The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Microgrid is a well known concept in present power system industry. Its increasing trends overcome the effect of depleting fossil fuels and increasing electricity demand. The energy scheduling of microgrid is an open issue in current power system industry and it is a difficult task. This paper presents the economic energy scheduling of a microgrid, consisting of wind turbine (WT) and photovoltaic...
In present scenario, electricity demand rise drastically and conversely fossil fuel resources are depleting from nature, continuously. Consequently, dependence on natural energy resources, such as solar, wind is increased. These are connected to the main grid to meet the increased electricity demand and the power system is transformed in to a hybrid power system (HPS). Battery storage (BS) is also...
This paper presents a protection scheme for wind power based Permanent Magnet Synchronous Generator (PMSG). To protect the connected equipment in the generation system, over-current and differential frequency relays, are used. Massive current flow and frequency change in the power line affect the equipments. Over-current relay and differential frequency relay are used to solve these problems. A study...
Recently, trends towards the integration of renewable energy sources (RES) like wind energy in to the microgrid increased sharply, so the requirement of battery storage system (BSS) also increased dramatically to make these sources dispatchable. Optimal scheduling of renewable energies along with BSS is a tedious task, as these sources are dependent on uncertainty of nature. In this paper various...
Optimal energy management of microgrid is essential for providing reliable and economical power to its customers. A grid connected microgrid that consists wind plant (WP), hydro plant, battery storage system (BSS) used as study system. Profit maximization with consideration of capital cost, operation and maintenance (OM) cost, replacement cost and salvage value of BSS and WP. Different diverse meteorological...
Load forecasting is an essential part of power system planning. This helps in proper and economic operation of power system plant. The problem of accurate load forecasting has become more baffling in current scenario since deregulation came into picture and industrial growth is positive. This brought highly non-linear electric load profile that is difficult to forecast. Artificial Intelligence (AI)...
Now-a-days, superconducting fault current limiters are in general found increasing in land-based power systems due to the enhancement of the distribution system. To limit this current, up-gradation of expensive current limiting equipments is investigated by several researchers, and various fault current limiting techniques have been developed. Superconducting fault current limiter (SFCL) is used to...
Fulfilling the energy demand is an important issue in the power system. As conventional fossil fuels based energy resources are limited on the earth and expected to be depleted in few decades. Microgrid with battery energy storage is commonly employed nowadays to full-fill the demand. For better performance of microgrid, optimal energy management is essential. Therefore, the optimal utilization of...
Electrical load forecasting is an important topic within the electrical market which has been done by a machine learning methodology: Support Vector Machines (SVM). Load forecasting with SVM will form the non-linear relations with the parameters that have an effect on the load; additionally to the correct modeling of the load curve on weekends and holidays. The past information is used as a sample...
In this paper a new passive islanding detection technique is proposed for distributed generation using rate of change of negative sequence voltage and current. The proposed technique is based on the voltage and current measurement at targeted distributed generation (DG). The retrieved voltage and current are converted into symmetrical component through sequence analyzer then rate of change of negative...
Load forecasting plays a significant role in power system planning. In today's scenario of deregulated electricity market as existing in New South Wales (NSW) Australia, an extremely accurate load/ price forecasting model is required because of several economic and operational advantages. It helps in dealing with the problems of economic load dispatch, unit commitment, protection, etc. Research shows...
Constant tariff scheme produces a large and continuously-changing difference between electricity cost and price. Consequently, the concern of power system planning and economic generation becomes significant. To overcome this problem accurate load forecasting is a field of immense importance. Conventional methods, i.e., Moving Average (MA) and Holt-Winter (HW) methods are inappropriate to forecast...
Accurate and robust load forecasting models play an important role in power system planning. Due to smaller size and inherent property of good classification, Radial Basis Function Neural Network (RBFNN) is always preferred over other neural network structures. It is used by researchers as an effective tool for Short-Term Load Forecasting (STLF). The smaller size of this network may lead its output...
Management and pricing of electricity in power system is largely influenced by Short-Term Load Forecasting (STLF). This paper presents a hybrid algorithm, where Radial Basis Function Neural Network (RBFNN) is optimized using Genetic Algorithm (GA) for STLF, with load and day-type as input parameters. Since, conventional training methods, viz., principle component analysis and least square method,...
In 1990s, after deregulation of Australian electricity market, electricity became a commodity that can be bought and sold. This led power industry to change their planning strategies. In this planning Short Term Load Forecasting (STLF) plays a vital role to provide unit commitment, economic generation scheduling etc. In this paper, RBF neural network (RBFNN) is applied as short term load as well as...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.