Hybrid neural network

About: Hybrid neural network is a research topic. Over the lifetime, 1305 publications have been published within this topic receiving 18223 citations.

Hybrid neural network–particle swarm method to predict global radiation over the Norte Chico (Chile)

Abstract: Solar energy estimation procedures are very important in the renewable energy field for development of mathematical models, optimization, and advanced control of processes. Solar radiation data provide information on how much of the sun’s energy strikes a surface at a location on earth during a particular time period. These data are needed for effective research into solar-energy utilization. Due to the cost and difficulty in measurement, these data are not readily available. Therefore, there is the need to develop alternative ways of generating these data. In this study, an artificial neural network (ANN) was used for the estimation of daily global solar radiation (RG) over the Norte Chico using 17 552 data measured from 21 meteorological stations (years 2004–2010) located in the south area of the Atacama Desert. The ANN was developed with particle swarm optimization. Six input parameters were used to train the network. These parameters were elevation, longitude, latitude, air temperature, relative humid...

Hybrid-neural-network-based estimation of line overloading

Abstract: Since the modern power systems are being operated under heavily stressed conditions, the cases of voltage limit violation and power system line overloading are occurring frequently. These events are responsible for several incidents of major network collapses leading to partial or even complete blackouts. Alleviation of line overloads is the suitable corrective action in this regard and for this, fast and accurate identification of overloaded lines is essential along with the estimation of the overloading amount in these lines. In this paper, an approach based on hybrid neural network (HNN) is presented for identification and estimation of line overloading in an efficient manner. The effectiveness of the proposed HNN-based approach is demonstrated by estimation of line overloading for different loading conditions in IEEE 14-bus system. The developed HNN provides accurate and quick results for previously unseen operating condition during testing phase.

Forecasting global crude oil price fluctuation by novel hybrid E-STERNN model and EMCCS assessment

Abstract: Energy futures are a very significant part of commodity futures, no less than the influence of the spot market. A novel hybrid neural network (denote by E-STERNN) is proposed through combining Elman recurrent neural network model with stochastic time strength (ST-ERNN), and ensemble empirical mode decomposition (EEMD) is also introduced to improve the performance of forecasting neural network system for energy markets. ST-ERNN model is established for taking into account the weight of energy historical data with time variations. EEMD is an algorithm that decomposes any non-stationary and nonlinear time series into simple and independent time sequence. From the empirical research for four global energy market prices, the proposed hybrid E-STERNN model is verified to have higher prediction accuracy compared with the original ERNN and the ST-ERNN models. Moreover, a new error evaluation approach, called the exponent of multi-scale composite complexity synchronization (EMCCS), is utilized to analyze and estimate the prediction performance, and the demonstration analyses confirm that the hybrid E-STERNN model has higher prediction accuracy for global energy futures indexes.

Development of 110-220 kV power transformer model for equipment functional state assessment system

Abstract: This paper addresses the problems, connected with implementation of 110-220 kV power transformer structural model for automated equipment functional state assessment system based on test and technical diagnostics data. This article describes the basic construction principles of hybrid neural network using Takagi-Sugeno fuzzy method. The paper also provides the statistical data analysis results for power transformers (of real energy grid part) to define fuzzy neural network criteria (layers).