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.

Non-cooperative intelligent control agents (ICAs) with dedicated cost functions, can lead the System to poor performance and in some cases, closed-loop instability. A robust solution to this challenge is to place the ICAs at the feedback Nash equilibrium point (FNEP) of the differential game between them. This paper introduces the designation of a robust decentralized infinite horizon LQR control System based on the FNEP for a linear time-invariant System. For this purpose, two control strategies are defined. The first one is a centralized infinite horizon LQR (CIHLQR) problem (i.e. a supervisory problem), and the second one is a decentralized control problem (i.e. an infinite horizon linear-quadratic differential game). Then, while examining the optimal solution of each of the above strategies on the performance of the other, the necessary and sufficient conditions for the equivalence of the two problems are presented. In the absence of the conditions, by using the least-squares error criterion, an approximated CIHLQR controller is presented. It is shown that the theorems could be extended from a two-agent control System to a multi-agent System. Finally, the results are evaluated using the Simulation results of a Two-Area non-reheat Power System.

This paper presents a new optimal adaptive neuro - fuzyy Power System stabilizer to damp out Power System oscillations. In this design, five layers of neuro-fuzzy network are used. Fuzzy rules and inferences, input out put membership functions can be adjusted by learning with back propagation and hybrid algorithms. Finally, a comparison between proposed controller and others is carried out in one-machine and multi-machine Power System.

Railway Power System planners and operators need a tool for testing their designs or new timetables. This paper presents a tool for the DC traction Systems, an algorithm called decoupled AC/DC load flow. The main feature of this algorithm is to separate the solution of AC and DC equations. Therefore, a minimum number of iterations is needed to avoid any convergence problems. Electrical variables of AC and DC Systems as a function of time and location can be determined. In this paper, for a better presentation of the real operating conditions, i.e. the uncertainties in the timetable, the trains departure time has been considered as a random variable. The Monte Carlo Simulation method has then been implemented to the AC/DC load flow algorithm in order to determine the worst operating conditions of the System. Simulation results indicate the effectiveness of the suggested AC/DC load flow algorithm, and the importance of the mentioned stochastic Simulation method.

Multiple variables and subSystems increase the complexity of safety-based maintenance modeling in large Systems, including Power plants. This paper is modeling the Power plant maintenance System by considering safety indicators. Then it simulates and analyzes the behavior of the designed model for a numerical sample. Regarding the literature review, a causal loop diagram of the maintenance System for Power plant was designed and a safety subSystem was added to the model, based on the experts’,opinion, and finally the stock and flow diagram was formulated using AnyLogic software. The model was simulated in three scenarios including scenario one, training, scenario two, adding new equipment in the seventh year and scenario three, a combination of adding new equipment and training. The first Simulation confirmed that increasing the training rate reduced the breakdown rate and equipment failure rate up to the sixth year, as well as reducing the accident rate and cost and increasing safety until the fifth year. Current costs increased. The second indicated that the addition of eight new equipment in the seventh year will improve the model up to 15 years later, and the amount of profit from the second year to the fifteenth year is more than scenario one and three. The combination of scenarios one and two, the optimal scenario, causes a greater amount of safety, a lower failure rate from the sixth year onward, a lower accident rate and cost, and a lower current cost from the fifth year onward.

The hydro turbine dynamics have a considerable influence on the dynamic stability of Power System. In the study of dynamic stability, the System is modeled by the linear differential equations (small signal analysis). Small signal stability of Power Systems is needed in all conditions and only is dependent on the conditions of Power System performance before commotion occurrence. This paper provides an analysis of the small signal stability in a hydroPower plant equipped with low head Kaplan turbine connected as single-machine infinite-bus (SMIB) Power System. The dynamical behaviors with representative characteristics are identified and studied in details. The model of System is described by state-space equations. The eigenvalues analysis is used to show the effects of change in parameters for damping load angle and speed oscillations through the excitation and governor subSystems. Finally, results of theoretical analyses are verified by time-domain Simulations under different System conditions and operating loads.