Laona??on Modified Spider Monkey Algorithm

Then I would like to show my own planning and suggestions for Beijing public transportation system based on energy conversion. And also an outlook of technologies on reducing the cost for these renewable power based buses.

Base layer is base for smart grid and self-healing control system and this base layer is collection of power grid and its equipment. The Support layer is collection of data and communication. Main function of this layer is to control the transmission of data and communication between different nodes. High speed, bi directional, real time transmission and communication is held by this layer. The application layer is collection of monitoring, decision-making, restoration.

Knowledge of human experts forms the base of the accuracy of fuzzy logic systems (FIS). The results of post contingent state of line power flows and performance indices are obtained using Newton Raphson or any other load flow method .The membership functions for these post contingent quantities are first recognized and defined and with these formed membership functions, the computation of overall severity index is done to obtain the contingency ranking. For each post contingent quantities which is obtained by the conventional load flow method is known by different linguistic variable and with the membership function associated with it. The inputs to the fuzzy inference system are line loadings, and voltage profiles indices and the outputs to the same FIS are the severity indices, which are computed using the simple set of rules of Fuzzy. The post contingent quantities of line flows and bus voltage must be

As the world is growing, demand for energy is also considerably increasing. Now a days due to more concern on pollution free power generation sources renewable energy sources are in high demand. Wind farm is also one of the renewable energy source and its use and implementation is considerably increasing. As the wind is not constant at all the time, so generated power may vary. We can get stable power by using particular control strategies but still we need to minimize transients available in power. As well characteristics should match between generator source and load. In this

Abstract-This paper suggests four heuristic algorithms for optimal loss reduction of power distribution network equipped with renewable energy resources. These algorithms are Gravitational Search Algorithm (GSA), Bat Algorithm (BA), Imperialist Competitive Algorithm (ICA) and Flower Pollination Algorithm (FPA). Placing renewable energy resources such as wind turbine (WT) and photovoltaic panels (PV) in certain locations in the power network with special sizes might share in reducing the power loss , and consequently, improving the voltage profile. In the present research, the heuristic algorithms are utilized to find the optimal

results obtained show that ALO have been successfully implemented to solve different ELD problems; moreover, ALO is able to provide very spirited results in terms of minimizing total fuel cost and lower transmission loss. Also, convergence of ALO is very fast as compared to lambda iteration method, particle swarm optimization (PSO) algorithm, genetic algorithm (GA), APSO, artificial bee colony (ABC), and Grey Wolf optimizer (GWO) for small-scale power systems. Also, it has been observed that the ALO has the ability to converge to a better quality near-optimal solution and possesses better convergence characteristics than other widespread techniques reported in the recent literature. It is also clear from the results obtained by different

Abstract— Voltage instability is one of the major power system problems which have appeared since the existence of the power systems. This paper investigates the performance of Recurrent Neural Networks (RNN) in voltage instability early prediction. Voltage instability early detection is necessary for avoiding voltage collapse which can lead to power system fast breakdown. In this research, the bus voltage phase is considered as the main factor of power system voltage instability early detection. Voltage instability leads to a considerable change in the difference between the consecutive buses voltage angles. A recurrent neural network trained with Particle Swarm Optimization (PSO) is proposed in this paper. The proposed method is examined on 14-bus and 30-bus IEEE standard systems. These systems are simulated using MATLAB/Power System Toolbox program. The performance of the proposed method is compared with Back-propagation (BP) training algorithm.

An accurate cost function for the transmission system is formulated where both fixed and variable costs for all planned facilities are includes, in addition to the cost energy losses. The cost function is then minimized, using (BBO) algorithms. We can be used to derive algorithms for optimization. We apply the BBO on the model of IEEE of 6-bus test system.

Abstract-Increased the numbers of Plug-in Hybrid Electric Vehicles (PHEVs) can have a significant impact on distribution system performance, such as reduction in power quality and efficiency, increase in power losses and voltage variations, as well as an adverse impact on the customers’ energy price. This paper proposed approach evaluates the effect of integrating a large number of PHEV on power system operation as well as appropriate operation of PHEVs can improve the voltage profile in the network by eliminating the voltage drops in highly-loaded buses with discharging reactive power. Furthermore, the optimal placements for charging stations and renewable energy sources (RES) are identified. The objective function includes minimizing the cost of power delivery to the PHEVs, the reducing losses as well as minimizing the voltage deviation between different buses in distinct hours. For increasing the load factor of the network, special coefficient is introduced into the problem. The ratio between the hourly demand and the peak in the day-ahead load characteristic is multiplied with the electricity tariffs which lead the aggregators to charge their PHEVs in off-peak hours. For this purpose, Differential Evolution (DE) algorithm is presented for optimal planning of charging stations and RES. The proposed method is implemented in 69-bus radial distribution system (RDS).

Relevant numerical techniques, which have been done with the help of MATLAB routines, are applied to solve the arising optimization problem and to find the optimum parameters of the TMD. For a given mass ratio, µ, one can assume different values of the frequency ratio, f, and for each frequency ratio assuming a range of damping factor ζ2 of the TMD and estimate the optimum parameters that minimize a certain desired output. Fig. 8 is an example of the numerical optimization conducted to estimate the optimal frequency ratio and damping factor of the TMD for two different mass ratios under wind loads modeled as white-noise. The optimization is based on the minimization of the displacement of the primary structure. In this numerical optimization, the responses of the primary structure are normalized, which means that the response obtained with the TMD when attached to the structure is divided by the corresponding response obtained without the TMD. The optimal values of the frequency ratio and the damping factor of the TMD are written on the subfigures. It is shown that a TMD with 1% mass ratio can provide a significant reduction in the displacement response of the primary structure. The reduction in the displacement depends very much on the tuning frequency and the damping ratio of the TMD. By increasing the mass ratio from 1% to 5%, the displacement response of the primary structure is reduced. However, the TMD with 5% mass ratio is more robust to the changes in the frequency

The computation involves the calculation of the loss of a power system and determination of flowing of real and reactive power for all connection at all busbars.

Feeder Reconfiguration is basically an optimization problem which has several objectives and operating constraints. A. Merlin and H. Black were the first to implement optimization techniques to solve a Feeder Reconfiguration problem. Optimization is a mathematical tool to obtain an optimized solution. There are numerous techniques to solve an optimization problem where in each technique will yield a different network topology and different optimized solution. Heuristic and meta-Heuristic methods were used in the early 90’s to solve feeder reconfiguration problem. These methods uses many assumptions to yield an optimal solution. However, the solution obtained by heuristic method is considered to be sub-optimal in compared with the

The stability of an interconnected power system is the ability of the system to return to its normal or stable condition after having been subjected to some form of disturbance. With the interconnected systems continually increasing in size and extending over whole geographical regions, it is becoming increasingly more difficult to maintain synchronism between various parts of the power system. This paper work presents an advanced adaptive Particle swarm optimization technique to optimize the SVC controller parameters for enhancement of the steady state stability & overcoming the premature convergence & stagnation problems as in basic PSO algorithm & Particle swarm optimization with shrinkage factor & inertia weight approach (PSO-SFIWA). In this paper SMIB system along with PID damped SVC controller is considered for study. The generator speed deviation is used as an auxiliary signal to SVC, to generate the desired damping. This controller improves the dynamic performance of power system by reducing the steady-state error. The controller parameters are optimized using basic PSO, PSO-SFIWA & Advanced Adaptive PSO. Computational results show that Advanced Adaptive based SVC controller is able to find better quality solution as compare to conventional PSO & PSO-SFIWA Techniques.

Index Terms - Photovoltaic; energy storage system; optimal control; artificial bee colony optimization algorithm; DC motor pump system.

In order to understanding the function of relays, software relay models must be realized and feasible alternative to studying the performance of protective relays. Relay models have been long used in a variety of tasks, such as designing new relaying algorithms, optimizing relay settings. Electric power utilities use compute-based relay models to confirm how the relay would perform during systems disturbances and normal operating conditions and to make the necessary corrective adjustment on the relay settings. [3][4]. One of the world-wide recognized, powerful analysis software package, is a MATLAB/SIMULINK, which has the capability for modeling, simulating and analyzing dynamic systems using SimPowerSystems toolbox, in side Simulink package, different parts of a system such as three phase transformer, three phase load, distributed parameters line, circuit breaker, etc can