scholarly journals Transient Stability Improvement of Wind Farm Integrated Power System using STATCOM

The need for Interconnected power system is increasing day by day because of continuous growth of Electrical energy demand and to transmit Electric power to remote places at minimum cost and minimum losses. With the operation of power system in interconnected manner, maintaining the system security is difficult task i.e. whenever a disturbance occurs, the system undergoes stability problems. Even though Conventional energy sources are available, Electrical Engineers prefer Renewable energy sources integration because of Energy crisis and pollution problems related to the former, one such Renewable energy source is Wind power. Wind energy has major share in Renewable energy sources because of its abundant availability in the nature. Whenever Wind generators coupled to the power system, the system exhibits drooping voltage characteristics and this situation becomes worse during faults. This condition can be neutralised with FACTS (Flexible AC transmission system) devices, one such FACTS device is STATCOM (static synchronous compensator). STATCOMsupports reactive and real power exchange and also improves Transient stability of the system because of its superior characteristics and quick response. In this paper a 9 bus Wind farm integrated test power system is taken and stability studies are done. Since, Wind farm is integrated with the system whenever a fault occurs, overall system stability is reduced i.e. the conventional synchronous generators can withstand it, whereas the Wind generators can’t. So to enhance the Transient stability of the system, a STATCOM is installed and the system behaviour is observed.

2018 ◽  
Vol 10 (11) ◽  
pp. 4140 ◽  
Author(s):  
Seungchan Oh ◽  
Heewon Shin ◽  
Hwanhee Cho ◽  
Byongjun Lee

Efforts to reduce greenhouse gas emissions constitute a worldwide trend. According to this trend, there are many plans in place for the replacement of conventional electric power plants operating using fossil fuels with renewable energy sources (RESs). Owing to current needs to expand the RES penetration in accordance to a new National power system plan, the importance of RESs is increasing. The RES penetration imposes various impacts on the power system, including transient stability. Furthermore, the fact that they are distributed at multiple locations in the power system is also a factor which makes the transient impact analysis of RESs difficult. In this study, the transient impacts attributed to the penetration of RESs are analyzed and compared with the conventional Korean electric power system. To confirm the impact of the penetration of RESs on transient stability, the effect was analyzed based on a single machine equivalent (SIME) configuration. Simulations were conducted in accordance to the Korean power system by considering the anticipated RES penetration in 2030. The impact of RES on transient stability was provided by a change in CCT by increasing of the RES penetration.


2020 ◽  
Vol 172 ◽  
pp. 25004
Author(s):  
Marcin Zygmunt ◽  
Dariusz Gawin

Worldwide policy referring to global warming and air pollution assumes several main guidelines, in which Renewable Energy Sources (RES) usage simultaneously with limitation of fossil fuels in energy production seems to be a major goal. Nowadays, the continuous growth of RES usage within final energy consumption is becoming an obvious part of many country’s development. Adding to that relentless pursuit for improvement of building energy efficiency results in prediction, that in nearest future one should expect the development of advanced city-scale areas constituting an Energy Cluster. The paradigm of Energy Cluster (EC) allows us to define an energy flexibility neighbourhood. This article presents the results of energy analysis of a model neighbourhood of single-family houses with possible usage of RES. The neighbourhood constituting an EC was defined considering the Polish household sector statistical study. The analyzed area consists of representative single-family houses of Poland, characterized by different built periods, building shape and geometry as well as building enclosure parameters. Within the analysis, a detailed examination of a defined EC was performed by means of TEAC – computer tool developed by authors. TEAC is based on the results of energy simulations obtained by means of Energy Plus software and Artificial Neural Network (ANN) usage. Artificial Intelligence (AI) was used for energy demand predictions of buildings. Among possible RES a detailed analysis of solar and wind energy usage was performed. As a result, we obtained an hourly energy demand space- and time distribution, RES outputs, ecological analysis concerning greenhouse gasses emission and profitability analysis of proposed modernizations for the neighbourhood.


Stable operation of electrical power systems is one of the crucial issues in the power industry. Current vo­lumes of electricity consumption cause the need to constantly increase the generated capacity, repeatedly modifying and complicating the original circuit. In addition to this, given the current trend towards the use of digital power systems and renewable energy sources, more and more uncertainties difficult to predict by standard mathematical methods appear. Events in the power system are deterministic, i.e. random. Thus, it is difficult to fully assess the system stability, voltage levels, currents, or possible power losses. Finding the probability distribution laws can give us an understanding of all the possible states in which an object can exist. Obtaining them is complicated by the difficulty of accounting for all the correlations between the random arguments of the source data. These laws are necessary to determine the optimal operating modes, the possibility of solving the problem of determining the optimal renewable energy sources installation locations and the required amount of generated energy in a non-deterministic way. The purpose of this article is to test the developed SIBD method for obtaining the full probabilistic characteristics. This method, unlike the Monte Carlo methods, does not use a random sample of initial data, but completely covers the studied functional dependence. The problem was solved using the provisions of probability theory and mathematical statistics, numerical optimization methods in particular. The MATLAB Matpower application package was also used to solve technical computing problems.


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