COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)
Year:2016 | Volume:7 | Issue:2|Papers Count:8

In this paper, the invasive weed optimization is used to determine the satellite orbit. The generated TLE can be used in antenna positioning, mission control and monitoring. Also, it can be sent to the satellite for on board applications. In the proposed method, the trial generated TLEs in each iteration are propagated to achieve the satellite position versus the time, and then they are compared with the real data position achieved from the tracking systems to build an error fitness function. Finally, the invasive weed optimization (IWO) algorithm is used to minimize this error. The proposed method is verified by using CHAMP satellite position data, as a real example.

In this paper, a methodology is presented for optimal placement of Power Quality meters for‎Harmonic State ‎Estimation. Single line and meter loss ‎contingencies are also considered in placement problem. For Harmonic State ‎Estimation in proposed method, an optimization problem for minimization the sum of the squares of the difference between the measured and estimated values is solved. Mean Square ‎Errors of estimated Total ‎Harmonic Distortions in normal condition is selected as the cost function, while single line and meter loss ‎contingencies are also considered by formulating ‎a multi-objective optimization exercise. To ‎solve this optimization problem, a SOA ‎algorithm based on Pareto optimum method is used. Simulation‎‎are performed on an IEEE 14-bus test system to demonstrate the effectiveness ‎of the proposed ‎methodology‎.‎

In this paper, Model Predictive Controller has been used for controlling the hybrid power system, which contains grid connected photovoltaic and energy storage systems (battery) considering the economic issues like optimization of profit from selling energy to grid by way of selling energy to grid during the high cost of energy and with regard to technical issues like improved dynamic response and smoothing power output of photovoltaic system and also maximization the batteries life by way of minimization the cycle of the change and discharge is used and such for optimization the results of model Predictive Control, PSO algorithm is used to obtain the coefficient of the objective function suggested controller. In addition, the suggested controller adapts itself. Dynamically and automatically and yet is able to respond external requests like price signals or satisfy the constraints of the power system or request of operator. Finally the simulation results of system with the proposed controller and result of PID controller is presented and compared.

This paper presents a Loop DC isolated network which is fed by photovoltaic (PV) renewable sources to supply unbalanced AC loads. The battery energy storage bank has been connected to the DC network via DC/DC converter called storage converter to control the network voltage and optimize the operation of the PV generation units. The PV units are connected to the DC network via its own DC/DC converter called, PV converter to ensure the required power flow. The unbalanced AC loads are connected to the DC network via its own DC/AC converter called, load converter without transformer. This paper proposes a novel control strategy for storage converter which has a DC voltage droop regulator. Also a novel control system based on Clark and Park rotating frame has been proposed for load converters. In this paper, the proposed operation method is demonstrated by simulation of power transfer between PV units, unbalanced AC loads and battery units. The simulation results based on PSCAD/EMTDC software show that DC isolated distribution system including PV units can provide the balanced voltages to supply unbalanced AC loads.

Incremental increasing use of the electrical energy and restructuring the power networks and the problems that they have been brought with them, cause increased the power operator’s interest to use of WAMS instead of SCADA. One of the main issues in the WAMS is the cost reduction of the optimal placement of PMU and correlated communication infrastructure. In this paper, a new method is presented for WAMS designing, with the aim of cost reduction. For this purpose, the greedy algorithm is used for optimal placement of PMUs and simultaneously, with the use of the Bellman-Ford algorithm, minimum communication infrastructure is designed so that the total cost of WAMS designing is reduced. Also the optimum location of the PDC is determined with the Bellman-Ford algorithm. This method is proposed in the normal operation condition and also in N-1 contingency conditions ( e.g. a single PMU loss and a single line outage). Simulations are conducted on the IEEE 30- and 118-bus power networks and the 230kv and 400kv of the Isfahan power network. Simulation results show good performance of the proposed method.

This paper proposes a synergetic combination of chaotic neural network with self-feedback, Lyapunov exponent, and simulated annealing for combinatorial optimization problems such as travelling salesman problem (TSP). Unlike conventional neural networks only with point attractors, the chaotic neural network has more flexible dynamics, so that it can be expected to have higher ability of searching for optimal or near-optimal global solutions. One of the most important problems related to conventional neural networks is becomies trapped into the local minimums. Although chaotic neural networks can solve this problem, but they have difficulty due to convergence towards the equilibrium point. Therefore, we have tried to add the Lyapunov exponent and gradual cooling factor as a simulated annealing process, until network converges to the global optimal solutions quickly. In order to evaluate the proposed approach, TSP with different cities is used. Numerical experiments of the propsed approach on 10-TSP are shown that it has high efficiency to converge to global optimal solutions.

Particle filter is one of the main filters to estimate of non-linear/ non- Gaussian systems. However, it is inconsistent over time. Since the choice of the proposal distribution and the resampling method are is very important to improve the accuracy and consistency, in this paper, increasing consistency of particle filter is done using improved sampling and resampling. To optimize the sampling step, particle swarm optimization (PSO) has been merged into the importance sampling. PSO causes the particles to move to the high probability region of the posterior before sampling and therefor the distribution of particles is improved. In order to reduce the impact of resampling on the accuracy and consistency, a new resampling approach is proposed. The new resampling method can maintain diversity among the particles and ensure that the resampled particles asymptotically approximate the samples from the posterior probability density function of the true state. The main advantage of the proposed method is that it reduces computational cost. This is because the proposed resampling method is performed on only part of the particles. The validity of the proposed filter is evaluated using simulation. The results show that the proposed method has better performance than classical filters.

Nowadays due to development of distribution systems and increase in electricity demand, the use of distributed generation sources and capacitor banks in parallel are increased. In this paper a modified multi-objective artificial bee colony (MABC) algorithm to solve the optimal placement and sizing of distributed generation and shunt capacitor is proposed. The objectives consist of minimization of costs, active losses, voltage deviation and improved voltage stability index incorporated with different load models. In the MABC, the bees adjust their trajectories based on the solutions also mutation and crossover operators are used to enhance the proposed algorithm. The proposed algorithm uses an external repository to save founded Pareto optimal solutions during the search process and fuzzy decision making process is employed to extract one of the Pareto-optimal solutions. The proposed algorithm is tested on the 69-bus IEEE test system and the results demonstrate the applicability and effectiveness of the proposed approach compared to that of other algorithms.