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.

n general, the Maximum practical transferable Power is different from the theoretical Power, and in most cases much less. This is due to the role of factors related to the geometric shape of the structure, the characteristics of the emitted wave and the propagation environment of the waves. This article deals with practical considerations in transmitting Maximum Power in waveguides. In this paper, the theoretical principles of electrical breakdown in gases and consequently the Maximum Power hanling in waveguides as well as the factors that affect the Power handling in waveguides are stated. Finally, a rectangular waveguide in the X band with the given wave characteristics and environmental conditions is investigated.

Maximum Power point tracking controller is essential to obtain the Maximum Power from a solar array in the photovoltaic systems as the PV Power module varies with the temperature and solar irradiation. In this paper, several methods for the MPPT of the PV systems are discussed and it can to be used as a Helpful reference for the upcoming MPPT user in the PV applications.

Solar panels exhibit non-linear current–voltage characteristics producing Maximum Power at only one particular operating point. The Maximum Power point changes with temperature and light intensity variations. Different methods have been introduced for tracking the Maximum Power point based on offline and online methods. In this paper a new method is presented to improve the performance of Maximum Power point tracking in off-grid solar panels. The proposed algorithm is a combination of two loops, set point calculation and fine tuning loops. First the set point loop approximates the Maximum Power using offline calculation of the open circuit voltage. The exact amount of the Maximum Power will, then, be tracked by the fine tuning loop which is based on perturbation and observation (P&O) method. The proposed method is simulated in Matlab/Simulink environment and experimentally verified using a laboratory prototype. In Maximum Power point tracking, the effects of frequency variation and disturbance amplitude on dynamic response and steady state performance are examined. Simulation and experimental results are compared with other methods and the effectiveness of the proposed method is evaluated.

This paper presents a model of solar cell by using MATLAB SIMULINK. P-V, I-V and P-I characteristics were studied for various values of irradiance at constant temperature. Genetic Algorithm (GA) was used for Maximum Power point tracking (MPPT) of Photovoltaic (PV) system using the direct control method. The main objective of this paper is to find out the optimal angle, which is used for the positional control of solar module and optimal Power tracking. The principle of GAs is searching for the Maximum of fitness function and not for the minimum of Power derivation; this gives more stability and minimize oscillation of output Power around the Maximum Power point (MPP). The main contribution of the proposed scheme is the elimination of PI control loop which normally exists to manipulate the duty cycle. Simulation results indicate that the proposed controller outperforms the others method for all type of environmental conditions.

This paper, firstly presents a model of solar cell is built using MATLAB SIMULINK and P-V, I-V & P-I characteristics are studied for various values of irradiance & a constant temperature, And then used of Genetic Algorithm (GA) for Maximum Power point tracking (MPPT) of Photovoltaic (PV) system using the direct control method. The main objective of this paper is to find out that optimal angle, which is used for positional control of solar module for optimal Power tracking and also the main contribution of the proposed scheme is the elimination of PI control loop which normally exists to manipulate the duty cycle. Simulation results indicate that proposed controller outperforms the others method for all type of environmental conditions. For efficient utilization of solar energy, the solar PV system must be able to track MPP and extract available Maximum Power in real-time. Thus, it becomes necessary to properly interface the PV module with a fixed load.

Let Γ = (V,E) be a simple and undirected graph. General Power graph of Γ, shown by Pg(Γ), is a graph with the vertex set P(V (Γ))\ϕ. Also two distinct vertices of B and C are adjacent if and only if every b ∈ B is adjacent to every c ∈ C \{b} in Γ. In this paper, we consider general Power graph related to graph Γ. Also we show that zero forcing number is equal to Maximum nullity, for general Power graph of some graphs.

In this paper, Power system restoration in the presence of SVC and TCSC in partial outage conditions is considered and a new method for Maximum load restoration using different control variables is presented. Control variables are tap of transformers, generation rescheduling and operating points of FACTS devices. Objective function is restored load (to be maximized) and constraints are voltage magnitudes in buses, carrying load in lines and Power generation limits in generators. Also SPA limits in voltage angles in both sides of circuit breakers before closing are considered. With respect to the number of control variables, optimization is done using Genetic Algorithm with IEEE-118bus network as the test system.