One–day wave forecasts based on artificial neural networks

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This paper presents an application of artificial neural networks for short-term times series electrical load forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of training process. Historical data of hourly power load as well as hourly wind power generation are sourced from European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training with the adaptive learning factor starting at different initial value and errors behave volatile with constant learning factors with different values

Short Term Electrical Load Forecasting by Artificial Neural Network

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The study investigates the applicability of linear regression and ANN models for estimating weekly reference evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar regions of Andhra Pradesh. The climatic parameters influencing ET0 were identified through multiple and partial correlation analysis. The sunshine, temperature, wind velocity and relative humidity mostly influenced the study area in the weekly ET0 estimation. Linear regression models in terms of the climatic parameters influencing the regions and, optimal neural network architectures considering these climatic parameters as inputs were developed. The models’ performance was evaluated with respect to ET0 estimated by FAO-56 Penman-Monteith method. The linear regression models showed a satisfactory performance in the weekly ET0 estimation in the regions selected for the present study. The ANN (4,4,1) models, however, consistently showed a slightly improved performance over linear regression models.

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This paper presents a generalized optimal placement of Phasor Measurement Units (PMUs) considering power system observability, reliability, Communication Infrastructure (CI), and latency time associated with this CI. Moreover, the economic study for additional new data transmission paths is considered as well as the availability of predefined locations of some PMUs and the preexisting communication devices (CDs) in some buses. Two cases for the location of the Control Center Base Station (CCBS) are considered; predefined case and free selected case. The PMUs placement and their required communication network topology and channel capacity are co-optimized simultaneously. In this study, two different approaches are applied to optimize the objective function; the first approach is combined from Binary Particle Swarm Optimization-Gravitational Search Algorithm (BPSOGSA) and the Minimum Spanning Tree (MST) algorithm, while the second approach is based only on BPSOGSA. The feasibility of the proposed approaches are examined by applying it to IEEE 14-bus and IEEE 118-bus systems.

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In the present paper the experimental study of Nanotechnology involves high cost for Lab set-up and the experimentation processes were also slow. Attempt has also been made to discuss the contributions towards the societal change in the present convergence of Nano-systems and information technologies. one cannot rely on experimental nanotechnology alone. As such, the Computer- simulations and modeling are one of the foundations of computational nanotechnology. The computer modeling and simulations were also referred as computational experimentations. The accuracy of such Computational nano-technology based experiment generally depends on the accuracy of the following things: Intermolecular interaction, Numerical models and Simulation schemes used. The essence of nanotechnology is therefore size and control because of the diversity of applications the plural term nanotechnology is preferred by some nevertheless they all share the common feature of control at the nanometer scale the latter focusing on the observation and study of phenomena at the nanometer scale. In this paper, a brief study of Computer-Simulation techniques as well as some Experimental result

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This paper proposes a new optimization algorithm known as Modified Shuffled Frog Leaping Algorithm (MSFLA) for optimal designing of PSSs controller. The design problem of the proposed controller is formulated as an optimization problem and MSFLA is employed to search for optimal controller parameters. An eigenvalue based objective function reflecting the combination of damping factor and damping ratio is optimized for different operating conditions. The proposed approach is applied to optimal design of multimachine power system stabilizers. Three different power systems, A Single Machine Infinite Bus (SMIB), four-machine of Kundur and ten-machine New England systems are considered. The obtained results are evaluated and compared with other results obtained by Genetic Algorithm (GA). Eigenvalue analysis and nonlinear system simulations assure the effectiveness and robustness of the proposed controller in providing good damping characteristic to system oscillations and enhancing the system dynamic stability under different operating conditions and disturbances.

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Continuous Depleting conventional fuel reserves and its impact as increasing global warming concerns have diverted world attention towards non-conventional energy sources. Out of different non-conventional energy sources wind energy can be consider as one of the cleanest source with minimum possible pollution or harmful emissions and has the potential to decrease the relying on conventional energy sources. Today Wind energy can play a vital role to meet our energy demands; however, it faces various issues such as intermittent nature and frequency instability. To reduce such issues the knowledge of futuristic weather conditions and wind speed trend are required. This work mainly describes the implementation of NARX Artificial neural network for wind speed & power forecasting with the help of historical data available from wind farms.

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The clustering approach is considered as a vital method for many fields suchas machine learning, pattern recognition, image processing, information retrieval, data compression, computer graphics, and others.Similarly, it hasgreat significance in wireless sensor networks (WSNs) by organizing thesensor nodes into specific clusters. Consequently, saving energy and prolonging network lifetime, which is totally dependent on the sensor’s battery, that is considered asa major challenge in the WSNs. Fuzzyc-means (FCM) is one of classification algorithm, which is widely used in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces this algorithm to produce unbalanced clusters, which adversely affects the lifetime of the network.To overcome this problem, a new clustering method called FCM-CMhas been proposed by improving the FCM algorithm to form balanced clustersfor random nodes deployment. The improvement is conductedby integrating the FCM with a centralized mechanism(CM).The proposed method will be evaluated based on four new parameters. Simulation result shows that our proposed algorithm is more superior to FCM by producing balanced clustersin addition to increasing the balancing of the intra-distances of the clusters, which leads to energy conservation and prolonging network lifespan.

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Cross-layer design is important in wireless ad hoc network and the power control methods. Power control is the intelligent selection of transmit power in a communication to achieve the better performance within the system. Cross-layer is used to sharing the information between the layers. CLD using LOADPOWER (LOADPOW) control protocol is reduce the overall end-end delay in transmission power. So many power control schemes are dealt in network layer but this work Power control protocol was done in MAC layer and it plays a vital role. A MAC approach to power control only does a local optimization whereas network layer is capable of a global optimization. Simulation was done in NS-2 simulator with the performance metrics as throughput, and energy consumption and end-end delay. The key concept is to improve the throughput, saves energy by sending all the packets with optimal transmit power according to the network load, transmission power was given, when the network load is low, higher transmission power gives lower end-end delay and viceversa.

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A multidisciplinary research area such as wireless sensor networks (WSN) have been invoked the monitoring of remote physical environment and are used for a wide range of applications ranging from defense personnel to many scientific research, statistical application, disaster area and War Zone. These networks are constraint with energy, memory and computing power enhance efficient techniques are needed for data aggregation, data collection, query processing, decision making and routing in sensor networks. The problem encountered in the recent past was of the more battery power consumption as activity increases, need more efficient data aggregation and collection techniques with right decision making capabilities. Therefore, this paper proposed the efficient and effective architecture and mechanism of energy efficient techniques for data aggregation and collection in WSN using principles like global weight calculation of nodes, data collection for cluster head and data aggregation techniques using data cube aggregation.

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One–day wave forecasts based on artificial neural networks

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