGs and capacitors compared to any other algorithm available in the literature.  The WCA is easy to implement and the search process is methodic.