COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)
Year:2012 | Volume:3 | Issue:3|Papers Count:7

Recent blackouts in power systems have shown the necessity of vulnerability assessment. Among all factors, transmission system components have a more important role. Power system vulnerability assessment could capture cascading outages which result in large blackouts and is an effective tool for power system engineers for defining power system bottlenecks and weak points. In this paper a new method based on fault chains concept is developed which uses new measures. Genetic algorithm with an effective structure is used for finding vulnerable branches in a practical power transmission system. Analytic hierarchy process is a technique used to determine the weighting factors in fitness function of genetic algorithm. Finally, the numerical results for Isfahan Regional Electric Company are presented which verifies the effectiveness and precision of the proposed method according to the practical expriments.

Biogeography-Based Optimization (BBO) which is a new population based evolutionary optimization method inspired by biogeography and Differential Evolution (DE) is a fast and robust evolutionary algorithm for optimization problems. DE algorithm is good at the exploration of the search space and finds global minimum but is not good in exploitation of solutions. In this paper, we combine the exploration of DE with the exploitation of BBO to solve multi-objective problems by introducing a hybrid migration operator effectively.The proposed algorithm (MOBBO/DE) makes the use of nondominated sorting approach improve the convergence ability efficiently and hence it can generate the promising candidate solutions. It also combines crowding distance to guarantee the diversity of Pareto optimal solutions. The proposed approach is validated using several test functions and some metrics taken from the standard literature on evolutionary multi-objective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multi-objective optimization problems.

The benefits of renewable energy have led to the high penetration of Distributed Generators (DG) in the distribution systems. Besides the technical, economical and environmental benefits, Microgrids can operate in islanded (autonomous) modes. Microgrids need an effective load shedding system for the control of system frequency and voltage profile in an autonomous operation mode. The generation uncertainty of wind turbines in microgrids is analyzed in this paper and a new load shedding criteria has been proposed. Reactive power balance is an important problem with considering short electrical distances in microgrids and considering this basic fact, the proposed load shedding method uses a combination of frequency and voltage criteria. The total load shedding amount is determined by using transient stability analysis. This method is implemented by using ANFIS network in microgrids. Simulation results show the effectiveness of the proposed load shedding method.

One of mal-operations of the transformer differential protection during the unload transformer energizing with additional line/load from the supplying side is ultra-saturation phenomenon. In this paper, first a new model according to Discrete Fourier Transform (DFT) algorithm for investigating the ultra-saturation phenomenon during the unload transformer energizing with additional line/load from the supplying side is presented and its effect on the differential protection of the transformer is considered. In this model, the nonlinear characteristic of the transformer core and the effect of current transformer are taken into account. It is assumed that the additional line/load from the supplying side of the power transformer is a resistive-inductive load. Also, the effect of the residual flux, inception angle and additional line/load from the supplying side on the ultra-saturation phenomenon is investigated. Then, the mal-operation of differential protection by using DFT algorithm is described.

Insulation failure caused by switching overvoltages (SOVs) is one of the main sources of transmission lines’ outage, specially, on voltage levels of 345 kV and above. Therefore, the estimation of SOVs is vital in order to control and/or to reduce the switching–related outages. Due to the stochastic behavior of some of the parameters affecting on SOVs, the study of this phenomenon should be carried out based on a statistical study of the switching. Also, in the case of surge arrester installation on the transmission lines, depending on the location of arrester, voltage profile on line is changed and all the simulation should be performed for each new location of arresters, separately. One can conclude that this procedure is complex and time consuming. In this paper, a fuzzy based meta-model is presented which is be able to estimate the switching surge flashover rate (SSFOR), the maximum value of SOVs on the network and the location where the maximum overvoltage takes place. In the proposed Meta model, the effect of altitude on SSFOR and the magnitude of SOVs is considered. This meta-model can be used, directly, for planning the insulation level of transmission lines in order to meet a certain number of outages and locating arresters on the region/nodes of the network of weak operation against SOVs. It is also possible to utilize the proposed Meta model, indirectly, for assigning the optimal location of any specified set of arresters on the network without simulating of real network by transient software, e.g. EMTP/ATP draw. The presented Meta model can also be used in the operating stage to decide on the sequence of energizing and re-energizing of different transmission lines connected to the substations with the aim of reducing of maximum SOVs.

In this paper, a new approach for solving short term hydrothermal scheduling problem is suggested, to minimize the total production cost and to produce electrical energy in an optimized way, by using honey-bee mating optimization algorithm. In the proposed method, lots of the hydrothermal system constraints such as power balance, water balance, time delay between reservoirs, volume limits and the operation limits of hydro and thermal plants, are considered. Therefore, the problem of short term hydrothermal scheduling becomes a complicated and nonlinear problem. In this paper, in addition to implementing the honey-bee mating optimization on a sample system, the improved honey-bee mating optimization algorithm has also been tested and analyzed. With regard to the simulation results, it is apparent that the improved honey-bee mating optimization has far higher convergence speed and takes less time, and less total cost in comparison with honey-bee mating optimization algorithm, genetic algorithm, particle swarm optimization algorithm and other optimization methods.