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In the PhotoVoltaic (PV) system characteristics, a maximum power operating point exists for each value of solar irradiance. The operating point continuously varies as the solar irradiance vary and therefore tracking of maximum power in PV system is significant. This paper introduces a new rule compressed fuzzy logic method to track optimal power operating point of photovoltaic system under non-uniform irradiance. The method has three-input parameters and a single output parameter. The input parameters are the change in power, the change in voltage and change in duty cycle and the output parameter is the reference current to the converter. With respect to each combination of two input parameters, three set of output rules are developed and then compressed to a single set of rules based on tracking conditions. The step response, analysis under partial shading and with one day solar irradiance data are implemented in Matlab/simulink platform. The method confirms its effectiveness by attaining the optimal power at 2.46s with a low steady state error of 0.35%. The energy capitulation efficiency is obtained as 99.5%. The performance of the proposed method is also evaluated experimentally and its efficiency is proved by comparing with perturb & observe algorithm. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new multifuzzy logic MPPT controller incorporating PV system and converter parameters is proposed. [•] Performance is evaluated with one day insolation data. [•] Compared with fuzzy logic and P&O algorithm. [Copyright &y& Elsevier]

Abstract: The analysis on reliability aspects of wind power finds more significance as compared to that in conventional power generation systems. In spite of the intermittent and variable nature of wind energy, it can be usefully tapped to generate electrical power for meeting part of the energy demand of the population. The present paper undertakes an analysis on the reliable aspects of wind energy conversion system and applies to seven wind farms in Muppandal region in India. For the purpose of analysis, two reliability indices are used; one is coined as the period during which the expected wind energy is not supplied and the other one is the loss of load expectation index which analyzes the degree of matching of wind farm power generation with the load model. The study also investigates the effect of increasing the hub height of wind energy conversion systems. [ABSTRACT FROM AUTHOR]

Abstract: This paper attempts to assess the adequacy of wind power generation systems using the data collected from seven wind farms in Muppandal, Tamilnadu (India) with a total capacity of 37 MW. A Monte Carlo model simulation is incorporated in the algorithm to obtain the hourly power output of wind farms, which also takes into account the unavailability of wind turbines. A typical load demand profile is used to examine the chronological hourly wind power generation for each month. The reliability index of LOLE (loss of load expectation) is used to estimate the reliable contribution of wind farm power generation. [ABSTRACT FROM AUTHOR]

This paper uses the data from seven wind farms at Muppandal, Tamil Nadu, India, collected for three years from April 2002 to March 2005 for the estimation of energy yield from wind farms. The model is developed with the help of neural network methodology, and it involves three input variables-wind speed, relative humidity, and generation hours-and one output variable, which give the energy output from wind farms. The modeling is done using MATLAB software. The most appropriate neural network configuration after trial and error is found to be 3-5-1 (3 input layer neurons, 5 hidden layer neurons, 1 output layer neuron). The mean square error for the estimated values with respect to the measured data is 7.6 × 10-3. The results demonstrate that this work is an efficient energy yield estimation tool for wind farms. [ABSTRACT FROM AUTHOR]

Diffusion bonding is a pressure welding technique to establish bonds by inter diffusion of atoms. Bonding characteristics were generated by varying the significant process conditions such as the bonding temperature, the pressing load and the duration of pressure while bonding the aluminium alloys AA5083 and AA7075. Deriving analytical correlation with the process variables to weld strength is quite involved due to the non-linear dependency of the process variables with the mechanical strength of the joints. An arbitrary function approximation mechanism, the artificial neural network (ANN) is therefore employed to develop the models for predicting the mechanical properties of the bonded joints. Back propagation technique, which alters the network weights to minimize the mean square error was used to develop the ANN models. The models were tested, validated and found to be satisfactory with good prediction accuracy. [ABSTRACT FROM AUTHOR]