In this paper, a novel risk-based, two-objective (technical and economical) optimal reactive power dispatch method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

In this Paper a New Technique to Design Receiver Protector Based on S-Parameters is Presented. The Main Goal is to Achieve Maximum power handling. In Conventional Way Analyzing and Optimizing power is Needed to Reach Maximum power handling. In the Proposed Method We Present a Protective Circuit With High power handling and Selective Frequency Characteristic that Uses Only the S- Parameters.

The present paper discusses the development of a Markov model for performance evaluation of coal handling unit of a thermal power plant using probabilistic approach. Coal handling unit ensures proper supply of coal for sound functioning of thermal power Plant. In present paper, the coal handling unit consists of two subsystems with two possible states i.e. working and failed. Failure and repair rates of both subsystems are taken to be constant. After drawing transition diagram, differential equations have been generated. After that, steady state probabilities are determined. Besides, some decision matrices are also developed, which provide various performance levels for different combinations of failure and repair rates of all subsystems. Based upon various performance values obtained in decision matrices and plots of failure rates/ repair rates of various subsystems, performance of each subsystem is analyzed and then maintenance decisions are made for all subsystems. The developed model helps in comparative evaluation of alternative maintenance strategies.

In this paper, a new method for extending and relaxing the noise-coupling (NC) technique is proposed to enhance the noise-shaping order without adding the number of integrators. The noise-shaping order of the introduced ∑∆ modulator whit applying a second-order noise-coupling technique is enhanced and its performance with optimizing the noise transfer function (NTF) zeros is improved. Also, by removing the analog adder at feedforward path and transferring it to a new feedback branch before the last integrator and adding second-order NC path can be decreased the input voltage swing to the quantizer. Thus, by improving the modulator resolution, power consumption can be reduced. Mathematical analyses and behavioural simulation results confirm the effectiveness of the new NC method. To examine its performance, a 2nd-order single loop ΣΔ modulator was designed. The new noise-coupling method is used to achieve the three-order noise shaping to increase the resolution with low complexity and low-power. The results show an outstanding improvement in signal-to-noise and distortion ratio (SNDR) compared to the conventional structure.

The proper forecasting of the intermittent wind power for optimal power systems operation is a really tough and complicated issue. In this paper, a successful method is proposed for predicting the wind power productions which is based on the self-organized neural networks called Group Method of Data handling (GMDH). By analyzing and discovering the hidden relationships between the inputs, the GMDH-based neural network intelligently presents the optimal model and predicts the output variable. Patterns used in this study are based on the two methods of artificial intelligence and information theory. At first, the effective variables are selected based on Mutual Information (MI) technique and the mixed particle swarm and genetic algorithm and after that the proposed forecast engine is used. In contrast to the mutual correlation method, in the proposed cross-entropy-based approach of this paper, non-linear relations between the variables are considered and the selection of effective variables in the forecasting of wind power in which nonlinear fluctuations and trends are observed are chosen more precisely and more accurately. In order to evaluate the ability, speed and accuracy of the proposed framework, real-world data of Sotavento wind farm in the Spain were used. The results of the study indicate that the proposed technique has a higher speed and accuracy than other methods.